Howell Stuart, Coory Michael, Martin Jennifer, Duckett Stephen
Centre for Healthcare Improvement, Queensland Health, Brisbane, Australia.
BMC Health Serv Res. 2009 Jun 9;9:96. doi: 10.1186/1472-6963-9-96.
A relatively small percentage of patients with chronic medical conditions account for a much larger percentage of inpatient costs. There is some evidence that case-management can improve health and quality-of-life and reduce the number of times these patients are readmitted. To assess whether a statistical algorithm, based on routine inpatient data, can be used to identify patients at risk of readmission and who would therefore benefit from case-management.
Queensland database study of public-hospital patients, who had at least one emergency admission for a chronic medical condition (e.g., congestive heart failure, chronic obstructive pulmonary disease, diabetes or dementia) during 2005/2006. Multivariate logistic regression was used to develop an algorithm to predict readmission within 12 months. The performance of the algorithm was tested against recorded readmissions using sensitivity, specificity, and Likelihood Ratios (positive and negative).
Several factors were identified that predicted readmission (i.e., age, co-morbidities, economic disadvantage, number of previous admissions). The discriminatory power of the model was modest as determined by area under the receiver operating characteristic (ROC) curve (c = 0.65). At a risk score threshold of 50, the algorithm identified only 44.7% (95% CI: 42.5%, 46.9%) of patients admitted with a reference condition who had an admission in the next 12 months; 37.5% (95% CI: 35.0%, 40.0%) of patients were flagged incorrectly (they did not have a subsequent admission).
A statistical algorithm based on Queensland hospital inpatient data, performed only moderately in identifying patients at risk of readmission. The main problem is that there are too many false negatives, which means that many patients who might benefit would not be offered case-management.
患有慢性疾病的患者中，相对较小比例的人群却占据了住院费用的较大比例。有证据表明，病例管理可以改善健康状况和生活质量，并减少这些患者再次入院的次数。本研究旨在评估基于常规住院数据的统计算法是否可用于识别有再次入院风险的患者，这些患者可能会从病例管理中受益。
对昆士兰公立医院患者进行数据库研究，这些患者在2005/2006年期间因慢性疾病（如充血性心力衰竭、慢性阻塞性肺疾病、糖尿病或痴呆症）至少有一次紧急入院治疗。采用多变量逻辑回归开发一种算法，以预测12个月内的再次入院情况。使用敏感性、特异性和似然比（阳性和阴性）对算法的性能进行测试，并与记录的再次入院情况进行对比。
确定了几个预测再次入院的因素（即年龄、合并症、经济劣势、既往入院次数）。根据受试者工作特征曲线（ROC）下的面积确定，该模型的鉴别能力一般（c = 0.65）。在风险评分阈值为50时，该算法仅识别出44.7%（95%置信区间：42.5%，46.9%）患有参考疾病且在接下来12个月内再次入院的患者；37.5%（95%置信区间：35.0%，40.0%）的患者被错误标记（他们没有随后的入院记录）。
基于昆士兰医院住院数据的统计算法在识别有再次入院风险的患者方面表现一般。主要问题是假阴性太多，这意味着许多可能受益的患者无法获得病例管理服务。
