Evidence map›Paper›PMID 41345999›Full record

ArticlePharmacoepidemiology and drug safety2025

High-Throughput Screening Using the Self-Controlled Tree-Based Scan Statistic to Identify Medications Associated With Hospitalization for Severe Acute Liver Injury.

Vincent Lo Re, Craig W Newcomb, Dean M Carbonari, Charles E Leonard, Christopher T Rentsch, Judith C Maro

Abstract read
In one paragraph

Article in Pharmacoepidemiology and drug safety, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Vincent Lo ReDivision of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0001-7955-0600
Craig W NewcombCenter for Clinical Epidemiology and Biostatistics, Center for Real-World Effectiveness and Safety of Therapeutics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Dean M CarbonariCenter for Clinical Epidemiology and Biostatistics, Center for Real-World Effectiveness and Safety of Therapeutics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0003-0310-270X
Charles E LeonardCenter for Clinical Epidemiology and Biostatistics, Center for Real-World Effectiveness and Safety of Therapeutics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Christopher T RentschDepartment of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.ORCID 0000-0002-1408-7907
Judith C MaroDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Healthcare Institute, Boston, Massachusetts, USA.ORCID 0000-0001-9900-2142

Funding

Alcohol Associated Outcomes Among HIV+/-Aging VeteransU01AA013566 · NIAAA · YALE UNIVERSITY · PI JUSTICE, AMY CAROLINE · 2002 to 2005
$10.1M
Alcohol and Multisubstance Use in the Veterans Aging Cohort StudyU01AA020790 · NIAAA · YALE UNIVERSITY · PI JUSTICE, AMY CAROLINE · 2011 to 2020
$7.7M
The HIV and Alcohol Research center focused on Polypharmacy (HARP)P01AA029545 · NIAAA · YALE UNIVERSITY · PI JUSTICE, AMY CAROLINE · 2021 to 2025
$6.3M
Sex Disparities in Overall Response to ART Among HIV Infected IndividualsU24AA020794 · NIAAA · YALE UNIVERSITY · PI JUSTICE, AMY CAROLINE · 2011 to 2020
$5.5M
Translational Research on Alcohol, Immunodeficiency, and Aging In COMpAAASU24AA022001 · NIAAA · YALE UNIVERSITY · PI BRANDT, CYNTHIA A. · 2012 to 2020
$3.2M
(PQ4)HIV and Aging Mechanisms for Hepatocellular CancerR01CA206465 · NCI · YALE UNIVERSITY · PI JUSTICE, AMY CAROLINE, LO RE, VINCENT · 2016 to 2020
$2.5M
NCI NIH HHS R01 CA206465NCI NIH HHS R01CA206465NIAAA NIH HHS P01 AA029545NIAAA NIH HHS U01 AA013566NIAAA NIH HHS U01AA013566NIAAA NIH HHS U01 AA020790NIAAA NIH HHS U01AA020790NIAAA NIH HHS U24 AA020794NIAAA NIH HHS U24AA020794NIAAA NIH HHS U24 AA022001NIAAA NIH HHS U24AA022001
6 · The paper itself

Abstract

backgroundMedications associated with acute liver injury (ALI) are primarily identified by case reports. High-throughput screening of real-world data could be leveraged to detect hepatotoxicity signals.

objectiveTo apply tree-based scan statistics in real-world data to identify drugs associated with hospitalization for severe ALI among patients without liver/biliary disease and with chronic liver disease (CLD).

methodsWe implemented a self-controlled case-crossover design in Veterans Health Administration data (2000-2023) among patients hospitalized for laboratory-confirmed severe ALI. We identified all newly dispensed drugs within 365 days prior to their hospitalization and used conditional Bernoulli tree-based scan statistics to identify potential associations (p < 0.3). We performed analyses separately in patients without liver/biliary disease and with CLD.

resultsAmong 12 860 patients without liver/biliary disease and 17 512 with CLD hospitalized for severe ALI, we evaluated associations with 450 and 543 drugs, respectively. Drugs associated with severe ALI among patients without liver/biliary disease included: acid-suppressives (ranitidine [p < 0.001], omeprazole [p = 0.004]), antiemetics (ondansetron [p < 0.001], promethazine [p = 0.06]), antibiotics (amoxicillin/clavulanate [p = 0.008], ciprofloxacin [p = 0.02], mupirocin [p = 0.032], ethambutol [p = 0.275]), anticoagulants (heparin [p = 0.015]), and chemotherapy (pazopanib [p = 0.275]). Drugs associated with severe ALI among CLD patients were: diuretics (spironolactone, furosemide [both p < 0.001]), antiemetics (ondansetron, metoclopramide, promethazine [all p < 0.001]), appetite stimulants (p < 0.001), analgesics (morphine, oxycodone, fentanyl [all p < 0.001]), chemotherapy (sorafenib [p < 0.001]), antibiotics (ciprofloxacin [p = 0.011], metronidazole [p = 0.020]), antipsychotics (prochlorperazine [p = 0.105]), vitamins (p = 0.134), acid-suppressives (omeprazole [p = 0.164]), and gastrointestinal/liver disease treatments (lactulose, senna, docusate, silicones, antiflatulents [all p < 0.001]; sucralfate [p = 0.005], albumin [p = 0.228]).

conclusionsHigh-throughput screening using tree-based scan statistics detected potentially hepatotoxic drugs for investigation in future pharmacoepidemiology studies.

Indexed as

Chemical and Drug Induced Liver InjuryHigh-Throughput Screening AssaysHospitalizationAgedCross-Over StudiesFemaleHumansMaleMiddle AgedSeverity of Illness IndexUnited Statesacute liver injurydrug‐induced liver injuryhepatotoxicityhigh‐throughput screeningtree‐based scan statistics

Identifiers

PMID41345999
PMCPMC12678845

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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.