Many factors predicting the outcome and prognosis of coronavirus disease 2019 (COVID-19) have been identified. These factors have proved valuable for determining prognosis and have guided the treatment of patients at risk for severe COVID-19. Procalcitonin (PCT) is a biomarker that has served as an indicator for bloodstream infections and has been used as a guide to antimicrobial management in sepsis and bacterial infections in the general population (ElGohary et al., 2022; Azzini et al., 2020; Kalil et al., 2016; Schuetz et al., 2013) and in cancer patients with and without neutropenia (Haddad et al., 2018; El Haddad et al., 2018; Chaftari et al., 2021b; Chaftari et al., 2021a). Randomized trials have shown PCT to be useful in guiding decisions regarding antimicrobial therapy for lower respiratory tract infections (Schuetz et al., 2017; Schuetz et al., 2009; Christ-Crain et al., 2006). Several PCT cut-off values have been evaluated and used in different treatment algorithms. PCT cut-off values of 0.25 and 0.5 ng/ml have been adopted for critically ill patients in the intensive care unit (ICU) (Bouadma et al., 2010), neutropenic patients (Azzini et al., 2020; Kalil et al., 2016; Chaftari et al., 2021a), and patients with lower respiratory tract infections (Christ-Crain et al., 2004).

In patients with coronavirus disease 2019 (COVID-19), elevated PCT levels and elevated levels of other inflammatory markers have been associated with more severe COVID-19 both in the general population (Haddad et al., 2018; Frater et al., 2020; Pink et al., 2021; Ponti et al., 2020; Lippi and Plebani, 2020) and in cancer patients (ElGohary et al., 2022; Cai et al., 2021).

Bacterial co-infections may not be prevalent in patients with COVID-19 (Rawson et al., 2020). However, because of the similarity in signs and symptoms between bacterial co-infections and COVID-19 and the difficulty of ruling out a bacterial infection in patients presenting with COVID-19 pneumonia, empirical treatment with antibiotics is often initiated in patients with COVID-19 without a confirmed bacterial co-infection (Rawson et al., 2020). This practice may lead to an emergence of antibiotic resistance, undesirable adverse events, and increase costs (Azzini et al., 2020; Kalil et al., 2016). One study showed that the use of antibiotics in patients with COVID-19 with a PCT level >0.25 ng/ml and with a low suspicion of bacterial infection did not improve clinical outcome (So et al., 2022). Little to no data have been published regarding PCT for antimicrobial stewardship among cancer patients with COVID-19.

Given the widespread use of empiric antibiotics in cancer patients admitted for COVID-19, we evaluated the role of PCT in decreasing the duration of empiric antibiotic therapy in this patient population.

We conducted a retrospective study of cancer patients who were admitted to The University of Texas MD Anderson Cancer Center between March 1, 2020, and June 28, 2021, for COVID-19 and the highest serum PCT level measured within 72 hr after admission was collected for the study. Patients were divided into two groups: PCT level <0.25 ng/ml and PCT level ≥0.25 ng/ml. This cut-off is conventionally suggested and has been used in different algorithms (Christ-Crain et al., 2006; Bouadma et al., 2010; Christ-Crain et al., 2004).

We reviewed the patients’ electronic medical records and collected data pertinent to demographics (age, sex, and race and ethnicity), type of cancer (hematological malignancy vs. solid tumor), cancer status (active vs. in remission), active cancer therapy, co-morbidities, tobacco use, and presence of pneumonia. We assessed laboratory test results, including absolute neutrophil count, PCT level, documented bacterial infections, and sources of cultures. We also extracted data on oxygen saturation, requirement for oxygen supplementation, need for and duration of intravenous (IV) antibiotic therapy, ICU admission, and 30 day mortality after COVID-19 diagnosis. Pneumonia was defined as an abnormal chest imaging (chest radiograph or computed tomography scan) in patients who present with respiratory symptoms.

Our study was approved by the Institutional Review Board of MD Anderson Cancer Center, and a waiver of informed consent was obtained.

We compared the clinical characteristics and outcomes of the patients in the PCT <0.25 ng/ml and PCT ≥0.25 ng/ml groups. We used the χ² or Fisher’s exact test, as appropriate, to compare categorical variables. We used Wilcoxon rank-sum tests to compare continuous variables because of the deviation of the data from the normal distribution. In addition, we used multivariable logistic regression model to evaluate the independent impact of PCT >0.25 ng/ml on each outcome we evaluated. We assessed negative predictive values of PCT levels for the prediction of the various outcomes. We also estimated the relative risks (RR) of various outcomes for a patient with PCT ≥0.25 ng/ml. All tests were two-sided at a significance level of 0.05. The statistical analyses were performed using SAS version 9.3 (SAS Institute Inc, Cary, NC).

We identified 530 patients, of whom 358 (68%) had a PCT level <0.25 ng/ml and 172 (32%) had a PCT level ≥0.25 ng/ml. Patients in the two PCT groups were similar in terms of age, sex, race and ethnicity, type and status of cancer, and active cancer therapy (Table 1). The proportion of patients with an absolute neutrophil count <1000/µl was 9% in both groups; however, the proportion of patients with an absolute lymphocyte count <1000/µl was lower in patients with PCT <0.25 ng/ml (63% vs. 75%; p=0.009) (Table 2).

