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

PURPOSE: Unrecognized 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. METHODS: We 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. RESULTS: In 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%). CONCLUSION: Our 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.

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Journal
European Journal of Clinical Pharmacology
Published
2026-07-16
DOI
https://doi.org/10.1007/s00228-026-04142-3
Primary Topic
Machine Learning in Healthcare
Type
article
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0.00

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article

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

Renke Maas, Wahram Andrikyan, Hanna M. Seidling, Sven Schmiedl et al.
European Journal of Clinical Pharmacology
Machine Learning in Healthcare
article

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

Renke Maas, Wahram Andrikyan, Hanna M. Seidling, Sven Schmiedl, Markus Loeffler, Tobias Dreischulte, Martin F. Fromm, Katrin Farker, Louisa Redeker, Martin Federbusch, F. Schmidt, Alexander Strübing, Theresa Terstegen, Ulrich Jaehde, Petra Thürmann, Daniel Neumann, Miriam Schechner, Michael I. Sponfeldner, Thomas Bauerdick
article en

Abstract

PURPOSE: Unrecognized 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. METHODS: We 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. RESULTS: In 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%). CONCLUSION: Our 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.

European Journal of Clinical PharmacologyVol. 82(8)
University of Bonn (DE), Witten/Herdecke University (DE), Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), Heidelberg University (DE), University Hospital Heidelberg (DE), LMU Klinikum (DE), Universitätsklinikum Erlangen (DE), University Hospital Leipzig (DE), Jena University Hospital (DE), Ludwig-Maximilians-Universität München (DE), Leipzig University (DE)
Friedrich-Alexander-Universität Erlangen-Nürnberg
Openalex Percentile: Top 6%
Machine Learning in Healthcare
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