Exploratory data on the clinical efficacy of monoclonal antibodies against SARS-CoV-2 Omicron variant of concern

  1. Fulvia Mazzaferri
  2. Massimo Mirandola
  3. Alessia Savoldi
  4. Pasquale De Nardo
  5. Matteo Morra
  6. Maela Tebon
  7. Maddalena Armellini
  8. Giulia De Luca
  9. Lucrezia Calandrino
  10. Lolita Sasset
  11. Denise D'Elia
  12. Emanuela Sozio
  13. Elisa Danese
  14. Davide Gibellini
  15. Isabella Monne
  16. Giovanna Scroccaro
  17. Nicola Magrini
  18. Annamaria Cattelan
  19. Carlo Tascini
  20. MANTICO Working Group
  21. Evelina Tacconelli  Is a corresponding author
  1. Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Italy
  2. Infectious Disease Unit, Padova University Hospital, Italy
  3. Infectious Diseases Division, Department of Medicine, University of Udine and Azienda Sanitaria Universitaria Friuli Centrale, Italy
  4. Section of Clinical Biochemistry, Department of Neuroscience, Biomedicine and Movement, University of Verona, Italy
  5. Microbiology and Virology Unit, Department of Diagnostics and Public Health, University of Verona, Italy
  6. Viral genomics and transcriptomics Laboratory, Istituto Zooprofilattico Sperimentale delle Venezie, Italy
  7. Direzione Farmaceutico, Protesica, Dispositivi Medici, Regione del Veneto, Italy
  8. Italian Medicines Agency, Italy

Abstract

Background:

Recent in-vitro data have shown that the activity of monoclonal antibodies (mAbs) targeting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) varies according to the variant of concern (VOC). No studies have compared the clinical efficacy of different mAbs against Omicron VOC.

Methods:

The MANTICO trial is a non-inferiority randomised controlled trial comparing the clinical efficacy of early treatments with bamlanivimab/etesevimab, casirivimab/imdevimab, and sotrovimab in outpatients aged 50 or older with mild-to-moderate SARS-CoV-2 infection. As the patient enrolment was interrupted for possible futility after the onset of the Omicron wave, the analysis was performed according to the SARS-CoV-2 VOC. The primary outcome was coronavirus disease 2019 (COVID-19) progression (hospitalisation, need of supplemental oxygen therapy, or death through day 14). Secondary outcomes included the time to symptom resolution, assessed using the product-limit method. Kaplan-Meier estimator and Cox proportional hazard model were used to assess the association with predictors. Log rank test was used to compare survival functions.

Results:

Overall, 319 patients were included. Among 141 patients infected with Delta, no COVID-19 progression was recorded, and the time to symptom resolution did not differ significantly between treatment groups (Log-rank Chi-square 0.22, p 0.90). Among 170 patients infected with Omicron (80.6% BA.1 and 19.4% BA.1.1), two COVID-19 progressions were recorded, both in the bamlanivimab/etesevimab group, and the median time to symptom resolution was 5 days shorter in the sotrovimab group compared with the bamlanivimab/etesevimab and casirivimab/imdevimab groups (HR 0.53 and HR 0.45, 95% CI 0.36–0.77 and 95% CI 0.30–0.67, p<0.01).

Conclusions:

Our data suggest that, among adult outpatients with mild-to-moderate SARS-CoV-2 infection due to Omicron BA.1 and BA.1.1, early treatment with sotrovimab reduces the time to recovery compared with casirivimab/imdevimab and bamlanivimab/etesevimab. In the same population, early treatment with casirivimab/imdevimab may maintain a role in preventing COVID-19 progression. The generalisability of trial results is substantially limited by the early discontinuation of the trial and firm conclusions cannot be drawn.

Funding:

This trial was funded by the Italian Medicines Agency (Agenzia Italiana del Farmaco, AIFA). The VOC identification was funded by the ORCHESTRA (Connecting European Cohorts to Increase Common and Effective Response to SARS-CoV-2 Pandemic) project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 101016167.

Clinical trial number:

NCT05205759.

Editor's evaluation

This paper will be of broad interest to clinicians and scientists in the area, providing clinical trial data on how the efficacy of monoclonal antibodies targeting SARS-CoV-2 varies according to the variant of concern. The clinical outcome data were consistent with previously reported in vitro data, which are being used to inform the clinical use of monoclonal antibodies.

https://doi.org/10.7554/eLife.79639.sa0

Introduction

Coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread globally and poses a major challenge to healthcare systems worldwide. A high incidence of hospitalisation and death due to COVID-19 has been reported among older patients and those with certain coexisting conditions, such as obesity, diabetes mellitus, cardiovascular disease, chronic obstructive pulmonary disease, and chronic kidney disease (Petrilli et al., 2020; Huang et al., 2020). The implementation of mass vaccination campaigns has markedly reduced the healthcare burden related to COVID-19. Nevertheless, SARS-CoV-2 vaccination rates differ considerably across countries, and growing evidence suggests a reduced efficacy of vaccines against new viral variants of concern (VOC) (Cao et al., 2022; Planas et al., 2022; Dejnirattisai et al., 2022; Andrews et al., 2022).

Therapeutic agents directed against SARS-CoV-2 have been developed to prevent the COVID-19 progression, especially addressing high-risk groups of patients. Neutralising monoclonal antibodies (mAbs) target the spike protein of SARS-CoV-2 that mediates viral entry into host cells (Benton et al., 2020). Based on the results of randomised placebo-controlled trials showing the efficacy in preventing COVID-19 progression, drug regulatory authorities, such as the US Food and Drug Administration (FDA), the European Medicines Agency, and the Italian Medicines Agency (AIFA), had granted the emergency use authorisation status for bamlanivimab 700 mg combined with etesevimab 1400 mg, casirivimab 600 mg combined with imdevimab 600 mg, and sotrovimab 500 mg to treat early COVID-19 in patients at high risk of progression (Dougan et al., 2021; Weinreich et al., 2021; Gupta et al., 2021).

To date, two randomised trials have compared the clinical outcomes of these mAbs in preventing severe COVID-19, showing similar effectiveness of bamlanivimab/etesevimab vs casirivimab/imdevimab in patients infected with the alpha VOC (McCreary et al., 2022) and casirivimab/imdevimab vs sotrovimab in patients infected with the Delta VOC, respectively (Huang et al., 2022).

This paper reports the results of the MANTICO trial, a non-inferiority randomised controlled trial comparing the clinical efficacy of routinely-used mAbs in a real-life setting of outpatients aged 50 or older with early mild-to-moderate COVID-19. The patient enrolment started in December 2021 and was interrupted after the publication of in-vitro evidence that two treatments under investigation (bamlanivimab/etesevimab and casirivimab/imdevimab) were not effective against the new emerging viral Omicron VOC (Cao et al., 2022; Planas et al., 2022; Dejnirattisai et al., 2022). The analysis is therefore restricted to 319 randomised patients, who were enrolled up to the interruption for possible futility, and was performed according to the SARS-CoV-2 VOC (Delta and Omicron).

Methods

Trial design

The trial was designed as a pragmatic, randomised, single-blind, non-inferiority, parallel group, multi-centre, and controlled trial. Eligible subjects were outpatients aged 50 years or older, presenting at three trial sites in Italy (Verona, Padua, and Udine) with a positive test (either direct antigen or nucleic acid SARS-CoV-2) and mild-to-moderate COVID-19 symptoms within 4 days of the onset (COVID-19 Treatment Guidelines Panel, 2019). COVID-19 symptoms included cough, nasal congestion, sore throat, feeling hot or feverish, myalgia, fatigue, headache, anosmia/ageusia, nausea, vomiting, and/or diarrhoea (U.S. Department of Health and Human Services Food and Drug Administration, 2022a). Predefined exclusion criteria included a peripheral oxygen saturation level of 93% or less on room air, a respiratory rate of 30 or more breaths per minute, a heart rate of 125 or more beats per minute, and previous COVID-19 treatments with mAbs.

Sample-size estimation was based on the only available double-blind, randomised, placebo-controlled trial assessing the clinical efficacy of casirivimab/imdevimab (reference standard) in preventing COVID-19 progression in adult outpatients with early mild-to-moderate SARS-CoV-2 (Weinreich et al., 2021). This study showed that the hospitalisation related to COVID-19 or all-cause mortality occurred in 7 of 736 patients in the casirivimab/imdevimab 1200 mg group (1.0%) and in 24 of 748 patients in the placebo group (3.2%) (relative risk reduction, 70.4%; Weinreich et al., 2021). Therefore, 5% COVID-19 progression was assumed in the casirivimab/imdevimab group. 5% non-inferiority margin was considered clinically relevant by the expert opinion of infectious disease and clinical trial specialists involved in the protocol development, taking into account both the estimates of COVID-19 progression in the study population in the absence of early treatment with mAbs (20%; Istituto Superiore di Sanità, 2021) and the efficacy of the reference standard (Weinreich et al., 2021). Using these parameters, 420 patients per group were needed to achieve 90% power with a one-sided α level of.025, allowing for 5% dropout.