Patients with PCT <0.25 ng/ml were less likely to require oxygen supplementation within 72 hr of admission (54% vs. 69%, p=0.001); were less likely to have a positive bacterial culture (6% vs. 17%; p<0.0001) from any source, including blood, lower respiratory tract, and urine; and had a lower proportion of patients with pneumonia, although the difference was not significant (76% vs. 82%; p=0.10). Patients with PCT <0.25 ng/ml were less likely to receive IV antibiotic therapy than were patients with PCT ≥0.25 ng/ml (76% vs. 90%; p<0.001). Furthermore, patients with PCT <0.25 ng/ml had a shorter median duration of IV antibiotic therapy (4 days vs. 6 days; p<0.0001) and were less likely to receive antibiotics for ≥72 hr compared to patients with PCT ≥0.25 (45% vs. 69%; p<0.0001) (Table 2). Similar results were found among patients with negative culture results: those with PCT <0.25 ng/ml were less likely to receive IV antibiotics for ≥72 hr than those with PCT ≥0.25 ng/ml (44% vs. 67%; p<0.0001). In addition, among patients with PCT <0.25 ng/ml and negative culture results, those who received a long course of IV antibiotics (≥72 hr) and those who received a shorter course had similar 30 day mortality rates (2% vs. 3%, p=0.71) (Table 3). Compared to patients with PCT ≥0.25 ng/ml, patients with PCT <0.25 ng/ml had shorter median duration of hospital stay (6 days vs. 10 days; p<0.0001), lower rate of ICU admission (14% vs. 29%; p<0.0001), and lower rate of mortality within 30 days of COVID diagnosis (6% vs. 19%; p<0.0001). By subset data analyses, we also found similar significant associations between PCT level and outcomes among patients under active cancer treatment (Supplementary file 2), but not among patients with ANC <1000/µl at admission. However, we need to point it out that the latter subset analyses were limited by a low statistical power due to the small sample size. Furthermore, multivariable logistic regression analyses showed that PCT >0.25 ng/ml was an independent predictor of every outcome we evaluated after adjusting for the possible confounders (Supplementary file 1).

We also evaluated the negative predictive values of PCT <0.25 ng/ml for different outcomes. Analyses showed that PCT <0.25 ng/ml had a high negative predictive value for bacteremia (NPV = 0.94, 95% CI = 0.92–0.97)and 30 day mortality (NPV = 0.94, 95% CI = 0.92–0.97), followed by ICU admission (NPV = 0.86, 95% CI = 0.82–0.89) and IV antibiotic use ≥7 days (NPV = 0.85, 95% CI = 0.81–0.88) (Table 4). Correspondingly, PCT level ≥0.25 ng/ml was associated with elevated RR for 30 day mortality (RR = 3.43, 95% CI = 2.03–5.80) followed by positive bacterial culture (RR = 3.12, 95% CI = 1.83–5.34), IV antibiotic use ≥7 days (RR = 2.08, 95% CI = 1.50–2.90), and ICU admission (RR = 2.04, 95% CI = 1.45–2.88) (Table 4).

In this study of cancer patients admitted for COVID-19, we found that PCT level <0.25 ng/ml was associated with a lower rate of bacterial co-infection, shorter hospital stay, shorter duration of IV antibiotics, and lower 30 day mortality. We also found that among the patients with PCT <0.25 ng/ml and negative bacterial cultures, 30 day mortality was similar for patients treated with IV antibiotics for ≥72 hr and those treated with IV antibiotics for shorter periods.

Our finding that the rate of microbiologically documented bacterial co-infections from any source, including blood, lower respiratory tract, and urine, was lower in patients with PCT <0.25 ng/ml is consistent with well-established findings that pure viral infections are unlikely to increase PCT levels (Gilbert, 2010). Furthermore, both in the general population (ElGohary et al., 2022; Azzini et al., 2020; Schuetz et al., 2013; Schuetz et al., 2017; Schuetz et al., 2009; Christ-Crain et al., 2006) and in immunocompromised patients (Haddad et al., 2018; El Haddad et al., 2018; Chaftari et al., 2021b; Chaftari et al., 2021a), patients with low PCT levels are unlikely to have bacterial infections. PCT levels increase in patients with many types of bacterial infections, including bacterial infections of the lower respiratory tract (Self et al., 2017), bacterial meningitis (Wei et al., 2016), acute pyelonephritis (Zhang et al., 2016), spontaneous bacterial peritonitis (Yang et al., 2015), and bloodstream bacterial infections (Shomali et al., 2012). Our findings regarding PCT levels and the risk of bacterial infection are also consistent with published data on patients with COVID-19 (So et al., 2022; Fabre et al., 2022). In a recent study of patients hospitalized with COVID-19, PCT levels were higher in patients with proven bacterial co-infections: PCT level ≥0.25 ng/ml was seen in 69% of patients with proven co-infection, compared to 35% of those with low suspicion of bacterial co-infection (p<0.001) (So et al., 2022). The low rate of bacterial co-infection in our cancer patients with COVID-19 (about 9%) is also consistent with rates reported in the literature (Rawson et al., 2020; Garcia-Vidal et al., 2021).

Another recent study showed that PCT could be abnormally elevated in patients with COVID-19 with no evidence of pneumonia and may result in overprescribing antibiotics in such patients (Fabre et al., 2022). In our current study, IV antibiotics were administered to 90% of patients with PCT ≥0.25 ng/ml and 76% of patients with PCT <0.25 ng/ml (p<0.001). These high rates are similar to rates reported earlier in the pandemic, which ranged from 70% to 90% (Wang et al., 2020; Chen et al., 2020). This high rate of use of IV antibiotics in our cancer patient population could be due to the vulnerability of our immunocompromised patients. The initial PCT level may not have influenced the decision of the treating physician to initiate IV antibiotics in our frail and immunocompromised cancer patient population.

In hospitalized patients with COVID-19, PCT level ≥0.25 ng/ml was previously found to be a good predictor of oxygen supplementation, ICU admission, mechanical ventilation, and antibiotic use (So et al., 2022). Similarly, in our study, cancer patients with higher PCT levels were more likely to require oxygen supplementation within 72 hr of admission, be admitted in ICU, had a higher 30 day mortality rate, had a longer median duration of hospital stay, and were more likely to receive IV antibiotics.

Our data demonstrate that administering IV antibiotics beyond 72 hr in patients with PCT <0.25 ng/ml and negative bacterial cultures does not improve outcome and may be redundant. Thus, just as PCT has been used to de-escalate antibiotic use in the general population (Schuetz et al., 2017; Schuetz et al., 2009), it can be used to de-escalate antibiotic use in cancer patients with COVID-19.

Our findings that PCT <0.25 ng/ml had a negative predictive value for bacteremia, 30 day mortality, ICU admission, and IV antibiotic use >7 days are consistent with previously published data from patients with COVID-19 (So et al., 2022; Heesom et al., 2020).

The use of PCT levels to guide antibiotic therapy decisions has been important in antimicrobial stewardship outside of the COVID-19 pandemic. However, our data suggest that in cancer patients with COVID-19, if the PCT level is <0.25 ng/ml, there is low suspicion for infection, and if bacterial cultures are negative, PCT could be used as an adjunct to clinical judgment to guide de-escalation of antibiotics after 72 hr. Incorporating PCT into future algorithms for treatment of patients with COVID-19 could be cost-effective and may decrease antibiotic overuse, which is associated with undesirable adverse events (such as Clostridium difficile infection, acute kidney injury, potential allergic reactions, and loss of microbiome diversity) and contributes to the emergence of antibiotic resistance (Azzini et al., 2020; Kip et al., 2015; Kip et al., 2018).

