Evidence map›Paper›PMID 42461433›Full record

ArticleEuropean journal of clinical pharmacology2026

Development and analysis of a SmPC-based machine-readable dataset of contraindications for clinical decision support and real-world data analysis.

Miriam Schechner, Wahram Andrikyan, Thomas Bauerdick, Theresa Terstegen, Louisa Redeker, Martin Federbusch, Katrin Farker, Alexander Strübing, Florian Schmidt, Petra Thürmann and 10 more

Abstract read
In one paragraph

Article in European journal of clinical pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

20 authors.

Miriam SchechnerInstitute of General Practice and Family Medicine, LMU University Hospital, LMU Munich, Munich, Germany.ORCID http://orcid.org/0009-0003-0861-5085
Wahram AndrikyanInstitute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID http://orcid.org/0000-0002-4885-9864
Thomas BauerdickDepartment of Clinical Pharmacy, Institute of Pharmacy, University of Bonn, Bonn, Germany.ORCID http://orcid.org/0009-0004-2858-2126
Theresa TerstegenMedical Faculty Heidelberg, Internal Medicine IX, Clinical Pharmacology and Pharmacoepidemiology, Cooperation Unit Clinical Pharmacy, Heidelberg University, Heidelberg University Hospital, Heidelberg, Germany.ORCID http://orcid.org/0009-0000-9689-6806
Louisa RedekerDepartment of Clinical Pharmacology, School of Medicine, Faculty of Health, Witten / Herdecke University, Wuppertal, Germany.ORCID http://orcid.org/0000-0003-4150-9084
Martin FederbuschDepartment for Clinical AI and Translational Medicine, University of Leipzig Medical Center, Leipzig, Germany.ORCID http://orcid.org/0000-0002-1126-5763
Katrin FarkerInstitute for Hospital Pharmacy, University Center for Pharmacotherapy and Pharmacoeconomics, Jena University Hospital, Jena, Germany.ORCID http://orcid.org/0000-0001-5768-0754
Alexander StrübingInstitute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Germany.ORCID http://orcid.org/0000-0001-5151-7665
Florian SchmidtInstitute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Germany.ORCID http://orcid.org/0000-0003-2027-8213
Petra ThürmannDepartment of Clinical Pharmacology, School of Medicine, Faculty of Health, Witten / Herdecke University, Wuppertal, Germany.ORCID http://orcid.org/0000-0001-9724-1422
Sven SchmiedlDepartment of Clinical Pharmacology, School of Medicine, Faculty of Health, Witten / Herdecke University, Wuppertal, Germany.ORCID http://orcid.org/0000-0003-4844-0112
Hanna M SeidlingMedical Faculty Heidelberg, Internal Medicine IX, Clinical Pharmacology and Pharmacoepidemiology, Cooperation Unit Clinical Pharmacy, Heidelberg University, Heidelberg University Hospital, Heidelberg, Germany.ORCID http://orcid.org/0000-0002-1215-634X
Tobias DreischulteInstitute of General Practice and Family Medicine, LMU University Hospital, LMU Munich, Munich, Germany.ORCID http://orcid.org/0000-0003-2345-5377
Daniel NeumannInstitute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Germany.ORCID http://orcid.org/0000-0002-4639-5189
Markus LoefflerInstitute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Germany.ORCID http://orcid.org/0000-0002-0424-9933
Renke MaasInstitute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID http://orcid.org/0000-0002-5498-9761
Ulrich JaehdeDepartment of Clinical Pharmacy, Institute of Pharmacy, University of Bonn, Bonn, Germany.ORCID http://orcid.org/0000-0002-2493-7370
Martin F FrommInstitute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID http://orcid.org/0000-0002-0334-7478
Michael I SponfeldnerInstitute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany. michael.sponfeldner@fau.de.ORCID http://orcid.org/0009-0000-8236-0074
INTERPOLAR project of the German Medical Informatics Initiative

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeUnrecognized contraindications pose risks for adverse drug reactions, hospitalizations or death. Clinical decision support systems (CDSS) aim to mitigate medication-related harm, particularly originating from contraindications. However, many CDSS provide limited benefit, as they focus largely on singular risk situations such as drug-drug interactions and often generate alerts of limited clinical relevance. Comprehensive integration of contraindications into CDSS may support more clinically meaningful alerts. The aim of this work was the development and analysis of machine-readable contraindication lists, including drug-clinical condition, drug-kidney function and drug-drug (group) contraindications, for integration into CDSS and real-world data analysis.

methodsWe extracted and operationalized contraindications, based on Summaries of Product Characteristics (SmPCs), of the 688 most prescribed drugs in Germany, leveraging common medical coding systems. Moreover, we analyzed extracted contraindications based on operationalizability, overall frequency and frequencies within different contraindication categories.

resultsIn total, we extracted 4676 contraindications, of which 2129 (45.5%) were deemed operationalizable. Of these 2129 contraindications, 1652 (77.6%) were attributed to drug-clinical condition, 83 (3.9%) to drug-kidney, 140 (6.6%) to drug-drug group and 254 (11.9%) to drug-drug. The most frequently mentioned contraindicated risk situations were 'severe liver insufficiency' (n = 74, 3.5%), 'pregnancy' (n = 66, 3.1%), 'MAO-inhibitors' (n = 44, 2.1%), and 'shock' (n = 44, 2.1%).

conclusionOur results show, that drug-clinical condition contraindications are listed far more frequently in SmPCs than other contraindication categories. Focusing on clinical condition-related contraindications within CDSS could improve the detection of clinically relevant contraindications in routine data and enhance medication safety. The clinical applicability is currently being evaluated in the INTERPOLAR study.

Indexed as

Contraindications, DrugDecision Support Systems, ClinicalDrug-Related Side Effects and Adverse ReactionsData AnalysisFemaleGermanyHumansCDSSContraindicationMedication safetyRoutine data

Identifiers

PMID42461433
PMCPMC13375740

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.