Pharmacogenomic and drug interaction risk associations with hospital length of stay among Medicare Advantage members with COVID-19

作者: Guo Y , Hall To , Reyes J , Schenning C , Erdemir Et

DOI: 10.1101/2021.05.06.21256769

关键词:

摘要: ImportanceCOVID-19 has severely impacted older populations and strained healthcare resources, with many patients requiring long periods of hospitalization. Reducing the hospital length stay (LOS) reduces patient burden. Given that adverse drug reactions are known to prolong LOS, unmanaged pharmacogenomic risk interactions among COVID-19 may be a factor for longer stays. ObjectiveThe objective this study was determine if interaction risks were associated lengths high-risk hospitalized COVID-19. DesignRetrospective cohort medical pharmacy claims SettingAdministrative database from large U.S. health insurance company ParticipantsMedicare Advantage members first hospitalization between January 2020 June 2020, who did not die during stay. Exposures(1) Pharmacogenetic probability (PIP) [≤]25% (low), 26%-50% (moderate), or >50% (high), which indicate likelihood one more clinically actionable gene-drug gene-drug-drug would identified testing; (2) drug-drug (DDI) severity minimal, minor, moderate, major, contraindicated, an two active medications. Main Outcomes MeasuresThe primary outcome Results stratified by hierarchical condition categories (HCC) counts chronic conditions. ResultsA total 6,025 included in study. Patients moderate high PIP 9% (CI: 4%-15%; p < 0.001) 16% 8%-24%; 0.001), respectively, compared those low PIP, whereas RAF score LOS. High significantly 12%-22% hypertension, hyperlipidemia, diabetes, COPD. Finally, 2 3 HCCs, 10% observed severe DDI minimal minor DDI. Conclusions RelevanceProactively mitigating potential reduce especially COPD, hypertension. Key PointsO_ST_ABSQuestionC_ST_ABSWhat is impact COVID-19? FindingsAmong COVID-19, greater had stays than lower risk, both within entire groups matched number type MeaningPreemptive testing shorten reducing seriously ill broadly improve classification, care utilization predictions, system performance.

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