Our study has limitations. First, the retrospective nature of this study may have masked confounding variables. Second, bacterial co-infections may have been overlooked given the limited face-to-face interactions with patients admitted with COVID-19 during the pandemic. Third, antimicrobials were administered empirically at the discretion of the team treating the patient. The patients were not on a defined protocol and the management of empiric antibiotic therapy as well as COVID-related therapies were not standardized which can lead to a wide variety of practices. Hence, we have not listed the type of antibiotics that were administered either as monotherapy or in combination. Similarly, we have not reported on COVID-19 targeted therapies including immunosuppressants received such as steroids or tocilizumab. Furthermore, the study spans the period from March 2020 to June 2021 through which our knowledge of COVID has evolved, multiple variants have emerged, immunization has become available in the later part of the study period, more therapies (antivirals, monoclonal antibodies) became available, all of which could affect COVID-related mortality and outcomes. Finally, this is a single-center study, which limits the generalizability of our results.

These are human subjects and we are unable to share data that contain patients' health information because of IRB restriction. We do not have the patients' consent to share their data. The study protocol, statistical analysis plan, lists of deidentified individual data, generated tables and figures will be made available upon request by qualified scientific and medical researchers for legitimate research purposes. Requests should be sent to achaftari@mdanderson.org and yijiang@mdanderson.org. Data will be available on request for 6 months from the date of publication. Investigators are invited to submit study proposal requests detailing research questions and hypotheses in order to receive access to these data. The software we used for data analysis is SAS version 9.3 (SAS Institute Inc, Cary, NC), and we have provided this information in Statistical analysis section of the manuscript.

1. 1. Azzini AM
   2. Dorizzi RM
   3. Sette P
   4. Vecchi M
   5. Coledan I
   6. Righi E
   7. Tacconelli E

   (2020) A 2020 review on the role of procalcitonin in different clinical settings: an update conducted with the tools of the evidence based laboratory medicine

   Annals of Translational Medicine 8:610.

   https://doi.org/10.21037/atm-20-1855

   • PubMed
   • Google Scholar
2. 1. Bouadma L
   2. Luyt CE
   3. Tubach F
   4. Cracco C
   5. Alvarez A
   6. Schwebel C
   7. Schortgen F
   8. Lasocki S
   9. Veber B
   10. Dehoux M
   11. Bernard M
   12. Pasquet B
   13. Régnier B
   14. Brun-Buisson C
   15. Chastre J
   16. Wolff M
   17. PRORATA trial group

   (2010) Use of procalcitonin to reduce patients’ exposure to antibiotics in intensive care units (PRORATA trial): a multicentre randomised controlled trial

   Lancet 375:463–474.

   https://doi.org/10.1016/S0140-6736(09)61879-1

   • PubMed
   • Google Scholar
3. 1. Cai G
   2. Gao Y
   3. Zeng S
   4. Yu Y
   5. Liu X
   6. Liu D
   7. Wang Y
   8. Yu R
   9. Desai A
   10. Li C
   11. Gao Q

   (2021) Immunological alternation in COVID-19 patients with cancer and its implications on mortality

   Oncoimmunology 10:1854424.

   https://doi.org/10.1080/2162402X.2020.1854424

   • PubMed
   • Google Scholar
4. 1. Chaftari P
   2. Chaftari AM
   3. Hachem R
   4. Yeung SCJ
   5. Dagher H
   6. Jiang Y
   7. Malek AE
   8. Dailey Garnes N
   9. Mulanovich VE
   10. Raad I

   (2021a) The role of procalcitonin in identifying high-risk cancer patients with febrile neutropenia: a useful alternative to the multinational association for supportive care in cancer score

   Cancer Medicine 10:8475–8482.

   https://doi.org/10.1002/cam4.4355

   • PubMed
   • Google Scholar
5. 1. Chaftari P
   2. Qdaisat A
   3. Chaftari AM
   4. Maamari J
   5. Li Z
   6. Lupu F
   7. Raad I
   8. Hachem R
   9. Calin G
   10. Yeung SCJ

   (2021b) Prognostic value of procalcitonin, C-reactive protein, and lactate levels in emergency evaluation of cancer patients with suspected infection

   Cancers 13:4087.

   https://doi.org/10.3390/cancers13164087

   • PubMed
   • Google Scholar
6. 1. Chen N
   2. Zhou M
   3. Dong X
   4. Qu J
   5. Gong F
   6. Han Y
   7. Qiu Y
   8. Wang J
   9. Liu Y
   10. Wei Y
   11. Xia J
   12. Yu T
   13. Zhang X
   14. Zhang L

   (2020) Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in wuhan, china: a descriptive study

   Lancet 395:507–513.

   https://doi.org/10.1016/S0140-6736(20)30211-7

   • PubMed
   • Google Scholar
7. 1. Christ-Crain M
   2. Jaccard-Stolz D
   3. Bingisser R
   4. Gencay MM
   5. Huber PR
   6. Tamm M
   7. Müller B

   (2004) Effect of procalcitonin-guided treatment on antibiotic use and outcome in lower respiratory tract infections: cluster-randomised, single-blinded intervention trial

   Lancet 363:600–607.

   https://doi.org/10.1016/S0140-6736(04)15591-8

   • PubMed
   • Google Scholar
8. 1. Christ-Crain M
   2. Stolz D
   3. Bingisser R
   4. Müller C
   5. Miedinger D
   6. Huber PR
   7. Zimmerli W
   8. Harbarth S
   9. Tamm M
   10. Müller B

   (2006) Procalcitonin guidance of antibiotic therapy in community-acquired pneumonia: a randomized trial

   American Journal of Respiratory and Critical Care Medicine 174:84–93.

