Objective To develop and externally validate the STRAtifying Treatments In the multi-morbid Frail elderlY (STRATIFY)-Falls clinical prediction model to identify the risk of hospital admission or death from a fall in patients with an indication for antihypertensive treatment. Design Retrospective cohort study. Setting Primary care data from electronic health records contained within the UK Clinical Practice Research Datalink (CPRD). Participants Patients aged 40 years or older with at least one blood pressure measurement between 130 mm Hg and 179 mm Hg. Main outcome measure First serious fall, defined as hospital admission or death with a primary diagnosis of a fall within 10 years of the index date (12 months after cohort entry). Model development was conducted using a Fine-Gray approach in data from CPRD GOLD, accounting for the competing risk of death from other causes, with subsequent recalibration at one, five, and 10 years using pseudo values. External validation was conducted using data from CPRD Aurum, with performance assessed through calibration curves and the observed to expected ratio, C statistic, and D statistic, pooled across general practices, and clinical utility using decision curve analysis at thresholds around 10%. Results Analysis included 1 772 600 patients (experiencing 62 691 serious falls) from CPRD GOLD used in model development, and 3 805 366 (experiencing 206 956 serious falls) from CPRD Aurum in the external validation. The final model consisted of 24 predictors, including age, sex, ethnicity, alcohol consumption, living in an area of high social deprivation, a history of falls, multiple sclerosis, and prescriptions of antihypertensives, antidepressants, hypnotics, and anxiolytics. Upon external validation, the recalibrated model showed good discrimination, with pooled C statistics of 0.833 (95% confidence interval 0.831 to 0.835) and 0.843 (0.841 to 0.844) at five and 10 years, respectively. Original model calibration was poor on visual inspection and although this was improved with recalibration, under-prediction of risk remained (observed to expected ratio at 10 years 1.839, 95% confidence interval 1.811 to 1.865). Nevertheless, decision curve analysis suggests potential clinical utility, with net benefit larger than other strategies. Conclusions This prediction model uses commonly recorded clinical characteristics and distinguishes well between patients at high and low risk of falls in the next 1-10 years. Although miscalibration was evident on external validation, the model still had potential clinical utility around risk thresholds of 10% and so could be useful in routine clinical practice to help identify those at high risk of falls who might benefit from closer monitoring or early intervention to prevent future falls. Further studies are needed to explore the appropriate thresholds that maximise the model’s clinical utility and cost effectiveness. Data were obtained via a Clinical Practice Research Datalink (CPRD) institutional licence. Requests for data sharing should be made directly to the CPRD. The algorithm is freely available for research use and can be downloaded from . Code lists used to define variables included in the dataset are available at

中文翻译：

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有抗高血压治疗指征的患者跌倒风险预测模型的开发和外部验证：回顾性队列研究

目的 开发并外部验证多病态衰弱老年人分层治疗 (STRATIFY)-跌倒临床预测模型，以确定有抗高血压治疗指征的患者因跌倒住院或死亡的风险。设计回顾性队列研究。根据英国临床实践研究数据链 (CPRD) 中包含的电子健康记录设置初级保健数据。参与者 40 岁或以上且至少一次血压测量结果在 130 毫米汞柱至 179 毫米汞柱之间的患者。主要结局指标 首次严重跌倒，定义为在索引日期后 10 年内（进入队列后 12 个月）初步诊断为跌倒而入院或死亡。模型开发是使用来自 CPRD GOLD 的数据的细灰色方法进行的，考虑了其他原因导致的死亡的竞争风险，随后使用伪值在一年、五年和十年进行了重新校准。使用 CPRD Aurum 的数据进行外部验证，通过校准曲线和观察到的预期比率、C 统计量和 D 统计量评估性能，汇总一般实践，并使用阈值约为 10% 的决策曲线分析进行临床效用。结果 模型开发中使用的 CPRD GOLD 分析了 1 772 600 名患者（经历了 62 691 次严重跌倒），外部验证中的 CPRD Aurum 分析了 3 805 366 名患者（经历了 206 956 次严重跌倒）。最终模型由 24 个预测因素组成，包括年龄、性别、种族、饮酒量、生活在社会贫困程度较高的地区、跌倒史、多发性硬化症以及抗高血压药、抗抑郁药、安眠药和抗焦虑药的处方。经过外部验证，重新校准的模型显示出良好的区分度，五年和十年的汇总 C 统计量分别为 0.833（95% 置信区间 0.831 至 0.835）和 0.843（0.841 至 0.844）。原始模型校准在目视检查方面较差，尽管通过重新校准得到了改善，但风险预测仍然不足（观察到的 10 年预期比率为 1.839，95% 置信区间为 1.811 至 1.865）。尽管如此，决策曲线分析表明潜在的临床实用性，其净效益大于其他策略。结论 该预测模型使用常见记录的临床特征，可以很好地区分未来 1-10 年内跌倒高风险和低风险的患者。尽管在外部验证中校准错误很明显，但该模型在 10% 的风险阈值左右仍具有潜在的临床实用性，因此可用于常规临床实践，以帮助识别那些跌倒高风险的人，这些人可能会受益于更密切的监测或早期干预以预防跌倒。未来落下。需要进一步的研究来探索最大化模型临床效用和成本效益的适当阈值。数据是通过临床实践研究数据链（CPRD）机构许可证获得的。数据共享请求应直接向CPRD提出。该算法可免费供研究使用，并且可以从以下地址下载。用于定义数据集中包含的变量的代码列表可在