Development and Validation of a Treatment Benefit Index to Identify Hospitalized Patients With COVID-19 Who May Benefit From Convalescent Plasma

Importance: Identifying which patients with COVID-19 are likely to benefit from COVID-19 convalescent plasma (CCP) treatment may have a large public health impact. Objective: To develop an index for predicting the expected relative treatment benefit from CCP compared with treatment without CCP for p...

Full description

Bibliographic Details
Main Authors: Agarwal, A. (Author), Antman, E.M (Author), Avendaño-Solá, C. (Author), Bar, K.J (Author), Belloso, W. (Author), Belov, A. (Author), Bhattacharya, P. (Author), Burgos Pratx, L. (Author), Duarte, R.F (Author), Forshee, R. (Author), Ganguly, D. (Author), Goldfeld, K. (Author), Hsue, P.Y (Author), Huang, Y. (Author), Li, Y. (Author), Liu, M. (Author), Luetkemeyer, A.F (Author), Meyfroidt, G. (Author), Mukherjee, A. (Author), Nicola, A.M (Author), Ortigoza, M.B (Author), Park, H. (Author), Paul, S.R (Author), Petkova, E. (Author), Pirofski, L.-A (Author), Ray, Y. (Author), Rijnders, B.J.A (Author), Scibona, P. (Author), Simonovich, V.A (Author), Tarpey, T. (Author), Troxel, A. (Author), Verdun, N.C (Author), Villa, C. (Author), Wu, D. (Author), Wu, Y. (Author), Yoon, H.A (Author), Zhang, J. (Author)
Format: Article
Language:English
Published: NLM (Medline) 2022
Subjects:
Online Access:View Fulltext in Publisher
LEADER 05971nam a2201033Ia 4500
001 10-1001-jamanetworkopen-2021-47375
008 220420s2022 CNT 000 0 und d
020 |a 25743805 (ISSN) 
245 1 0 |a Development and Validation of a Treatment Benefit Index to Identify Hospitalized Patients With COVID-19 Who May Benefit From Convalescent Plasma 
260 0 |b NLM (Medline)  |c 2022 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1001/jamanetworkopen.2021.47375 
520 3 |a Importance: Identifying which patients with COVID-19 are likely to benefit from COVID-19 convalescent plasma (CCP) treatment may have a large public health impact. Objective: To develop an index for predicting the expected relative treatment benefit from CCP compared with treatment without CCP for patients hospitalized for COVID-19 using patients' baseline characteristics. Design, Setting, and Participants: This prognostic study used data from the COMPILE study, ie, a meta-analysis of pooled individual patient data from 8 randomized clinical trials (RCTs) evaluating CCP vs control in adults hospitalized for COVID-19 who were not receiving mechanical ventilation at randomization. A combination of baseline characteristics, termed the treatment benefit index (TBI), was developed based on 2287 patients in COMPILE using a proportional odds model, with baseline characteristics selected via cross-validation. The TBI was externally validated on 4 external data sets: the Expanded Access Program (1896 participants), a study conducted under Emergency Use Authorization (210 participants), and 2 RCTs (with 80 and 309 participants). Exposure: Receipt of CCP. Main Outcomes and Measures: World Health Organization (WHO) 11-point ordinal COVID-19 clinical status scale and 2 derivatives of it (ie, WHO score of 7-10, indicating mechanical ventilation to death, and WHO score of 10, indicating death) at day 14 and day 28 after randomization. Day 14 WHO 11-point ordinal scale was used as the primary outcome to develop the TBI. Results: A total of 2287 patients were included in the derivation cohort, with a mean (SD) age of 60.3 (15.2) years and 815 (35.6%) women. The TBI provided a continuous gradation of benefit, and, for