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

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.

[1]  L. Pirofski,et al.  Association of Convalescent Plasma Treatment With Clinical Status in Patients Hospitalized With COVID-19 , 2022, JAMA network open.

[2]  T. Tarpey,et al.  A Single-Index Model With a Surface-Link for Optimizing Individualized Dose Rules , 2020, J. Comput. Graph. Stat..

[3]  T. Tarpey,et al.  Prospective individual patient data meta‐analysis: Evaluating convalescent plasma for COVID‐19 , 2021, Statistics in medicine.

[4]  C. Seymour,et al.  Precision Medicine for COVID-19: Phenotype Anarchy or Promise Realized? , 2021, JAMA.

[5]  Patrick W. Johnson,et al.  Program and patient characteristics for the United States Expanded Access Program to COVID-19 convalescent plasma , 2021, medRxiv.

[6]  S. Madhi,et al.  Efficacy of the ChAdOx1 nCoV-19 Covid-19 Vaccine against the B.1.351 Variant , 2021, The New England Journal of Medicine.

[7]  Patrick W. Johnson,et al.  Convalescent Plasma Antibody Levels and the Risk of Death from Covid-19 , 2021, The New England journal of medicine.

[8]  A. Tewari,et al.  Clinical and immunological benefits of convalescent plasma therapy in severe COVID-19: insights from a single center open label randomised control trial , 2020, medRxiv.

[9]  D. Follmann,et al.  A Randomized Trial of Convalescent Plasma in Covid-19 Severe Pneumonia , 2020, The New England journal of medicine.

[10]  Danilo Bzdok,et al.  Prediction, Not Association, Paves the Road to Precision Medicine. , 2020, JAMA psychiatry.

[11]  Eva Petkova,et al.  Pooling Data From Individual Clinical Trials in the COVID-19 Era. , 2020, JAMA.

[12]  L. Tian,et al.  How to Quantify and Interpret Treatment Effects in Comparative Clinical Studies of COVID-19 , 2020, Annals of Internal Medicine.

[13]  T. Tarpey,et al.  A constrained single‐index regression for estimating interactions between a treatment and covariates , 2020, Biometrics.

[14]  Mike Clarke,et al.  A minimal common outcome measure set for COVID-19 clinical research , 2020, The Lancet Infectious Diseases.

[15]  T. Tarpey,et al.  A single-index model with multiple-links. , 2020, Journal of statistical planning and inference.

[16]  E. Blackstone.  Precision Medicine Versus Evidence-Based Medicine: Individual Treatment Effect Versus Average Treatment Effect. , 2019, Circulation.

[17]  Ying Liu,et al.  Augmented outcome‐weighted learning for estimating optimal dynamic treatment regimens , 2018, Statistics in medicine.

[18]  B. Shirts,et al.  Knowledge for Precision Medicine: Mechanistic Reasoning and Methodological Pluralism. , 2017, JAMA.

[19]  Eva Petkova,et al.  Generated effect modifiers (GEM's) in randomized clinical trials. , 2017, Biostatistics.

[20]  Eric B. Laber,et al.  Tree-based methods for individualized treatment regimes. , 2015, Biometrika.

[21]  Donglin Zeng,et al.  On sparse representation for optimal individualized treatment selection with penalized outcome weighted learning , 2015, Stat.

[22]  Donglin Zeng,et al.  Estimating Individualized Treatment Rules Using Outcome Weighted Learning , 2012, Journal of the American Statistical Association.

[23]  Holly Janes,et al.  Assessing the Value of Risk Predictions by Using Risk Stratification Tables , 2008, Annals of Internal Medicine.

[24]  R. Gray.  A Class of $K$-Sample Tests for Comparing the Cumulative Incidence of a Competing Risk , 1988 .