Kitchen Christopher, Chang Hsien-Yen, Weiner Jonathan P, Kharrazi Hadi
Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Division of Health Sciences Informatics, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Risk Manag Healthc Policy. 2022 Sep 5;15:1671-1682. doi: 10.2147/RMHP.S356080. eCollection 2022.
Patient vital signs are related to specific health risks and outcomes but are underutilized in the prediction of health-care utilization and cost. To measure the added value of electronic health record (EHR) extracted Body Mass Index (BMI) and blood pressure (BP) values in improving healthcare risk and utilization predictions.
A sample of 12,820 adult outpatients from the Johns Hopkins Health System (JHHS) were identified between 2016 and 2017, having high data quality and recorded values for BMI and BP. We evaluated the added value of BMI and BP in predicting health-care utilization and cost through a retrospective cohort design. BMI, mean arterial pressure (MAP), systolic and diastolic BPs were summarized as annual aggregated values. Concurrent annual BMI and MAP changes were quantified as the difference between maximum and minimum recorded values. Model performance estimates consisted of repeated 10-fold cross validation, compared to base model point estimates for demographic and diagnostic, coded events: (1) patient age and sex, (2) age, sex, and the Charlson weighted index, (3) age, sex and the Johns Hopkins ACG system's DxPM risk score.
Both categorical BMI and BP were progressively indicative of disease comorbidity, but not uniformly related to health-care utilization or cost. Annual change in BMI and MAP improved predictions for most concurrent year outcomes when compared to base models.
When a healthcare system lacks relevant diagnostic or risk assessment information for a patient, vital signs may be useful for a simple estimation of disease risk, cost and utilization.
患者生命体征与特定健康风险及结果相关，但在预测医疗保健利用情况和成本方面未得到充分利用。本研究旨在衡量从电子健康记录（EHR）中提取的体重指数（BMI）和血压（BP）值在改善医疗保健风险和利用情况预测方面的附加价值。
选取了2016年至2017年间约翰霍普金斯健康系统（JHHS）的12820名成年门诊患者作为样本，这些患者数据质量高，且记录了BMI和BP值。我们通过回顾性队列设计评估了BMI和BP在预测医疗保健利用情况和成本方面的附加价值。BMI、平均动脉压（MAP）、收缩压和舒张压以年度汇总值表示。同时将年度BMI和MAP变化量化为记录的最大值与最小值之间的差值。模型性能评估包括重复的10倍交叉验证，并与基于人口统计学和诊断编码事件的基础模型点估计进行比较：（1）患者年龄和性别，（2）年龄、性别和查尔森加权指数，（3）年龄、性别和约翰霍普金斯ACG系统的DxPM风险评分。
分类BMI和BP均逐渐表明疾病共病情况，但与医疗保健利用情况或成本并非始终相关。与基础模型相比，BMI和MAP的年度变化改善了大多数同期结果的预测。
当医疗系统缺乏患者的相关诊断或风险评估信息时，生命体征可能有助于简单估计疾病风险、成本和利用情况。
