Institute for Computational Medicine, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, USA,
Pac Symp Biocomput. 2023;28:31-42.
The objective of this research was to build and assess the performance of a prediction model for post-operative recovery status measured by quality of life among individuals experiencing a variety of surgery types. In addition, we assessed the performance of the model for two subgroups (high and moderately consistent wearable device users). Study variables were derived from the electronic health records, questionnaires, and wearable devices of a cohort of individuals with one of 8 surgery types and that were part of the NIH All of Us research program. Through multivariable analysis, high frailty index (OR 1.69, 95% 1.05-7.22, p<0.006), and older age (OR 1.76, 95% 1.55-4.08, p<0.024) were found to be the driving risk factors of poor recovery post-surgery. Our logistic regression model included 15 variables, 5 of which included wearable device data. In wearable use subgroups, the model had better accuracy for high wearable users (81%). Findings demonstrate the potential for models that use wearable measures to assess frailty to inform clinicians of patients at risk for poor surgical outcomes. Our model performed with high accuracy across multiple surgery types and were robust to variable consistency in wearable use.
本研究旨在构建和评估一个预测模型，该模型用于测量生活质量来评估接受各种手术类型的个体的术后恢复情况。此外，我们评估了该模型在两个亚组（高和中度一致使用可穿戴设备的用户）中的表现。研究变量来自 NIH All of Us 研究计划中一个队列的个体的电子健康记录、问卷和可穿戴设备。通过多变量分析，发现较高的衰弱指数（OR 1.69，95%置信区间 1.05-7.22，p<0.006）和年龄较大（OR 1.76，95%置信区间 1.55-4.08，p<0.024）是术后恢复不良的驱动风险因素。我们的逻辑回归模型包含 15 个变量，其中 5 个变量包含可穿戴设备数据。在可穿戴设备使用亚组中，该模型对高可穿戴设备用户的准确性更高（81%）。研究结果表明，使用可穿戴措施评估衰弱的模型具有为临床医生提供手术结局不良风险患者的潜力。我们的模型在多种手术类型中表现出高精度，并且对可穿戴设备使用的一致性变化具有稳健性。
