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Record W4328049154 · doi:10.1001/jama.2023.3651

Heterogeneous Treatment Effects of Therapeutic-Dose Heparin in Patients Hospitalized for COVID-19

2023· article· en· W4328049154 on OpenAlexaff
Ewan C. Goligher, Patrick R. Lawler, Thomas P. Jensen, Victor B. Talisa, Lindsay R. Berry, Elizabeth Lorenzi, Bryan J. McVerry, Chung‐Chou Ho Chang, Eric Leifer, Charlotte Bradbury, Jeffrey S. Berger, B. J. Hunt, Lana A. Castellucci, Lucy Z. Kornblith, Anthony Gordon, Colin McArthur, Steven Webb, Judith S. Hochman, Matthew D. Neal, Ryan Zarychanski, Scott Berry, Derek Angus, Aaron W. Aday, Tania Ahuja, Farah Al-Beidh, Djillali Annane, Yaseen M. Arabi, Diptesh Aryal, Lisa Baumann Kreuziger, Abi Beane, Zahra Bhimani, Shailesh Bihari, Henny H. Billett, Lindsay Bond, Marc J. M. Bonten, Maria M. Brooks, Frank Brunkhorst, Meredith Buxton, Adrian Buzgau, Marc Carrier, Lana A. Castelucci, Sweta Chekuri, Jen‐Ting Chen, Allen Cheng, Tamta Chkhikvadze, Benjamin Coiffard, Aira Contreras, Todd W. Costantini, Mary Cushman, Sophie de Brouwer, Lennie Derde, Michelle A. Detry, Abhijit Duggal, Vladimír Džavík, Mark B. Effron, Heather Eng, Jorge Escobedo, Lise J Estcourt, Brendan M. Everett, Micheal E. Farkough, Dean Fergusson, Mark Fitzgerald, Rob Fowler, Joshua D. Froess, Zhuxuan Fu, J.‐P. Galanaud, Benjamin Galen, Sheetal Gandotra, Timothy D. Girard, Lucus D. Godoy, Michelle N. Gong, Andrew L. Goodman, Herman Goossens, Cameron Green, Yonatan Greenstein, Peter L. Gross, Raquel Morillo Guerrero, Naomi M. Hamburg, Rashan Haniffa, George P. Hanna, Nicholas Hanna, Sheila M. Hedge, Carolyn M. Hendrickson, Alisa M. Higgins, Alexander Hindenburg, R. Duncan Hite, Aluko A. Hope, James M Horowitz, Christopher M. Horvat, Brett L. Houston, David T. Huang, Kristin Hudock, Beverley J. Hunt, Mansoor Husain, Robert C. Hyzy, Vivek Iyer, Jeff R. Jacobson, Devachandran Jayakumar, Susan R. Kahn, Norma Keller, Akram Khan, Yuri Kim, Keri S. Kim, Andrei Kindzelski, Andrew J. King, Bridget‐Anne Kirwan, M. Margaret Knudson, Aaron E. Kornblith, Vidya Krishnan, Anand Kumar, Matthew Kutcher, Michael Laffan, François Lamontagne, Grégoire Le Gal, Christine M. Leeper, Roger Lewis, George Lim, Felipe Gallego Lima, Kelsey Linstrum, Edward Litton, José López‐Sendón, José Luis López-Sendón, Sylvain Lother, Sebastian García Madrona, Saurabh Malhotra, Miguel Marcos Martin, John C. Marshall, Nicole Marten, Andrea Saud Martinez, Mary Martinez, Eduardo Mateos Garcia, Michael A. Matthay, Stephanie Mavromichalis, Daniel F. McAuley, Emily G. McDonald, Anna McGlothlin, Shay McGuinness, Zoe McQuilten, Saskia Middeldorp, Stephanie K. Montgomery, S.C. Moore, Paul Mouncey, Srinivas Murthy, Girish B. Nair, Rahul Nair, Alistair Nichol, José Carlos Nicolau, Brenda Nunez‐Garcia, Ambarish Pandey, John J. Park, Pauline K. Park, Rachael Parke, Sam Parnia, Jonathan Paul, Mauricio Pompilio, Matt Prekker, John G. Quigley, Harmony R. Reynolds, Robert S Rosenson, Natalia S. Rost, Kathy Rowan, Mayler Olombrada Nunes de Santos, Fernanda O Santos, Marlene Santos, Lewis Satterwhite, Christina Saunders, Jake Schreiber, Roger Schutgens, Christopher Seymour, Manu Shankar‐Hari, John P. Sheehan, Deborah Siegal, Delcio Goncalves Silva, Aneesh B. Singhal, Arthur S. Slutsky, Dayna Solvason, Simon Stanworth, Tobias Tritschler, Alexis F. Turgeon, Anne Turner, Wilma van Bentum-Puijk, Frank L. van de Veerdonk, Sean van Diepen, Gloria Vazquez Grande, Lana Wahid, Vanessa Wareham, Steve Webb, Bryan J. Wells, R. Jay Widmer, Jennifer G. Wilson, Eugene Yuriditsky, Fernando G Zampieri, Yongqi Zhong

