Novel fast pathogen diagnosis method for severe pneumonia patients in the intensive care unit: randomized clinical trial

  1. Yan Wang
  2. Xiaohui Liang
  3. Yuqian Jiang
  4. Danjiang Dong
  5. Cong Zhang
  6. Tianqiang Song
  7. Ming Chen
  8. Yong You
  9. Han Liu
  10. Min Ge
  11. Haibin Dai
  12. Fengchan Xi
  13. Wanqing Zhou
  14. Jian-Qun Chen
  15. Qiang Wang  Is a corresponding author
  16. Qihan Chen  Is a corresponding author
  17. Wenkui Yu  Is a corresponding author
  1. Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, China
  2. The State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, China
  3. Department of Critical Care Medicine, Nanjing First Hospital, Nanjing Medical University, China
  4. Department of Cardiothoracic Surgery Intensive Care Unit, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, China
  5. Department of Neurosurgery Intensive Care Unit, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, China
  6. Research Institute of General Surgery, Affiliated Jinling Hospital, Medical School of Nanjing University, China
  7. Department of Laboratory Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, China
  8. Medical School of Nanjing University, China

Abstract

Background:

Severe pneumonia is one of the common acute diseases caused by pathogenic microorganism infection, especially by pathogenic bacteria, leading to sepsis with a high morbidity and mortality rate. However, the existing bacteria cultivation method cannot satisfy current clinical needs requiring rapid identification of bacteria strain for antibiotic selection. Therefore, developing a sensitive liquid biopsy system demonstrates the enormous value of detecting pathogenic bacterium species in pneumonia patients.

Methods:

In this study, we developed a tool named Species-Specific Bacterial Detector (SSBD, pronounce as ‘speed’) for detecting selected bacterium. Newly designed diagnostic tools combining specific DNA-tag screened by our algorithm and CRISPR/Cas12a, which were first tested in the lab to confirm the accuracy, followed by validating its specificity and sensitivity via applying on bronchoalveolar lavage fluid (BALF) from pneumonia patients. In the validation I stage, we compared the SSBD results with traditional cultivation results. In the validation II stage, a randomized and controlled clinical trial was completed at the ICU of Nanjing Drum Tower Hospital to evaluate the benefit SSBD brought to the treatment.

Results:

In the validation stage I, 77 BALF samples were tested, and SSBD could identify designated organisms in 4 hr with almost 100% sensitivity and over 87% specific rate. In validation stage II, the SSBD results were obtained in 4 hr, leading to better APACHE II scores (p=0.0035, ANOVA test). Based on the results acquired by SSBD, cultivation results could deviate from the real pathogenic situation with polymicrobial infections. In addition, nosocomial infections were found widely in ICU, which should deserve more attention.

Conclusions:

SSBD was confirmed to be a powerful tool for severe pneumonia diagnosis in ICU with high accuracy.

Funding:

National Natural Science Foundation of China. The National Key Scientific Instrument and Equipment Development Project. Project number: 81927808.

Clinical trial number:

This study was registered at https://clinicaltrials.gov/ (NCT04178382).

Editor's evaluation

Current culture-based, gold standard methods used for diagnosing the cause of sepsis provide results in 48-96 hours slowing antibiotic treatment initiation and leading to poor patient recovery. This work provides a new tool for identifying sepsis- and pneumonia-causing pathogens in less than 4 hours with species-specificity with the hope that the fast turnaround time leads to early treatment and improved clinical outcomes. Using an optimized PCR+CRISPR-Cas12a DNA detection method, the assay demonstrates good analytical sensitivity and specificity for 10 common bacterial pathogens that cause pneumonia. The method is validated in a clinical cohort and the clinical benefit is analyzed using a second cohort which is an intervention study used to guide clinicians on treatment choice.

https://doi.org/10.7554/eLife.79014.sa0

Introduction

Sepsis is associated with high morbidity and mortality (Singer et al., 2016). Adequate antibiotic therapy in time could decrease mortality and reduce the length of stay in ICU for patients with sepsis or septic shock (Ferrer et al., 2018; Kumar et al., 2006; Pulia and Redwood, 2020; Seymour et al., 2017). As reported in the previous study, the mortality rate of patients increased approximately 7.6% for every hour delayed (Kumar et al., 2006). Therefore, rapid diagnosis of pathogenic microorganisms is crucial for shortening the time of empirical antibiotic therapy and improving the prognosis of patients with sepsis.

Conventional culture test (CCT) is the most commonly used and golden standard identification method of pathogenic microorganisms in most countries. However, it showed two critical limitations: long time-consuming (2–5 days) and low sensitivity (30–50%), which limited the application of this method in the ICU (Abd El-Aziz et al., 2021; Zhou et al., 2014). To overcome this bottleneck, several new tools were developed and showed significant improvement in time consumption and accuracy. Recently, next-generation sequencing (NGS) technology was applied to acquire the entire information of microorganisms and demonstrated great ability in diagnosing rare pathogens. However, the whole process still needs at least 2 days for the full diagnostic report with high cost (Chen et al., 2020; Wang et al., 2020). On the other hand, NGS provided too much information about microorganisms but only semi-quantification of pathogens, which was hard for most clinical doctors to extract the most important information to determine antibiotic usage. Other new emerging detection techniques designed by BioFire and Curetis are much superior in detection time than these above. However, its original principle was based on nucleotide diversity of conserved genes among species, which could not satisfy the application in the ICU due to potential false-positive results (Edin et al., 2020; Jamal et al., 2014; Trotter et al., 2019). Therefore, a unique diagnosis tool aimed at faster and more accurate pathogen identification in the ICU was still a great challenge.

In this study, we aimed to design a simple and convenient diagnosis tool for sepsis patients in the ICU, which covered the most common pathogenic bacteria and completed the detection process in the shortest possible time with low cost and minimum instrument requirements. A clinical trial with two stages was applied to evaluate the accuracy of the tool and the clinical benefits.

Materials and methods

Study design

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The full study design was shown in Figure 1. In the discovery stage, we screened species-specific DNA tags of 10 epidemic pathogenic bacteria in the ICU. In the training stage, we optimized reaction conditions and sample preparation process, including detection concentration limitation, DNA purification, and incubation time of the CRISPR/Cas12a reaction. The finalized experiment operating procedure of SSBD was used in the subsequent stages (detailed protocol was shown in Appendix 1).

Study design.

This study contained four stages: discovery stage, training stage, validation stage I and validation stage II. All patients were from the Department of Critical Care Medicine, Nanjing Drum Tower Hospital. Patients were randomly divided into two groups for the clinical trial.

In validation stage I, 77 specimens of bronchoalveolar lavage fluid (BALF) directly acquired from patients in ICU were finally detected by SSBD to confirm the specificity and sensitivity of SSBD compared to CCT results. Based on clinical needs, some of the samples were diagnosed by NGS technology in third party commercial company, which provided additional information for reference.

After the stability and accuracy of SSBD were thoroughly evaluated, the validation stage II, a preliminary clinical intervention experiment, was launched to verify the clinical application of SSBD.

Screening species-specific DNA tags

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We designed a process to find the species-specific DNA tags according to the basic principle, intraspecies-conserved and interspecies-specific sequences (illustrated in Figure 2A). A total of 1791 high-quality genomes of 232 microorganism species from the public databases were included in the screening process. To accelerate the screening process, we developed a linear comparison algorithm instead of comparing every two genomes, which could save more than 90% of calculation time cost (Appendix 1—figure 1). According to the epidemiological data by previous retrospective study (Zhou et al., 2014) and 2017 data in ICU of Nanjing Drum Tower Hospital (Appendix 1—figure 2), 10 species of bacteria covered 76% sepsis pathogenic bacteria and therefore were selected as targets for subsequent detecting process, including Acinetobacter baumannii (A. baumannii), Escherichia coli (E. coli), Klebsiella pneumoniae (K. pneumoniae), Pseudomonas aeruginosa (P. aeruginosa), Stenotrophomonas maltophilia (S. maltophilia), Staphylococcus aureus (S. aureus), Staphylococcus epidermidis (S. epidermidis), Staphylococcus capitis (S. capitis), Enterococcus faecalis (E. faecalis) and Enterococcus faecium (E. faecium). Then we designed different DNA primers targeting selected species-specific DNA tags from each species (Appendix 1—tables 1 and 2).

Screening workflow and statistics of species-specific DNA tags.

(A) Schematic diagram of screening species-specific DNA tags. (B) Genomic distribution of species-specific DNA tags in 10 bacteria. (C) Genomic proportion of species-specific DNA tags in 10 bacteria.

To evaluate our primers' specificity in identifying species, we chose S. aureus and S. epidermidis from the same genus as our cross-validated target species. We extracted DNA sequences of the S. aureus and the S. epidermidis amplified by primers used in FilmArray Pneumonia Panel developed by BioFire and in our protocol, which were acquired from NCBI Reference Prokaryotic Representative genomes. We then aligned S. aureus-specific DNA sequences with the representative genome of S. epidermidis using blast to search the most similar DNA sequences. In the FilmArray Pneumonia Panel, DNA amplified sequences from the S. aureus and the S. epidermidis were aligned to each other (two different gene regions, rpoB and gyrB, were used to separate two species).

Patients

Patients admitted to ICUs and diagnosed with severe pneumonia were recruited from Aug 27, 2019. The recruit criteria for patients were: (1) age ≥18 years; (2) had artificial airway and expected to retain for more than 48 hr; (3) clinically diagnosed as pneumonia, and the microbiology of etiology was unclear; (4) signed informed consent; (5) the expected length of staying in ICU was more than 3 days. According to previous mortality acquired from the adequate anti-infective group, the sample size calculation (two-group rate) for patients was done, and a sample size of 73 patients in each group was needed. The enrolled participants formed a consecutive and convenient series, who were randomly assigned to experimental or control groups as described in the appendix 1.

Clinical outcomes

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BALFs were obtained from all the patients from 2 groups on day 1, day 3–5, and day 7–10 after recruitment and were sent directly to the hospital diagnostic microbiology laboratory for CCT and susceptibility testing. CCT results were obtained in strict accordance with international ERS/ESICM/ESCMID/ALAT guidelines for the management of hospital-acquired pneumonia and ventilator-associated pneumonia, which is currently the guideline for clinical gold standard. BALFs from patients of the experiment group were also sent for SSBD tests immediately after sampling. The time from sample acquisition to feedback of results was defined as the turnaround time, as well as being estimated and compared between SSBD and CCT. Other clinical records included blood routine tests, CRP and PCT examinations.

All enrolled patients received primary empirical antibiotic therapy. Once the SSBD results of the patients in the experiment group were obtained, the decisions about whether antibiotics were adjusted or not were made by two senior doctors according to the SSBD results and other clinical information. While in the control group, adjustment depended on conventional culture results and clinical data. Patient demographics and other vital clinical parameters were recorded. Acute Physiology and Chronic Health Evaluation II (APACHE II) scores and Sequential Organ Failure Assessment (SOFA) scores were calculated and recorded for patients on days 1, 3, 7, 10, and 14 to assess their disease severity and organ function.

