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

Educational Objective
To identify the key insights or developments described in this article
1 Credit CME
Key Points

Question  What patient characteristics are associated with benefit from treatment with COVID-19 convalescent plasma (CCP)?

Findings  This prognostic study of 2287 patients hospitalized with COVID-19 identified a combination of baseline characteristics that predict a gradation of benefit from CCP compared with treatment without CCP. Preexisting health conditions (diabetes, cardiovascular and pulmonary diseases), blood type A or AB, and earlier stage of COVID-19 were associated with a larger treatment benefit.

Meaning  These findings suggest that simple patient information collected at hospitalization can be used to guide CCP treatment decisions for patients with COVID-19.

Abstract

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.

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CME Disclosure Statement: Unless noted, all individuals in control of content reported no relevant financial relationships. If applicable, all relevant financial relationships have been mitigated.

Article Information

Accepted for Publication: December 15, 2021.

Published: January 25, 2022. doi:10.1001/jamanetworkopen.2021.47375

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2022 Park H et al. JAMA Network Open.

Corresponding Author: Eva Petkova, PhD, Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, 180 Madison Ave, Room 2-55, New York, NY 10016 (eva.petkova@nyulangone.org).

Author Contributions: Drs Park and Petkova had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Tarpey, Liu, Goldfeld, Ray, Villa, Verdun, Scibona, Burgos Pratx, Duarte, Hsue, Meyfroidt, Ortigoza, Pirofski, Troxel, Antman, Petkova.

Acquisition, analysis, or interpretation of data: Park, Tarpey, Liu, Y. Wu, D. Wu, Li, Zhang, Ganguly, Ray, Paul, Bhattacharya, Belov, Huang, Forshee, Verdun, Yoon, Agarwal, Simonovich, Burgos Pratx, Belloso, Avendaño-Solá, Bar, Duarte, Luetkemeyer, Meyfroidt, Nicola, Mukherjee, Ortigoza, Pirofski, Rijnders, Antman, Petkova.

Drafting of the manuscript: Park, Tarpey, Liu, Y. Wu, Li, Zhang, Burgos Pratx, Hsue, Pirofski, Petkova.

Critical revision of the manuscript for important intellectual content: Park, Tarpey, Goldfeld, D. Wu, Zhang, Ganguly, Ray, Paul, Bhattacharya, Belov, Huang, Villa, Forshee, Verdun, Yoon, Agarwal, Simonovich, Scibona, Burgos Pratx, Belloso, Avendaño-Solá, Bar, Duarte, Luetkemeyer, Meyfroidt, Nicola, Mukherjee, Ortigoza, Pirofski, Rijnders, Troxel, Antman, Petkova.

Statistical analysis: Park, Tarpey, Liu, Goldfeld, Y. Wu, D. Wu, Li, Zhang, Ganguly, Paul, Bhattacharya, Belov, Forshee, Burgos Pratx, Troxel, Petkova.

Obtained funding: Scibona, Hsue, Luetkemeyer, Petkova.

Administrative, technical, or material support: Park, Ganguly, Ray, Paul, Bhattacharya, Belov, Huang, Villa, Simonovich, Scibona, Burgos Pratx, Avendaño-Solá, Bar, Petkova.

Supervision: Liu, Ray, Forshee, Verdun, Scibona, Duarte, Hsue, Nicola, Pirofski, Antman, Petkova.

Conflict of Interest Disclosures: Dr Yoon reported receiving grants from the G. Harold and Leila Y. Mathers Foundation during the conduct of the study. Dr Duarte reported receiving personal fees from Amgen, Astellas, Bristol Myers Squibb, Gilead Sciences, Jazz Pharmaceuticals, Kiadis Pharma, Miltenyi Biotec, Merck Sharp and Dohme, Omeros, Pfizer, Sanofi Oncology, Sobi, and Takeda outside the submitted work. Dr Hsue reported receiving honoraria from Gilead Sciences and Merck and receiving grants from Novartis outside the submitted work. Dr Luetkemeyer reported receiving grants from the Steve and Marti Diamond Charitable Foundation during the conduct of the study and grants from Gilead Sciences, Eli Lilly and Co, and EMD Serono outside the submitted work. Dr Meyfroidt reported receiving grants from the Belgian Health Care Knowledge Center and the Research Foundation Flanders during the conduct of the study. Dr Pirofski reported receiving grants the G. Harold and Leila Y. Mathers Foundation during the conduct of the study. Dr Rijnders reported receiving grants from Erasmus MC Foundation during the conduct of the study. No other disclosures were reported.

Funding/Support: The design and conduct of the study; data collection, management, analysis, and interpretation of the data; and preparation of the manuscript for publication were supported by grant UL1TR001445 from the National Center for Advancing Translational Sciences of the National Institutes of Health (NIH). The statistical methodology and the extensions necessary for its applicability in the context of convalescent plasma use for treating COVID-19 were developed with support from grant R01MH099003 from National Institute of Mental Health of the NIH.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the US Food and Drug Administration.

