Evidence map›Paper›PMID 35076698›Full record

ArticleJAMA network open2022

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

Hyung Park, Thaddeus Tarpey, Mengling Liu, Keith Goldfeld, Yinxiang Wu, Danni Wu, Yi Li, Jinchun Zhang, Dipyaman Ganguly, Yogiraj Ray and 27 more

Open access · goldAbstract readValidation Study
In one paragraph

Article in JAMA network open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 3 pooled it
6.8field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 3 syntheses or guidelines pooled it, 65 citations in OpenAlex.

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  13. Rethinking the role of COVID-19 convalescent plasma in the critically ill.Transfusion and apheresis science : official journal of the World Apheresis Association : official journal of the European Society for Haemapheresis · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

37 authors at 18 institutions in 7 countries.

Hyung ParkDivision of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York.
Thaddeus TarpeyDivision of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York.
Mengling LiuDivision of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York.
Keith GoldfeldDivision of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York.
Yinxiang WuDepartment of Biostatistics, School of Public Health, University of Washington, Seattle.
Danni WuDivision of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York.
Yi LiDivision of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York.
Jinchun ZhangBiostatistics and Research Decision Sciences, Merck Research Labortory, Merck & Co Inc, Rahway, New Jersey.
Dipyaman GangulyTranslational Research Unit of Excellence, Council Of Scientific And Industrial Research-Indian Institute of Chemical Biology, Kolkata, India.
Yogiraj RayInfectious Disease, Beleghata General Hospital, Kolkata, India.
Shekhar Ranjan PaulInfectious Disease, Beleghata General Hospital, Kolkata, India.
Prasun BhattacharyaMedical College Hospital, Kolkata, India.
Artur BelovCenter for Biologics Evaluation and Research, Office of Biostatistics and Epidemiology, Analytics and Benefit-Risk Assessment Team, US Food and Drug Administration, Silver Spring, Maryland.
Yin HuangCenter for Biologics Evaluation and Research, Office of Biostatistics and Epidemiology, Analytics and Benefit-Risk Assessment Team, US Food and Drug Administration, Silver Spring, Maryland.
Carlos VillaCenter for Biologics Evaluation and Research, Office of Biostatistics and Epidemiology, Analytics and Benefit-Risk Assessment Team, US Food and Drug Administration, Silver Spring, Maryland.
Richard ForsheeCenter for Biologics Evaluation and Research, Office of Biostatistics and Epidemiology, Analytics and Benefit-Risk Assessment Team, US Food and Drug Administration, Silver Spring, Maryland.
Nicole C VerdunOffice of Blood Research and Review, Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland.
Hyun Ah YoonAlbert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York.
Anup AgarwalIndian Council of Medical Research, New Delhi, India.
Ventura Alejandro SimonovichClinical Pharmacology Section, Department of Internal Medicine and Department of Research, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
Paula ScibonaClinical Pharmacology Section, Internal Medicine Service, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
Leandro Burgos PratxTransfusional Medicine Service, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
Waldo BellosoDepartment of Research, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
Cristina Avendaño-SoláHospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain.
Katharine J BarDepartment of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia.
Rafael F DuarteHospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain.
Priscilla Y HsueZuckerberg San Francisco General, University of California, San Francisco.
Anne F LuetkemeyerZuckerberg San Francisco General, University of California, San Francisco.
Geert MeyfroidtDepartment of Intensive Care Medicine, University Hospitals Leuven, Leuven, Belgium.
André M NicolaHospital Universitário de Brasília, University of Brasília, Brasília, Brazil.
Aparna MukherjeeIndian Council of Medical Research, New Delhi, India.
Mila B OrtigozaDepartments of Medicine and Microbiology, New York University Grossman School of Medicine, New York.
Liise-Anne PirofskiAlbert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York.
Bart J A RijndersDepartment of Internal Medicine, Section of Infectious Diseases, Erasmus University Medical Center, Rotterdam, the Netherlands.
Andrea TroxelDivision of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York.
Elliott M AntmanBrigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
Eva PetkovaDivision of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York.
New York University · USCenter for Biologics Evaluation and Research · USHospital Italiano de Buenos Aires · ARHospital Universitario Puerta de Hierro Majadahonda · ESIndian Council of Medical Research · INMontefiore Medical Center · USBrigham and Women's Hospital · USErasmus MC · NLIndian Institute of Chemical Biology · INKU Leuven · BEMedical College and Hospital, Kolkata · INMerck & Co., Inc., Rahway, NJ, USA (United States) · USSan Francisco General Hospital · USUnited States Food and Drug Administration · USUniversidade de Brasília · BRUniversity of California, San Francisco · USUniversity of Pennsylvania · USUniversity of Washington · US

Funding

Project-005UL1TR001445 · NCATS · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI BREDELLA, MIRIAM ANTOINETTE, HOCHMAN, JUDITH S · 2015 to 2025
$103.5M
CTSA Administrative Supplement for Informatics Core: A novel AI/ML system to predict respiratory failure and ARDS in Covid-19 patientsUL1TR002556 · NCATS · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI KELLER, MARLA J, KIM, MIMI Y · 2018 to 2022
$26.7M
Characterizing Placebo ResponseR01MH099003 · NIMH · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI PETKOVA, EVA, TARPEY, THADDEUS · 2013 to 2021
$4.5M
NCATS NIH HHS UL1 TR001445NCATS NIH HHS UL1 TR002556
6 · The paper itself

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.

Indexed as

HospitalizationPatient SelectionPlasmaTherapeutic IndexAgedBlood Grouping and CrossmatchingComorbidityCOVID-19COVID-19 SerotherapyFemaleHumansImmunization, PassiveMaleMiddle AgedOdds RatioPandemics

Identifiers

PMID35076698
PMCPMC8790670
OpenAlexW4206922908

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.