Statistical analysis

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The number of improved patients on different clinical indicators of different days was calculated and tested by Fisher’s exact test. APACHE II scores and SOFA scores were tested as a series by two-way ANOVA. Different clinical outcomes were tested by the Mann-Whitney test.

Funding support

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This study was funded by National Natural Science Foundation of China. The National Key Scientific Instrument and Equipment Development Project. Project number: 81927808. The funders were not involved in the initiation or design of this study, collection of samples, analysis and interpretation of data, writing of the paper, or the submission for publication. The study and researchers are independent of the funders.

Results

The identification of species-specific DNA fragments

The first step to identify pathogenic bacteria was to figure out the specific genome information of each species. Bacteria were quite similar between close-related species but sometimes quite different among different strains of one species due to fast evolution and horizontal gene transfer (Dombrowski et al., 2020; Brito, 2021; Groussin et al., 2021), which makes it hard to figure out great species-specific DNA fragments. For example, two typical strains PAO1 and PAO7 of P. aeruginosa (NCBI representative genome database) demonstrate less than 94% nucleotide identity, while E. coli and Shigella sonnei both belong to Enterobateriaceae and their representative genomes share more than 98% nucleotide identity. Therefore, the widely used method to identify bacteria with conserved genes may not be a good choice (Maslunka et al., 2015; Liu et al., 2021). We developed an innovative algorithm and designed a workflow to figure out the best DNA tag for each species for diagnostic application based on 1791 microbe genomes from 232 species (Figure 2A). The details could be found in Appendix 1.

We started from 10 common bacteria contributing to sepsis infection as the initial panel according to local epidemic data from ICU of Drum Tower hospital and previous studies about pathogens in ICU (Appendix 1—figure 2; De Pascale et al., 2012; Sakr et al., 2018). To our surprise, bacteria-specific DNA sequences showed a random distribution and turned out to be only 0.3–4.1% in the whole genomes of 10 bacteria (Figure 2B and C). Considering the application scenario of ICU with only basic instruments, PCR +CRISPR/Cas12a system was chosen for the following detection. Based on the identified species-specific DNA fragments, related primers and crRNAs (CRISPR RNA) were designed according to each species (Appendix 1—tables 1 and 2).

The establishment of species-specific bacteria detection tool

Briefly, CRISPR/Cas12a with designed crRNA could be activated by its target, which could be told by whether the reporter probe was cleaved and demonstrated signal as previously reported (Chen et al., 2018).

To optimize the working conditions of the detection tool in ICU, multiple experiments were applied to optimize the sample preparation and detection process. With the gradient concentration of DNA templates, we confirmed that the lowest detection limit was 10–15 M with PCR amplification and 10–8M without amplification step (Figure 3B), which was consistent with previous studies (Gootenberg et al., 2018). In addition, 30 min’ incubation of CRISPR/Cas12a with PCR products was enough to demonstrate signals (Figure 3B). An additional purification step right after PCR amplification appeared unnecessary to acquire the positive result but helpful for weaker signal (Appendix 1—figure 3A). In addition, the comparison of CRISPR/Cas incubation duration confirmed that fluorescence value showed a significant difference from 5 min and reached its maximum after 30 min compared to the negative control (Appendix 1—figure 3B).

SSBD development and effectiveness validation.

(A) SSBD workflow for clinical validation stages. (B) Cas12a and Cas12a-after-PCR detection of different concentrations and reaction times including 30 min (left) and 60 min (right). Blue bars indicated the Cas12a-after-PCR test. Brown bars indicated Cas12a test only. The concentration gradient of pGL3 plasmid from 10–17 M-10–7M was established as the test group. NC stood for the fluorescence values of PCR products of using DEPC-H2O as input. Each group had three repeats. Error bars indicated mean ± SEM of fluorescence value. ** indicated p-value <0.01 and *** indicated p-value <0.001 of unpaired t-test. (C) SSBD results of 10 pathogenic bacteria. Every test panel for each of 10 bacteria was used to detect genome DNA samples of 10 bacteria by SSBD. NC stood for the fluorescence values of PCR products of using DEPC-H2O as input. Each group had three repeats. Error bars indicated mean ± SEM of fluorescence value. *** indicated p-value <0.001 of unpaired t-test.

Figure 3—source data 1

The reaction condition test of Cas12a detection.

https://cdn.elifesciences.org/articles/79014/elife-79014-fig3-data1-v2.xlsx
Figure 3—source data 2

The cross-validation of 10 selected bacteria using SSBD.

https://cdn.elifesciences.org/articles/79014/elife-79014-fig3-data2-v2.xlsx

To confirm the primary behavior of SSBD, two clinical strains separated from different patients for each of 10 selected bacteria species were collected and tested by SSBD as the positive control, which showed clear positive results (Appendix 1—figure 3C). To further confirm the specificity of SSBD, each bacteria strain was tested by 10 SSBD test panels targeting different bacteria. Compared to negative control, only SSBD targeting the tested bacteria showed a positive result, which confirmed its high specificity (Figure 3C).

Putting these results together, a standard operating procedure was finally established for the following validation stages (Figure 3A), which was capable of providing the information about the ten most common pathogenic bacteria in ICU. Since this method was a quite fast and species-specific bacteria detection tool, we named it SSBD.

The accuracy and clinical benefits of SSBD

We started our study with validation stage I, which was a non-intervention study with 77 samples of BALF extracted from patients. Samples were detected both by SSBD and CCT, and the results were compared (raw detection results were shown in Appendix 1—table 3).

Generally, 5 of 10 selected bacteria were detected by both tests, including A. baumannii, K. pneumoniae, P. aeruginosa, S. aureus, and S. maltophilia. SSBD could detect those five bacteria separately with 100% sensitivity and over 87% specificity, which were calculated by the results of CCT as golden standard (Figure 4A). The other five bacteria were detected by SSBD but not CCT, including E. coli, S. epidermidis, S. capitis, E. faecalis, and E. faecium. Among all samples, 11 of them were determined by patients to acquire results with NGS, which provided extra information to evaluate the results (Appendix 1—table 4). Based on the results, SSBD was highly consistent with NGS, which implied that SSBD might provide more accurate and complete pathogenic information than CCT in the selected panel.

Statistical analysis of test results and clinical outcomes in the two validation stages.

(A) Cross-tables for 5 of 10 bacteria by both SSBD and CCT in the validation stage I. (B) Cross-tables for 5 of 10 bacteria by both SSBD and CCT in the validation stage II. (C) Antibiotics coverage rate of each test in the two groups. Exp meant the experimental group, and Con meant the control group. Test 1: Day 1. Test 2: Day 3–5. Test 3: Day 7+. Raw antibiotics coverage results of each patient were available in Appendix 1—figure 4B. Detailed judging guidelines were shown in Appendix 1. (D and E) Line charts for APACHE II and SOFA scores, respectively. Error bars indicated mean ± SEM of scores of all the recorded patients. * indicated a significant difference between the two groups using two-way ANOVA.

Based on these accurate results, we started the validation stage II, which was an intervention study aiming to evaluate the clinical benefits of SSBD compared to the current diagnosis and treatment strategy in ICU. Although the study was paused due to the outbreak of SARS-CoV2, 22 patients were recruited into the experiment group and 24 patients into the control group. The baseline characteristics had no significant difference except ages (Table 1).

Table 1
Demographic and baseline characteristics of the patients in the validation stage II.
Experimental group (n=22)Control group(n=24)p value
Women9 (40.9%)11 (45.8%)0.774
Men13 (59.1%)13 (54.2%)0.774
Age, years (SD)58 (17.4)68 (9.5)0.015*
Patients' numbers of chronic comorbidities
 Hypertension9 (40.9%)17 (70.8%)0.073
 Coronary artery disease1 (4.5%)3 (12.5%)0.609
 Chronic pulmonary disease2 (9.1%)4 (16.7%)0.667
 Chronic kidney disease2 (9.1%)6 (25.0%)0.247
 Diabetes5 (22.7%)12 (50.0%)0.072
 Malignancy0 (0.0%)2 (8.3%)0.490
 Stroke3 (13.6%)8 (33.3%)0.171
Immunodeficiency/immune suppressive therapy5 (22.7%)3 (12.5%)0.451
 Recent surgery4 (18.2%)3 (12.5%)0.694
Hemodynamic support (using vasoactive drugs)7 (31.8%)7 (29.2%)1.000
 Norepinephrine ≤0.1 μg/(kg•min)23
 Norepinephrine >0.1 μg/(kg•min)11
 Dopamine ≤5 μg/(kg•min)32
 Dopamine >5 μg/(kg•min)01
 Dobutamine ≤5 μg/(kg•min)10
 Dobutamine >5 μg/(kg•min)00
Status at randomization (D1)
 Temperature, °C38.4 (0.6)38.3 (0.7)0.345
 Coma6 (27.3%)6 (25.0%)1.000
 Systolic blood pressure, mmHg112.2 (19.3)121.6 (17.8)0.057
 Invasive mechanical ventilation20 (90.9%)24 (100.0%)0.223
 Renal replacement therapy0 (0.0%)2 (8.3%)0.493
 SOFA score6.3 (0.7)6.0 (0.5)0.935
 APACHE II score17.3 (1.6)18.9 (1.5)0.422
 Albumin, g/L32.1 (5.1)31.0 (3.7)0.442
 Globulin, g/L21.8 (3.9)23.1 (5.9)0.489
 Absolute lymphocyte count, 109 /L0.9 (0.7)0.7 (0.3)0.909
 White blood cells, 109 /L11.0 (5.7)12.7 (6.3)0.210
 CRP, mg/L94.7 (101.2)108.3 (84.0)0.424
  1. SOFA score and APACHE II score are mean (SEM), other data are mean (SD), n (%). Mean (SEM/SD) is compared using Mann-Whitney test, and n (%) is compared using Fisher’s exact test. * indicated p-value <0.05.

We finally got 57 BALF results tested by SSBD, which included 43 results that also had CCT results among them in the experiment group. While in the control group, we got 63 samples tested only by CCT. In the experiment group, 47 samples were positive among 57 samples tested by SSBD, while 28 samples were positive among 43 samples tested by CCT (raw detection results were shown in Appendix 1—table 5). In the control group, 41 samples showed positive among 63 samples. It was shown that SSBD could detect each bacterium with similar high sensitivity and specificity in validation stage II (Figure 4B). Consistent with the local epidemic data, the most frequent occurrence was A. baumannii (Figure 4B). Similar to stage I, 12 samples were determined by patients to test with NGS help us to draw the same conclusion that SSBD seemed to be better that CCT (Appendix 1—table 6).