Bibliographic record

VenueJAMA · 2023
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsCancerCare ManitobaUniversity of ManitobaOttawa HospitalUniversity of OttawaMcGill University Health CentreToronto General HospitalUniversity of TorontoUniversity Health Network
FundersNational Institute of General Medical SciencesNational Human Genome Research InstituteNational Heart, Lung, and Blood InstituteNational Institute for Health and Care Research
KeywordsMedicineRandomized controlled trialHeparinPopulationClinical trialTherapeutic indexIntensive care medicineInternal medicineEmergency medicinePharmacology

Abstract

fetched live from OpenAlex

Importance: Randomized clinical trials (RCTs) of therapeutic-dose heparin in patients hospitalized with COVID-19 produced conflicting results, possibly due to heterogeneity of treatment effect (HTE) across individuals. Better understanding of HTE could facilitate individualized clinical decision-making. Objective: To evaluate HTE of therapeutic-dose heparin for patients hospitalized for COVID-19 and to compare approaches to assessing HTE. Design, Setting, and Participants: Exploratory analysis of a multiplatform adaptive RCT of therapeutic-dose heparin vs usual care pharmacologic thromboprophylaxis in 3320 patients hospitalized for COVID-19 enrolled in North America, South America, Europe, Asia, and Australia between April 2020 and January 2021. Heterogeneity of treatment effect was assessed 3 ways: using (1) conventional subgroup analyses of baseline characteristics, (2) a multivariable outcome prediction model (risk-based approach), and (3) a multivariable causal forest model (effect-based approach). Analyses primarily used bayesian statistics, consistent with the original trial. Exposures: Participants were randomized to therapeutic-dose heparin or usual care pharmacologic thromboprophylaxis. Main Outcomes and Measures: Organ support-free days, assigning a value of -1 to those who died in the hospital and the number of days free of cardiovascular or respiratory organ support up to day 21 for those who survived to hospital discharge; and hospital survival. Results: Baseline demographic characteristics were similar between patients randomized to therapeutic-dose heparin or usual care (median age, 60 years; 38% female; 32% known non-White race; 45% Hispanic). In the overall multiplatform RCT population, therapeutic-dose heparin was not associated with an increase in organ support-free days (median value for the posterior distribution of the OR, 1.05; 95% credible interval, 0.91-1.22). In conventional subgroup analyses, the effect of therapeutic-dose heparin on organ support-free days differed between patients requiring organ support at baseline or not (median OR, 0.85 vs 1.30; posterior probability of difference in OR, 99.8%), between females and males (median OR, 0.87 vs 1.16; posterior probability of difference in OR, 96.4%), and between patients with lower body mass index (BMI <30) vs higher BMI groups (BMI ≥30; posterior probability of difference in ORs >90% for all comparisons). In risk-based analysis, patients at lowest risk of poor outcome had the highest propensity for benefit from heparin (lowest risk decile: posterior probability of OR >1, 92%) while those at highest risk were most likely to be harmed (highest risk decile: posterior probability of OR <1, 87%). In effect-based analysis, a subset of patients identified at high risk of harm (P = .05 for difference in treatment effect) tended to have high BMI and were more likely to require organ support at baseline. Conclusions and Relevance: Among patients hospitalized for COVID-19, the effect of therapeutic-dose heparin was heterogeneous. In all 3 approaches to assessing HTE, heparin was more likely to be beneficial in those who were less severely ill at presentation or had lower BMI and more likely to be harmful in sicker patients and those with higher BMI. The findings illustrate the importance of considering HTE in the design and analysis of RCTs. Trial Registration: ClinicalTrials.gov Identifiers: NCT02735707, NCT04505774, NCT04359277, NCT04372589.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.055
GPT teacher head0.429
Teacher spread0.375 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations98
Published2023
Admission routes1
Has abstractyes

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