Smith Matthew I, de Lusignan Simon, Mullett David, Correa Ana, Tickner Jermaine, Jones Simon
Department of Clinical and Experimental Medicine, University of Surrey, Guildford, United Kingdom.
Inner North West London Integrated Care Programme, London, United Kingdom.
PLoS One. 2016 Jul 22;11(7):e0159365. doi: 10.1371/journal.pone.0159365. eCollection 2016.
Falls are the leading cause of injury in older people. Reducing falls could reduce financial pressures on health services. We carried out this research to develop a falls risk model, using routine primary care and hospital data to identify those at risk of falls, and apply a cost analysis to enable commissioners of health services to identify those in whom savings can be made through referral to a falls prevention service.
Multilevel logistical regression was performed on routinely collected general practice and hospital data from 74751 over 65's, to produce a risk model for falls. Validation measures were carried out. A cost-analysis was performed to identify at which level of risk it would be cost-effective to refer patients to a falls prevention service. 95% confidence intervals were calculated using a Monte Carlo Model (MCM), allowing us to adjust for uncertainty in the estimates of these variables.
A risk model for falls was produced with an area under the curve of the receiver operating characteristics curve of 0.87. The risk cut-off with the highest combination of sensitivity and specificity was at p = 0.07 (sensitivity of 81% and specificity of 78%). The risk cut-off at which savings outweigh costs was p = 0.27 and the risk cut-off with the maximum savings was p = 0.53, which would result in referral of 1.8% and 0.45% of the over 65's population respectively. Above a risk cut-off of p = 0.27, costs do not exceed savings.
This model is the best performing falls predictive tool developed to date; it has been developed on a large UK city population; can be readily run from routine data; and can be implemented in a way that optimises the use of health service resources. Commissioners of health services should use this model to flag and refer patients at risk to their falls service and save resources.
跌倒是老年人受伤的主要原因。减少跌倒可减轻医疗服务的经济压力。我们开展这项研究以开发一个跌倒风险模型，利用常规初级保健和医院数据识别有跌倒风险的人群，并进行成本分析，以使医疗服务专员能够确定哪些人通过转介至跌倒预防服务可实现节约成本。
对从74751名65岁以上人群中常规收集的全科医疗和医院数据进行多水平逻辑回归分析，以生成跌倒风险模型。进行了验证措施。开展成本分析以确定将患者转介至跌倒预防服务在哪一风险水平上具有成本效益。使用蒙特卡洛模型（MCM）计算95%置信区间，使我们能够针对这些变量估计中的不确定性进行调整。
生成了一个跌倒风险模型，其受试者工作特征曲线下面积为0.87。灵敏度和特异度组合最高时的风险临界值为p = 0.07（灵敏度为81%，特异度为78%）。节约成本超过成本时的风险临界值为p = 0.27，节约成本最大值时的风险临界值为p = 0.53，这将分别导致65岁以上人群中1.8%和0.45%的人被转介。在风险临界值p = 0.27以上，成本不超过节约的成本。
该模型是迄今为止开发的性能最佳的跌倒预测工具；它是基于英国一个大城市的大量人群开发的；可以很容易地从常规数据运行；并且可以以优化医疗服务资源利用的方式实施。医疗服务专员应使用此模型标记有风险的患者并将其转介至跌倒服务，以节约资源。