To explore clinical benefits with the help of SSBD, effective antibiotic coverage rate, APACHE II scores and SOFA scores were calculated and compared to evaluate the rationalization of antibiotic therapy and patients' disease severity and organ function status in the two groups (Figure 4C-E). Effective antibiotic coverage rates for each test were significantly higher in the experimental group than those in the control group in three tests (Figure 4C). The definition of antibiotic coverage and the original calculation results were shown in Appendix 1—figure 4A and B. APACHE II scores were significantly lower in the experimental group than those in the control group after day 1 (p=0.0035, two-way ANOVA); the separation between two groups of patients increased progressively until day 14 (Figure 4D). SOFA scores showed no difference between the groups (p=0.8918, two-way ANOVA) (Figure 4E).

Polymicrobial infection and nosocomial events observed by SSBD

Based on the previous studies, CCT had defects in the evaluation of polymicrobial infection events due to the limitations of its technology (Azevedo et al., 2017). Therefore, we tried to evaluate whether SSBD demonstrated better performance with polymicrobial infection. Here, we defined situations of infection with more than one pathogenic microorganism as polymicrobial infection events to assess the performance based on the results of both methods. From the results, the detection rate of polymicrobial infection events by SSBD was 41.8% (55/134) in two validation stages, which was significantly higher than 11.7% (14/120) of CCT (Figure 5A). Polymicrobial infection events were compared among SSBD, CCT and NGS, which demonstrated high consistency of SSBD and NGS (Figure 5B).

Statistical analysis of polymicrobial infection and nosocomial infection in the two validation stages.

(A) Statistics of pathogenic infection status of BALF samples in the two validation stages. (B) Verification from NGS results for 6 samples identified as polymicrobial infection by SSBD but not CCT or missed pathogens by CCT. (C) Case study of polymicrobial infection detected by SSBD and CCT. (D) Statistics of pathogens involved in polymicrobial infections in the two stages. (E) Case study of nosocomial infection identified by SSBD. (F) Case study of nosocomial infection identified by CCT. (G) Percentage of nosocomial infection identified by SSBD and CCT.

Since both SSBD and NGS were based on target DNA, we wanted to confirm if some polymicrobial infection events were ‘false positive’ and caused by dead bacteria. Here, we showed patient B19 as an example, who received three times tests at days 1, 3, and 7 by both CCT and SSBD. Based on the results, S. maltophilia was detected as level II in test1 with SSBD but not CCT. Later on, S. maltophilia was detected by CCT in test2 as well with few days’ development from level II to level III based on result of SSBD, which means SSBD discovered the true polymicrobial infection event earlier than CCT (Figure 5C). From the aspect of pathogen species participated in polymicrobial infection events, both methods demonstrated similar results with A. baumannii, S. maltophilia, P. aeruginosa, K. pneumoniae and S. aureus in top 5 (Figure 5D), which were consistent with the frequency of pathogens in ICU (De Pascale et al., 2012).

Hospital infections, also known as nosocomial infections, are an important factor in the incidence rate and mortality of ICU patients with severe pneumonia (Zaragoza et al., 2020). Since CCT has a long delay in clinical feedback of pathogenic results, there is no effective monitoring method in clinical practice. Here, we tried to evaluate nosocomial infections based on the test results. We defined a case as a nosocomial infection event if a pathogenic bacterium was newly detected in the current time point but not before. For example, B17 (K. pneumoniae at test 2, E. faecalis at test 3) and B19 (S. maltophilia at test 2, K. pneumoniae at test 3) patients were discovered as nosocomial infection cases for SSBD and CCT (Figure 5E and F). Based on the results of SSBD, 47.6% (10/21) of patients had nosocomial infections at the test 2, and 28.6% (4/14) of patients had nosocomial infections at the test 3. Similarly, 40% (4/10) of patients were identified as nosocomial infections by CCT at test 2, and 27.3% (3/11) of patients were identified as nosocomial infections at test 3 (Figure 5G).

Discussion

In this study, we developed a rapid bacteria detection technique based on CRISPR/Cas12a using species-specific DNA tags and detected common bacteria taken from pneumonia in 4 hr with 100% sensitivity and over 87% specificity in the validation stage I. Currently, there are already some market-oriented detection technologies for pneumonia patients, such as FilmArray Pneumonia Panel by BioFire and Curetis Unyvero system, which also could detect microorganisms in several hours (Trotter et al., 2019). However, based on the information in their product instruction, false positive results were widely seen in close relative species. Such problem may due to the marker selection strategy. For example, sequences used by FilmArray Pneumonia Panel from two gene regions had highly similar DNA sequences in the S. epidermidis representative genome (E-value=5e-40, rpoB; E-value=8e-39, gyrB), which could interfere with pathogen identification between species from the same genus. It was ideal for early and rapid screening of infectious diseases but was not applicable in the ICU, considering the complexity and urgency of infection events within the ICU. We have adopted a completely different strategy from the existing methods, getting specific gene regions from species for further test using our developed bioinformatics workflow and algorithm. It was shown that our sequences used for S. aureus diagnosis had no similar fragments in S. epidermidis, which avoided distinguishing different species by gene diversity. It was likely to get the species-specific DNA tags from such amount genomes when aligned bacterial genomes with each other but consuming computational cost. We optimized calculation processes by rescheduling steps and then made it possible for us to acquire species-specific DNA regions after shortening time to a range bearable.

NGS technology is useful in species identification and also shows its advantages in clinical diagnosis. It is valuable to detect uncommon pathogens because of its unique capability in detecting multiple agents across the full microbial spectrum contributing to disease and has already been developed as a new detection platform (Wang et al., 2020). However, in the majority of cases of common pathogens, redundant microorganism results were probably unhelpful to the anti-infection regimen. In addition, the high cost and relatively long turnaround time prevent its widespread application, especially in the ICU circumstance. Therefore, our SSBD method seemed more advantageous in time-consuming and information effectiveness than other mentioned methods, especially when we could quantify bacterial load based on fluorescence intensity for better antibiotic therapy strategy. CRISPR/Cas12a and qPCR are both quantitative methods, but CRISPR/Cas12a shows its robustness and lower equipment requirement, which satisfied our needs for most of the ICU. There are still several challenges in implementing POCT in developing countries, especially the qPCR/POCT system, which will be an alternative. Here, we listed a table to demonstrated the comparison among SSBD, CCT and NGS from main aspects (Appendix 1—table 10).

The results of SSBD demonstrated high sensitivity and specificity. However, we discovered several ‘false positive’ results compared to CCT, which might be caused by two reasons: (1) The low bacterial load of the patient sample was probably not enough or needed much longer time than expected to be cultivated. SSBD provided a lower threshold of detection (10–15 M) than CCT, which could detect pathogens that even existed in trace amounts which unable to be cultivated. In our study, the fluorescence intensity obtained from SSBD was divided into three intervals (level I: 10–15-10–14 M, level II: 10–14 M-10–13 M, level III: over 10–13 M), representing the different strengths of bacteria (roughly equivalent to bacteria amounts according to our lowest detection thresholds, dividing details in SSBD diagnostic report of Appendix 1). All false-positive results were calculated on the count of species and strengths, mostly belonging to the level I or II (Appendix 1—figure 5). Considering most of those false positive samples were also validated by NGS technology, it suggested that some pathogens might be missed in the CCT results. (2) Cultivation could fail in detecting pathogens that failed in competitive growth environments. It was interesting to see that many patients were infected by more than one pathogen, which might cause potential competition between different pathogens in CCT process (Appendix 1—table 9). For example, A. baumannii was found to be the most competitive bacteria in cultivation, which may be due to its fastest growth rate. On the other hand, P. aeruginosa seemed to be relatively the weakest one among them, which was usually concealed in the cultivation with other species existing (sample A16, B19-3, B21-1, B21-2 after we excluded all samples with A. baumannii existing).

When evaluating the clinical benefit from SSBD, the quicker directed therapy adjustment for patients in the experiment group (Exp: 10.2±8.8 hours vs. Con: 96.0±35.1 hr, p<0.0001, Mann-Whitney test) could shorten the empirical anti-infection time and seemed to alleviate illness severity (APACHE II score) during the validation stage II with the help of the SSBD. As showed in Appendix 1—table 8, patients in experiment groups for example demonstrated significant better measures of temperature improvement at day 3, WBC improvement at day 7. It implied that appropriate antibiotic treatment guided by in-time pathogenic information would alleviate acute physiological illness. Nevertheless, at the endpoint, clinical outcomes showed no differences between the two groups, which may due to the insufficient patient numbers.

Despite the size in our intervention stage, there were still some aspects that have not been considered. (1) Resistance genes were not included in the study. Multi-drug resistant organisms (MDROs) prevailed in ICU (Liu et al., 2020; Yang et al., 2020), which might not improve the situation of patients even with accurate pathogenic information. There were a few cases (e.g. B07, B25, and B35 patients) showing no signs of clearing the bacterial infection. (2) The 10 designed pathogens were originated from sepsis, which might not completely overlap with pathogens of severe pneumonia, though pneumonia is one of the most common causes of sepsis. The panel pathogens could be optimized flexibly for meeting diverse clinical needs in the ICU. (3) Other potential pathogenic microbes, such as viruses and fungus, might affect the clinical outcomes considering the complexity of ICU patients.

Previous studies showed that polymicrobial pneumonia is related to an increased risk of inappropriate antimicrobial treatment (Karner et al., 2020). In both phases, a total of 55 samples were identified as polymicrobial infections by SSBD, while only 14 samples were identified as polymicrobial infections by CCT, which suggested that SSBD could provide more precise pathogenic bacteria information than CCT, especially for those patients with polymicrobial infections. On the other hand, nosocomial infections contribute to a considerable proportion of deaths in ICU patients with severe pneumonia (Zaragoza et al., 2020). Although SSBD identified similar ratio of nosocomial infection events with CCT (Figure 5G), SSBD provided more timely information for clinical control and response, which might improve the clinical medication decision in ICU.

As anticipated, SSBD performed well with high sensitivity and specificity in rapid pathogens identification, and it possessed shorter turnover time, which was associated with more rapid administration of appropriate antimicrobial therapy in the experiment cases. SSBD also has enormous potential in expanding pathogens from different diseases with much more pathogen genomes included. We believe that SSBD is an accurate tool with great potential but need to be applied in more clinical research.

Appendix 1

Principles of screening species-specific DNA-tags

Our core principles for screening species-specific DNA fragments were as follows: (1) Multiple isolated strains from the same bacterial species were included to ensure the intra-species conservation of selected DNA fragments; (2) Those similar DNA fragments of intra-species conserved fragments in genomes from other bacteria were excluded to ensure inter-species specificity. We developed an original workflow and optimized the algorithm to more efficiently achieve our purposes compared with conventional pairwise alignment (Appendix 1—figure 1A). Firstly, conserved DNA regions were obtained by aligning the genomes of two strains within a given species, downsizing the genome to the regional scale. Those conserved DNA regions were performed alignments with the genomes of other strains from the same species, only shared DNA regions in all the strains are retained, downsizing conserved genomic regions to intra-species conserved DNA fragments (Appendix 1—figure 1B). Secondly, to achieve inter-species specificity of DNA tags, intra-species conserved DNA fragments were performed alignments with the genomes of other bacterial species. Those similar fragments of intra-species conserved DNA fragments in genomes from other bacteria species were excluded. After two-step screening, we finally obtained species-specific DNA tags (Appendix 1—figure 1C). The software for screening species-specific DNA fragments could be obtained from the following URL: https://github.com/wang-q/App-Egaz; (Wang, 2022 copy archived at swh:1:rev:58b19aa6a5d540030ff43796c141e810cced07ce).