Participants were randomly assigned in a 1:1:1 ratio to receive a single intravenous infusion over a period of 1 hr, consisting of a combination of 700 mg of bamlanivimab and 1400 mg of etesevimab or 500 mg of sotrovimab or a combination of 600 mg of casirivimab and 600 mg of imdevimab. The study drugs were diluted to 250 mL with normal saline. Patients were masked to treatment group assignment. Randomisation was computer generated in permuted blocks with a stratification based on site. The allocated drug was revealed to the investigator using an online randomisation module within the REDCap data management system (Harris et al., 2009).

The trial was conducted in accordance with the principles of the Declaration of Helsinki, the international ethical guidelines of the Council for International Organisations of Medical Sciences, the International Council for Harmonisation Good Clinical Practice guidelines, and applicable laws and regulations. All patients or their legally authorised representatives provided written informed consent. This study is registered with ClinicalTrials.gov, NCT05205759.

Outcomes

The composite primary outcome was the COVID-19 progression, defined as hospitalisation, need of supplemental oxygen therapy, or death from any cause through day 14. The presence of any of the three variables qualified the presence of the COVID-19 progression. Prespecified secondary outcomes were emergency department visits through day 28, all-cause mortality through day 28, duration of supplemental oxygen therapy, rate and duration of non-invasive ventilation and mechanical ventilation, and time to sustained patient-reported symptom resolution, which was defined as the absence of any symptom related to COVID-19 for at least 24 hr (U.S. Department of Health and Human Services Food and Drug Administration, 2022b).

Predictors

The main predictor was the treatment regimen randomised at enrolment (bamlanivimab/etesevimab, casirivimab/imdevimab, and sotrovimab). All patients were assessed at baseline for the following predictors to be tested for association with the time to symptom resolution: age, sex, BMI, relevant comorbidities (diabetes for which medication was warranted, cardiovascular disease [hypertension, coronary artery disease, and congestive heart failure], chronic kidney disease, chronic liver disease, chronic pulmonary disease, active cancer, transplant, and other immunocompromising conditions), SARS-CoV-2 serological status (anti-spike IgG), and SARS-CoV-2 vaccination status. The SARS-CoV-2 serological status was categorised as serum antibody-negative (if test results were negative), serum antibody-positive (if test results were positive), or other (inconclusive or unknown results). The SARS-CoV-2 vaccination status was categorised as not vaccinated, partial or complete primary COVID-19 vaccination series administered more than 120 days before the enrolment, complete primary COVID-19 vaccination series administered 120 days or less before the enrolment, and booster vaccination (Andrews et al., 2022). These categories were further collapsed as not vaccinated and partial or complete primary COVID-19 vaccination series administered more than 120 days before the enrolment vs complete primary COVID-19 vaccination series administered 120 days or less before the enrolment and booster vaccination.

Procedures and tools

Outpatient visits were scheduled at baseline, 14±3 days and 30±3 days after the randomisation. Patients were considered lost to follow-up if they repeatedly did not participate in scheduled visits and could not be contacted by the investigators. Medical evaluation, vital signs recording, and laboratory tests were performed at each visit. If patients missed the visits, they were called by telephone to assess clinical conditions.

The SARS-CoV-2 serological status was assessed using LIAISON SARS-CoV-2 TrimericS IgG assay (DiaSorin), an indirect chemiluminescence immunoassay detecting IgG against the spike viral protein in its native trimeric conformation, which includes receptor-binding domain and N-terminal domain sites from the three subunit S1. According to the manufacturer’s instructions, binding antibody units (BAU)/mL ≥33.8 were considered positive for anti-trimeric spike protein specific IgG antibodies.

Nasopharyngeal swabs were processed using MagMAX Viral/Pathogen Nucleic Acid Isolation Kit and KingFisher automated extraction system (ThermoFisher Scientific). Viral RNA was detected using COVIDSeq amplicon-based Next Generation Sequencing Test combined with COVIDSeq V4 Primer Pool (Illumina, Inc). Sequencing libraries were synthesised using automated Microlab STAR liquid handler (Hamilton Company). Pooled samples were quantified using Qubit 2.0 fluorometer (Invitrogen Inc). Next generation sequencing was performed in 150 PE mode on NextSeq 550 Sequencing System (Illumina, Inc) or MiSeq System (Illumina, Inc) using the NextSeq 500/550 Mid Output Kit v2.5 or the Miseq Reagent Kit v3, respectively.

Statistical analysis

For continuous variables, mean and SD or median and IQR were calculated. For categorical variables, count and percentages were used. All outcome variables estimates were reported with 95% CI (95% CI). Wilcoxon–Mann–Whitney test was used to compare independent groups. The association between categorical variables was assessed using the Fisher’s test. The product-limit method (Kaplan and Meier) was used to describe the time to symptom resolution. Kaplan-Meier estimator and Cox proportional hazard model were used to assess the bivariate association of independent variables with the time-dependent outcome. Kaplan-Meier curves were plotted to depict the association between each predictor and symptom persistence, and the Log-rank test was used to compare survival functions. Predictors associated with the time to symptom resolution with a probability <0.05 were considered significant. A two-sided test of less than 0.05 was considered statistically significant in all analyses. All statistical analyses were performed with the use of Stata Version 17.0 (College Station, TX: StataCorp LP).

Results

The first patient was enrolled on 9 December 2021. Overall, 319 patients underwent randomisation by 20 January 2022 and were assigned to receive bamlanivimab/etesevimab (106 patients), sotrovimab (107 patients), or casirivimab/imdevimab (106 patients). No patients reported previous SARS-CoV-2 infections. No patients were lost to follow-up. VOC data were available for 311 patients: 170 (53.3%) were infected with Omicron and 141 (44.2%) with Delta. Eight (2.5%) patients were excluded from this analysis due to the lack of SARS-CoV-2 VOC identification. Figure 1 shows the flow diagram of the progress through the trial phases. Baseline characteristics of the population by type of SARS-CoV-2 VOC are reported in Table 1.

Flow diagram of the progress through the phases of the MANTICO trial according to the CONSORT statement.
Table 1
Baseline characteristics of the overall study population by type of variant of concern.
CharacteristicDeltaN=141OmicronN=170p value
Sex (male) – n (%)69 (48.94)101 (59.41)0.068
Age – median (IQR) (range)65.7 (15.4) (50–92)64.5 (14.8) (50–90)0.585
Smoking status – n (%)
Smoker8 (5.67)24 (14.12)0.015
Former smoker32 (22.70)28 (16.47)0.194
Non-smoker101 (71.63)118 (69.41)0.709
BMI – n (%)
≤29101 (71.63)132 (77.65)0.239
≥3040 (28.37)38 (22.35)0.239
SARS-CoV-2 serological status – n (%)
Antibody-positive70 (49.65)134 (78.82)<0.001
Antibody-negative68 (48.23)35 (20.59)<0.001
Other3 (2.13)1 (0.59)
Anti-SARS-CoV-2 vaccination status – n (%)
3 doses or 2 doses ≤120 days23 (16.31)66 (38.82)<0.001
1 or 2 doses ≥120 days or not vaccinated113 (80.14)99 (58.24)<0.001
Other5 (3.55)5 (2.94)
Comorbidities – n (%)
Diabetes3 (2.13)6 (3.53)0.519
Cardiovascular disease56 (39.72)61 (35.88)0.557
Chronic kidney disease7 (4.96)9 (5.29)1.000
Chronic liver disease3 (2.13)12 (7.06)0.061
Chronic pulmonary disease16 (11.35)33 (19.41)0.061
Immunocompromising conditions17 (12.06)35 (20.59)0.048
Symptoms at enrolment – n (%)
Cough96 (68.09)118 (69.41)0.807
Nasal congestion69 (48.94)69 (40.59)0.169
Sore throat32 (22.70)69 (40.59)0.001
Feeling hot or feverish103 (73.05)99 (58.24)0.008
Myalgia46 (32.62)54 (31.76)0.903
Fatigue47 (33.33)75 (44.12)0.062
Headache59 (41.84)60 (35.29)0.244
Anosmia/ageusia39 (27.66)4 (2.35)<0.001
Nausea/vomiting28 (19.86)11 (6.47)<0.001
Diarrhoea15 (10.64)12 (7.06)0.314
Serum C-reactive protein level – n136161
Mean (SD), mg/L20.58 (29.00)14.29 (21.72)0.022

Comparing symptoms at enrolment by VOC, anosmia/ageusia (p<0.001), nausea/vomiting (p<0.001), and feeling feverish or hot (p<0.01) were significantly more frequent among patients infected with Delta, while sore throat (p<0.001) was significantly more frequent among patients infected with Omicron. Serological positivity to anti-SARS-CoV-2 antibodies (p<0.001) and complete primary COVID-19 vaccination series within 120 days of the enrolment or booster vaccination (p<0.001) were significantly more frequent among patients infected with Omicron. Table 2 shows the bivariate Cox regression of symptom resolution predictors by type of SARS-CoV-2 VOC. No predictors were associated with the time to symptom resolution in both SARS-CoV-2 VOC.

