Frydenlund Juliane, Cosgrave Nicole, Moriarty Frank, Wallace Emma, Kirke Ciara, Williams David J, Bennett Kathleen, Cahir Caitriona
Data Science Centre, School of Population Health, RCSI University of Medicine and Health Science, Lower Mercer Street, Dublin 2, Ireland.
Department of Geriatric and Stroke Medicine, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
Eur Geriatr Med. 2025 Apr;16(2):573-581. doi: 10.1007/s41999-024-01152-1. Epub 2025 Jan 17.
Older people are at an increased risk of developing adverse drug reactions (ADR) and adverse drug events (ADE). This study aimed to develop and validate a risk prediction model (ADAPTiP) for ADR/ADE in older populations.
We used the adverse drug reactions in an Ageing PopulaTion (ADAPT) cohort (N = 798; 361 ADR-related admissions; 437 non-ADR-related admissions), a cross-sectional study designed to examine the prevalence and risk factors for ADR-related hospital admissions in patients aged ≥ 65 years. Twenty predictors (categorised as sociodemographic-related, functional ability-related, disease-related, and medication-related) were considered in the development of the model. The model was developed using multivariable logistic regression and was internally validated by fivefold cross-validation. The model was externally validated in a separate prospective cohort from the Centre for Primary Care Research (CPCR) study of ADES. The cross-validated and externally validated model performance was evaluated by discrimination and calibration.
The final prediction model, ADAPTiP, included nine predictors: age, chronic lung disease, the primary presenting complaints of respiratory, bleeding and gastrointestinal disorders and syncope on hospital admission and antithrombotics, diuretics, and renin-angiotensin-aldosterone system drug classes. ADAPTiP demonstrated good performance with cross-validated area under the curve of 0.75 [95% CI 0.72;79] and 0.83 [95% CI 0.80;0.87] in the external validation.
Using accessible information from medical records, ADAPTiP can help clinicians to identify those older people at risk of an ADR/ADE who should be monitored and/or have their medications reviewed to avoid potentially harmful prescribing.
老年人发生药物不良反应（ADR）和药物不良事件（ADE）的风险增加。本研究旨在开发并验证一种针对老年人群ADR/ADE的风险预测模型（ADAPTiP）。
我们使用了老年人群药物不良反应（ADAPT）队列（N = 798；361例与ADR相关的入院病例；437例非ADR相关的入院病例），这是一项横断面研究，旨在检查年龄≥65岁患者中与ADR相关的住院患病率及风险因素。在模型开发过程中考虑了20个预测因素（分为社会人口统计学相关、功能能力相关、疾病相关和药物相关）。该模型采用多变量逻辑回归开发，并通过五折交叉验证进行内部验证。该模型在来自初级保健研究中心（CPCR）的ADES研究的另一个独立前瞻性队列中进行了外部验证。通过区分度和校准来评估交叉验证和外部验证后的模型性能。
最终的预测模型ADAPTiP包括9个预测因素：年龄、慢性肺病、入院时呼吸、出血、胃肠道疾病和晕厥的主要主诉以及抗血栓药、利尿剂和肾素-血管紧张素-醛固酮系统药物类别。ADAPTiP表现良好，交叉验证的曲线下面积为0.75 [95% CI 0.72；0.79]，外部验证中的曲线下面积为0.83 [95% CI 0.80；0.87]。
利用病历中的可获取信息，ADAPTiP可帮助临床医生识别那些有ADR/ADE风险的老年人，这些老年人应接受监测和/或对其用药进行审查，以避免潜在的有害处方。