   https://doi.org/10.1164/rccm.200512-1922OC

   • PubMed
   • Google Scholar
9. 1. ElGohary GM
   2. Hashmi S
   3. Styczynski J
   4. Kharfan-Dabaja MA
   5. Alblooshi RM
   6. de la Cámara R
   7. Mohmed S
   8. Alshaibani A
   9. Cesaro S
   10. Abd El-Aziz N
   11. Almaghrabi R
   12. Gergis U
   13. Majhail NS
   14. El-Gohary Y
   15. Chemaly RF
   16. Aljurf M
   17. El Fakih R

   (2022) The risk and prognosis of COVID-19 infection in cancer patients: a systematic review and meta-analysis

   Hematology/Oncology and Stem Cell Therapy 15:45–53.

   https://doi.org/10.1016/j.hemonc.2020.07.005

   • PubMed
   • Google Scholar
10. 1. El Haddad H
    2. Chaftari A-M
    3. Hachem R
    4. Chaftari P
    5. Raad II

    (2018) Biomarkers of sepsis and bloodstream infections: the role of procalcitonin and proadrenomedullin with emphasis in patients with cancer

    Clinical Infectious Diseases 67:971–977.

    https://doi.org/10.1093/cid/ciy331

    • PubMed
    • Google Scholar
11. 1. Fabre V
    2. Karaba S
    3. Amoah J
    4. Robinson M
    5. Jones G
    6. Dzintars K
    7. Katz M
    8. Landrum BM
    9. Qasba S
    10. Gupta P
    11. Klein E
    12. Cosgrove SE

    (2022) The role of procalcitonin results in antibiotic decision-making in coronavirus disease 2019 (COVID-19)

    Infection Control and Hospital Epidemiology 43:570–575.

    https://doi.org/10.1017/ice.2021.175

    • PubMed
    • Google Scholar
12. 1. Frater JL
    2. Zini G
    3. d’Onofrio G
    4. Rogers HJ

    (2020) COVID-19 and the clinical hematology laboratory

    International Journal of Laboratory Hematology 42:11–18.

    https://doi.org/10.1111/ijlh.13229

    • PubMed
    • Google Scholar
13. 1. Garcia-Vidal C
    2. Sanjuan G
    3. Moreno-García E
    4. Puerta-Alcalde P
    5. Garcia-Pouton N
    6. Chumbita M
    7. Fernandez-Pittol M
    8. Pitart C
    9. Inciarte A
    10. Bodro M
    11. Morata L
    12. Ambrosioni J
    13. Grafia I
    14. Meira F
    15. Macaya I
    16. Cardozo C
    17. Casals C
    18. Tellez A
    19. Castro P
    20. Marco F
    21. García F
    22. Mensa J
    23. Martínez JA
    24. Soriano A
    25. COVID-19 Researchers Group

    (2021) Incidence of co-infections and superinfections in hospitalized patients with COVID-19: a retrospective cohort study

    Clinical Microbiology and Infection 27:83–88.

    https://doi.org/10.1016/j.cmi.2020.07.041

    • PubMed
    • Google Scholar
14. 1. Gilbert DN

    (2010) Use of plasma procalcitonin levels as an adjunct to clinical microbiology

    Journal of Clinical Microbiology 48:2325–2329.

    https://doi.org/10.1128/JCM.00655-10

    • PubMed
    • Google Scholar
15. 1. Haddad HE
    2. Chaftari A-M
    3. Hachem R
    4. Michael M
    5. Jiang Y
    6. Yousif A
    7. Raad S
    8. Jordan M
    9. Chaftari P
    10. Raad I

    (2018) Procalcitonin guiding antimicrobial therapy duration in febrile cancer patients with documented infection or neutropenia

    Scientific Reports 8:1099.

    https://doi.org/10.1038/s41598-018-19616-3

    • PubMed
    • Google Scholar
16. 1. Heesom L
    2. Rehnberg L
    3. Nasim-Mohi M
    4. Jackson AIR
    5. Celinski M
    6. Dushianthan A
    7. Cook P
    8. Rivinberg W
    9. Saeed K

    (2020) Procalcitonin as an antibiotic stewardship tool in COVID-19 patients in the intensive care unit

    Journal of Global Antimicrobial Resistance 22:782–784.

    https://doi.org/10.1016/j.jgar.2020.07.017

    • PubMed
    • Google Scholar
17. 1. Kalil AC
    2. Metersky ML
    3. Klompas M
    4. Muscedere J
    5. Sweeney DA
    6. Palmer LB
    7. Napolitano LM
    8. O’Grady NP
    9. Bartlett JG
    10. Carratalà J
    11. El Solh AA
    12. Ewig S
    13. Fey PD
    14. File TM Jr
    15. Restrepo MI
    16. Roberts JA
    17. Waterer GW
    18. Cruse P
    19. Knight SL
    20. Brozek JL

    (2016) Management of adults with hospital-acquired and ventilator-associated pneumonia: 2016 clinical practice guidelines by the infectious diseases Society of America and the American thoracic Society

    Clinical Infectious Diseases 63:e61–e111.

    https://doi.org/10.1093/cid/ciw353

    • PubMed
    • Google Scholar
18. 1. Kip MMA
    2. Kusters R
    3. IJzerman MJ
    4. Steuten LMG

    (2015) A PCT algorithm for discontinuation of antibiotic therapy is a cost-effective way to reduce antibiotic exposure in adult intensive care patients with sepsis

    Journal of Medical Economics 18:944–953.

    https://doi.org/10.3111/13696998.2015.1064934

    • PubMed
    • Google Scholar
19. 1. Kip MMA
    2. van Oers JA
    3. Shajiei A
    4. Beishuizen A
    5. Berghuis AMS
    6. Girbes AR
    7. de Jong E
    8. de Lange DW
    9. Nijsten MWN
    10. IJzerman MJ
    11. Koffijberg H
    12. Kusters R

    (2018) Cost-effectiveness of procalcitonin testing to guide antibiotic treatment duration in critically ill patients: results from a randomised controlled multicentre trial in the netherlands

    Critical Care 22:293.

    https://doi.org/10.1186/s13054-018-2234-3

    • PubMed
    • Google Scholar
20. 1. Lippi G
    2. Plebani M

    (2020) Procalcitonin in patients with severe coronavirus disease 2019 (COVID-19): a meta-analysis

    Clinica Chimica Acta; International Journal of Clinical Chemistry 505:190–191.