Table 2
Bivariate Cox regression of symptom resolution predictors by type of variant of concern.
DeltaOmicron
PredictorHR (95% CI)p valueHR
(95% CI)
p value
Gender0.80
(0.57–1.11)
0.1820.84
(0.61–1.14)
0.257
Age1.00
(0.98–1.02)
0.9521.00
(0.98–1.01)
0.626
BMI1.03
(0.72–1.50)
0.8551.17
(0.82–1.68)
0.393
SARS-CoV-2 serological status0.93
(0.67–1.31)
0.6900.82
(0.57–1.20)
0.307
Anti-SARS-CoV-2 vaccination status1.30
(0.83–2.04)
0.2570.91
(0.66–1.24)
0.539
Diabetes0.63
(0.34–1.18)
0.1501.19
(0.76–1.88)
0.444
Cardiovascular disease0.96
(0.69–1.35)
0.8310.85
(0.62–1.17)
0.319
Chronic kidney disease1.24
(0.58–2.66)
0.5811.12
(0.57–2.21)
0.733
Chronic liver disease2.42
(0.76–7.68)
0.1351.33
(0.74–2.40)
0.341
Chronic pulmonary disease0.78
(0.46–1.31)
0.3460.98
(0.67–1.43)
0.902
Immunocompromising conditions1.00
(0.60–1.66)
0.9890.80
(0.55–1.17)
0.252

Delta VOC

Baseline characteristics of 141 patients infected with Delta VOC by type of treatment are reported in Table 3. The main detected lineages were 34 AY.4 (24.1%), 33 AY.43 (23.4%), and 26 AY.122 (18.4%). 69 (48.9%) were male, median age was 65.7 years (IQR ±15.4), 115 (78.8%) had at least one comorbidity, 70 (49.6%) were serum antibody-positive at the enrolment, and 23 (16.3%) received complete primary COVID-19 vaccination series within 120 days of the enrolment or booster vaccination.

Table 3
Baseline characteristics of the study population infected with Delta by type of treatment.
CharacteristicTotalN=141SotrovimabN=43Bamlanivimab/etesevimabN=48Casirivimab/imdevimabN=50
Sex (male) – n (%)69 (48.94)22 (51.16)21 (43.75)26 (52.00)
Age – median (IQR) (range)65.7 (15.4) (50–92)65.8 (16.4) (50–90)68.6 (11.8) (50–92)63.2 (12) (50–89)
Smoking status – n (%)
Smoker8 (5.67)2 (4.65)4 (8.33)2 (4.00)
Former smoker32 (22.70)8 (18.60)11 (22.92)13 (26.00)
Non-smoker101 (71.63)33 (76.74)33 (68.75)35 (70.00)
BMI – n (%)
≤29101 (71.63)29 (67.44)36 (75.00)36 (72.00)
≥3040 (28.37)14 (32.56)12 (25.00)14 (28.00)
SARS-CoV-2 serological status – n (%)
Antibody-positive70 (49.65)20 (46.51)29 (61.70)21 (43.75)
Antibody-negative68 (48.23)23 (53.49)18 (38.30)27 (56.25)
Other3 (2.13)01 (2.08)2 (4.00)
Anti-SARS-CoV-2 vaccination status – n (%)
3 doses16 (11.35)6 (13.95)3 (6.25)7 (14.00)
2 doses ≤120 days7 (4.96)2 (4.65)2 (4.17)3 (6.00)
1 or 2 doses ≥120 days54 (38.30)14 (32.56)26 (54.17)14 (28.00)
Not vaccinated59 (41.84)19 (44.19)15 (31.25)25 (50.00)
Other5 (3.55)2 (4.65)2 (4.17)1 (2.00)
Comorbidities – n (%)
Diabetes3 (2.13)02 (4.17)1 (2.00)
Cardiovascular disease56 (39.72)18 (41.86)20 (41.67)18 (36.00)
Chronic kidney disease7 (4.96)1 (2.33)2 (4.17)4 (8.00)
Chronic liver disease3 (2.13)01 (2.08)2 (4.00)
Chronic pulmonary disease16 (11.35)6 (13.95)4 (8.33)6 (12.00)
Immunocompromising conditions17 (12.06)6 (13.95)6 (12.50)5 (10.00)
Symptoms at enrolment – n (%)
Cough96 (68.09)28 (65.12)36 (75.00)32 (64.00)
Nasal congestion69 (48.94)20 (46.51)22 (45.83)27 (54.00)
Sore throat32 (22.70)10 (23.26)8 (16.67)14 (28.00)
Feeling hot or feverish103 (73.05)31 (72.09)36 (75.00)36 (72.00)
Myalgia46 (32.62)11 (25.58)16 (33.33)19 (38.00)
Fatigue47 (33.33)13 (30.23)15 (31.25)19 (38.00)
Headache59 (41.84)15 (34.88)15 (31.25)29 (58.00)
Anosmia/ageusia39 (27.66)12 (27.91)15 (31.25)12 (24.00)
Nausea/vomiting28 (19.86)6 (13.95)9 (18.75)13 (26.00)
Diarrhoea15 (10.64)1 (2.33)5 (10.42)9 (18.00)
Serum C-reactive protein level – n136414649
Mean (SD), mg/L20.58 (29.00)22.84 (33.70)25.27 (34.20)14.29 (15.99)

Primary and secondary outcomes of the study population infected with Delta VOC by type of treatment are reported in Table 4 with the exclusion of time to sustained patient-reported symptom resolution. No COVID-19 progression was recorded in Delta infections. All-cause mortality through day 28 was the same as that through day 14. An emergency department visit without hospitalisation was observed once in one patient in the casirivimab/imdevimab group. This visit was deemed to be unrelated to COVID-19.

Table 4
Efficacy outcomes of the study population infected with Delta by type of treatment with the exclusion of time to sustained patient-reported symptom resolution.
OutcomeTotalN=141SotrovimabN=44Bamlanivimab/etesevimabN=47Casirivimab/imdevimabN=50
Composite primary outcome – n (%)0000
Hospitalisation0000
Need of supplemental oxygen therapy0000
Death from any cause through day 140000
Secondary outcomes
Emergency department visits through day 28 – n (%)1 (0.71)001 (2)
All-cause mortality through day 28 – n (%)0000
Duration of supplemental oxygen therapy – days0000
Rate of non-invasive ventilation – n (%)0000
Duration of non-invasive ventilation – days0000
Rate of mechanical ventilation – n (%)0000
Duration of mechanical ventilation – days0000

The median time to symptom resolution was 7 days (95% CI 6–13) in the bamlanivimab/etesevimab group, 10 days (95% CI 7–14) in the sotrovimab group, and 10 days (95% CI 7–15) in the casirivimab/imdevimab group, not differing significantly across the overall groups of treatment (Log-rank Chi-square 0.22, p 0.895) and for each comparison between treatment groups, namely bamlanivimab/etesevimab with casirivimab/imdevimab (Log-rank Chi-square 0.08, p 0.776), sotrovimab with casirivimab/imdevimab (Log-rank Chi-square 0.40, p 0.527), and bamlanivimab/etesevimab with sotrovimab (Log-rank Chi-square 0.01, p 0.92). Figure 2A shows the time to symptom resolution by type of treatment in the Delta study population. The Cox regression analysis confirmed the non-significantly different effects upon the time to symptom resolution between casirivimab/imdevimab (reference standard according to the original trial protocol) and both bamlanivimab/etesevimab and sotrovimab (HR 1.052 and HR 1.097, 95% CI 0.70–1.57 and 0.73–1.65, p 0.805 and 0.657, respectively).

Time to symptom resolution by type of treatment in the study population infected with Delta (A) and Omicron (B).

Omicron VOC

Baseline characteristics of 170 patients infected with Omicron VOC by type of treatment are reported in Table 5. The detected lineages were 137 (80.6%) BA.1 and 33 (19.4%) BA.1.1. 101 (59.4%) were male, median age was 64.5 years (IQR ±14.8), 135 (79.4%) had at least one comorbidity, 134 (78.8%) were serum antibody-positive at the enrolment, and 66 (38.8%) received complete primary COVID-19 vaccination series within 120 days of the enrolment or booster vaccination.

