BACKGROUND The healthcare for older adults is insufficient in many countries, not designed to meet their needs and is often described as disorganized and reactive. Prediction of older persons at risk of admission to hospital may be one important way for the future healthcare system to act proactively when meeting increasing needs for care. Therefore, we wanted to develop and test a clinically useful model for predicting hospital admissions of older persons based on routine healthcare data. METHODS We used the healthcare data on 40,728 persons, 75-109 years of age to predict hospital in-ward care in a prospective cohort. Multivariable logistic regression was used to identify significant factors predictive of unplanned hospital admission. Model fitting was accomplished using forward selection. The accuracy of the prediction model was expressed as area under the receiver operating characteristic (ROC) curve, AUC. RESULTS The prediction model consisting of 38 variables exhibited a good discriminative accuracy for unplanned hospital admissions over the following 12 months (AUC 0.69 [95% confidence interval, CI 0.68-0.70]) and was validated on external datasets. Clinically relevant proportions of predicted cases of 40 or 45% resulted in sensitivities of 62 and 66%, respectively. The corresponding positive predicted values (PPV) was 31 and 29%, respectively. CONCLUSION A prediction model based on routine administrative healthcare data from older persons can be used to find patients at risk of admission to hospital. Identifying the risk population can enable proactive intervention for older patients with as-yet unknown needs for healthcare.

中文翻译：

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对老年人群住院的临床有用预测。

背景技术在许多国家，老年人的医疗保健不足，不能满足他们的需求，通常被描述为无组织的和反应性的。预测面临住院风险的老年人可能是未来的医疗体系在满足日益增长的护理需求时采取积极行动的重要方式之一。因此，我们希望开发和测试一种基于常规医疗保健数据的临床上有用的模型，用于预测老年人的住院人数。方法我们使用了40728名75-109岁的人的医疗保健数据来预测未来人群的医院内科护理。使用多因素逻辑回归分析来确定可预测意外住院的重要因素。使用正向选择完成模型拟合。预测模型的准确性表示为接收器工作特征（ROC）曲线AUC下的面积。结果由38个变量组成的预测模型在接下来的12个月内对计划外的住院人数表现出良好的判别准确性（AUC 0.69 [95％置信区间，CI 0.68-0.70]），并在外部数据集上进行了验证。预计病例的临床相关比例为40或45％，敏感性分别为62％和66％。相应的阳性预测值（PPV）分别为31％和29％。结论基于老年人常规行政医疗数据的预测模型可用于发现有入院风险的患者。识别风险人群可以对尚未确定的医疗保健需求的老年患者进行积极干预。