Sample size and randomized double-blind trial

Based on the local epidemiology, the mortality rate of the early stage of sepsis in the adequate anti-infective treatment group was 48%, and was 65% in the inadequate anti-infective treatment group. The sample size was estimated through the formula: α=0.05, 1- β=0.80, Pt: 0.65, Pc: 0.42, Nt/Nc = 1, Power = 0.802, H0: Pt - Pc = 0; H1: Pt - Pc ≠ 0. Nt = 73, Nc = 73. The sample size for the clinical study was finally determined to be 73 patients for each group. The randomized process was conducted by SAS 9.4 software to determine whether patients were selected for the experiment group or the control group. The random numbers were generated using the PLAN process. Then all numbers are concealed in random envelopes. Patients were assigned by opening the random number in random envelopes. For CCT and SSBD detection, the two processes were carried out independently in the hospital microbiology laboratory and research lab, which were blinded to each other. The detection results were finally unblinded by the clinician of ICU with non-testing procedures.

Extracting samples

Extracting bronchoalveolar lavage fluid (BALF) for the enrolled patients were under mild anesthesia via tracheal intubation or tracheotomy entering the infected bronchus. 30 mL saline was injected into batches quickly and recollected by using negative pressure lower than 13.3 kPa. In the validation stage I, 5 mL collected samples were cultivated as usual, and another 5 mL samples were sent for SSBD testing. As a liquid biopsy technique, SSBD was not involved any additional operations that could harm the patients’ health compared to CCT.

Reaction process

Briefly, DNA of BALF samples was extracted using Quick–DNA/RNA Pathogen Miniprep Kit (Zymo Research) according to the manual and diluted to 100 ng/μL. The DNA samples were then amplified using designed specific primers for 10 bacteria. Then, PCR products, Cas12a-crRNA complexes and the reporter DNA probe were added to the reaction system. Finally, the fluorescence signals were detected after incubation for 30 mins at 37 ℃. Some samples were sent for next-generation sequencing (IngeniGen XunMinKang Biotechnology Inc Hangzhou, Zhejiang, China).

Clinical operation process

In the validation stage II, BALFs were collected from each patient on the first day, day 3–5, and day 7+unless the airway was removed. All samples were sent for microbial cultivation, and simultaneously the samples taken from the experiment group were tested by SSBD. Once BALF results were available, at least two clinical experts (antimicrobial stewardship) discussed and decided on antibiotics adjustment according to the results and other clinical data. Additionally, the experts would assess each patient's receiving antibiotics coverage rate at different times and other clinical outcomes retrospectively.

Treatment and outcomes data, such as evaluation of therapeutic effectiveness at day 3, 7, 10 and 14, time of mechanical ventilation and vasopressor support from enrollment to day 28, occurrence of antibiotic-associated diarrhea, and mortality on day 28 were also recorded and analyzed. Evaluation of therapeutic effectiveness was conducted by two senior clinicians according to these parameters.: (1) whether fever and purulent secretion were improved or not; (2) whether leukocytosis or leukopenia got better or not; (3) whether radiological pulmonary infiltrate absorbed partly or not; (4) whether oxygenation index was improved or not; (5) whether hemodynamic instability was rectified gradually or not.

SSBD diagnostic report

For every single experiment, we test the fluorescence value of clinical separated positive bacteria strains as positive control (PC) and DEPC-H2O as negative control (NC). For each experiment, we test the fluorescence value of BALF (shown as F) with a microplate reader. When F/NC >2, it is the signal that bacteria detected by our method. We use I (interval) as our point of distinction. I = (PC - 2NC) / 3. When 2NC <F < 2 NC+I, the bacteria strength level is defined as level I. When 2NC + I < F<2 NC+2 I, the bacteria strength level is defined as level II. When F>2 NC+2 I, the bacteria strength level is defined as level III. Different levels of bacterial strength can roughly represent different bacterial copies according to our method lowest detection rate (Level I: 10–15 M–10–14 M, Level II: 10–14 M–10–13 M, Level III: over 10–13 M, Figure 3B). We separated different testing values as different levels, suggesting that our method could test pathogen strength to some extent and give pathogens strength for drug usage. If the sample was discovered to be affected by any unexpected condition (e.g. contamination during the test, etc.), the detection results of the sample would be excluded from comparative analysis of diagnostic accuracy. If any missing data was critical for result judgment, such result would be excluded from subsequent analysis and statistics.

Evaluation of antibiotics coverage

For three individually designated BALF tests, the rate of antibiotics coverage was calculated in all groups. We calculated coverage rate from two aspects. For each test, we calculated rate of covered samples among all samples tested. For each patient, we calculated their coverage rate by counting covered test numbers within all tests that had been taken. Evaluation of antibiotics coverage was made by two experts retrospectively, according to microbial, antimicrobial susceptibility tests (AST) and clinical treatment effect. The details were as follow:

  1. Our test result is negative (from the experiment group), or microbial cultivation is negative (from the control group). If it is deemed effective on the clinical signs (1), it is judged as antibiotics covering. If it is deemed clinically invalid, it is judged as antibiotics uncovering.

  2. Our test result is positive (from the experiment group), or microbial cultivation is positive (from the control group). If AST (from experiment group or control group) is shown sensitive to the antibiotics, no matter the clinical signs are effective or not, it is judged as antibiotics covering. If AST is shown resistant to using antibiotics, whether antibiotics covering or not is judged by clinical effectiveness. It is identified as covered when clinically effective and uncovered when clinically invasive.

When BALF is not collected for a test or microbial cultivation from the day on, whether with covering antibiotics or not on the day is judged on clinical effectiveness.

Appendix 1—figure 1
Diagram of core principles for screening species-specific DNA-tags.

(A) Optimizing the algorithm of sequence alignment. Abbreviations: SA, sequence alignment; N, number of double sequence alignment; n, number of sequences. (B) Schematic map of screening intra-species conserved DNA fragments. (C) Schematic map of screening species-specific DNA tags.

Appendix 1—figure 2
Epidemic data of pathogens in the Nanjing Drum Tower Hospital ICU in 2017.

10 targeted bacteria were indicated with the box.

Appendix 1—figure 2—source data 1

Epidemic data of pathogens in the ICU of Nanjing Drum Tower Hospital in 2017.

https://cdn.elifesciences.org/articles/79014/elife-79014-app1-fig2-data1-v2.xlsx
Appendix 1—figure 3
SSBD development and effectiveness validation.

(A) SSBD results of purified and unpurified DNA. NC, namely the fluorescence values of PCR products of using DEPC-H2O as input. Each group had three repeats. Error bars indicated mean ± SEM of fluorescence value. *** indicated p-value <0.001 of unpaired t-test. (B) SSBD results of reaction time gradient with Cas12a. Fluorescence values of K. pneumoniae and E. faecium by Cas12a through different incubation times after PCR. Gray represented NC, namely the fluorescence values of PCR products of using DEPC-H2O as input. Green and blue represented the fluorescence values of bacteria strains from different patients. Each group had three repeats. Error bars indicated mean ± SEM of fluorescence value. ** indicated p-value <0.01 and *** indicated p-value <0.001 of unpaired t-test. (C) SSBD results of 10 pathogenic bacteria with Cas12a. Gray represented NC, namely the fluorescence values of PCR products of using DEPC-H2O as input. Green and blue represented the fluorescence values of bacteria strains from different patients. Each group had three repeats. Error bars indicated mean ± SEM of fluorescence value. *** indicated p-value <0.001 of unpaired t-test.

Appendix 1—figure 3—source data 1

The test of SSBD with or without DNA purification.

https://cdn.elifesciences.org/articles/79014/elife-79014-app1-fig3-data1-v2.xlsx
Appendix 1—figure 3—source data 2

The test of SSBD with different incubation times.

https://cdn.elifesciences.org/articles/79014/elife-79014-app1-fig3-data2-v2.xlsx
Appendix 1—figure 3—source data 3

The validation of SSBD with clinical strains.

https://cdn.elifesciences.org/articles/79014/elife-79014-app1-fig3-data3-v2.xlsx
Appendix 1—figure 4
Judgment process and results of antibiotics coverage.

(A) Judgment process of antibiotics coverage. (B) The raw results of antibiotics coverage in two groups. Exp meant the experimental group, and Con meant the control group.

Appendix 1—figure 4—source data 1

Classification of antibiotic coverage for each test in two groups.

https://cdn.elifesciences.org/articles/79014/elife-79014-app1-fig4-data1-v2.xlsx
Appendix 1—figure 5
Analysis of false-positive samples.

Numbers and fractions of different strength levels among all false-positive samples of each bacteria species in the validation stage I (A) and II (B). Strength could be seen roughly as bacterial amounts (level I-level III, the definition could be seen in the Appendix 1). False-positive situations meant pathogenic bacteria detected by SSBD but not by CCT in a given BALF sample.