Table 5
Baseline characteristics of the study population infected with Omicron by type of treatment.
CharacteristicTotalN=170SotrovimabN=61Bamlanivimab/etesevimabN=57Casirivimab/imdevimabN=52
Sex (male) – n (%)101 (59.41)36 (59.02)30 (52.63)35 (67.31)
Age – median (IQR) (range)64.5 (14.8) (50–90)64.2 (15) (50–90)64.8 (14.6) (50–86)65.3 (14.8) (50–86)
Smoking status – n (%)
Smoker24 (14.12)6 (9.84)11 (19.30)7 (13.46)
Former smoker28 (16.47)9 (14.75)11 (19.30)8 (15.38)
Non-smoker118 (69.41)46 (75.41)35 (61.40)37 (71.15)
BMI – n (%)
≤29132 (77.65)53 (86.89)42 (73.68)37 (71.15)
≥3038 (22.35)8 (13.11)15 (26.32)15 (28.85)
SARS-CoV-2 serological status – n (%)
Antibody-positive134 (78.82)45 (73.77)45 (78.95)44 (84.62)
Antibody-negative35 (20.59)16 (26.23)11 (19.30)8 (15.38)
Other1 (0.59)01 (1.75)0
Anti-SARS-CoV-2 vaccination status – n (%)
3 doses62 (36.47)24 (39.34)19 (33.33)19 (36.54)
2 doses ≤120 days4 (2.35)2 (3.28)1 (1.75)1 (1.92)
1 or 2 doses ≥120 days57 (33.53)16 (26.23)22 (38.60)19 (36.54)
Not vaccinated42 (24.71)18 (29.51)13 (22.81)11 (21.15)
Other5 (2.94)1 (1.64)2 (3.51)2 (3.85)
Comorbidities – n (%)
Diabetes6 (3.53)2 (3.28)2 (3.51)2 (3.85)
Cardiovascular disease61 (35.88)18 (29.51)17 (29.82)26 (50.00)
Chronic kidney disease9 (5.29)4 (6.56)2 (3.51)3 (5.77)
Chronic liver disease12 (7.06)4 (6.56)5 (8.77)3 (5.77)
Chronic pulmonary disease33 (19.41)11 (18.03)15 (26.32)7 (13.46)
Immunocompromising conditions35 (20.59)15 (24.59)11 (19.30)9 (17.31)
Symptoms at enrolment – n (%)
Cough118 (69.41)42 (68.85)37 (64.91)39 (75.00)
Nasal congestion69 (40.59)28 (45.90)25 (43.86)16 (30.77)
Sore throat69 (40.59)22 (36.07)27 (47.37)20 (38.46)
Feeling hot or feverish99 (58.28)37 (60.66)32 (56.14)30 (57.69)
Myalgia54 (31.76)20 (32.79)18 (31.58)16 (30.77)
Fatigue75 (44.12)31 (50.82)20 (35.09)24 (46.15)
Headache60 (35.29)23 (37.70)20 (35.09)17 (32.69)
Anosmia/ageusia4 (2.35)1 (1.64)2 (3.51)1 (1.92)
Nausea/vomiting11 (6.47)4 (6.56)5 (8.77)2 (3.85)
Diarrhoea12 (7.06)5 (8.20)4 (7.02)3 (5.77)
Serum C-reactive protein level – n161575648
Mean (SD), mg/L14.29 (21.72)12.65 (15.97)17.19 (31.07)12.87 (12.55)

Primary and secondary outcomes of the study population infected with Omicron VOC by type of treatment are reported in Table 6 with the exclusion of time to sustained patient-reported symptom resolution. Two of 57 in the bamlanivimab/etesevimab group (3.5%) had COVID-19 progression leading to hospitalisation, and no COVID-19 progression was recorded in the casirivimab/imdevimab and sotrovimab groups. The primary reasons for the two hospitalisations were deemed to be related to COVID-19. Both patients admitted to hospital were serum antibody-negative at enrolment and underwent non-invasive mechanical ventilation at hospital admission. One of these patients, a man aged 71–75 who received three doses of SARS-CoV-2 vaccine and was affected by non-Hodgkin lymphoma under active chemotherapy and chronic heart failure, died 12 days after the symptom onset, 10 days after the administration of bamlanivimab/etesevimab, and 4 days after the hospitalisation. The other patient, a man aged 66–70 who was not vaccinated against SARS-CoV-2 and was affected by obesity (BMI, 31) and type 2 diabetes, was admitted 7 days after the symptom onset and 4 days after the administration of bamlanivimab/etesevimab; the length of his hospital stay was 22 days, including non-invasive mechanical ventilation for 13 days and low-flow oxygen therapy for 8 days. All-cause mortality through day 28 was the same as that through day 14.

Table 6
Efficacy outcomes of the study population infected with Omicron by type of treatment with the exclusion of time to sustained patient-reported symptom resolution.
OutcomeTotalN=170SotrovimabN=61Bamlanivimab/etesevimabN=57Casirivimab/imdevimabN=52
Composite primary outcome – n (%)2 (1.18)02 (3.51)0
Hospitalisation2 (1.18)02 (3.51)0
Need of supplemental oxygen therapy2 (1.18)02 (3.51)0
Death from any cause through day 141 (0.59)01 (1.75)0
Secondary outcomes
Emergency department visits through day 28 – n (%)1 (0.59)01 (1.75)0
All-cause mortality through day 28 – n (%)2 (1.18)02 (3.51)0
Duration of supplemental oxygen therapy – days4 (patient 1)
22 (patient 2)
04 (patient 1)
22 (patient 2)
0
Rate of non-invasive ventilation – n (%)2 (1.18)02 (3.51)0
Duration of non-invasive ventilation – days4 (patient 1)
13 (patient 2)
04 (patient 1)
13 (patient 2)
0
Rate of mechanical ventilation – n (%)0000
Duration of mechanical ventilation – days0000

An emergency department visit without hospitalisation was observed once in one patient in the bamlanivimab/etesevimab group. This visit was deemed to be unrelated to COVID-19.

The median time to symptom resolution was 12 days (95% CI 8–14) in the bamlanivimab/etesevimab group, 12 days in the casirivimab/imdevimab group (95% CI 9–16), and 7 days in the sotrovimab group (95% CI 6–9), differing significantly across the overall groups of treatment (Log-rank Chi-square 20.29, p 0.0001) and between sotrovimab and both bamlanivimab/etesevimab (Log-rank Chi-square 11.09, p 0.009) and casirivimab/imdevimab (Log-rank Chi-square 19.51, p 0.0001), whereas the comparison between bamlanivimab/etesevimab and casirivimab/imdevimab was not significant (Log-rank Chi-square 0.63, p 0.427). The Cox regression analysis confirmed the significantly different effects upon the time to symptom resolution between sotrovimab and both bamlanivimab/etesevimab and casirivimab/imdevimab (HR 0.53 and HR 0.45, 95% CI 0.36–0.77 and 95% CI 0.30–0.67, p 0.001 and 0.0001, respectively). Figure 2B shows the time to symptom resolution by type of treatment in the Omicron study population. In each of the assessed subgroups (SARS-CoV-2 serological and vaccination status), sotrovimab showed a significantly shorter time to symptom resolution compared with bamlanivimab/etesevimab and casirivimab/imdevimab, as reported in Table 7.

Table 7
Cox regression to assess the difference between treatment effects upon the time to symptom resolution in selected subgroups of interest in the study population infected with Omicron.
SubgroupSotrovimabBamlanivimab/etesevimabCasirivimab/imdevimab
HRHR (95% CI)p valueHR (95% CI)p value
SARS-CoV-2 serological status
Antibody-negative10.34 (0.16–0.75)0.0080.41 (0.18–0.97)0.043
Antibody-positive10.40 (0.22–0.71)0.0020.32 (0.18–0.57)<0.001
Anti-SARS-CoV-2 vaccination status
1 or 2 doses ≥120 days or not vaccinated10.47 (0.30–0.77)0.0030.49 (0.30–0.82)0.006
3 doses or 2 doses ≤120 days10.50 (0.28–0.89)0.0190.35 (0.19–0.62)<0.001

Discussion

During the SARS-CoV-2 pandemic, the paradigm of discovering and implementing mAb and antiviral treatments based on randomised controlled trials has lagged significantly behind the new evidence coming from in-vitro studies, which has driven clinical recommendations causing ethical dilemmas on the continuation of ongoing trials. At the time of approving the MANTICO trial protocol (November 2021), casirivimab/imdevimab, bamlanivimab/etesevimab, and, later, sotrovimab were the only therapies recommended by the COVID-19 treatment guidelines for outpatients with mild-to-moderate COVID-19 at high risk of progressing to severe COVID-19. Delta was the SARS-CoV-2 dominant VOC worldwide, and the selection of the study mAbs was based on their in-vitro activity against the circulating variants and on the existing evidence of their clinical efficacy. Since mid-December 2021, the Omicron VOC has been spreading worldwide, rapidly becoming the dominant VOC. Preliminary in-vitro studies on Omicron demonstrated numerous mutations in the gene encoding the spike protein, predicting a markedly reduced susceptibility to bamlanivimab/etesevimab and casirivimab/imdevimab (Cao et al., 2022; Planas et al., 2022; Dejnirattisai et al., 2022). According to these findings, FDA and AIFA have revised the emergency use authorisation for bamlanivimab/etesevimab and casirivimab/imdevimab, halting their use, in line with the National Institutes of Health COVID-19 Treatment Guidelines Panel, which advised against the use of these mAbs due to reduced activity against Omicron and because real-time testing to identify rare, non-Omicron variants is not readily available (National Institutes of Health, 2022). Therefore, the study enrolment in a real-life outpatient setting was prematurely discontinued for possible futility, after the inclusion of barely one fourth of the predefined sample size (1260 patients). Nevertheless, the recruitment timeframe provided a unique opportunity to collect data on the clinical efficacy of bamlanivimab/etesevimab, casirivimab/imdevimab, and sotrovimab in patients infected with Omicron.

Overall, the three treatment groups appeared to be balanced with respect to the predictors of outcomes in both Delta and Omicron population, as expected under the randomised allocation design. As reported by previous studies, patients infected with Omicron, compared with patients infected with Delta, were more likely to present with symptoms limited to the upper respiratory tract and to have pre-existing immunity, considering that Omicron is better equipped than Delta to infect people with pre-existing immunity (Nyberg et al., 2022).