    https://doi.org/10.1016/j.cca.2020.03.004

    • PubMed
    • Google Scholar
21. 1. Pink I
    2. Raupach D
    3. Fuge J
    4. Vonberg R-P
    5. Hoeper MM
    6. Welte T
    7. Rademacher J

    (2021) C-Reactive protein and procalcitonin for antimicrobial stewardship in COVID-19

    Infection 49:935–943.

    https://doi.org/10.1007/s15010-021-01615-8

    • PubMed
    • Google Scholar
22. 1. Ponti G
    2. Maccaferri M
    3. Ruini C
    4. Tomasi A
    5. Ozben T

    (2020) Biomarkers associated with COVID-19 disease progression

    Critical Reviews in Clinical Laboratory Sciences 57:389–399.

    https://doi.org/10.1080/10408363.2020.1770685

    • PubMed
    • Google Scholar
23. 1. Rawson TM
    2. Moore LSP
    3. Zhu N
    4. Ranganathan N
    5. Skolimowska K
    6. Gilchrist M
    7. Satta G
    8. Cooke G
    9. Holmes A

    (2020) Bacterial and fungal coinfection in individuals with coronavirus: a rapid review to support COVID-19 antimicrobial prescribing

    Clinical Infectious Diseases 71:2459–2468.

    https://doi.org/10.1093/cid/ciaa530

    • PubMed
    • Google Scholar
24. 1. Schuetz P
    2. Christ-Crain M
    3. Thomann R
    4. Falconnier C
    5. Wolbers M
    6. Widmer I
    7. Neidert S
    8. Fricker T
    9. Blum C
    10. Schild U
    11. Regez K
    12. Schoenenberger R
    13. Henzen C
    14. Bregenzer T
    15. Hoess C
    16. Krause M
    17. Bucher HC
    18. Zimmerli W
    19. Mueller B
    20. ProHOSP Study Group

    (2009) Effect of procalcitonin-based guidelines vs standard guidelines on antibiotic use in lower respiratory tract infections: the prohosp randomized controlled trial

    JAMA 302:1059–1066.

    https://doi.org/10.1001/jama.2009.1297

    • PubMed
    • Google Scholar
25. 1. Schuetz P
    2. Raad I
    3. Amin DN

    (2013) Using procalcitonin-guided algorithms to improve antimicrobial therapy in ICU patients with respiratory infections and sepsis

    Current Opinion in Critical Care 19:453–460.

    https://doi.org/10.1097/MCC.0b013e328363bd38

    • PubMed
    • Google Scholar
26. 1. Schuetz P
    2. Wirz Y
    3. Sager R
    4. Christ-Crain M
    5. Stolz D
    6. Tamm M
    7. Bouadma L
    8. Luyt CE
    9. Wolff M
    10. Chastre J
    11. Tubach F
    12. Kristoffersen KB
    13. Burkhardt O
    14. Welte T
    15. Schroeder S
    16. Nobre V
    17. Wei L
    18. Bucher HC
    19. Bhatnagar N
    20. Annane D
    21. Reinhart K
    22. Branche A
    23. Damas P
    24. Nijsten M
    25. de Lange DW
    26. Deliberato RO
    27. Lima SS
    28. Maravić-Stojković V
    29. Verduri A
    30. Cao B
    31. Shehabi Y
    32. Beishuizen A
    33. Jensen J-US
    34. Corti C
    35. Van Oers JA
    36. Falsey AR
    37. de Jong E
    38. Oliveira CF
    39. Beghe B
    40. Briel M
    41. Mueller B

    (2017) Procalcitonin to initiate or discontinue antibiotics in acute respiratory tract infections

    The Cochrane Database of Systematic Reviews 10:CD007498.

    https://doi.org/10.1002/14651858.CD007498.pub3

    • PubMed
    • Google Scholar
27. 1. Self WH
    2. Balk RA
    3. Grijalva CG
    4. Williams DJ
    5. Zhu Y
    6. Anderson EJ
    7. Waterer GW
    8. Courtney DM
    9. Bramley AM
    10. Trabue C
    11. Fakhran S
    12. Blaschke AJ
    13. Jain S
    14. Edwards KM
    15. Wunderink RG

    (2017) Procalcitonin as a marker of etiology in adults hospitalized with community-acquired pneumonia

    Clinical Infectious Diseases 65:183–190.

    https://doi.org/10.1093/cid/cix317

    • PubMed
    • Google Scholar
28. 1. Shomali W
    2. Hachem R
    3. Chaftari AM
    4. Jiang Y
    5. Bahu R
    6. Jabbour J
    7. Raad S
    8. Al Shuaibi M
    9. Al Wohoush I
    10. Raad I

    (2012) Can procalcitonin distinguish infectious fever from tumor-related fever in non-neutropenic cancer patients?

    Cancer 118:5823–5829.

    https://doi.org/10.1002/cncr.27602

    • PubMed
    • Google Scholar
29. 1. So W
    2. Simon MS
    3. Choi JJ
    4. Wang TZ
    5. Williams SC
    6. Chua J
    7. Kubin CJ

    (2022) Characteristics of procalcitonin in hospitalized COVID-19 patients and clinical outcomes of antibiotic use stratified by procalcitonin levels

    Internal and Emergency Medicine 17:1405–1412.

    https://doi.org/10.1007/s11739-022-02955-5

    • PubMed
    • Google Scholar
30. 1. Wang D
    2. Hu B
    3. Hu C
    4. Zhu F
    5. Liu X
    6. Zhang J
    7. Wang B
    8. Xiang H
    9. Cheng Z
    10. Xiong Y
    11. Zhao Y
    12. Li Y
    13. Wang X
    14. Peng Z

    (2020) Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China

    JAMA 323:1061–1069.

    https://doi.org/10.1001/jama.2020.1585

    • PubMed
    • Google Scholar
31. 1. Wei T-T
    2. Hu Z-D
    3. Qin B-D
    4. Ma N
    5. Tang Q-Q
    6. Wang L-L
    7. Zhou L
    8. Zhong R-Q

    (2016) Diagnostic accuracy of procalcitonin in bacterial meningitis versus nonbacterial meningitis: a systematic review and meta-analysis

    Medicine 95:e3079.

    https://doi.org/10.1097/MD.0000000000003079

    • PubMed
    • Google Scholar
32. 1. Yang Y
    2. Li L
    3. Qu C
    4. Zeng B
    5. Liang S
    6. Luo Z
    7. Wang X
    8. Zhong C