Additional Contributions: Alison Bateman-House, PhD (New York University), Eric Boersma, PhD (Erasmus University), David Glidden, PhD (University of California, San Francisco), Lakshmanan Jeyaseelan, PhD (Christian Medical College), Emmanuel Lesaffre, PhD (Katholieke University of Leuven), Grigorios Papageorgiou, (Erasmus University), Aitor Perez, PhD (Pivotal CRO), Suman Pramanik, MD (Army Hospital), Aranzazu Sancho-Lopez, MD, PhD (Hospital Universitario Puerta de Hierro Majadahonda), André Siqueira, MD (University of Brasilia), John Szumowski, MD, MPH (University of California, San Francisco), Séverine Vermeire , MD, PhD (Universitait Ziekenhuis Leuven), John Younger, MD (University City Science Center), Pamela Shaw, PhD (Kaiser Permanente Washington Health Research Institute), and Geert Verbeke, PhD (Katholieke University of Leuven), served on the data safety monitoring board for the COMPILE study. Barbee Whitaker, PhD (Center for Biologics Evaluation and Research, Office of Biostatistics and Epidemiology, Food and Drug Administration), facilitated the validation of TBI in 1 of the external data sets. Michael Joyner, MD, (Mayo Clinic, Human and Integrative Physiology and Clinical Pharmacology Laboratory), conducted the study that collected 1 of the validation data sets and allowed the use of those data for validation. Rickey Carter, PhD (Mayo Clinic, Biostatistics), was the biostatistician for a study that provided 1 of the external data sets for validation of the TBI and commented on the TBI development and validation. R. Todd Ogden, PhD (Columbia University, Department of Biostatistics), and David DeMets, PhD (University of Wisconsin, Biostatistics), discussed with the study team the methodology for the TBI and its application to this study. Judith S. Hochman, MD (New York University, School of Medicine), and Corita Grudzen, MD (New York University, School of Medicine), provided administrative support for the study. None of these individuals were compensated for their time.