clinical utility, it was operationalized into groups of expected large clinical benefit (B1; 629 participants in the derivation cohort [27.5%]), moderate benefit (B2; 953 [41.7%]), and potential harm or no benefit (B3; 705 [30.8%]). Patients with preexisting conditions (diabetes, cardiovascular and pulmonary diseases), with blood type A or AB, and at an early COVID-19 stage (low baseline WHO scores) were expected to benefit most, while those without preexisting conditions and at more advanced stages of COVID-19 could potentially be harmed. In the derivation cohort, odds ratios for worse outcome, where smaller odds ratios indicate larger benefit from CCP, were 0.69 (95% credible interval [CrI], 0.48-1.06) for B1, 0.82 (95% CrI, 0.61-1.11) for B2, and 1.58 (95% CrI, 1.14-2.17) for B3. Testing on 4 external datasets supported the validation of the derived TBIs. Conclusions and Relevance: The findings of this study suggest that the CCP TBI is a simple tool that can quantify the relative benefit from CCP treatment for an individual patient hospitalized with COVID-19 that can be used to guide treatment recommendations. The TBI precision medicine approach could be especially helpful in a pandemic. 
650 0 4 |a aged 
650 0 4 |a Aged 
650 0 4 |a artificial ventilation 
650 0 4 |a blood group typing 
650 0 4 |a Blood Grouping and Crossmatching 
650 0 4 |a comorbidity 
650 0 4 |a Comorbidity 
650 0 4 |a COVID-19 
650 0 4 |a female 
650 0 4 |a Female 
650 0 4 |a hospitalization 
650 0 4 |a Hospitalization 
650 0 4 |a human 
650 0 4 |a Humans 
650 0 4 |a Immunization, Passive 
650 0 4 |a male 
650 0 4 |a Male 
650 0 4 |a middle aged 
650 0 4 |a Middle Aged 
650 0 4 |a odds ratio 
650 0 4 |a Odds Ratio 
650 0 4 |a pandemic 
650 0 4 |a Pandemics 
650 0 4 |a passive immunization 
650 0 4 |a patient selection 
650 0 4 |a Patient Selection 
650 0 4 |a plasma 
650 0 4 |a Plasma 
650 0 4 |a Respiration, Artificial 
650 0 4 |a SARS-CoV-2 
650 0 4 |a severity of illness index 
650 0 4 |a Severity of Illness Index 
650 0 4 |a therapeutic index 
650 0 4 |a Therapeutic Index 
650 0 4 |a therapy 
650 0 4 |a treatment outcome 
650 0 4 |a Treatment Outcome 
650 0 4 |a World Health Organization 
650 0 4 |a World Health Organization 
700 1 0 |a Agarwal, A.  |e author 
700 1 0 |a Antman, E.M.  |e author 
700 1 0 |a Avendaño-Solá, C.  |e author 
700 1 0 |a Bar, K.J.  |e author 
700 1 0 |a Belloso, W.  |e author 
700 1 0 |a Belov, A.  |e author 
700 1 0 |a Bhattacharya, P.  |e author 
700 1 0 |a Burgos Pratx, L.  |e author 
700 1 0 |a Duarte, R.F.  |e author 
700 1 0 |a Forshee, R.  |e author 
700 1 0 |a Ganguly, D.  |e author 
700 1 0 |a Goldfeld, K.  |e author 
700 1 0 |a Hsue, P.Y.  |e author 
700 1 0 |a Huang, Y.  |e author 
700 1 0 |a Li, Y.  |e author 
700 1 0 |a Liu, M.  |e author 
700 1 0 |a Luetkemeyer, A.F.  |e author 
700 1 0 |a Meyfroidt, G.  |e author 
700 1 0 |a Mukherjee, A.  |e author 
700 1 0 |a Nicola, A.M.  |e author 
700 1 0 |a Ortigoza, M.B.  |e author 
700 1 0 |a Park, H.  |e author 
700 1 0 |a Paul, S.R.  |e author 
700 1 0 |a Petkova, E.  |e author 
700 1 0 |a Pirofski, L.-A.  |e author 
700 1 0 |a Ray, Y.  |e author 
700 1 0 |a Rijnders, B.J.A.  |e author 
700 1 0 |a Scibona, P.  |e author 
700 1 0 |a Simonovich, V.A.  |e author 
700 1 0 |a Tarpey, T.  |e author 
700 1 0 |a Troxel, A.  |e author 
700 1 0 |a Verdun, N.C.  |e author 
700 1 0 |a Villa, C.  |e author 
700 1 0 |a Wu, D.  |e author 
700 1 0 |a Wu, Y.  |e author 
700 1 0 |a Yoon, H.A.  |e author 
700 1 0 |a Zhang, J.  |e author 
773 |t JAMA network open