Considering the time to symptom resolution, no differences in the effect between treatment groups were found in Delta infections, whereas sotrovimab seems to show a benefit in patients infected with Omicron BA.1 and BA.1.1. This benefit was consistent across all Omicron subgroups, regardless of the SARS-CoV-2 serology and vaccination status, confirming the preliminary in-vitro evidence on the mAbs activity against Omicron BA.1 and BA.1.1 (Cao et al., 2022; Planas et al., 2022; Dejnirattisai et al., 2022).

The COVID-19 progression was recorded in two patients infected with Omicron, who were both randomised to receive bamlanivimab/etesevimab. On the one hand, these findings seem consistent with recent in-vitro data showing that all study treatments were active against Delta, and both casirivimab/imdevimab and sotrovimab retained a residual neutralising activity against Omicron BA.1/BA.1.1, whereas bamlanivimab/etesevimab did not neutralise Omicron (Takashita et al., 2022b; Iketani et al., 2022; Takashita et al., 2022a; Arora et al., 2022). Nevertheless, the above-mentioned results are severely limited by the early discontinuation of the trial, and firm conclusions on the primary outcome parameters cannot be drawn. Furthermore, the observed rate of COVID-19 progression (2/319, 0.6%) was lower than the one used to inform the sample size calculation (5% in the casirivimab/imdevimab arm, reference standard; NCT05205759). This overestimation of the primary outcome could be influenced by the lower intrinsic-severity of Omicron, the high vaccination rate in Italy, and the prioritisation of the booster vaccination for the elderly (Bhattacharyya and Hanage, 2022). Another limitation of this study is the lack of data on the clinical efficacy of the study mAbs, as well as other commercially available early COVID-19 treatments (mAbs, such as tixagevimab/cilgavimab and bebtelovimab, and antiviral drugs, such as remdesivir, nirmatrelvir/ritonavir, and molnupiravir), against the currently circulating VOC (BA.2 subvariants, BA.4, or BA.5; CoVariants, 2022). Following an adaptive design in a real-life setting, the MANTICO trial is actively recruiting to compare the clinical efficacy of commercially available early COVID-19 treatments against the currently circulating VOC (tixagevimab/cilgavimab, nirmatrelvir/ritonavir, and sotrovimab; NCT05321394).

Additional clinical studies with an adequate sample size are required to determine whether casirivimab/imdevimab and sotrovimab are indeed effective in preventing COVID-19 progression due to Omicron infection. Should the role of casirivimab/imdevimab in preventing severe COVID-19 due to Omicron infections be confirmed, this mAb could represent a readily available and well-tolerated treatment option in case of shortages of mAbs supplies and contraindication to other early COVID-19 treatments.

The MANTICO trial provides the first data on the clinical efficacy of bamlanivimab/etesevimab, casirivimab/imdevimab, and sotrovimab against Omicron VOC. There is an urgent need for adaptive clinical trials comparing anti-SARS-CoV-2 treatments by the currently circulating VOC to promptly inform recommendations for the management of early COVID-19.

Data availability

The trial dataset has been uploaded to the Dryad repository (https://doi.org/10.5061/dryad.tdz08kq2w). As per predefined protocol, personally identifiable information (such as gender, date of birth, age, and weight) has been removed from the dataset to keep the records completely anonymous. In addition, the dataset record order was randomised so the resulting dataset is a file very similar in terms of length, fields and content to the original version, except for row order which is now completely random and the record id variable deleted.

The following data sets were generated
    1. Tacconelli E
    (2022) Dryad Digital Repository
    Adaptive, Randomized, Non-inferiority Trial to Evaluate the Efficacy of Monoclonal Antibodies in Outpatients With Mild or Moderate COVID-19.
    https://doi.org/10.5061/dryad.tdz08kq2w

References

Decision letter

  1. Zoe McQuilten
    Reviewing Editor; Monash University, Australia
  2. Miles P Davenport
    Senior Editor; University of New South Wales, Australia
  3. David Huang
    Reviewer; University of Pittsburgh, United States

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 "Exploratory data on the clinical efficacy of monoclonal antibodies against SARS-CoV-2 Omicron Variant of Concern" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Miles Davenport as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: David Huang (Reviewer #3).

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) As this is a report of a randomised trial, please report according to CONSORT guidelines/checklist and provide a CONSORT figure.

2) Clearer presentation of primary and secondary outcome data.

3) Revision of the discussion and conclusion to ensure that all statements regarding the efficacy of interventions and interpretations of findings are supported and consistent with the data presented (see reviewer comments below for examples).

4) Inclusion of discussion of limitations of the study, including potential sources of bias

Reviewer #1 (Recommendations for the authors):

The authors followed STROBE guidelines. However, reporting an RCT and not an observational study, they should have followed CONSORT guidelines.

Could the authors explain why the type of administered mAb was blinded to the patient?

Reviewer #2 (Recommendations for the authors):

Thank you for the opportunity to review this manuscript. I provide the below feedback in an effort to improve the manuscript and in addition to my evaluation summary and public review.

The authors could be more consistent with regard to the description of the primary outcome (e.g. "COVID-19 progression" on lines 38/39 of the abstract cf "disease progression" on line 44).

On lines 46/47, the authors could consider editing the statement "two disease progressions were recorded in the bamlanivimab/etesevimab group" to specify that there were no cases of disease progression in the other groups.

On lines 54/55, the evidence for the claim re: Casirivimab/imdevimab is insufficient (see statements above) but even as it is currently written it is not quite accurate. The manuscript states "Casirivimab/imdevimab seems to maintain a role in preventing severe COVID-19 in the Omicron population" whereas it could be better worded in terms of preventing progression of COVID-19 in adults with mild-moderate COVID-19 due to the omicron variant of SARS-CoV-2.

The ClinicalTrials.gov entry for your trial says 320 participants were recruited. Is there a reason that this manuscript says 319?

Is there a CONSORT diagram? Is there a CONSORT checklist that could be submitted with this manuscript? I see there is a STROBE checklist, and I guess in some ways this is an observational (δ vs omicron) study housed within an RCT. But I think it would be useful to understand (and report) more about the recruitment to the RCT, even as an appendix, and include a CONSORT diagram.

Were any participants lost to follow-up? Were any data points missing?

Figure 1 would usually show the primary outcome, rather than a secondary outcome. I can see why the authors would choose to show the secondary outcome (time to symptom resolution) in the figure, as this is a useful way of showing those data. But the discussion is focused on a signal in the primary outcome (which I do not think is justified) and I think if the authors wish to focus on that (hypothesis-generating only) finding of only 2 cases of COVID-19 progression, only occurring in the bamlanivimab/etesevimab group, the readers should see how these groups 'differ' visually too.

Could the word 'survival' lead to misinterpretation of the data in Figure 1? I would suggest removing 'survival' from the figure title (currently: "Figure 1 – Survival time to symptom resolution by type of treatment in the study population infected with Δ". Same for Figure 2).

Could Figure 1 and Figure 2 be combined into a multi-panel figure so the data are next to each other for comparison?

On lines 222/223, this statement is redundant when you have presented the days of symptoms in each group "turning out to be 5 days shorter in the sotrovimab group compared to both bamlanivimab/etesevimab and casirivimab/imdevimab groups"

I was confused about the presentation of the statistical analyses of the time to symptom resolution by treatment assignment in the two cohorts (δ and omicron). For the δ data, an analysis was presented assessing whether the duration of symptoms differed by treatment assignment (log-rank chi-square), and then each arm vs arm comparison was also presented as log-rank chi-square (lines 189-194). Cox regression was then used to confirm no difference with casirivimab/imdevimab as the reference. For omicron, where the data suggest there may be a difference, we do not present the chi-square analyses (lines 220-225). Is there a reason for this? The data should be presented the same way for both VOC.

Lines 224-226 too strongly imply a benefit here with casirivimab/imdevimab. And could be read as suggesting both casirivimab/imdevimab and sotrovimab retain comparable activity in vitro. Of course, it depends on the assay used, etc. And these in vitro assays have significant limitations, in terms of their utility to make treatment decisions (and decisions re: guideline recommendations!). But I think this section could be re-worded to improve accuracy.

When this is published, we will be on to the next variant (BA.2, BA.4, etc), in most places we already are. Therefore, consideration of how the authors frame the results in that context, ensuring we do not necessarily imply that all variants termed 'omicron' are equal with regards to mAb neutralisation, will be important (and challenging).

The authors could consider more of a discussion on the paucity of clinical data on these mAbs and these variants from well-designed trials, and that we do not know whether the in vitro findings have informed so many treatment/guideline decisions, do correlate with in vivo activity. Is this the first study to report clinical outcomes randomising patients across δ and omicron with these mAb agents? The authors could consider referring to this study, the other large comparative effectiveness study comparing monoclonal antibodies (particularly if published soon). https://www.medrxiv.org/content/10.1101/2021.09.03.21262551v1

Reviewer #3 (Recommendations for the authors):

My overall review is in the public review. What follows is details. Also, authors should check and review extensively for improvements to the use of English.

Abstract

– Notes AIFA and ORCHESTRA funding, but I believe the electronic form says no funding.

Introduction

– Other comparative effectiveness trials have been conducted.

https://www.medrxiv.org/content/10.1101/2021.12.23.21268244v1

https://www.sciencedirect.com/science/article/pii/S1551714422001483

Methods

– Please describe which centers participated and where they were located.