    (2015) Diagnostic accuracy of serum procalcitonin for spontaneous bacterial peritonitis due to end-stage liver disease: a meta-analysis

    Medicine 94:e2077.

    https://doi.org/10.1097/MD.0000000000002077

    • PubMed
    • Google Scholar
33. 1. Zhang H
    2. Yang J
    3. Lin L
    4. Huo B
    5. Dai H
    6. He Y

    (2016) Diagnostic value of serum procalcitonin for acute pyelonephritis in infants and children with urinary tract infections: an updated meta-analysis

    World Journal of Urology 34:431–441.

    https://doi.org/10.1007/s00345-015-1630-4

    • PubMed
    • Google Scholar

Decision letter after peer review:

Thank you for submitting your article "Procalcitonin for Antimicrobial Stewardship Among Cancer Patients Admitted with COVID-19" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Lisa L Dever (Reviewer #1); Vincent Yeung (Reviewer #2).

As is customary in eLife, the reviewers have discussed their critiques with one another. What follows below is the Reviewing Editor's edited compilation of the essential and ancillary points provided by reviewers in their critiques and in their interaction post-review. Please submit a revised version that addresses these concerns directly. Although we expect that you will address these comments in your response letter, we also need to see the corresponding revision clearly marked in the text of the manuscript. Some of the reviewers' comments may seem to be simple queries or challenges that do not prompt revisions to the text. Please keep in mind, however, that readers may have the same perspective as the reviewers. Therefore, it is essential that you attempt to amend or expand the text to clarify the narrative accordingly.

Essential revisions:

1. Please reconsider the use of the word "unnecessary" throughout the manuscript (used thrice) as it might come across as dogmatic and formulaic, but it should be contextual. In other words, there may well be instances when a patient has negative cultures and low PCT in which antibiotics are perhaps clinically indicated.

2. Please clarify certain timing issues regarding the PCT measurements, namely: A single initial PCT was used to initially sort patients. (i) Did the patients have a repeat PCT level after a few days of therapy? If PCT levels were repeated, were the results collected for the study? (ii) If the patient had a PCT <0.25 ng/ml initially, but had a subsequent value >0.25 ng/ml, were they still included in the study in the <0.25 group? (iii) If so, did these PCT changes influence clinical outcomes and antibiotic therapy duration?

3. Please clarify the criteria by which the presence of "pneumonia" is defined. Is it a clinical definition based on signs/symptoms? Or a radiographic definition based on some imaging findings? Or is the diagnosis based on documentation by the treating physician? And does this determination refer to any pneumonia, COVID-19 pneumonia, or just bacterial pneumonia?

4. Please state the antimicrobial therapy used. The institution has an antimicrobial stewardship program, but no standardized empiric therapy has been used in this setting. Therefore, the management of antimicrobial therapy has been left at the discretion of the treating physician, but the class and nature of the drugs administered is not reported here. Moreover, the authors use the terms antimicrobial, antibacterial, and antibiotic therapy interchangeably: Does antibiotic refer to antibacterial only? Finally, the use of antifungal therapy is not fully addressed, but there have evidently been more patients that had positive fungal cultures in the high PCT group. Please review the wording throughout the manuscript to ensure consistency and appropriateness.

5. Please comment on the use of COVID-19 targeted therapies, including immunosuppressants (such as steroids and tocilizumab), which may in turn increase the risk of secondary infections, but perhaps decrease the risk of mortality.

Reviewer #1 (Recommendations for the authors):

I have a few comments and queries for the authors to improve the manuscript.

The paper states that a single initial PCT was used to sort patients. If PCTs were repeated on patients, were the results collected for the study?

If the patient had a PCT <0.25 ng/ml initially, but had a subsequent value >0.25 ng/ml, were they still included in the study in the <0.25 group?

Were PCT levels in hemodialysis patients collected before dialysis?

Were there other patient variables associated with unreliable PCT levels?

I assume the authors' center has a robust antimicrobial stewardship program. Did this influence prescribing of antibiotics? Does the center have a protocol that PCT are collected on patients with COVID-19, or only co-infection is suspected.

The authors use antimicrobial, antibacterial and antibiotic therapy. Does antibiotic refer to antibacterial only? I would review the use in the manuscript to make sure it is consistent and appropriate. Use of antifungal therapy is not addressed, but there were more patients that had positive fungal cultures in the PCT> 0.25 group.

What criteria were used for presence of pneumonia? Imaging results? Is this bacterial pneumonia? COVID-19 pneumonia?

I would rethink the use of "unnecessary" in the discussion, conclusions and abstract. It is used 3 times in the manuscript. It comes across as dogmatic. There may be times when a patient has negative cultures and low PCT where antibiotics may be appropriate. The use of "unnecessary" is contextual.

Reviewer #2 (Recommendations for the authors):

– Table 1 is quite long and busy, consider shortening the list of patient characteristics.

– Would be interesting/useful to see subgroup analysis of more specific scenarios among cancer patients with COVID 19 i.e neutropenic patients, patients under active treatment, patients requiring icu level care.

Reviewer #3 (Recommendations for the authors):

1. Lines 94, 95, 100: I would refrain from using the word rate to describe characteristics or outcomes that do not include a time component. A better wording would be "the proportion of patients with".

2. Line 100: It might be best if the authors clarify how they defined pneumonia: Was it a clinical definition based on signs/symptoms? Or a radiographic definition based on some imaging findings? Or was it based on documentation by the treating physician? And does this refer to any pneumonia, or just bacterial pneumonia?

3. Lines 102-103: Was there a protocol of ABx de-escalation in place based on serum procalcitonin levels that prompted physicians to stop ABx early? Even as part of the institutional COVID protocol? Or was the early discontinuation of ABx solely at the discretion of the treating physician?

4. Lines 113-116: if the word count allows, I would expand a bit more on the NPV and RR values since the conclusions are based on the results of this specific analysis.

5. Lines 143-144: The authors mention the initial PCT level. Did the patients have a repeat PCT level after a few days of therapy? and if so, did this influence clinical outcomes and ABx duration?

6. Lines 147-149: The authors state that cancer patients with higher PCT levels were more likely to require O2 supplementation, be admitted to the ICU etc. Can this finding be only attributed to a bacterial coinfection?

