Pharmacoepidemiology, Machine Learning, and COVID-19: An Intent-to-Treat Analysis of Hydroxychloroquine, With or Without Azithromycin, and COVID-19 Outcomes Among Hospitalized US Veterans
Gerlovin H, Posner DC, Ho YL, Rentsch CT, Tate JP, King JT, Kurgansky KE, Danciu I, Costa L, Linares FA, Goethert ID, Jacobson DA, Freiberg MS, Begoli E, Muralidhar S, Ramoni RB, Tourassi G, Gaziano JM, Justice AC, Gagnon DR, Cho K. Pharmacoepidemiology, Machine Learning, and COVID-19: An Intent-to-Treat Analysis of Hydroxychloroquine, With or Without Azithromycin, and COVID-19 Outcomes Among Hospitalized US Veterans. American Journal Of Epidemiology 2021, 190: 2405-2419. PMID: 34165150, PMCID: PMC8384407, DOI: 10.1093/aje/kwab183.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overAnti-Bacterial AgentsAzithromycinCOVID-19COVID-19 Drug TreatmentDrug Therapy, CombinationFemaleHospitalizationHumansHydroxychloroquineIntention to Treat AnalysisMachine LearningMaleMiddle AgedPharmacoepidemiologyRetrospective StudiesSARS-CoV-2Treatment OutcomeUnited StatesVeteransConceptsUS veteransCOVID-19Veterans Affairs Health Care SystemRecent randomized clinical trialsAdministration of hydroxychloroquineEffectiveness of hydroxychloroquineRisk of intubationEffect of hydroxychloroquineElectronic health record dataRandomized clinical trialsTreatment of patientsUS veteran populationCOVID-19 outcomesCoronavirus disease 2019Health record dataRigorous study designsHealth care systemSurvival benefitTreat analysisEarly therapyHospitalized populationClinical trialsObservational studyDisease 2019Hydroxychloroquine