| アブストラクト | BACKGROUND: Polypharmacy can be a consequence of overprescribing that is prevalent in older adults with multimorbidity. Polypharmacy can cause adverse reactions and result in hospital admission. This study predicted risks of adverse drug reaction (ADR)-related and emergency hospital admissions by medicine classes. METHODS: We used electronic health record data from general practices of Clinical Practice Research Datalink (CPRD GOLD) and Aurum. Older patients who received at least five medicines were included. Medicines were classified using the British National Formulary sections. Hospital admission cases were propensity-matched to controls by age, sex, and propensity for specific diseases. The matched data were used to develop and validate random forest (RF) models to predict the risk of ADR-related and emergency hospital admissions. Shapley Additive eXplanation (SHAP) values were calculated to explain the predictions. RESULTS: In total, 89,235 cases with polypharmacy and hospitalised with an ADR-related admission were matched to 443,497 controls. There were over 112,000 different combinations of the 50 medicine classes most implicated in ADR-related hospital admission in the RF models, with the most important medicine classes being loop diuretics, domperidone and/or metoclopramide, medicines for iron-deficiency anaemias and for hypoplastic/haemolytic/renal anaemias, and sulfonamides and/or trimethoprim. The RF models strongly predicted risks of ADR-related and emergency hospital admission. The observed Odds Ratio in the highest RF decile was 7.16 (95% CI 6.65-7.72) in the validation dataset. The C-statistics for ADR-related hospital admissions were 0.58 for age and sex and 0.66 for RF probabilities. CONCLUSIONS: Polypharmacy involves a very large number of different combinations of medicines, with substantial differences in risks of ADR-related and emergency hospital admissions. Although the medicines may not be causally related to increased risks, RF model predictions may be useful in prioritising medication reviews. Simple tools based on few medicine classes may not be effective in identifying high risk patients. |
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| 投稿者 | Fahmi, Ali; Wong, David; Walker, Lauren; Buchan, Iain; Pirmohamed, Munir; Sharma, Anita; Cant, Harriet; Ashcroft, Darren M; van Staa, Tjeerd Pieter |
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| 組織名 | Centre for Health Informatics & Health Data Research UK North, Division of;Informatics, Imaging and Data Science, School of Health Sciences, Faculty of;Biology, Medicine and Health, The University of Manchester, Manchester Academic;Health Science Centre, Manchester, United Kingdom.;Institute of Population Health, NIHR Applied Research Collaboration North West;Coast, University of Liverpool, Liverpool, United Kingdom.;Centre for Drug Safety Science, Institute of Systems, Molecular and Integrative;Biology (ISMIB) University of Liverpool, Liverpool, United Kingdom.;Chadderton South Health Centre, Eaves Lane, Chadderton, United Kingdom.;Centre for Pharmacoepidemiology and Drug Safety, NIHR Greater Manchester Patient;Safety Translational Research Centre, School of Health Sciences, Faculty of;Biology, Medicine and Health, The University of Manchester, Manchester, United;Kingdom. |
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