7. Lines 154-159: These seem like a repetition of some of the results.

8. Table 1: I would split table 1 into two tables to provide better clarity for the reader:

– Baseline patient characteristics (all the way until obstructive sleep apnea).

– Characteristics of COVID hospital admission (from ANC until the end of the table).

9. Would review column width distribution in table 2.

Essential revisions:

1. Please reconsider the use of the word "unnecessary" throughout the manuscript (used thrice) as it might come across as dogmatic and formulaic, but it should be contextual. In other words, there may well be instances when a patient has negative cultures and low PCT in which antibiotics are perhaps clinically indicated.

The use of the word “unnecessary” has been revised and toned down according to the context.

2. Please clarify certain timing issues regarding the PCT measurements, namely: A single initial PCT was used to initially sort patients. (i) Did the patients have a repeat PCT level after a few days of therapy? If PCT levels were repeated, were the results collected for the study? (ii) If the patient had a PCT <0.25 ng/ml initially, but had a subsequent value >0.25 ng/ml, were they still included in the study in the <0.25 group? (iii) If so, did these PCT changes influence clinical outcomes and antibiotic therapy duration?

Thank you for the above question. Patients may have had repeated PCT levels. For the purpose of this study we only collected the highest PCT level within the first 72 hours and classified the patients accordingly. We have clarified this point in the revised manuscript.

3. Please clarify the criteria by which the presence of "pneumonia" is defined. Is it a clinical definition based on signs/symptoms? Or a radiographic definition based on some imaging findings? Or is the diagnosis based on documentation by the treating physician? And does this determination refer to any pneumonia, COVID-19 pneumonia, or just bacterial pneumonia?

We used a radiographic definition based on imaging findings in the right clinical context in the presence of signs and symptoms to determine the presence of pneumonia by reviewing each patient’s Chest X ray and CT scan imaging report. We did not distinguish between types of pneumonia. We have added the following definition of pneumonia in the Methods section “Pneumonia was defined as an abnormal chest imaging (Chest radiograph or Computed tomography (CT) scan) in patients who present with respiratory symptoms”.

4. Please state the antimicrobial therapy used. The institution has an antimicrobial stewardship program, but no standardized empiric therapy has been used in this setting. Therefore, the management of antimicrobial therapy has been left at the discretion of the treating physician, but the class and nature of the drugs administered is not reported here. Moreover, the authors use the terms antimicrobial, antibacterial, and antibiotic therapy interchangeably: Does antibiotic refer to antibacterial only? Finally, the use of antifungal therapy is not fully addressed, but there have evidently been more patients that had positive fungal cultures in the high PCT group. Please review the wording throughout the manuscript to ensure consistency and appropriateness.

We have revised the manuscript and used antibacterial throughout for consistency. We however used the general term “antimicrobial” when referring to antimicrobial stewardship program.

We did not collect data on antifungal therapy.

We did not report on the type of antibiotics, given that this was a retrospective study, patients were not on a defined protocol, the management of empiric antibiotic therapy was not standardized but was left at the discretion of the treating physician. This was also added as a limitation in the Discussion section. The class and nature of the antibiotics used as monotherapy or in combination included: fluoroquinolones (such as levofloxacin, ciprofloxacin and moxifloxacin,), antipseudomonal/antipneumococcal β-lactams (piperacillin-tazobactam, cefepime, ceftazidime, cefazolin, ceftriaxone, meropenem, ertapenem, cefiderocol), anti-MRSA agents (vancomycin, linezolid, dalbavancin, daptomycin, clindamycin, doxycycline, linezolid, minocycline, rifampin, tigecycline, trimethoprim-sulfamethoxazole), and aminoglycoside (amikacin).

5. Please comment on the use of COVID-19 targeted therapies, including immunosuppressants (such as steroids and tocilizumab), which may in turn increase the risk of secondary infections, but perhaps decrease the risk of mortality.

We agree with the reviewer. However, we did not collect such information. This was added as a limitation in the Discussion section.

Reviewer #1 (Recommendations for the authors):

I have a few comments and queries for the authors to improve the manuscript.

The paper states that a single initial PCT was used to sort patients. If PCTs were repeated on patients, were the results collected for the study?

If the patient had a PCT <0.25 ng/ml initially, but had a subsequent value >0.25 ng/ml, were they still included in the study in the <0.25 group?

We only collected the highest serum PCT level measured within 72 hours after admission and did not look at subsequent PCTs

Were PCT levels in hemodialysis patients collected before dialysis?

We did not collect dialysis information.

Were there other patient variables associated with unreliable PCT levels?

As shown in Tables 1 and 2 in the revised manuscript, some other variables were also significantly associated with higher PCT by univariate analysis, including chronic kidney disease, ALC<1000/ul at admission, pneumonia and oxygen supplementation within 72 hours. We thank the reviewer for this comment and following it, we performed multivariable logistic regression analyses to evaluate the independent impact of PCT >0.25 ng/ml on each outcome adjusting for possible confounders. The analyses showed that PCT>0.25 was an independent predictor of every outcome we evaluated. We have included the analyses results in Supplemental Table 1 in the revised manuscript. On the other hand, we have tried to consider all the relevant variables that could affect the outcomes being studied. We however acknowledge that because of the retrospective nature of the study (listed as a limitation), potential unmeasured confounding variables could have impacted our results.

I assume the authors' center has a robust antimicrobial stewardship program. Did this influence prescribing of antibiotics? Does the center have a protocol that PCT are collected on patients with COVID-19, or only co-infection is suspected.

PCT is ordered in our emergency center on all patients who present with a suspicion for sepsis. PCT level did not influence the physician’s prescription of antibiotics.

The authors use antimicrobial, antibacterial and antibiotic therapy. Does antibiotic refer to antibacterial only? I would review the use in the manuscript to make sure it is consistent and appropriate. Use of antifungal therapy is not addressed, but there were more patients that had positive fungal cultures in the PCT> 0.25 group.

We have revised the manuscript and used antibacterial throughout for consistency. We however used the general term “antimicrobial” when referring to antimicrobial stewardship program.

We did not collect data on antifungal therapy. We only looked at bacterial infections and antibacterial therapy.

What criteria were used for presence of pneumonia? Imaging results? Is this bacterial pneumonia? COVID-19 pneumonia?