Results

– 319 pts enrolled in 5 weeks, over the winter holidays is impressive.

– Leveraging of the ~50/50 δ/omicron breakdown is very nice.

– For completeness, would note the #s of all the secondary outcomes, even if they are very small in # or even zero.

Discussion

– Would note that there was a signal in only 1 secondary outcome – could note it is perhaps due to the fact that very few patients had "progression" – but that nonetheless this signal is from 1 of multiple examined outcomes

– On page 10, line 255, "significant benefit" is somewhat problematic – statistically for the reason noted above, and 5d less symptoms is of debatable significance if ultimate outcome is the same. Would use more objective/neutral words.

– On page 11, line 265 – what are the specific findings that support that all study treatments were active against Δ (when there was no "control" arm) – the fact that no disease progression occurred – if so, pls say so?

Also, what are the specific findings that support that C-I and S both retain activity against Omicron when S was superior to both C-I and B-E for symptom resolution, and C-I and B-E had the same 12d median symptom duration? Ie. what is evidence that C-I retained activity v Omicron?

– Would note that currently the dominant Omicron variants are now BA.2 and beyond, and for this reason, FDA revoked all EUAs for mAbs, except for bebtelovimb.

Tables + Figures

– Please shorten everything to 1 or 2 decimal points.

– The K-M curves are hard to read – try dropping the 95% CIs.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Exploratory data on the clinical efficacy of monoclonal antibodies against SARS-CoV-2 Omicron Variant of Concern" for further consideration by eLife. Your revised article has been evaluated by Miles Davenport (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

As recommended by the reviewers, please edit the abstract to ensure the conclusion reflects the study design and data. The abstract should state that the trial was ceased early and the conclusion amended as per reviewer 2 comments below.

For the study methods, the original sample size calculation should be included in the methods section (rather than referring readers to clinical trial register).

Reviewer #2 (Recommendations for the authors):

Thank you for submitting your revised manuscript and for addressing the suggestions raised in previous reviews. The revised manuscript is clearer, in terms of the study design and the important limitations, and the conclusions are now better supported by the data.

Given the uncertainty, the limitations, etc, the conclusion of the abstract is important. I suggest a minor edit of this sentence:

"Casirivimab/imdevimab seems to maintain a role in preventing COVID-19 progression in adult outpatients with early mild-to-moderate SARS-CoV-2 infection due to Omicron."

Which could be changed to:

"Casirivimab/imdevimab may maintain a role in preventing COVID-19 progression in adult outpatients with early mild-to-moderate SARS-CoV-2 infection due to Omicron."

This is particularly true when you are not suggesting it is used for treatment (which could be implied by "seems to maintain a role") and your overarching conclusion is that adaptive clinical studies are required (although casirivimab/imdevimab is not included in the next phase of MANTICO, presumably because these data are only available now and the VOCs have continued to change).

Reviewer #3 (Recommendations for the authors):

Revisions are appropriate, however, manuscript revisions (esp Discussion revisions) have not extended to the Abstract.

For Abstract, I recommend:

1. Cut decimal places to two.

2. Also add to the 2nd Conclusion sentence the specific Omicron variants analyzed in this study. Or reword so it's clear that the specific variants apply to all Conclusion sentences / mAbs.

3. Add to Conclusion a sentence reflective of the "results are severely limited" sentence added to Discussion.

E.g., could note that time to symptom resolution was 1 of X secondary outcomes, and no conclusions could be made about the primary outcome.

Specific text up to the authors – my main residual concern is simply that the Abstract's Conclusion is considerably "stronger" than the manuscript's conclusion/discussion, and should be appropriately caveated.

https://doi.org/10.7554/eLife.79639.sa1

Author response

Essential revisions:

1) As this is a report of a randomised trial, please report according to CONSORT guidelines/checklist and provide a CONSORT figure.

The CONSORT flow diagram has been added as Figure 1 and the CONSORT checklist has been included among the uploaded files.

2) Clearer presentation of primary and secondary outcome data.

Primary and secondary outcome data have been reported in Table 4 (Δ variant) and Table 6 (Omicron variant) of the revised version of the manuscript (pages 20 and 22).

The previous Figure 1 and 2 have been combined into a multi-panel figure, now renamed Figure 2A/2B (without 95% CIs, as suggested by Reviewer 3).

3) Revision of the discussion and conclusion to ensure that all statements regarding the efficacy of interventions and interpretations of findings are supported and consistent with the data presented (see reviewer comments below for examples).

The discussion and conclusion have been revised accordingly (page 12).

4) Inclusion of discussion of limitations of the study, including potential sources of bias

Limitations of the study, including potential sources of bias, have been included in the discussion of the revised version of the manuscript (page 12, lines 290-301).

Reviewer #1 (Recommendations for the authors):

The authors followed STROBE guidelines. However, reporting an RCT and not an observational study, they should have followed CONSORT guidelines.

Could the authors explain why the type of administered mAb was blinded to the patient?

The CONSORT flow diagram has been added as Figure 1 and the CONSORT checklist has been included among the uploaded files.

The type of administered mAb was blinded to the patient to reduce the risk of performance bias.

Reviewer #2 (Recommendations for the authors):

Thank you for the opportunity to review this manuscript. I provide the below feedback in an effort to improve the manuscript and in addition to my evaluation summary and public review.

The authors could be more consistent with regard to the description of the primary outcome (e.g. "COVID-19 progression" on lines 38/39 of the abstract cf "disease progression" on line 44).

The primary outcome has been consistently reported as “COVID-19 progression” all throughout the revised version of the manuscript.

On lines 46/47, the authors could consider editing the statement "two disease progressions were recorded in the bamlanivimab/etesevimab group" to specify that there were no cases of disease progression in the other groups.

The sentence has been amended accordingly in the revised version of the manuscript (page 2, line 47).

On lines 54/55, the evidence for the claim re: Casirivimab/imdevimab is insufficient (see statements above) but even as it is currently written it is not quite accurate. The manuscript states "Casirivimab/imdevimab seems to maintain a role in preventing severe COVID-19 in the Omicron population" whereas it could be better worded in terms of preventing progression of COVID-19 in adults with mild-moderate COVID-19 due to the omicron variant of SARS-CoV-2.

The sentence has been amended accordingly in the revised version of the manuscript (page 2, line 55-56).

The ClinicalTrials.gov entry for your trial says 320 participants were recruited. Is there a reason that this manuscript says 319?

Thanks for pointing out this typo. The right number is 319, as reported in the manuscript. The ClinicalTrials.gov entry has been corrected.

Is there a CONSORT diagram? Is there a CONSORT checklist that could be submitted with this manuscript? I see there is a STROBE checklist, and I guess in some ways this is an observational (δ vs omicron) study housed within an RCT. But I think it would be useful to understand (and report) more about the recruitment to the RCT, even as an appendix, and include a CONSORT diagram.

The CONSORT flow diagram has been added as Figure 1 and the CONSORT checklist has been included among the uploaded files.

Were any participants lost to follow-up? Were any data points missing?

No patients were lost to follow-up and no data points were missing.

Figure 1 would usually show the primary outcome, rather than a secondary outcome. I can see why the authors would choose to show the secondary outcome (time to symptom resolution) in the figure, as this is a useful way of showing those data. But the discussion is focused on a signal in the primary outcome (which I do not think is justified) and I think if the authors wish to focus on that (hypothesis-generating only) finding of only 2 cases of COVID-19 progression, only occurring in the bamlanivimab/etesevimab group, the readers should see how these groups 'differ' visually too.

We thank the reviewer for the suggestion. Primary and secondary outcomes have been reported in Table 4 (Δ variant) and Table 6 (Omicron variant) of the revised version of the manuscript (pages 20 and 22).

Could the word 'survival' lead to misinterpretation of the data in Figure 1? I would suggest removing 'survival' from the figure title (currently: "Figure 1 – Survival time to symptom resolution by type of treatment in the study population infected with Δ". Same for Figure 2).

We do agree that the word “survival” can be misleading. The word has been deleted from footnotes and figures.

Could Figure 1 and Figure 2 be combined into a multi-panel figure so the data are next to each other for comparison?

The previous Figure 1 and 2 have been combined into a multi-panel figure, now renamed Figure 2A/2B (without 95% CIs, as suggested by Reviewer 3).

On lines 222/223, this statement is redundant when you have presented the days of symptoms in each group "turning out to be 5 days shorter in the sotrovimab group compared to both bamlanivimab/etesevimab and casirivimab/imdevimab groups"

The statement has been removed.

I was confused about the presentation of the statistical analyses of the time to symptom resolution by treatment assignment in the two cohorts (δ and omicron). For the δ data, an analysis was presented assessing whether the duration of symptoms differed by treatment assignment (log-rank chi-square), and then each arm vs arm comparison was also presented as log-rank chi-square (lines 189-194). Cox regression was then used to confirm no difference with casirivimab/imdevimab as the reference. For omicron, where the data suggest there may be a difference, we do not present the chi-square analyses (lines 220-225). Is there a reason for this? The data should be presented the same way for both VOC.

We thank the reviewer for the comment. The Chi-square analyses on the Omicron group, which were improperly missing in the previous version of the manuscript, have been added (page 10, lines 237-242).

