Evidence map›Paper›PMID 40956484›Full record

ArticleDrug safety2026

Implied ADR-Admissions: A Cohort Study Introducing a Novel Administrative Data Approach for Identifying Drug-Related Hospitalisations.

Miriam Schechner, Marietta Rottenkolber, Clara Weglage, Vita Brišnik, Annette Haerdtlein, Bruce Guthrie, Ulrich Jaehde, Eva Grill, Tobias Dreischulte

Abstract read
In one paragraph

Article in Drug safety, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. [Falls and polypharmacy-an exploration of both the frequency and approaches to prevention].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2026
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Miriam SchechnerInstitute of General Practice and Family Medicine, LMU University Hospital, LMU Munich, Nußbaumstr. 5, 80336 Munich, Germany.
Marietta RottenkolberInstitute of General Practice and Family Medicine, LMU University Hospital, LMU Munich, Nußbaumstr. 5, 80336 Munich, Germany.
Clara WeglageInstitute of General Practice and Family Medicine, LMU University Hospital, LMU Munich, Nußbaumstr. 5, 80336 Munich, Germany.
Vita BrišnikInstitute of General Practice and Family Medicine, LMU University Hospital, LMU Munich, Nußbaumstr. 5, 80336 Munich, Germany.
Annette HaerdtleinInstitute of General Practice and Family Medicine, LMU University Hospital, LMU Munich, Nußbaumstr. 5, 80336 Munich, Germany.
Bruce GuthrieAdvanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, UK.
Ulrich JaehdeDepartment of Clinical Pharmacy, Institute of Pharmacy, University of Bonn, Bonn, Germany.
Eva GrillInstitute for Medical Information Processing, Biometry, and Epidemiology (IBE), Faculty of Medicine, LMU Munich, Munich, Germany.
Tobias DreischulteInstitute of General Practice and Family Medicine, LMU University Hospital, LMU Munich, Nußbaumstr. 5, 80336 Munich, Germany. Tobias.Dreischulte@med.uni-muenchen.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAdverse drug reactions (ADRs) are a key contributor to unplanned hospitalisations, particularly in patients with polypharmacy. Traditional detection methods, such as expert reviews or diagnostic coding, are limited in scalability and sensitivity.

objectiveThis study introduces and evaluates a novel scalable method, implied ADR-admissions, that links drug exposures to adverse events using administrative data to improve the detection of plausible drug-related hospitalisations.

methodsA retrospective cohort study was conducted using linked health data from 123,662 individuals aged ≥ 40 years with polypharmacy in two Scottish health boards. Implied ADR-admissions were defined as emergency hospitalisations with one of 15 adverse events plausibly linked to drug exposure (based on a structured consensus process) within the prior 90 days. Incidence was compared with three existing approaches: adverse event-admissions (regardless of drug exposure), explicit ADR-admissions (explicitly coded as ADRs) and preventable ADR-admissions (with prior medication error). Multivariate logistic regression was used to identify predictors of implied ADR-admissions.

resultsOver 1 year, 2.6% experienced an implied ADR-admission, compared with 5.7% with adverse event-admissions, and 0.4% with explicit ADR-admissions. For gastrointestinal bleeding, the implied ADR-admission incidence was 20 times higher than the preventable ADR-admission incidence. Key predictors for implied ADR-admissions included prior hypokalaemia-related hospitalisation and use of potentially inappropriate medications.

conclusionsThe implied ADR-admission approach has improved specificity relative to broad adverse event definitions while enhancing sensitivity beyond methods that rely solely on explicit ADR codes or pre-specified medication errors. It offers a scalable automated tool for pharmacovigilance, though further validation is needed prior to routine use in medication safety monitoring.

Indexed as

Adverse Drug Reaction Reporting SystemsDrug-Related Side Effects and Adverse ReactionsHospitalizationAdultAgedAged, 80 and overCohort StudiesFemaleHumansMaleMiddle AgedPolypharmacyRetrospective StudiesScotland

Identifiers

PMID40956484
PMCPMC12924847

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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.