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Войти в исследования International journal of medical informatics · 2026
From bedside observations to clinical decision support system (CDSS) rules: using real-world adverse drug events (ADEs) data to identify high-risk iatrogenic situations.
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Dintilhac Audrey, Lohan Laura, Laureau Marion, Perier Damien, Cestac Philippe, Juillard-Condat Blandine, Breuker Cyril
Аннотация
INTRODUCTION: Adverse drug events (ADEs) constitute a major clinical and economic burden in Europe. While hospital pharmacy activities improve prescription safety, pharmacists cannot review all orders in time and must prioritize high-risk patients. Rule-based clinical decision support systems (CDSS) offer an additional preventive strategy but often generate excessive, low-relevance alerts.
OBJECTIVE: To develop rules for detecting iatrogenic risk in accordance with methodological standards reported in the literature, using ADEs identified in a prospective cohort of adult patients admitted to the emergency department of a French healthcare institution (2,600-bed tertiary care center).
METHODS: ADEs were identified through a structured medication history interview conducted by a trained clinical pharmacist upon the patient's admission to the emergency department. To focus on the most critical situations, drug classes defined at the fourth level of the Anatomical Therapeutic Chemical (ATC) classification system (ATC4) and associated with the highest risk were identified by considering prescription frequency, ADE occurrence, and ADE severity. For each selected ATC level 4 class, logistic regression models were used to assess the association between ADE probability and specific explanatory factors. These factors were then operationalized into rules designed to detect iatrogenic risk.
RESULTS: A total of 245 ATC4 classes were involved in at least one ADE. Among these, 22 classes were identified as high iatrogenic risk, accounting for approximately 50% of prescriptions leading to an ADE, with vitamin K antagonists and heparins showing the highest risk. Regression analyses resulted in 58 distinct rules: 31 (53.4%) combined prescription data with at least one laboratory parameter, 8 (13.8%) incorporated demographic variables (age or sex), and 19 (32.8%) were based solely on medication prescription data.
CONCLUSION: The clinical and pharmaceutical relevance of the proposed rules must be further evaluated to reduce excessive alert generation, which may lead to disengagement from both pharmacists and prescribers. The institutional health data warehouse could provide an appropriate environment for this evaluation.
OBJECTIVE: To develop rules for detecting iatrogenic risk in accordance with methodological standards reported in the literature, using ADEs identified in a prospective cohort of adult patients admitted to the emergency department of a French healthcare institution (2,600-bed tertiary care center).
METHODS: ADEs were identified through a structured medication history interview conducted by a trained clinical pharmacist upon the patient's admission to the emergency department. To focus on the most critical situations, drug classes defined at the fourth level of the Anatomical Therapeutic Chemical (ATC) classification system (ATC4) and associated with the highest risk were identified by considering prescription frequency, ADE occurrence, and ADE severity. For each selected ATC level 4 class, logistic regression models were used to assess the association between ADE probability and specific explanatory factors. These factors were then operationalized into rules designed to detect iatrogenic risk.
RESULTS: A total of 245 ATC4 classes were involved in at least one ADE. Among these, 22 classes were identified as high iatrogenic risk, accounting for approximately 50% of prescriptions leading to an ADE, with vitamin K antagonists and heparins showing the highest risk. Regression analyses resulted in 58 distinct rules: 31 (53.4%) combined prescription data with at least one laboratory parameter, 8 (13.8%) incorporated demographic variables (age or sex), and 19 (32.8%) were based solely on medication prescription data.
CONCLUSION: The clinical and pharmaceutical relevance of the proposed rules must be further evaluated to reduce excessive alert generation, which may lead to disengagement from both pharmacists and prescribers. The institutional health data warehouse could provide an appropriate environment for this evaluation.