Lines 224-226 too strongly imply a benefit here with casirivimab/imdevimab. And could be read as suggesting both casirivimab/imdevimab and sotrovimab retain comparable activity in vitro. Of course, it depends on the assay used, etc. And these in vitro assays have significant limitations, in terms of their utility to make treatment decisions (and decisions re: guideline recommendations!). But I think this section could be re-worded to improve accuracy.

We do agree with the reviewer. The trial is underpowered to reach any firm conclusion. This major limitation has been better pointed out in the discussion of the revised version of the manuscript (page 12, lines 290-298).

When this is published, we will be on to the next variant (BA.2, BA.4, etc), in most places we already are. Therefore, consideration of how the authors frame the results in that context, ensuring we do not necessarily imply that all variants termed 'omicron' are equal with regards to mAb neutralisation, will be important (and challenging).

We thank the reviewer for the suggestion. This limitation has been pointed out in the discussion of the revised version of the manuscript (page 12, lines 298-301).

The authors could consider more of a discussion on the paucity of clinical data on these mAbs and these variants from well-designed trials, and that we do not know whether the in vitro findings have informed so many treatment/guideline decisions, do correlate with in vivo activity. Is this the first study to report clinical outcomes randomising patients across δ and omicron with these mAb agents? The authors could consider referring to this study, the other large comparative effectiveness study comparing monoclonal antibodies (particularly if published soon). https://www.medrxiv.org/content/10.1101/2021.09.03.21262551v1

We thank the reviewer for the suggested reference, which has been added in the revised version of the manuscript (page 4, lines 85-86). This study reports results as of June 25, 2021. During the trial the Α variant was the dominant variant of concern, while the Δ variant became more prevalent in the final time period. To date, our study is actually the first one to report clinical outcomes against the Omicron variant.

Reviewer #3 (Recommendations for the authors):

My overall review is in the public review. What follows is details. Also, authors should check and review extensively for improvements to the use of English.

Abstract

– Notes AIFA and ORCHESTRA funding, but I believe the electronic form says no funding.

As stated in the manuscript, AIFA and ORCHESTRA provided funding for the study. This information has been added in the electronic form as well.

Introduction

– Other comparative effectiveness trials have been conducted.

https://www.medrxiv.org/content/10.1101/2021.12.23.21268244v1

https://www.sciencedirect.com/science/article/pii/S1551714422001483

Thanks for the suggestion. The references have been added in the revised version of the manuscript (page 4, lines 84-87).

Methods

– Please describe which centers participated and where they were located.

The information on the trial centers has been added in the revised version of the manuscript (page 5, lines 98-99).

Results

– 319 pts enrolled in 5 weeks, over the winter holidays is impressive.

– Leveraging of the ~50/50 δ/omicron breakdown is very nice.

– For completeness, would note the #s of all the secondary outcomes, even if they are very small in # or even zero.

The numbers of all secondary outcomes have been reported in Table 4 (Δ variant) and Table 6 (Omicron variant) of the revised version of the manuscript (pages 20 and 22).

Discussion

– Would note that there was a signal in only 1 secondary outcome – could note it is perhaps due to the fact that very few patients had "progression" – but that nonetheless this signal is from 1 of multiple examined outcomes

We do agree with the reviewer. The trial is underpowered to reach any firm conclusion. This major limitation has been better pointed out in the discussion of the revised version of the manuscript (page 12, lines 290-298).

– On page 10, line 255, "significant benefit" is somewhat problematic – statistically for the reason noted above, and 5d less symptoms is of debatable significance if ultimate outcome is the same. Would use more objective/neutral words.

We thank the reviewer for the suggestion. The statement has been reworded accordingly, replacing “sotrovimab showed a significant benefit” with “sotrovimab seemed to show a benefit” in the revised version of the manuscript (page 11, line 277).

– On page 11, line 265 – what are the specific findings that support that all study treatments were active against Δ (when there was no "control" arm) – the fact that no disease progression occurred – if so, pls say so?

We are sorry for the misleading sentence, which has been rephrased in the revised version of the manuscript (page 12, lines 283-290).

We fully agree with the reviewer on the limitation of the study design.

Also, what are the specific findings that support that C-I and S both retain activity against Omicron when S was superior to both C-I and B-E for symptom resolution, and C-I and B-E had the same 12d median symptom duration? Ie. what is evidence that C-I retained activity v Omicron?

– Would note that currently the dominant Omicron variants are now BA.2 and beyond, and for this reason, FDA revoked all EUAs for mAbs, except for bebtelovimb.

We thank the reviewer for the suggestion. This limitation has been pointed out in the discussion of the revised version of the manuscript (page 12, lines 298-301).

Tables + Figures

– Please shorten everything to 1 or 2 decimal points.

All numbers in Tables and Figures have been shortened to 2 decimal points (p value excluded).

– The K-M curves are hard to read – try dropping the 95% CIs.

We have provided Kaplan-Meier curves without 95% CIs (Figure 2A/2B).

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

As recommended by the reviewers, please edit the abstract to ensure the conclusion reflects the study design and data. The abstract should state that the trial was ceased early and the conclusion amended as per reviewer 2 comments below.

The abstract has been revised accordingly.

For the study methods, the original sample size calculation should be included in the methods section (rather than referring readers to clinical trial register).

The original sample size calculation has been included in the method section.

Reviewer #2 (Recommendations for the authors):

Thank you for submitting your revised manuscript and for addressing the suggestions raised in previous reviews. The revised manuscript is clearer, in terms of the study design and the important limitations, and the conclusions are now better supported by the data.

Given the uncertainty, the limitations, etc, the conclusion of the abstract is important. I suggest a minor edit of this sentence:

"Casirivimab/imdevimab seems to maintain a role in preventing COVID-19 progression in adult outpatients with early mild-to-moderate SARS-CoV-2 infection due to Omicron."

Which could be changed to:

"Casirivimab/imdevimab may maintain a role in preventing COVID-19 progression in adult outpatients with early mild-to-moderate SARS-CoV-2 infection due to Omicron."

This is particularly true when you are not suggesting it is used for treatment (which could be implied by "seems to maintain a role") and your overarching conclusion is that adaptive clinical studies are required (although casirivimab/imdevimab is not included in the next phase of MANTICO, presumably because these data are only available now and the VOCs have continued to change).

We thank the reviewer for the suggestion. The sentence has been amended accordingly in the revised version of the abstract.

Reviewer #3 (Recommendations for the authors):

Revisions are appropriate, however, manuscript revisions (esp Discussion revisions) have not extended to the Abstract.

For Abstract, I recommend:

1. Cut decimal places to two.

All numbers have been shortened to 2 decimal points in the revised version of the abstract (p value included).

2. Also add to the 2nd Conclusion sentence the specific Omicron variants analyzed in this study. Or reword so it's clear that the specific variants apply to all Conclusion sentences / mAbs.

The conclusion of the abstract has been reworded to clarify that the Omicron variants analysed in this study apply to all sentences

3. Add to Conclusion a sentence reflective of the "results are severely limited" sentence added to Discussion.

E.g., could note that time to symptom resolution was 1 of X secondary outcomes, and no conclusions could be made about the primary outcome.

Specific text up to the authors – my main residual concern is simply that the Abstract's Conclusion is considerably "stronger" than the manuscript's conclusion/discussion, and should be appropriately caveated.

We do agree with the reviewer. A sentence pointing out the severe limitations of the results has been added to the abstract.

https://doi.org/10.7554/eLife.79639.sa2

Article and author information

Author details

  1. Fulvia Mazzaferri

    Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Supervision, Methodology, Writing - original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  2. Massimo Mirandola

    Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Supervision, Validation, Visualization, Methodology, Writing - original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2342-5867
  3. Alessia Savoldi

    Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    Contribution
    Resources, Data curation, Software, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  4. Pasquale De Nardo

    Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    Contribution
    Resources, Data curation, Software, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  5. Matteo Morra

    Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    Contribution
    Data curation, Software, Supervision, Validation, Investigation, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
  6. Maela Tebon

    Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    Contribution
    Data curation, Software, Supervision, Validation, Investigation, Project administration
    Competing interests
    No competing interests declared
  7. Maddalena Armellini

    Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    Contribution
    Data curation, Validation, Investigation, Project administration
    Competing interests
    No competing interests declared
  8. Giulia De Luca

    Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    Contribution
    Data curation, Validation, Investigation
    Competing interests
    No competing interests declared
  9. Lucrezia Calandrino

    Infectious Disease Unit, Padova University Hospital, Padua, Italy
    Contribution
    Data curation, Supervision, Investigation
    Competing interests
    No competing interests declared
  10. Lolita Sasset

    Infectious Disease Unit, Padova University Hospital, Padua, Italy
    Contribution
    Data curation, Supervision, Investigation
    Competing interests
    Lolita Sasset has served as a paid consultant to Abbvie, Janssen, MSD, Gilead Sciences, Janssen, MSD and ViiV Healthcare; she does not have any financial competing interests with this study
  11. Denise D'Elia

    Infectious Diseases Division, Department of Medicine, University of Udine and Azienda Sanitaria Universitaria Friuli Centrale, Udine, Italy
    Contribution
    Resources, Data curation, Supervision, Investigation
    Competing interests
    No competing interests declared
  12. Emanuela Sozio