Appendix 1—table 1
Primers used in experiments.
NameSequence (5' ->3')TargetProduct length (bp)
pGL3-amplify-FGAAGATGGAACCGCTGGAGApGL3597
pGL3-amplify-RGCAGGCAGTTCTATGAGGCA
Aba-amplify-FCACAGCGTTTAACCCATGCCA. baumannii564
Aba-amplify-RTATCGCCACCTGCACAGAAG
Eco-amplify-FGTTCCTGACTATCTGGCGGGE. coli371
Eco-amplify-RGCTTCCTGACTCCAGACACC
Kpn-amplify-FCATGGGCATATCGACGGTCAK. pneumoniae740
Kpn-amplify-RCCTGCAACATAGGCCAGTGA
Sau-amplify-FAGGTGCAGTAGACGCATAGCS. aureus563
Sau-amplify-RCATTCGCTGCGCCAATACAA
Pae-amplify-FTCTCTCTATCACGCCGGTCAP. aeruginosa467
Pae-amplify-RTCGCATCGAGGTATTCCAGC
Sep-amplify-FCACGCATGGCACTAGGTACAS. epidermidis383
Sep-amplify-RCGAAAAAGAGTTGTCCTTGTTGA
Sca-amplify-FGGTTCAGTCATCCCCACGTTS. capitis591
Sca-amplify-RCAGCTGCGACAACTGCTTAC
Efa-amplify-FCGGCAAGTTTGGAAGCAGACE. faecalis627
Efa-amplify-RCAGCGCCTAGTCCTTGTGAT
Efm-amplify-FATCGGAAATCGGTGTGGCTTE. faecium507
Efm-amplify-RTCAAATGCATCCCTGTGCCT
Sma-amplify-FCGCCTCCCGTTTACAGATTAS. maltophilia356
Sma-amplify-RTCGGCTCCACCACATACAC
Appendix 1—table 2
Oligonucleotide templates for synthesis of crRNAs.
NameSequence (5′->3′)Target
T7-ForwardTAATACGACTCACTATAGGT
LbcrRNA-Reverse-PGL3ATGTGACGAACGTGTACATCGACTATCTACACTTAGTAGAAATTACCTATAGTGAGTCGTATTAPGL3
LbcrRNA-Reverse-AbaTTCAAGTAATTCTTCTTTACATCTACACTTAGTAGAAATTACCTATAGTGAGTCGTATTAA. baumannii
LbcrRNA-Reverse-EcoCTTGCCATCATAGCGACCGTATCTACACTTAGTAGAAATTACCTATAGTGAGTCGTATTAE. coli
LbcrRNA-Reverse-KpnAAGATGGCGATTACCGCAGTATCTACACTTAGTAGAAATTACCTATAGTGAGTCGTATTAK. pneumoniae
LbcrRNA-Reverse-SauCCTCAGCAAGTTCACGTTGTATCTACACTTAGTAGAAATTACCTATAGTGAGTCGTATTAS. aureus
LbcrRNA-Reverse-PaeCTCCTCATGTGTGTTTACAAATCTACACTTAGTAGAAATTACCTATAGTGAGTCGTATTAP. aeruginosa
LbcrRNA-Reverse-SepTCTCTAATTGATGAATATTAATCTACACTTAGTAGAAATTACCTATAGTGAGTCGTATTAS. epidermidis
LbcrRNA-Reverse-ScaTATTGATTAATAAGGTGATTATCTACACTTAGTAGAAATTACCTATAGTGAGTCGTATTAS. capitis
LbcrRNA-Reverse-EfaTCAGCTGTGTTATTTGGTGCATCTACACTTAGTAGAAATTACCTATAGTGAGTCGTATTAE. faecalis
LbcrRNA-Reverse-EfmTGTATATAAGTTCAGGTAGTATCTACACTTAGTAGAAATTACCTATAGTGAGTCGTATTAE. faecium
LbcrRNA-Reverse-SmaCCTGGTCGCAGGTGTCATGCATCTACACTTAGTAGAAATTACCTATAGTGAGTCGTATTAS. maltophilia
Appendix 1—table 3
SSBD and CCT results of BALF samples in the validation stage I.
Sample IDSSBD ResultsCCT ResultsNGS Results
A01S. aureus (I)NS. aureus
A02A. baumannii (III)A. baumannii
A03A. baumannii (III)
S. aureus (I)
A. baumannii
S. aureus
A. baumannii
S. aureus
A04A. baumannii (III)
K. pneumoniae (II)
S. maltophilia (II)
N
A05A. baumannii (III)
K. pneumoniae (III)
P. aeruginosa (III)
K. pneumoniae
P. aeruginosa
A06A. baumannii (III)A. baumannii
A07S. maltophilia (II)S. maltophilia
A08A. baumannii (III)A. baumannii
A09A. baumannii (II)
S. aureus (I)
A. baumannii
A10A. baumannii (III)
K. pneumoniae (III)
P. aeruginosa (I)
A. baumannii
K. pneumoniae
A11A. baumannii (III)
K. pneumoniae (I)
A. baumannii
A12NN
A13A. baumannii (II)A. baumannii
A14NN
A15K. pneumoniae (III)K. pneumoniae
A16K. pneumoniae (III)
P. aeruginosa (III)
K. pneumoniae
A17NN
A18K. pneumoniae (I)
P. aeruginosa (III)
K. pneumoniae
P. aeruginosa
A19P. aeruginosa (I)P. aeruginosa
A20E. faecium (III)
S. capitis (II)
NE. faecium
S. capitis
A21A. baumannii (III)
K. pneumoniae (III)
S. aureus (I)
A. baumannii
S. aureus
A22A. baumannii (III)
S. epidermidis (III)
A. baumannii
A23NN
A24A. baumannii (III)A. baumannii
A25NNN
A26P. aeruginosa (III)P. aeruginosa
A27A. baumannii (III)
S. epidermidis (III)
A. baumanniiA. baumannii
S. epidermidis
A28A. baumannii (II)
P. aeruginosa (III)
A. baumannii
A29S. epidermidis (I)
S. maltophilia (I)
N
A30A. baumannii (III)
S. aureus (III)
S. epidermidis (I)
A. baumannii
A31K. pneumoniae (I)
P. aeruginosa (I)
S. epidermidis (I)
NK. pneumoniae
A32P. aeruginosa (I)
S. epidermidis (I)
N
A33S. aureus (I)
S. maltophilia (II)
S. aureus
A34NNA. baumannii
A35A. baumannii (III)A. baumannii
A36A. baumannii (III)
S. maltophilia (I)
A. baumannii
A37A. baumannii (III)
S. epidermidis (III)
A. baumannii
A38A. baumannii (III)A. baumannii
A39A. baumannii (III)A. baumannii
A40NN
A41NN
A42A. baumannii (I)N
A43A. baumannii (III)A. baumannii
A44A. baumannii (III)A. baumanniiA. baumannii
A45A. baumannii (III)
K. pneumoniae (II)
A. baumannii
A46A. baumannii (III)A. baumannii
A47A. baumannii (I)A. baumannii
A48A. baumannii (III)A. baumannii
A49A. baumannii (III)
S. aureus (III)
S. maltophilia (III)
A. baumannii
S. aureus
A. baumannii
S. aureus
S. maltophilia
A50S. epidermidis (III)N
A51NN
A52NN
A53K. pneumoniae (III)K. pneumoniae
A54NN
A55A. baumannii (III)
E. coli (II)
K. pneumoniae (III)
S. maltophilia (III)
A. baumannii
A56S. epidermidis (III)
E. faecalis (I)
N
A57NN
A58P. aeruginosa (III)N
A59S. maltophilia (I)S. maltophiliaS. aureus
S. maltophilia
A60A. baumannii (I)
P. aeruginosa (III)
A. baumannii
P. aeruginosa
A61A. baumannii (II)A. baumannii
A62K. pneumoniae (II)
S. maltophilia (III)
K. pneumoniae
S. maltophilia
A63A. baumannii (III)A. baumannii
A64A. baumannii (III)A. baumannii
A65A. baumannii (III)
P. aeruginosa (I)
S. maltophilia (II)
A. baumannii
P. aeruginosa
A66K. pneumoniae (II)K. pneumoniae
A67S. aureus (III)S. aureus
A68A. baumannii (III)
P. aeruginosa (III)
A. baumannii
A69A. baumannii (I)
P. aeruginosa (III)
A. baumanniiP. aeruginosa
A70A. baumannii (I)A. baumannii
A71NN
A72A. baumannii (III)
S. maltophilia (I)
A. baumannii
A73A. baumannii (III)
K. pneumoniae (III)
P. aeruginosa (III)
A. baumannii
P. aeruginosa
A74A. baumannii (III)A. baumannii
A75A. baumannii (III)
P. aeruginosa (III)
A. baumannii
A76A. baumannii (I)
P. aeruginosa (III)
S. maltophilia (I)
P. aeruginosa
A77S. epidermidis (I)N
Appendix 1—table 4
Comparative analysis of test results by SSBD, CCT and NGS in the validation stage I.
Sample IDSSBD ResultsCCT ResultsNGS Results
A01S. aureus (I)NS. aureus
A03A. baumannii (III)
S. aureus (I)
A. baumannii
S. aureus
A. baumannii
S. aureus
A20E. faecium (III)
S. capitis (II)
NE. faecium
S. capitis
A25NNN
A27A. baumannii (III)
S. epidermidis (III)
A. baumanniiA. baumannii
S. epidermidis
A31K. pneumoniae (I)
P. aeruginosa (I)
S. epidermidis (I)
NK. pneumoniae
A34NNA. baumannii
A44A. baumannii (III)A. baumanniiA. baumannii
A49A. baumannii (III)
S. aureus (III)
S. maltophilia (III)
A. baumannii
S. aureus
A. baumannii
S. aureus
S. maltophilia
A59S. maltophilia (I)S. maltophiliaS. maltophilia
S. aureus
A69A. baumannii (I)
P. aeruginosa (III)
A. baumanniiP. aeruginosa
Appendix 1—table 5
SSBD and CCT results of BALF samples from experiment group (n=22) and control group (n=24) during the validation stage II.
Patient IDSample IDTest No.SSBD ResultsCCT ResultsNGS Results
Exp.
B01B01-1Test 1NN
B01-2Test 2NNN
B01-3Test 3A. baumannii (III)A. baumannii
B03B03-1Test 1NNN
B03-2Test 2A. baumannii (III)A. baumannii
B05B05-1Test 1A. baumannii (III)
S. maltophilia (II)
A. baumanniiA. baumannii
S. maltophilia
B05-2Test 2A. baumannii (III)
S. maltophilia (I)
A. baumannii
B05-3Test 3A. baumannii (III)
S. maltophilia (I)
B07B07-1Test 1P. aeruginosa (III)P. aeruginosaP. aeruginosa
B07-2Test 2P. aeruginosa (III)P. aeruginosa
B07-3Test 3P. aeruginosa (III)P. aeruginosa
B09B09-1Test 1A. baumannii (III)
S. aureus (III)
S. capitis (III)
S. maltophilia (III)
A. baumannii
B11B11-1Test 1A. baumannii (III)
P. aeruginosa (III)
S. maltophilia (II)
A. baumanniiA. baumannii
P. aeruginosa
S. maltophilia
B11-2Test 2A. baumannii (III)
S. aureus (I)
S. maltophilia (III)
B11-3Test 3P. aeruginosa (III)N
B13B13-1Test 1S. epidermidis (I)NN
B13-2Test 2K. pneumoniae (I)K. pneumoniae
B15B15-1Test 1A. baumannii (I)
E. coli (I)
K. pneumoniae (II)
N
B15-2Test 2K. pneumoniae (I)
E. faecium (I)
B15-3Test 3E. faecium (III)
B17B17-1Test 1A. baumannii (III)
B17-2Test 2A. baumannii (III)
K. pneumoniae (III)
B17-3Test 3A. baumannii (I)
K. pneumoniae (I)
E. faecalis (I)
A. baumannii
B19B19-1Test 1A. baumannii (III)
P. aeruginosa (I)
S. maltophilia (II)
A. baumannii
B19-2Test 2A. baumannii (III)
P. aeruginosa (III)
S. maltophilia (III)
A. baumannii
S. maltophilia
B19-3Test 3K. pneumoniae (III)
P. aeruginosa (III)
S. maltophilia (III)
K. pneumoniae
S. maltophilia
B21B21-1Test 1P. aeruginosa (III)
S. aureus (III)
S. maltophilia (III)
S. aureus
S. maltophilia
B21-2Test 2P. aeruginosa (III)
S. aureus (III)
S. maltophilia (III)
S. maltophilia
B21-3Test 3S. aureus (I)
S. maltophilia (II)
N
B23B23-1Test 1S. capitis (I)
S. maltophilia (III)
S. maltophilia
B23-2Test 2S. maltophilia (III)S. maltophilia
B25B25-1Test 1A. baumannii (II)A. baumannii
B25-2Test 2A. baumannii (III)
B25-3Test 3A. baumannii (III)A. baumannii
B27B27-1Test 1S. maltophilia (I)NN
B27-2Test 2A. baumannii (III)
B29B29-1Test 1K. pneumoniae (III)K. pneumoniaeK. pneumoniae
B29-2Test 2K. pneumoniae (III)
B31B31-1Test 1A. baumannii (III)
S. capitis (III)
A. baumanniiA. baumannii
S. capitis
B31-2Test 2A. baumannii (III)
B31-3Test 3A. baumannii (I)N
B33B33-1Test 1NN
B33-2Test 2A. baumannii (I)
S. epidermidis (II)
N
B33-3Test 3NN
B35B35-1Test 1A. baumannii (III)A. baumanniiA. baumannii
B35-2Test 2A. baumannii (III)A. baumannii
B35-3Test 3A. baumannii (III)A. baumannii
B37B37-1Test 1NNA. baumannii
B37-2Test 2P. aeruginosa (I)
B39B39-1Test 1NP. aeruginosa
B39-2Test 2A. baumannii (I)
E. coli (I)
K. pneumoniae (III)
P. aeruginosa (III)
K. pneumoniae
P. aeruginosa
B39-3Test 3K. pneumoniae (I)
P. aeruginosa (III)
B41B41-1Test 1NNN
B41-2Test 2N
B43B43-1Test 1NN
B43-2Test 2A. baumannii (I)
S. epidermidis (I)
B43-3Test 3A. baumannii (II)A. baumannii
Con.
C02Test 1A. baumannii
Test 2A. baumannii
Test 3A. baumannii
C04Test 1N
Test 2N
Test 3N
C06Test 1N
Test 2A. baumannii
C08Test 1E. coli
Test 2E. coli
Test 3K. pneumoniae
C10Test 1N
Test 2N
Test 3A. baumannii
C12Test 1N
Test 2A. baumannii
Test 3A. baumannii
C14Test 1A. baumannii
Test 2A. baumannii
Test 3A. baumannii
C16Test 1N
Test 2N
C18Test 1A. baumannii
Test 2A. baumannii
Test 3A. baumannii
C20Test 1N
Test 2A. baumannii
Test 3A. baumannii
C22Test 1N
Test 2N
Test 3A. baumannii
C24Test 1N
Test 2N
C26Test 1A. baumannii
Test 2A. baumannii
Test 3A. baumannii
C28Test 1S. aureus
Test 2N
Test 3A. baumannii
C30Test 1N
Test 2A. baumannii
Test 3A. baumannii
C32Test 1N
Test 2K. pneumoniae
Test 3A. baumannii
K. pneumoniae
C34Test 1N
C36Test 1S. aureus
Test 2A. baumannii
Test 3A. baumannii
C38Test 1A. baumannii
Test 2A. baumannii
Test 3A. baumannii
C40Test 1P. aeruginosa
Test 2P. aeruginosa
C42Test 1N
Test 2N
C44Test 1S. aureus
Test 2A. baumannii
C46Test 1N
Test 2N
Test 3A. baumannii
C48Test 1A. baumannii
Test 2A. baumannii
Appendix 1—table 6
Comparative analysis of test results by SSBD, CCT and NGS in the validation stage II.
Patient IDSample IDTest No.SSBD ResultsCCT ResultsNGS Results
B01B01-2Test 2NNN
B03B03-1Test 1NNN
B05B05-1Test 1A. baumannii (III)
S. maltophilia (II)
A. baumanniiA. baumannii
S. maltophilia
B07B07-1Test 1P. aeruginosa (III)P. aeruginosaP. aeruginosa
B11B11-1Test 1A. baumannii (III)
P. aeruginosa (III)
S. maltophilia (II)
A. baumanniiA. baumannii
P. aeruginosa
S. maltophilia
B13B13-1Test 1S. epidermidis (I)NN
B27B27-1Test 1S. maltophilia (I)NN
B29B29-1Test 1K. pneumoniae (III)K. pneumoniaeK. pneumoniae
B31B31-1Test 1A. baumannii (III)
S. capitis (III)
A. baumanniiA. baumannii
S. capitis
B35B35-1Test 1A. baumannii (III)A. baumanniiA. baumannii
B37B37-1Test 1NNA. baumannii
B41B41-1Test 1NNN
Appendix 1—table 7
Antibiotic use of the patients prior to clinical trial in the experimental group and control group.
Empirical antibiotic therapy
Experimental group
 B01Piperacillin -tazobactam
 B03Biapenem
 B05Biapenem, Teicoplanin and Tigecycline
 B07Biapenem
 B09Biapenem and vancomycin
 B11Cefoperazone-sulbactam and Tigecycline
 B13Piperacillin-Tazobactam, trimethoprim-sulfamethoxazole and Teicoplanin
 B15Imipenem-cilastatin and Linezolid
 B17Biapenem
 B19Biapenem
 B21Ceftazidine-avibatam
 B23Imipenem-Cilastatin
 B25Imipenem-Cilastatin and Linezolid
 B27Piperacillin-Tazobactam and trimethoprim-sulfamethoxazole
 B29Imipenem-Cilastatin
 B31Imipenem-Cilastatin and Teicoplanin
 B33Piperacillin-tazobactam
 B35Moxifloxacin and Piperacillin -tazobactam
 B37Biapenem, trimethoprim-sulfamethoxazole and Linezolid
 B39Piperacillin-tazobactam
 B41Piperacillin-tazobactam
 B43Meropenem and Linezolid
Control group
 C02Meropenem and vancomycin
 C04Biapenem and vancomycin
 C06Piperacillin-tazobactam
 C08Imipenem-Cilastatin
 C10Cefoperazone-sulbactam
 C12Moxifloxacin
 C14Biapenem and Linezolid
 C16Cefoperazone-sulbactam
 C18Moxifloxacin
 C20Piperacillin-tazobactam
 C22Piperacillin-tazobactam and vancomycin
 C24Cefoperazone-sulbactam, Linezolid and trimethoprim-sulfamethoxazole
 C26Cefoperazone-sulbactam and Moxifloxacin
 C28Piperacillin-tazobactam
 C30Piperacillin-tazobactam
 C32Piperacillin-tazobactam and Moxifloxacin
 C34Meropenem
 C36Cefoperazone-sulbactam
 C38Piperacillin-tazobactam
 C40Imipenem-Cilastatin
 C42Cefoperazone-sulbactam
 C44Piperacillin-tazobactam
 C46Cefoperazone-sulbactam
 C48Piperacillin-tazobactam and Linezolid
Appendix 1—table 8
Patients' clinical outcomes.
Experimental group (n=22)Control group(n=24)p
Number of patients who have clinical indexes improved
Day 3 vs. Day 1
 Temperature, °C15 (68.2%)9 (37.5%)0.045*
 WBC, 109 /L15 (68.2%)12 (50.0%)0.211
 PCT, ng/mL18 (81.8%)19 (82.6%)0.945
Day 7 vs. Day 1
 Temperature, °C13 (72.2%)12 (54.5%)0.332
 WBC, 109 /L16 (84.2%)11 (50.0%)0.021*
 PCT, ng/mL13 (68.4%)19 (86.4%)0.166
Day 10 vs. Day 1
 Temperature, °C13 (82.6%)12 (70.6%)0.688
 WBC, 109 /L9 (56.3%)8 (47.1%)0.598
 PCT, ng/mL12 (75.0%)15 (88.2%)0.325
Number of patients undergoing effective treatment
Day 313 (59.1%)11 (45.8%)0.395
Day 716 (84.2%)11 (50.0%)0.046*
Day 1013 (81.3%)10 (58.8%)0.259
Clinical endpoint outcomes
Hospital stay duration, days21 (13.7)23.5 (17.2)0.987
28 days mortality8 (36.4%)8 (33.3%)1.000
Mechanical ventilation from randomization to 28th day, days11.3 (7.7)11.5 (7.7)0.970
Shock from randomization to 28th day, days3.1 (4.3)2.3 (3.6)0.456
Numbers of antibiotic-associated diarrhea0 (0.0%)2 (8.3%)0.490
  1. For those data are n (%), all p values are calculated using Fisher’s exact tests. For those data are mean (SD), all p values are calculated using Mann-Whitney tests. * indicated P-value <0.05.