As mentioned above, we looked at abnormal imaging results in the right clinical context and we did not distinguish between types of pneumonia.

I would rethink the use of "unnecessary" in the discussion, conclusions and abstract. It is used 3 times in the manuscript. It comes across as dogmatic. There may be times when a patient has negative cultures and low PCT where antibiotics may be appropriate. The use of "unnecessary" is contextual.

As mentioned above, the use of the word “unnecessary” has been revised and toned down according to the context and the manuscript revised accordingly.

Reviewer #2 (Recommendations for the authors):

– Table 1 is quite long and busy, consider shortening the list of patient characteristics.

– Would be interesting/useful to see subgroup analysis of more specific scenarios among cancer patients with COVID 19 i.e neutropenic patients, patients under active treatment, patients requiring icu level care.

We have shortened the list of patient characteristics and split table 1 into two tables (1 and 2) to provide better clarity for the reader. We adjusted the number of the following tables accordingly.

Following the reviewer comment, we performed the association analyses between PCT levels (PCT< 0.25 vs PCT ≥ 0.25) and outcomes among: (1) patients with ANC <1000 at admission; (2) patients under active cancer treatment. However, we were not able to perform such analyses among patients requiring ICU admission because we did not collect data on ICU admission at baseline. The ICU admission data we collected were an outcome data of the study. The analyses results have been added in the manuscript in Supplemental Table 2 and summarized in Results section.

Reviewer #3 (Recommendations for the authors):

1. Lines 94, 95, 100: I would refrain from using the word rate to describe characteristics or outcomes that do not include a time component. A better wording would be "the proportion of patients with".

As suggested, we changed the word “rate” to “proportion of patients with” in lines 94, 95 and 100.

2. Line 100: It might be best if the authors clarify how they defined pneumonia: Was it a clinical definition based on signs/symptoms? Or a radiographic definition based on some imaging findings? Or was it based on documentation by the treating physician? And does this refer to any pneumonia, or just bacterial pneumonia?

We relied on patient imaging to determine the presence or absence of pneumonia in patients with respiratory symptoms; we did not differentiate between types of pneumonia.

3. Lines 102-103: Was there a protocol of ABx de-escalation in place based on serum procalcitonin levels that prompted physicians to stop ABx early? Even as part of the institutional COVID protocol? Or was the early discontinuation of ABx solely at the discretion of the treating physician?

The discontinuation of ABx was solely at the discretion of the treating physician.

4. Lines 113-116: if the word count allows, I would expand a bit more on the NPV and RR values since the conclusions are based on the results of this specific analysis.

We agree and thank the reviewer for the advice. We have expanded our description on NPV and RR data by providing details in the Results section.

5. Lines 143-144: The authors mention the initial PCT level. Did the patients have a repeat PCT level after a few days of therapy? and if so, did this influence clinical outcomes and ABx duration?

For this study we only collected and analyzed the highest initial PCT level within the first 72 hours and not subsequent PCT levels.

6. Lines 147-149: The authors state that cancer patients with higher PCT levels were more likely to require O2 supplementation, be admitted to the ICU etc. Can this finding be only attributed to a bacterial coinfection?

We performed Cochran-Mantel-Haenszel (CMH) test to evaluate the association between PCT level and oxygen supplementation requirement while adjusting for bacterial coinfection and found that their association after the adjustment remains significant (p=0.002). Similarly, the association between PCT and ICU admission remains significant after adjustment for bacterial coinfection (p<.0001) by a CMH test. In addition, we performed multivariable logistic regression analysis on each outcome and found that PCT>=0.25 was independently associated with each outcome we evaluated after adjusting for possible confounders. The analyses results were added in Supplemental Table 1 in the revised manuscript.

7. Lines 154-159: These seem like a repetition of some of the results.

This paragraph was deleted.

8. Table 1: I would split table 1 into two tables to provide better clarity for the reader:

– Baseline patient characteristics (all the way until obstructive sleep apnea).

– Characteristics of COVID hospital admission (from ANC until the end of the table).

Thank you for your suggestion. We have split the table into Table 1 and Table 2 according to your suggestion and edited the number of the other tables accordingly.

9. Would review column width distribution in table 2.

We have reformatted old tables 2 and 3 (that are now tables 3 and4).

Author details

1. Hiba Dagher

   Department of Infectious Diseases, Infection Control and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, United States

   Contribution

   Conceptualization, Writing – original draft, Writing – review and editing, acquisition of data critical review, commentary

   Competing interests

   No competing interests declared

2. Anne-Marie Chaftari

   Department of Infectious Diseases, Infection Control and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, United States

   Contribution

   Conceptualization, Supervision, Writing – original draft, Writing – review and editing, oversight and leadership responsibilities, including mentorship

   For correspondence

   achaftari@mdanderson.org

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8097-8452

3. Patricia Mulanovich

   Department of Infectious Diseases, Infection Control and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, United States

   Contribution

   Writing – review and editing, acquisition of data critical review, commentary

   Competing interests

   No competing interests declared

4. Ying Jiang

   Department of Infectious Diseases, Infection Control and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, United States

   Contribution

   Software, Formal analysis, Visualization

   Competing interests

   No competing interests declared

5. Ray Hachem

   Department of Infectious Diseases, Infection Control and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, United States

   Contribution

   Conceptualization, Writing – review and editing, oversight and leadership responsibilities, including mentorship

   Competing interests

   No competing interests declared

6. Alexandre E Malek

   Department of Infectious Diseases, Infection Control and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, United States

   Contribution

   Conceptualization, Writing – review and editing, critical review, commentary, acquisiton of data

   Competing interests

   No competing interests declared

7. Jovan Borjan

   Pharmacy Clinical Programs, The University of Texas MD Anderson Cancer Center, Houston, United States

   Contribution

   Writing – review and editing, critical review, commentary, acquisition of data

   Competing interests

   No competing interests declared

8. George M Viola

   Department of Infectious Diseases, Infection Control and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, United States

   Contribution

   Conceptualization, Writing – review and editing, critical review, commentary, acquisition of data

   Competing interests

   No competing interests declared

9. Issam Raad

   Department of Infectious Diseases, Infection Control and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, United States

   Contribution

   Conceptualization, Supervision, Writing – review and editing, oversight and leadership responsibilities, including mentorship, critical review, commentary

   Competing interests

   No competing interests declared

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