    Infectious Diseases Division, Department of Medicine, University of Udine and Azienda Sanitaria Universitaria Friuli Centrale, Udine, Italy
    Contribution
    Resources, Data curation, Formal analysis, Supervision, Investigation, Writing - original draft
    Competing interests
    No competing interests declared
  13. Elisa Danese

    Section of Clinical Biochemistry, Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, Italy
    Contribution
    Resources, Validation, Investigation
    Competing interests
    No competing interests declared
  14. Davide Gibellini

    Microbiology and Virology Unit, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    Contribution
    Resources, Supervision, Validation, Investigation
    Competing interests
    No competing interests declared
  15. Isabella Monne

    Viral genomics and transcriptomics Laboratory, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Italy
    Contribution
    Resources, Supervision, Investigation, Writing - original draft
    Competing interests
    No competing interests declared
  16. Giovanna Scroccaro

    Direzione Farmaceutico, Protesica, Dispositivi Medici, Regione del Veneto, Venice, Italy
    Contribution
    Conceptualization, Resources, Supervision, Project administration
    Competing interests
    No competing interests declared
  17. Nicola Magrini

    Italian Medicines Agency, Rome, Italy
    Contribution
    Conceptualization, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
  18. Annamaria Cattelan

    Infectious Disease Unit, Padova University Hospital, Padua, Italy
    Contribution
    Resources, Supervision, Writing – review and editing
    Competing interests
    Annamaria Cattelan has served as a paid consultant to Abbvie, Janssen, MSD, and received research fundings from Gilead Sciences, Janssen, MSD and ViiV Healthcare; she does not have any financial competing interests with this study
  19. Carlo Tascini

    Infectious Diseases Division, Department of Medicine, University of Udine and Azienda Sanitaria Universitaria Friuli Centrale, Udine, Italy
    Contribution
    Conceptualization, Supervision, Writing – review and editing
    Competing interests
    Carlo Tascini has received grants from Correvio, Biotest, Biomerieux, Gilead, Angelini, MSD, Pfizer, Thermofisher, Zambon, Shionogi, Avir Pharma and Hikma outside the submitted work in the last two years
  20. MANTICO Working Group

    Contribution
    Data curation, Investigation, Validation
    Competing interests
    No competing interests declared
    1. Francesca Simbeni, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    2. Alessandro Castelli, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    3. Ilaria Dalla Vecchia, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    4. Chiara Konishi de Toffoli, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    5. Gaia Maccarrone, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    6. Marco Meroi, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    7. Chiara Perlini, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    8. Matilde Rocchi, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    9. Giulia Rosini, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    10. Laura Rovigo, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    11. Lorenzo Tavernaro, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    12. Amina Zaffagnini, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    13. Ilaria Currò, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    14. Ruth Joanna Davis, Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    15. Elena Agostini, Infectious Disease Unit, Padova University Hospital, Padua, Italy
    16. Carla Benfatto, Infectious Disease Unit, Padova University Hospital, Padua, Italy
    17. Nicola Bonadiman, Infectious Disease Unit, Padova University Hospital, Padua, Italy
    18. Marco Canova, Infectious Disease Unit, Padova University Hospital, Padua, Italy
    19. Giuseppe Lombardo, Infectious Disease Unit, Padova University Hospital, Padua, Italy
    20. Daniele Mengato, Infectious Disease Unit, Padova University Hospital, Padua, Italy
    21. Danilo Puntrello, Infectious Disease Unit, Padova University Hospital, Padua, Italy
    22. Francesca Prataviera, Infectious Diseases Division, Department of Medicine, University of Udine and Azienda Sanitaria Universitaria Friuli Centrale, Udine, Italy
    23. Tosca Semenzin, Infectious Diseases Division, Department of Medicine, University of Udine and Azienda Sanitaria Universitaria Friuli Centrale, Udine, Italy
    24. Dario Carloni, Infectious Diseases Division, Department of Medicine, University of Udine and Azienda Sanitaria Universitaria Friuli Centrale, Udine, Italy
    25. Giuseppe Lippi, Section of Clinical Biochemistry, Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, Italy
    26. Davide Negrini, Section of Clinical Biochemistry, Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, Italy
    27. Martina Montagnana, Section of Clinical Biochemistry, Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, Italy
    28. Riccardo Cecchetto, Microbiology and Virology Unit, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    29. Alice Fusaro, Viral genomics and transcriptomics Laboratory, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Italy
    30. Francesco Bonfante, Viral genomics and transcriptomics Laboratory, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Italy
    31. Elisa Palumbo, Viral genomics and transcriptomics Laboratory, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Italy
    32. Edoardo Giussani, Viral genomics and transcriptomics Laboratory, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Italy
  21. Evelina Tacconelli

    Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Visualization, Methodology, Project administration, Writing – review and editing
    For correspondence
    evelina.tacconelli@univr.it
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2010-4977

Funding

Agenzia Italiana del Farmaco, Ministero della Salute

  • Evelina Tacconelli

Horizon 2020 Framework Programme (101016167)

  • Evelina Tacconelli

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 NCT05205759.

All recruited subjects provided the informed consent to participate to the MANTICO trial. The IRB approval was provided by the Ethics Committee of the National Institute for Infectious Diseases "Lazzaro Spallanzani" (468_2021) and by the Scientific Technical Committee of the Italian Medicines Agency (28 OCT 2021).

Senior Editor

  1. Miles P Davenport, University of New South Wales, Australia

Reviewing Editor

  1. Zoe McQuilten, Monash University, Australia

Reviewer

  1. David Huang, University of Pittsburgh, United States

Publication history

  1. Received: April 20, 2022
  2. Preprint posted: May 9, 2022 (view preprint)
  3. Accepted: November 6, 2022
  4. Version of Record published: November 22, 2022 (version 1)

Copyright

© 2022, Mazzaferri 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. Fulvia Mazzaferri
  2. Massimo Mirandola
  3. Alessia Savoldi
  4. Pasquale De Nardo
  5. Matteo Morra
  6. Maela Tebon
  7. Maddalena Armellini
  8. Giulia De Luca
  9. Lucrezia Calandrino
  10. Lolita Sasset
  11. Denise D'Elia
  12. Emanuela Sozio
  13. Elisa Danese
  14. Davide Gibellini
  15. Isabella Monne
  16. Giovanna Scroccaro
  17. Nicola Magrini
  18. Annamaria Cattelan
  19. Carlo Tascini
  20. MANTICO Working Group
  21. Evelina Tacconelli
(2022)
Exploratory data on the clinical efficacy of monoclonal antibodies against SARS-CoV-2 Omicron variant of concern
eLife 11:e79639.
https://doi.org/10.7554/eLife.79639
  1. Further reading

Further reading

    1. Epidemiology and Global Health
    2. Medicine
    Qing Shen, Huan Song ... Unnur Valdimarsdóttir
    Research Article Updated

    Background:

    The association between cardiovascular disease (CVD) and selected psychiatric disorders has frequently been suggested while the potential role of familial factors and comorbidities in such association has rarely been investigated.

    Methods:

    We identified 869,056 patients newly diagnosed with CVD from 1987 to 2016 in Sweden with no history of psychiatric disorders, and 910,178 full siblings of these patients as well as 10 individually age- and sex-matched unrelated population controls (N = 8,690,560). Adjusting for multiple comorbid conditions, we used flexible parametric models and Cox models to estimate the association of CVD with risk of all subsequent psychiatric disorders, comparing rates of first incident psychiatric disorder among CVD patients with rates among unaffected full siblings and population controls.

    Results:

    The median age at diagnosis was 60 years for patients with CVD and 59.2% were male. During up to 30 years of follow-up, the crude incidence rates of psychiatric disorder were 7.1, 4.6, and 4.0 per 1000 person-years for patients with CVD, their siblings and population controls. In the sibling comparison, we observed an increased risk of psychiatric disorder during the first year after CVD diagnosis (hazard ratio [HR], 2.74; 95% confidence interval [CI], 2.62–2.87) and thereafter (1.45; 95% CI, 1.42–1.48). Increased risks were observed for all types of psychiatric disorders and among all diagnoses of CVD. We observed similar associations in the population comparison. CVD patients who developed a comorbid psychiatric disorder during the first year after diagnosis were at elevated risk of subsequent CVD death compared to patients without such comorbidity (HR, 1.55; 95% CI, 1.44–1.67).

    Conclusions:

    Patients diagnosed with CVD are at an elevated risk for subsequent psychiatric disorders independent of shared familial factors and comorbid conditions. Comorbid psychiatric disorders in patients with CVD are associated with higher risk of cardiovascular mortality suggesting that surveillance and treatment of psychiatric comorbidities should be considered as an integral part of clinical management of newly diagnosed CVD patients.

    Funding:

    This work was supported by the EU Horizon 2020 Research and Innovation Action Grant (CoMorMent, grant no. 847776 to UV, PFS, and FF), Grant of Excellence, Icelandic Research Fund (grant no. 163362-051 to UV), ERC Consolidator Grant (StressGene, grant no. 726413 to UV), Swedish Research Council (grant no. D0886501 to PFS), and US NIMH R01 MH123724 (to PFS).

    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.