Appendix 1—table 9
Potential competitive analysis among bacteria.
Sample IDBacteria detected by SSBD grow in CCT testsBacteria detected by SSBD could not grow in CCT testsProbable relations among bacteria
A05K. pneumoniae (III)
P. aeruginosa (III)
A. baumannii (III)K. pneumoniae + P. aeruginosa 
> A. baumannii
A09A. baumannii (II)S. aureus (I)Strength: II > I
A10A. baumannii (III)
K. pneumoniae (III)
P. aeruginosa (I)Strength: III + III > I
A11A. baumannii (III)K. pneumoniae (I)Strength: III > I
A16K. pneumoniae (III)P. aeruginosa (III)K. pneumoniae > P. aeruginosa
A21A. baumannii (III)
S. aureus (I)
K. pneumoniae (III)Strength: III + I > III
A22A. baumannii (III)S. epidermidis (III)A. baumannii > S. epidermidis
A27A. baumannii (III)S. epidermidis (III)A. baumannii > S. epidermidis
A28A. baumannii (II)P. aeruginosa (III)A. baumannii > P. aeruginosa
A30A. baumannii (III)S. aureus (III)
S. epidermidis (I)
A. baumannii
> S. aureus + S. epidermidis
A33S. aureus (I)S. maltophilia (II)S. aureus > S. maltophilia
A36A. baumannii (III)S. maltophilia (I)A. baumannii > S. maltophilia
A37A. baumannii (III)S. epidermidis (III)A. baumannii > S. epidermidis
A45A. baumannii (III)K. pneumoniae (II)A. baumannii > K. pneumoniae
A49A. baumannii (III)
S. aureus (III)
S. maltophilia (III)A. baumannii + S. aureus 
> S. maltophilia
A55A. baumannii (III)E. coli (II)
K. pneumoniae (III)
S. maltophilia (III)
A. baumannii > E. coli. coli + K. pneumoniae + S. maltophilia
A65A. baumannii (III)
P. aeruginosa (I)
S. maltophilia (II)Strength: III + I > II
A68A. baumannii (III)P. aeruginosa (III)A. baumannii > P. aeruginosa
A69A. baumannii (I)P. aeruginosa (III)A. baumannii > P. aeruginosa
A72A. baumannii (III)S. maltophilia (I)A. baumannii > S. maltophilia
A73A. baumannii (III)
P. aeruginosa (III)
K. pneumoniae (III)A. baumannii + P. aeruginosa 
> K. pneumoniae
A75A. baumannii (III)P. aeruginosa (III)A. baumannii > P. aeruginosa
A76P. aeruginosa (III)A. baumannii (I)
S. maltophilia (I)
Strength: III > I + I
B05-1A. baumannii (III)S. maltophilia (II)A. baumannii > S. maltophilia
B05-2A. baumannii (III)S. maltophilia (I)A. baumannii > S. maltophilia
B09-1A. baumannii (III)S. aureus (III)
S. capitis (III)
S. maltophilia (III)
A. baumannii > S. aureus
+ S. capitis + S. maltophilia
B11-1A. baumannii (III)P. aeruginosa (III)
S. maltophilia (II)
A. baumannii
> P. aeruginosa + S. maltophilia
B17-3A. baumannii (I)K. pneumoniae (I)
E. faecalis (I)
A. baumannii
> K. pneumoniae +E. faecalis
B19-1A. baumannii (III)P. aeruginosa (I)
S. maltophilia (II)
Strength: III >I + II
B19-2A. baumannii (III), S. maltophilia (III)P. aeruginosa (III)A. baumannii + S. maltophilia > P. aeruginosa
B19-3K. pneumoniae (III), S. maltophilia (III)P. aeruginosa (III)K. pneumoniae + S. maltophilia
> P. aeruginosa
B21-1S. aureus (III)
S. maltophilia (III)
P. aeruginosa (III)S. maltophilia + S. aureus 
> P. aeruginosa
B21-2S. maltophilia (III)S. aureu (III), P. aeruginosa (III)S. maltophilia > S. aureus + P. aeruginosa
B23-1S. maltophilia (III)S. capitis (I)Strength: III > I
B31-1A. baumannii (III)S. capitis (III)A. baumannii > S. capitis
B39-2K. pneumoniae (III)
P. aeruginosa (III)
A. baumannii (I)
E. coli (I)
Strength: III + III > I+I
Appendix 1—table 10
Comparation of CCT, SSBD and NGS.
CCTSSBDNGS
Turnover time2–5 daysLess than 4 hours2–3 days
CostLowLowHigh
Detection targetCulturable bacteriaSelected targetsAll microorganisms in the sample
QuantificationSemiRelativeSemi
Instrument requirementLowLowHigh

