Systems Research Initiative, Kaiser Permanente Division of Research, 2000 Broadway Avenue, Oakland, CA, 94612, USA.
Intensive Care Unit, Kaiser Permanente Medical Center, 700 Lawrence Expressway, Santa Clara, CA, 95051, USA.
BMC Health Serv Res. 2022 Apr 29;22(1):574. doi: 10.1186/s12913-022-07910-w.
Increasing evidence suggests that social factors and problems with physical and cognitive function may contribute to patients' rehospitalization risk. Understanding a patient's readmission risk may help healthcare providers develop tailored treatment and post-discharge care plans to reduce readmission and mortality. This study aimed to evaluate whether including patient-reported data on social factors; cognitive status; and physical function improves on a predictive model based on electronic health record (EHR) data alone.
We conducted a prospective study of 1,547 hospitalized adult patients in 3 Kaiser Permanente Northern California hospitals. The main outcomes were non-elective rehospitalization or death within 30 days post-discharge. Exposures included patient-reported social factors and cognitive and physical function (obtained in a pre-discharge interview) and EHR-derived data for comorbidity burden, acute physiology, care directives, prior utilization, and hospital length of stay. We performed bivariate comparisons using Chi-square, t-tests, and Wilcoxon rank-sum tests and assessed correlations between continuous variables using Spearman's rho statistic. For all models, the results reported were obtained after fivefold cross validation.
The 1,547 adult patients interviewed were younger (age, p = 0.03) and sicker (COPS2, p < 0.0001) than the rest of the hospitalized population. Of the 6 patient-reported social factors measured, 3 (not living with a spouse/partner, transportation difficulties, health or disability-related limitations in daily activities) were significantly associated (p < 0.05) with the main outcomes, while 3 (living situation concerns, problems with food availability, financial problems) were not. Patient-reported cognitive (p = 0.027) and physical function (p = 0.01) were significantly lower in patients with the main outcomes. None of the patient-reported variables, singly or in combination, improved predictive performance of a model that included acute physiology and longitudinal comorbidity burden (area under the receiver operator characteristic curve was 0.716 for both the EHR model and maximal performance of a random forest model including all predictors).
In this insured population, incorporating patient-reported social factors and measures of cognitive and physical function did not improve performance of an EHR-based model predicting 30-day non-elective rehospitalization or mortality. While incorporating patient-reported social and functional status data did not improve ability to predict these outcomes, such data may still be important for improving patient outcomes.
越来越多的证据表明，社会因素以及身体和认知功能问题可能会导致患者的再住院风险增加。了解患者的再入院风险有助于医疗保健提供者制定个性化的治疗和出院后护理计划，以降低再入院率和死亡率。本研究旨在评估在基于电子健康记录（EHR）数据的预测模型中加入患者报告的社会因素、认知状态和身体功能数据是否会提高预测能力。
我们对北加州 3 家 Kaiser Permanente 医院的 1547 名住院成年患者进行了前瞻性研究。主要结局是出院后 30 天内非择期再住院或死亡。暴露因素包括患者报告的社会因素以及认知和身体功能（在出院前访谈中获得）以及 EHR 衍生的合并症负担、急性生理、护理指令、既往使用情况和住院时间数据。我们使用卡方检验、t 检验和 Wilcoxon 秩和检验进行了双变量比较，并使用 Spearman rho 统计量评估了连续变量之间的相关性。对于所有模型，报告的结果都是经过五重交叉验证后获得的。
接受访谈的 1547 名成年患者比住院患者整体更年轻（年龄，p=0.03）且病情更重（COPS2，p<0.0001）。在测量的 6 项患者报告的社会因素中，有 3 项（未与配偶/伴侣同住、交通困难、日常活动受限与健康或残疾相关）与主要结局显著相关（p<0.05），而 3 项（生活状况担忧、食物供应问题、经济问题）则没有。报告的认知功能（p=0.027）和身体功能（p=0.01）在有主要结局的患者中明显较低。患者报告的任何变量，单独或组合使用，都不能提高包含急性生理和纵向合并症负担的模型的预测性能（EHR 模型的受试者工作特征曲线下面积为 0.716，包括所有预测因素的随机森林模型的最大性能也是 0.716）。
在这个有保险的人群中，纳入患者报告的社会因素以及认知和身体功能测量并不能提高基于 EHR 的模型预测 30 天非择期再住院或死亡的能力。虽然纳入患者报告的社会和功能状态数据并没有提高预测这些结果的能力，但这些数据对于改善患者的预后可能仍然很重要。
