Walsh Mary E, Galvin Rose, Boland Fiona, Williams David, Harbison Joseph A, Murphy Sean, Collins Ronan, Crowe Morgan, McCabe Dominick J H, Horgan Frances
School of Physiotherapy, Royal College of Surgeons in Ireland,123 St Stephens Green, Dublin 2, Ireland.
Department of Clinical Therapies, Faculty of Education and Health Sciences, University of Limerick, Health Research Institute, Limerick, Ireland.
Age Ageing. 2017 Jul 1;46(4):642-648. doi: 10.1093/ageing/afw255.
several multivariable models have been derived to predict post-stroke falls. These require validation before integration into clinical practice. The aim of this study was to externally validate two prediction models for recurrent falls in the first year post-stroke using an Irish prospective cohort study.
stroke patients with planned home-discharges from five hospitals were recruited. Falls were recorded with monthly diaries and interviews 6 and 12 months post-discharge. Predictors for falls included in two risk-prediction models were assessed at discharge. Participants were classified into risk groups using these models. Model 1, incorporating inpatient falls history and balance, had a 6-month outcome. Model 2, incorporating inpatient near-falls history and upper limb function, had a 12-month outcome. Measures of calibration, discrimination (area under the curve (AUC)) and clinical utility (sensitivity/specificity) were calculated.
128 participants (mean age = 68.6 years, SD = 13.3) were recruited. The fall status of 117 and 110 participants was available at 6 and 12 months, respectively. Seventeen and 28 participants experienced recurrent falls by these respective time points. Model 1 achieved an AUC = 0.56 (95% CI 0.46-0.67), sensitivity = 18.8% and specificity = 93.6%. Model 2 achieved AUC = 0.55 (95% CI 0.44-0.66), sensitivity = 51.9% and specificity = 58.7%. Model 1 showed no significant difference between predicted and observed events (risk ratio (RR) = 0.87, 95% CI 0.16-4.62). In contrast, model 2 significantly over-predicted fall events in the validation cohort (RR = 1.61, 95% CI 1.04-2.48).
both models showed poor discrimination for predicting recurrent falls. A further large prospective cohort study would be required to derive a clinically useful falls-risk prediction model for a similar population.
已推导了多个多变量模型来预测中风后跌倒情况。在将这些模型整合到临床实践之前，需要进行验证。本研究的目的是利用一项爱尔兰前瞻性队列研究，对两种中风后第一年复发性跌倒的预测模型进行外部验证。
招募了五家医院计划出院的中风患者。通过每月的日记记录跌倒情况，并在出院后6个月和12个月进行访谈。在出院时评估两种风险预测模型中包含的跌倒预测因素。使用这些模型将参与者分为风险组。模型1纳入了住院期间跌倒史和平衡能力，有6个月的结果。模型2纳入了住院期间险些跌倒史和上肢功能，有12个月的结果。计算校准、区分度（曲线下面积（AUC））和临床效用（敏感性/特异性）的指标。
招募了128名参与者（平均年龄=68.6岁，标准差=13.3）。分别有117名和110名参与者在6个月和12个月时的跌倒状态可用。在这些相应的时间点，分别有17名和28名参与者经历了复发性跌倒。模型1的AUC=0.56（95%可信区间0.46-0.67），敏感性=18.8%，特异性=93.6%。模型2的AUC=0.55（95%可信区间0.44-0.66），敏感性=51.9%，特异性=58.7%。模型1显示预测事件与观察事件之间无显著差异（风险比（RR）=0.87，95%可信区间0.16-4.62）。相比之下，模型2在验证队列中显著高估了跌倒事件（RR=1.61，95%可信区间1.04-2.48）。
两种模型在预测复发性跌倒方面的区分度都很差。需要进一步开展大规模前瞻性队列研究，以得出适用于类似人群的临床有用的跌倒风险预测模型。