Data availability

All data generated or analysed during this study are included in the manuscript and supporting file; Source Data files have been provided for Figures 3-5, Appendix figures 2-5, and Appendix tables 3-9.

References

Decision letter

  1. Bavesh D Kana
    Senior and Reviewing Editor; University of the Witwatersrand, South Africa

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Novel fast pathogen diagnosis method for severe pneumonia patients in the intensive care unit: randomized clinical trial" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Bavesh Kana as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) It is not clear which epidemiological data these pathogens were selected from. Moreover, the selected pathogens are only bacteria. Can the authors expand?

2) The authors imply that the implementation of their method has a clear clinical benefit but the data supporting this claim is sparse. Although the method seemed to improve disease severity, no improvement was indicated for important clinical measures including organ function, time of ventilation, and mortality. Testing more samples may provide a better understanding of the clinical benefit of the method but the data presented provide limited evidence. Are further data available? Hospital stay duration, survival? If not, these statements should be reconsidered.

3) BALF is the primary sample used for diagnosis in this work but can be difficult to obtain in patients with severe symptoms and young children. As a result, it may not be as useful in these patient populations thus reducing the application of this method. Were other sample types tested? Can the authors comment on this limitation?

4) The advantage of SSBD over NGS is clear but the benefit of SSBD compared to tests such as BioFire and Curetis could be made clearer. It seems that these two methods show low clinical specificity and thus cannot be used in the ICU but data or further explanation of this claim would validate the advantage of SSBD to current sepsis detection methods.

5) Consider creating a table for direct comparison of SSBD to CCT and NGS, rather than simply using 12 random samples for NGS.

6) Consider adding time taken to receive results for cultures, SSBD, and NGS to highlight the rapidity of the novel diagnostic test.

7) Consider investigating potential ways to distinguish polymicrobial results found on SSBD testing from colonization/non-pathogenic infections.

Other concerns that must be addressed:

1) Page 9. Please explain how primer specificity was evaluated.

2) Page 9. Were patients on antibiotics before getting into the trial? Add this detail.

3) Page 10 At which timepoint the patients received different treatment based on the results of the culture or SSBD? Was this consistent?

4) Page 11. The second sentence of 3.1 section in results is not clear.

5) How were patients allocated to groups? Randomised?

6) The table describing the patient cohort is in supplementary information. It should be in the main manuscript. It seems that the control and experimental groups were not balanced. Please explain.

7) What was the threshold level of fluorescence (in Figure 3) which was considered important?

Reviewer #1 (Recommendations for the authors):

Did the experimental groups have any differences in hospital stay, or survival? It would be nice to show if there is a correlation with important clinical outcomes.

Reviewer #2 (Recommendations for the authors):

The advantage of SSBD over NGS is clear but the benefit of SSBD compared to BioFire and Curetis could be improved. It seems that these two methods show low clinical specificity and thus cannot be used in the ICU but data or further explanation of this claim would further validate the advantage of SSBD to current sepsis detection methods.

Reviewer #3 (Recommendations for the authors):

Consider creating a table for direct comparison of SSBD to CCT and NGS rather than simply 12 random samples for NGS.

– Consider adding time until results received for cultures, SSBD, and NGS to highlight the rapidity of the novel diagnostic test.

– Consider investigating potential ways to distinguish polymicrobial results found on SSBD testing from colonization/non-pathogenic infections?

https://doi.org/10.7554/eLife.79014.sa1

Author response

Essential revisions:

(1) It is not clear which epidemiological data these pathogens were selected from. Moreover, the selected pathogens are only bacteria. Can the authors expand?

Thank you for the question and sorry for the unclear explanation.

Here we added the reason in Methods part section 2.3 (page 8, line 160-162) and labeled as red. Briefly, based on the pathogenic bacteria record of China and worldwide in both previous studies (Zhou et al., 2014. PLoS One; Sakr et al., 2018. Open Forum Infectious Diseases) and local record (2017, appendix 1-figure 2), the selected 10 species covered 76% sepsis pathogenic bacteria in ICU.

In ICU, many microbes could cause sepsis other than bacteria, such as fungus, viruses, mycoplasma. However, bacteria belong to the major pathogenic microbes that infected more than 84% patients (Sakr et al., 2018. Open Forum Infectious Diseases). On the other hand, according to the high similarity and huge differences in medication decision, identifying pathogenic bacteria is the most difficult but important task among all pathogenic microbes (Rhodes et al., 2017. Intensive Care Medicine). Based on these two aspects, we started this study from bacteria.

The new tool SSBD was based on specie-specific target of pathogenic microbes, which means it can expend to any species other than bacteria. The identification targets could also expend to common drug-resistant genes to provide further information for clinical decision. We have already started to launch a multicenter clinical trial to further evaluate this new tool, which fungus and drug resistant genes were added to the diagnosis targets.

(2) The authors imply that the implementation of their method has a clear clinical benefit but the data supporting this claim is sparse. Although the method seemed to improve disease severity, no improvement was indicated for important clinical measures including organ function, time of ventilation, and mortality. Testing more samples may provide a better understanding of the clinical benefit of the method but the data presented provide limited evidence. Are further data available? Hospital stay duration, survival? If not, these statements should be reconsidered.

We appreciate the comments and the questions. In this study, we planned to evaluate the accuracy and the clinical benefit of the new method SSBD. For the first purpose, we did see the high sensitivity and specificity of SSBD in bacteria detection. For the second purpose, the SSBD significantly shorten the turnover time of obtaining the pathogenic information and definitely help the clinical doctors to make better decisions. In addition, the experimental group demonstrated faster antibiotic coverage and better APACHE II scores comparing with control group. Based on this information, we drew the conclusion that SSBD was an accurate tool with great potential but need “to be applied in more clinical research” (page 21, line 449-450).

As suggested by the reviewer, we added the discussion about the important clinical measures in the discussion (page 20, line 417-419). As showed in appendix table 7, patients in the experimental group for example demonstrated significant better measures of temperature improvement at day 3, WBC improvement at day 7. However, other measures did not demonstrate significant difference between patients from two groups. As we mentioned in the discussion part, the samples size (22 vs 24), potential drug-resistant genes, other pathogenic microbes other than 10 selected bacteria may limit the clinical benefit of SSBD.

To better evaluate SSBD, we have already started to launch a multi-center clinical trial to further evaluate this new tool. Hopefully we could have a better answer to reviewer’s concerns when we finished this trial with better design and larger size.

(3) BALF is the primary sample used for diagnosis in this work but can be difficult to obtain in patients with severe symptoms and young children. As a result, it may not be as useful in these patient populations thus reducing the application of this method. Were other sample types tested? Can the authors comment on this limitation?

We appreciate your suggestions and questions.

First, please allow us to explain why we chose BALF as samples in this study. Based on the previous studies and international guidance, BALF is the best sample which contains less contamination and non-colonized bacteria (Torres et al., 2017. European Respiratory Journal). Considering the real-world situation of patients in ICU, all participants were older than 18 and applied artificial airway, which means the acquisition of BALF is general operation with no difficulty.

On the other hand, we highly agree with reviewer’s opinion that various types of samples would expend the value of SSBD. Based on the principle, any samples with enough DNA information could be test samples for SSBD. In our preliminary tests and other studies, we successfully acquired accurate pathogenic microbe information from sputum, blood, amniotic fluid, cerebrospinal fluid, pus and tissues. Among them, extra sample preparation processes were needed for sputum due to its viscous status. Multiple mature methods were reported in previous studies (Kim et al., 2014. Annals of Laboratory Medicine; Stokell et al., 2014. Journal of Clinical Microbiology).

(4) The advantage of SSBD over NGS is clear but the benefit of SSBD compared to tests such as BioFire and Curetis could be made clearer. It seems that these two methods show low clinical specificity and thus cannot be used in the ICU but data or further explanation of this claim would validate the advantage of SSBD to current sepsis detection methods.

Thank you for the suggestions.

Since the diagnosis products from BioFire and Curetis were neither approved by CFDA nor widely used in China, we had no access to comparing these two methods head-to-head in our study. Based on our understanding, both methods were approved by FDA and used in hospitals in the United States. According to their product instruction and clinical trial results, FilmArray Pneumonia Panel (BioFire) demonstrated various false positive results in bacteria detection.

Author response image 1

Based on the open information about the primers and probes, such false positive results may due to the highly similarities in the selected gene regions. This bottleneck was solved in SSBD due to better DNA markers. To better describe this information, we rewrote relative part in the revised manuscript (page 17, line 359-361).

(5) Consider creating a table for direct comparison of SSBD to CCT and NGS, rather than simply using 12 random samples for NGS.

Thank you for the suggestions.

We extracted all the sample results with all three tests: SSBD, CCT and NGS, and listed as appendix 1-table 5 for stage I (11 samples) and appendix 1-table 6 for stage II (12 samples). We are sorry for the misunderstanding expression about “random samples” in the last version of manuscript. In our clinical trial design, NGS was not included as a mandatory test. Therefore, such information was only acquired when patients determined to get extra information by NGS, which was neither a requirement of current guidance nor covered by medical insurance in China. Luckily, we had 23 samples with NGS results in two stages, and we think a comparison among three methods would be interesting to demonstrate in the manuscript.