References
1.
Tonelli  MR , Shirts  BH .  Knowledge for precision medicine: mechanistic reasoning and methodological pluralism.   JAMA. 2017;318(17):1649-1650. doi:10.1001/jama.2017.11914PubMedGoogle ScholarCrossref
2.
Blackstone  EH .  Precision medicine versus evidence-based medicine: individual treatment effect versus average treatment effect.   Circulation. 2019;140(15):1236-1238. doi:10.1161/CIRCULATIONAHA.119.043014PubMedGoogle ScholarCrossref
3.
DeMerle  K , Angus  DC , Seymour  CW .  Precision medicine for COVID-19: phenotype anarchy or promise realized?   JAMA. 2021;325(20):2041-2042. doi:10.1001/jama.2021.5248PubMedGoogle ScholarCrossref
4.
Bzdok  D , Varoquaux  G , Steyerberg  EW .  Prediction, not association, paves the road to precision medicine.   JAMA Psychiatry. 2021;78(2):127-128. doi:10.1001/jamapsychiatry.2020.2549PubMedGoogle ScholarCrossref
5.
Zhao  Y , Zeng  D , Rush  AJ , Kosorok  MR .  Estimating individualized treatment rules using outcome weighted learning.   J Am Stat Assoc. 2012;107(449):1106-1118. doi:10.1080/01621459.2012.695674PubMedGoogle Scholar
6.
Song  R , Kosorok  M , Zeng  D , Zhao  Y , Laber  E , Yuan  M .  On sparse representation for optimal individualized treatment selection with penalized outcome weighted learning.   Stat. 2015;4(1):59-68. doi:10.1002/sta4.78PubMedGoogle ScholarCrossref
7.
Laber  EB , Zhao  YQ .  Tree-based methods for individualized treatment regimes.   Biometrika. 2015;102(3):501-514. doi:10.1093/biomet/asv028PubMedGoogle ScholarCrossref
8.
Petkova  E , Tarpey  T , Su  Z , Ogden  RT .  Generated effect modifiers (GEM’s) in randomized clinical trials.   Biostatistics. 2017;18(1):105-118. doi:10.1093/biostatistics/kxw035PubMedGoogle ScholarCrossref
9.
Liu  Y , Wang  Y , Kosorok  MR , Zhao  Y , Zeng  D .  Augmented outcome-weighted learning for estimating optimal dynamic treatment regimens.   Stat Med. 2018;37(26):3776-3788. doi:10.1002/sim.7844PubMedGoogle ScholarCrossref
10.
Park  H , Petkova  E , Tarpey  T , Ogden  RT .  A single-index model with multiple-links.   J Stat Plan Inference. 2020;205:115-128. doi:10.1016/j.jspi.2019.05.008PubMedGoogle ScholarCrossref
11.
Park  H , Petkova  E , Tarpey  T , Ogden  RT .  A constrained single-index regression for estimating interactions between a treatment and covariates.   Biometrics. 2021;77(2):506-518.PubMedGoogle ScholarCrossref
12.
Petkova  E , Antman  EM , Troxel  AB .  Pooling data from individual clinical trials in the COVID-19 era.   JAMA. 2020;324(6):543-545. doi:10.1001/jama.2020.13042PubMedGoogle ScholarCrossref
13.
Troxel  AB , Petkova  E , Goldfeld  K ,  et al.  Association of convalescent plasma treatment with clinical status in patients hospitalized with COVID-19: a meta-analysis.   JAMA Netw Open. 2022;5(1):e2147331. doi:10.1001/jamanetworkopen.2021.47331Google Scholar
14.
Goldfeld  K , Wu  D , Tarpey  T ,  et al.  Prospective individual patient data meta-analysis: Evaluating convalescent plasma for COVID-19.   Stat Med. 2021;40(24):5131-5151. doi:10.1002/sim.9115PubMedGoogle ScholarCrossref
15.
WHO Working Group on the Clinical Characterisation and Management of COVID-19 infection.  A minimal common outcome measure set for COVID-19 clinical research.   Lancet Infect Dis. 2020;20(8):e192-e197. doi:10.1016/S1473-3099(20)30483-7PubMedGoogle ScholarCrossref
16.
Park  H , Petkova  E , Tarpey  T , Ogden  RT .  A single-index model with a surface-link for optimizing individualized dose rules.   J Computational Graphical Stat. Published online June 21, 2021. doi:10.1080/10618600.2021.1923521Google Scholar
17.
Gray  RJ .  A class of k-sample tests for comparing the cumulative incidence of a competing risk.   Ann Stat. 1988;16(3):1141-1154. Accessed December 20, 2021. https://www.jstor.org/stable/2241622Google ScholarCrossref
18.
McCaw  ZR , Tian  L , Vassy  JL ,  et al.  How to quantify and interpret treatment effects in comparative clinical studies of COVID-19.   Ann Intern Med. 2020;173(8):632-637. doi:10.7326/M20-4044PubMedGoogle ScholarCrossref
19.
Janes  H , Pepe  MS , Gu  W .  Assessing the value of risk predictions by using risk stratification tables.   Ann Intern Med. 2008;149(10):751-760. doi:10.7326/0003-4819-149-10-200811180-00009PubMedGoogle ScholarCrossref
20.
Senefeld  JW , Johnson  PW , Kunze  KL ,  et al.  Program and patient characteristics for the United States Expanded Access Program to COVID-19 convalescent plasma.   medRxiv. Preprint posted online April 11, 2021. doi:10.1101/2021.04.08.21255115Google Scholar
21.
Joyner  MJ , Carter  RE , Senefeld  JW ,  et al.  Convalescent plasma antibody levels and the risk of death from COVID-19.   N Engl J Med. 2021;384(11):1015-1027. doi:10.1056/NEJMoa2031893PubMedGoogle ScholarCrossref
22.
US Food and Drug Administration. FDA issues Emergency Use Authorization for convalescent plasma as potential promising COVID-19 treatment, another achievement in administration’s fight against pandemic. April 23, 2020. Accessed December 20, 2021. https://www.fda.gov/news-events/press-announcements/fda-issues-emergency-use-authorization-convalescent-plasma-potential-promising-covid-19-treatment
23.
Ray  Y , Paul  SR , Bandopadhyay  P ,  et al.  Clinical and immunological benefits of convalescent plasma therapy in severe COVID-19: insights from a single center open label randomised control trial.   medRxiv. Preprint posted online November 29, 2020. doi:10.1101/2020.11.25.20237883Google Scholar
24.
Simonovich  VA , Burgos Pratx  LD , Scibona  P ,  et al; PlasmAr Study Group.  A randomized trial of convalescent plasma in COVID-19 severe pneumonia.   N Engl J Med. 2021;384(7):619-629. doi:10.1056/NEJMoa2031304PubMedGoogle ScholarCrossref
25.
Madhi  SA , Baillie  V , Cutland  CL ,  et al; NGS-SA Group; Wits-VIDA COVID Group.  Efficacy of the ChAdOx1 nCoV-19 COVID-19 Vaccine against the B.1.351 variant.   N Engl J Med. 2021;384(20):1885-1898. doi:10.1056/NEJMoa2102214PubMedGoogle ScholarCrossref
26.
Park H, Tarpey T, Li Y, et al. Convalescent plasma treatment benefit index calculator. January 21, 2022. http://covid-convalescentplasma-tbi-calc.org
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