Op den Buijs Jorn, Pijl Marten, Landgraf Andreas
Philips Research, Eindhoven, Netherlands.
Philips DACH, Hamburg, Germany.
JMIR Med Inform. 2021 Mar 8;9(3):e25121. doi: 10.2196/25121.
Predictive analytics based on data from remote monitoring of elderly via a personal emergency response system (PERS) in the United States can identify subscribers at high risk for emergency hospital transport. These risk predictions can subsequently be used to proactively target interventions and prevent avoidable, costly health care use. It is, however, unknown if PERS-based risk prediction with targeted interventions could also be applied in the German health care setting.
The objectives were to develop and validate a predictive model of 30-day emergency hospital transport based on data from a German PERS provider and compare the model with our previously published predictive model developed on data from a US PERS provider.
Retrospective data of 5805 subscribers to a German PERS service were used to develop and validate an extreme gradient boosting predictive model of 30-day hospital transport, including predictors derived from subscriber demographics, self-reported medical conditions, and a 2-year history of case data. Models were trained on 80% (4644/5805) of the data, and performance was evaluated on an independent test set of 20% (1161/5805). Results were compared with our previously published prediction model developed on a data set of PERS users in the United States.
German PERS subscribers were on average aged 83.6 years, with 64.0% (743/1161) females, with 65.4% (759/1161) reported 3 or more chronic conditions. A total of 1.4% (350/24,847) of subscribers had one or more emergency transports in 30 days in the test set, which was significantly lower compared with the US data set (2455/109,966, 2.2%). Performance of the predictive model of emergency hospital transport, as evaluated by area under the receiver operator characteristic curve (AUC), was 0.749 (95% CI 0.721-0.777), which was similar to the US prediction model (AUC=0.778 [95% CI 0.769-0.788]). The top 1% (12/1161) of predicted high-risk patients were 10.7 times more likely to experience an emergency hospital transport in 30 days than the overall German PERS population. This lift was comparable to a model lift of 11.9 obtained by the US predictive model.
Despite differences in emergency care use, PERS-based collected subscriber data can be used to predict use outcomes in different international settings. These predictive analytic tools can be used by health care organizations to extend population health management into the home by identifying and delivering timelier targeted interventions to high-risk patients. This could lead to overall improved patient experience, higher quality of care, and more efficient resource use.
基于美国通过个人应急响应系统（PERS）对老年人进行远程监测的数据进行的预测分析，可以识别出有紧急住院运输高风险的用户。这些风险预测随后可用于主动进行干预，并防止不必要的、昂贵的医疗保健使用。然而，基于PERS的风险预测与针对性干预措施是否也能应用于德国的医疗保健环境尚不清楚。
目标是基于德国PERS供应商的数据开发并验证一个30天紧急住院运输的预测模型，并将该模型与我们之前基于美国PERS供应商的数据开发并发表的预测模型进行比较。
使用德国PERS服务的5805名用户的回顾性数据来开发并验证一个30天住院运输的极端梯度提升预测模型，包括从用户人口统计学、自我报告的医疗状况以及2年病例数据历史中得出的预测因素。模型在80%（4644/5805）的数据上进行训练，并在20%（1161/5805）的独立测试集上评估性能。将结果与我们之前基于美国PERS用户数据集开发并发表的预测模型进行比较。
德国PERS用户的平均年龄为83.6岁，女性占64.0%（743/1161），65.4%（759/1161）的用户报告有3种或更多慢性病。在测试集中，共有1.4%（350/24847）的用户在30天内有一次或多次紧急运输，这与美国数据集（2455/109966，2.2%）相比显著更低。通过受试者操作特征曲线下面积（AUC）评估的紧急住院运输预测模型的性能为0.749（95%CI 0.721 - 0.777），这与美国预测模型（AUC = 0.778 [95%CI 0.769 - 0.788]）相似。预测的高风险患者中前1%（12/1161）在30天内经历紧急住院运输的可能性是德国PERS总体人群的10.7倍。这种提升与美国预测模型获得的11.9的模型提升相当。
尽管在紧急护理使用方面存在差异，但基于PERS收集的用户数据可用于预测不同国际环境下的使用结果。这些预测分析工具可被医疗保健组织用于通过识别并向高风险患者提供更及时的针对性干预措施，将人群健康管理扩展到家庭。这可能会带来整体改善的患者体验、更高的护理质量以及更有效的资源利用。