(6) Consider adding time taken to receive results for cultures, SSBD, and NGS to highlight the rapidity of the novel diagnostic test.

Thank you for the great suggestion. We added an appendix 1-table 10 to better compare SSBD, CCT and NGS from several main aspects, including the most important one “turnover time” (page 31, Appendix 1)

(7) Consider investigating potential ways to distinguish polymicrobial results found on SSBD testing from colonization/non-pathogenic infections.

We highly appreciate this suggestion.

In fact, this is a problem that has puzzled clinical doctors for a long time. Such distinguish of colonization/non-colonization is a challenge for CCT and NGS. One of the most important reason we chose BALF as sample was to try the best to avoid the effect of colonization. Based on the previous studies, most nonpathogenic bacteria might be eliminated during the acquisition of BALF (Fagon et al., 2000. Annals of Internal Medicine). On the other hand, it is more important to determine the antibiotic that covers all pathogenic bacteria of sepsis patient in ICU rather than miss some of them, which means nonpathogenic infection was not the top priority issue.

Recently, several studies provided some promising solutions to this problem (Dickson et al., 2014. Lancet Respiratory Medicine; Pettigrew et al., 2020).

Journal of Infectious Diseases. In our study, we are trying to target mRNA fragments instead of genome DNA to distinguish colonization and noncolonization by different gene expression patterns for some critical bacteria in sepsis treatment. Hopefully we can demonstrate some promising results in our next publication.

Other concerns that must be addressed:

(1) Page 9. Please explain how primer specificity was evaluated.

Thank you for the suggestions and sorry for the unclear expression. As we mentioned in the answer to question 4, the diagnosis products from BioFire and Curetis were neither approved by CFDA nor widely used in China, we had no access to test the primers on bench. Instead, we selected one example based on the open information, and evaluated the alignment of DNA regions amplified by primers to multiple reference genomes as described in Method part section 2.3 (page 9, line 170-179).

For the primers we designed in SSBD, similar process was applied and no unspecific target was discovered in the representative genome from S. epidermidis. Besides, the primers in SSBD were further tested with different clinical strains of target species and cross validated (page 13, line 265-268, Figure 3C).

(2) Page 9. Were patients on antibiotics before getting into the trial? Add this detail.

Thank you for the question. An appendix 1-table 7 was added to demonstrate this information (page 24-25, Appendix 1).

(3) Page 10 At which timepoint the patients received different treatment based on the results of the culture or SSBD? Was this consistent?

Thank you for the question.

Based on the clinical design, patients from both groups would receive treatment every day during the study, which are consistent in experimental and control groups. Besides the clinical measures, clinical doctors have timely extra pathogenic information about the patients in experimental group to make better treatment decision due to shorter turnover time of SSBD. For example, at day 1, SSBD reported the pathogenic information in 4 hours, while CCT reported the pathogenic information after 2 days.

Author response image 2

(4) Page 11. The second sentence of 3.1 section in results is not clear.

Thank you for the suggestion and sorry for the unclear expression. We rewrote this sentence and provided some examples (page 11, line 227231).

(5) How were patients allocated to groups? Randomised?

Sorry for the inconspicuous expression.

The randomized allocation of patients to two groups was introduced in section 2.4 “patients” in the manuscript (page 10, line 188-190) and detailly described in the appendix file section “Sample size and randomized double-blind trial” (page 2, line 28-32, Appendix 1).

(6) The table describing the patient cohort is in supplementary information. It should be in the main manuscript. It seems that the control and experimental groups were not balanced. Please explain.

Thank you for the important suggestions.

We have moved the patient cohort information table to the main table part as table 1. We are not sure what does the reviewer mean “not balanced”. If it refers to the patient number (22 vs 24), it was due to the randomized process (see line 28-32 in appendix 1). If it refers to the baseline characteristics, only age demonstrated significant difference between control and experimental groups (68 ±9.5 vs 58±17.4), which might be due to the limit size of patients. All other baseline characteristics had no significant difference between two groups.

(7) What was the threshold level of fluorescence (in Figure 3) which was considered important?

We appreciate the question and sorry for the unclear expression.

In figure 3, which aimed to test whether SSBD could identify the target bacteria, the positive threshold level of fluorescence was set to be significantly higher than negative control. Negative control stood for the fluorescence values of PCR products of using DEPC-H2O as input, and the significance was estimated using an unpaired t-test.

Later on, in the clinical study, a more detailed standard was applied to further determine the relative amount of target bacteria (appendix 1, page 4, line 7180)

https://doi.org/10.7554/eLife.79014.sa2

Article and author information

Author details

  1. Yan Wang

    Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
    Contribution
    Resources, Project administration
    Contributed equally with
    Xiaohui Liang
    Competing interests
    No competing interests declared
  2. Xiaohui Liang

    The State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China
    Contribution
    Validation, Writing – original draft
    Contributed equally with
    Yan Wang
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8065-8168
  3. Yuqian Jiang

    The State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China
    Contribution
    Software
    Competing interests
    No competing interests declared
  4. Danjiang Dong

    Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
    Contribution
    Supervision
    Competing interests
    No competing interests declared
  5. Cong Zhang

    The State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  6. Tianqiang Song

    The State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China
    Contribution
    Software
    Competing interests
    No competing interests declared
  7. Ming Chen

    Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
    Contribution
    Supervision
    Competing interests
    No competing interests declared
  8. Yong You

    Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  9. Han Liu

    Department of Critical Care Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
    Contribution
    Supervision
    Competing interests
    No competing interests declared
  10. Min Ge

    Department of Cardiothoracic Surgery Intensive Care Unit, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
    Contribution
    Writing – review and editing
    Competing interests
    No competing interests declared
  11. Haibin Dai

    Department of Neurosurgery Intensive Care Unit, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
    Contribution
    Supervision
    Competing interests
    No competing interests declared
  12. Fengchan Xi

    Research Institute of General Surgery, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  13. Wanqing Zhou

    Department of Laboratory Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
    Contribution
    Supervision
    Competing interests
    No competing interests declared
  14. Jian-Qun Chen

    The State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China
    Contribution
    Writing – review and editing
    Competing interests
    No competing interests declared
  15. Qiang Wang

    The State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China
    Contribution
    Data curation, Methodology, Writing – original draft
    For correspondence
    wangq@nju.edu.cn
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2907-9851
  16. Qihan Chen

    1. The State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China
    2. Medical School of Nanjing University, Nanjing, China
    Contribution
    Supervision, Writing – review and editing
    For correspondence
    chenqihan@nju.edu.cn
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0062-8434
  17. Wenkui Yu

    1. Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
    2. Medical School of Nanjing University, Nanjing, China
    Contribution
    Methodology, Writing – review and editing
    For correspondence
    yudrnj@nju.edu.cn
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4218-0321

Funding

National Natural Science Foundation of China (The National Key Scientific Instrument and Equipment Development Project.)

  • Wenkui Yu

National Natural Science Foundation of China (81927808)

  • Wenkui Yu

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Clinical trial registration NCT04178382.

We acquired the ethics approval (2019-197-01) from the ethics committee of Nanjing Drum Tower Hospital Affiliated to Nanjing University Medical School in July 2019, registered and posted the complete research protocol, informed consent, subject materials, case report form, researcher manual, the introduction of main researchers and other information in Chinese. Later on, this study was registered in English at https://clinicaltrials.gov/ (NCT04178382) in November 2019.

Senior and Reviewing Editor

  1. Bavesh D Kana, University of the Witwatersrand, South Africa

Publication history

  1. Preprint posted: March 25, 2022 (view preprint)
  2. Received: March 28, 2022
  3. Accepted: October 5, 2022
  4. Accepted Manuscript published: October 7, 2022 (version 1)
  5. Version of Record published: October 26, 2022 (version 2)

Copyright

© 2022, Wang, Liang et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Yan Wang
  2. Xiaohui Liang
  3. Yuqian Jiang
  4. Danjiang Dong
  5. Cong Zhang
  6. Tianqiang Song
  7. Ming Chen
  8. Yong You
  9. Han Liu
  10. Min Ge
  11. Haibin Dai
  12. Fengchan Xi
  13. Wanqing Zhou
  14. Jian-Qun Chen
  15. Qiang Wang
  16. Qihan Chen
  17. Wenkui Yu
(2022)
Novel fast pathogen diagnosis method for severe pneumonia patients in the intensive care unit: randomized clinical trial
eLife 11:e79014.
https://doi.org/10.7554/eLife.79014

Further reading

    1. Epidemiology and Global Health
    2. Medicine
    Sonali Amarasekera, Prabhat Jha
    Insight

    Individuals recently diagnosed with a cardiovascular disease are at higher risk of developing a mental illness, with mortality increasing when both conditions are present.

    1. Cell Biology
    2. Medicine
    Sharon Elliot, Paola Catanuto ... Marilyn K Glassberg
    Research Article

    Background:

    MicroRNAs (miRNA) and other components contained in extracellular vesicles may reflect the presence of a disease. Lung tissue, sputum, and sera of individuals with idiopathic pulmonary fibrosis (IPF) show alterations in miRNA expression. We designed this study to test whether urine and/or tissue derived exosomal miRNAs from individuals with IPF carry cargo that can promote fibrosis.

    Methods:

    Exosomes were isolated from urine (U-IPFexo), lung tissue myofibroblasts (MF-IPFexo), serum from individuals with IPF (n=16) and age/sex-matched controls without lung disease (n=10). We analyzed microRNA expression of isolated exosomes and their in vivo bio-distribution. We investigated the effect on ex vivo skin wound healing and in in vivo mouse lung models.

    Results:

    U-IPFexo or MF-IPFexo expressed miR-let-7d, miR-29a-5p, miR-181b-3p and miR-199a-3p consistent with previous reports of miRNA expression obtained from lung tissue/sera from patients with IPF. In vivo bio-distribution experiments detected bioluminescent exosomes in the lung of normal C57Bl6 mice within 5 min after intravenous infusion, followed by distribution to other organs irrespective of exosome source. Exosomes labeled with gold nanoparticles and imaged by transmission electron microscopy were visualized in alveolar epithelial type I and type II cells. Treatment of human and mouse lung punches obtained from control, non-fibrotic lungs with either U-IPFexo or MF-IPFexo produced a fibrotic phenotype. A fibrotic phenotype was also induced in a human ex vivo skin model and in in vivo lung models.

    Conclusions:

    Our results provide evidence of a systemic feature of IPF whereby exosomes contain pro-fibrotic miRNAs when obtained from a fibrotic source and interfere with response to tissue injury as measured in skin and lung models.

    Funding:

    This work was supported in part by Lester and Sue Smith Foundation and The Samrick Family Foundation and NIH grants R21 AG060338 (SE and MKG), U01 DK119085 (IP, RS, MTC).