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All Studies   Meta Analysis    Recent:   
0 0.5 1 1.5 2+ Mortality -27% Improvement Relative Risk Acetaminophen  Lerner et al.  LATE TREATMENT Is late treatment with acetaminophen beneficial for COVID-19? Retrospective 5,783 patients in France (February 2020 - June 2021) Higher mortality with acetaminophen (not stat. sig., p=0.097) c19early.org Lerner et al., JMIR Medical Informatics, Mar 2022 Favors acetaminophen Favors control

Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS)

Lerner et al., JMIR Medical Informatics, doi:10.2196/35190
Mar 2022  
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1st treatment shown to increase risk in November 2020
 
*, now known with p = 0.00000029 from 28 studies, but still recommended in 46 countries.
* From meta analysis with ≥3 clinical studies.
4,100+ studies for 60+ treatments. c19early.org
Retrospective 5,783 hospitalized patients in France, showing higher mortality with paracetamol use, without statistical significance.
Paracetamol is also known as acetaminophen, Tylenol, Panadol, Calpol, Tempra, Calprofen, Doliprane, Efferalgan, Grippostad C, Dolo, Acamol, Fevadol, Crocin, and Perfalgan.
risk of death, 26.9% higher, RR 1.27, p = 0.10, odds ratio converted to relative risk, weighted and trimmed, day 28, control prevalance approximated with overall prevalence.
Effect extraction follows pre-specified rules prioritizing more serious outcomes. Submit updates
Lerner et al., 30 Mar 2022, retrospective, France, peer-reviewed, median age 69.2, 7 authors, study period 1 February, 2020 - 15 June, 2021. Contact: antoine.neuraz@aphp.fr.
This PaperAcetaminophenAll
Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS)
Ivan Lerner, MD Arnaud Serret-Larmande, MD Bastien Rance, PhD Nicolas Garcelon, PhD; Anita Burgun, PhD Laurent Chouchana, PharmD Antoine Neuraz
JMIR Medical Informatics, doi:10.2196/35190
Background: Patients hospitalized for a given condition may be receiving other treatments for other contemporary conditions or comorbidities. The use of such observational clinical data for pharmacological hypothesis generation is appealing in the context of an emerging disease but particularly challenging due to the presence of drug indication bias. Objective: With this study, our main objective was the development and validation of a fully data-driven pipeline that would address this challenge. Our secondary objective was to generate pharmacological hypotheses in patients with COVID-19 and demonstrate the clinical relevance of the pipeline. Methods: We developed a pharmacopeia-wide association study (PharmWAS) pipeline inspired from the PheWAS methodology, which systematically screens for associations between the whole pharmacopeia and a clinical phenotype. First, a fully data-driven procedure based on adaptive least absolute shrinkage and selection operator (LASSO) determined drug-specific adjustment sets. Second, we computed several measures of association, including robust methods based on propensity scores (PSs) to control indication bias. Finally, we applied the Benjamini and Hochberg procedure of the false discovery rate (FDR). We applied this method in a multicenter retrospective cohort study using electronic medical records from 16 university hospitals of the Greater Paris area. We included all adult patients between 18 and 95 years old hospitalized in conventional wards for COVID-19 between February 1, 2020, and June 15, 2021. We investigated the association between drug prescription within 48 hours from admission and 28-day mortality. We validated our data-driven pipeline against a knowledge-based pipeline on 3 treatments of reference, for which experts agreed on the expected association with mortality. We then demonstrated its clinical relevance by screening all drugs prescribed in more than 100 patients to generate pharmacological hypotheses. Results: A total of 5783 patients were included in the analysis. The median age at admission was 69.2 (IQR 56.7-81.1) years, and 3390 (58.62%) of the patients were male. The performance of our automated pipeline was comparable or better for controlling bias than the knowledge-based adjustment set for 3 reference drugs: dexamethasone, phloroglucinol, and paracetamol. After
Authors' Contributions IL contributed to conceptualization, data curation, formal analysis, methodology, software, and writing (original draft). ASL contributed to methodology and writing (reviewing and editing). LC contributed to conceptualization, formal analysis, and writing (reviewing and editing). BR and NG contributed to validation, and writing (reviewing and editing). AB contributed to project administration, resources, supervision, validation, and writing (reviewing and editing). AN contributed to conceptualization, data curation, formal analysis, software, resources, supervision, validation, writing (original draft), and writing (reviewing and editing). Conflicts of Interest None declared. Multimedia Appendix 1 Supplementary materials.
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Our secondary objective was ' 'to generate pharmacological hypotheses in patients with COVID-19 and demonstrate the clinical ' 'relevance of the pipeline.</jats:p>\n' ' </jats:sec>\n' ' <jats:sec>\n' ' <jats:title>Methods</jats:title>\n' ' <jats:p>We developed a pharmacopeia-wide association study (PharmWAS) pipeline ' 'inspired from the PheWAS methodology, which systematically screens for associations between ' 'the whole pharmacopeia and a clinical phenotype. First, a fully data-driven procedure based ' 'on adaptive least absolute shrinkage and selection operator (LASSO) determined drug-specific ' 'adjustment sets. Second, we computed several measures of association, including robust ' 'methods based on propensity scores (PSs) to control indication bias. Finally, we applied the ' 'Benjamini and Hochberg procedure of the false discovery rate (FDR). We applied this method in ' 'a multicenter retrospective cohort study using electronic medical records from 16 university ' 'hospitals of the Greater Paris area. We included all adult patients between 18 and 95 years ' 'old hospitalized in conventional wards for COVID-19 between February 1, 2020, and June 15, ' '2021. We investigated the association between drug prescription within 48 hours from ' 'admission and 28-day mortality. We validated our data-driven pipeline against a ' 'knowledge-based pipeline on 3 treatments of reference, for which experts agreed on the ' 'expected association with mortality. We then demonstrated its clinical relevance by screening ' 'all drugs prescribed in more than 100 patients to generate pharmacological ' 'hypotheses.</jats:p>\n' ' </jats:sec>\n' ' <jats:sec>\n' ' <jats:title>Results</jats:title>\n' ' <jats:p>A total of 5783 patients were included in the analysis. The median age at ' 'admission was 69.2 (IQR 56.7-81.1) years, and 3390 (58.62%) of the patients were male. The ' 'performance of our automated pipeline was comparable or better for controlling bias than the ' 'knowledge-based adjustment set for 3 reference drugs: dexamethasone, phloroglucinol, and ' 'paracetamol. After correction for multiple testing, 4 drugs were associated with increased ' 'in-hospital mortality. Among these, diazepam and tramadol were the only ones not discarded by ' 'automated diagnostics, with adjusted odds ratios of 2.51 (95% CI 1.52-4.16, Q=.01) and 1.94 ' '(95% CI 1.32-2.85, Q=.02), respectively.</jats:p>\n' ' </jats:sec>\n' ' <jats:sec>\n' ' <jats:title>Conclusions</jats:title>\n' ' <jats:p>Our innovative approach proved useful in generating pharmacological ' 'hypotheses in an outbreak setting, without requiring a priori knowledge of the disease. Our ' 'systematic analysis of early prescribed treatments from patients hospitalized for COVID-19 ' 'showed that diazepam and tramadol are associated with increased 28-day mortality. 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Late treatment
is less effective
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