Using routine inpatient data to identify patients at risk of hospital readmission.
Using routine inpatient data to identify patients at risk of hospital readmission.
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DOI：
10.1186/1472-6963-9-96
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发表时间：
2009-06-09
影响因子：
2.8
通讯作者：
Duckett S
中科院分区：
文献类型：
--
作者：
Howell S;Coory M;Martin J;Duckett S
相对较小比例的慢性病患者占住院费用的比例要大得多。有一些证据表明，病例管理可以改善健康和生活质量，减少这些患者再次入院的次数。评估基于常规住院患者数据的统计算法是否可用于识别有再入院风险的患者，并因此从病例管理中获益。昆士兰州公立医院患者的数据库研究，这些患者至少有一次因慢性疾病（例如，充血性心力衰竭、慢性阻塞性肺病、糖尿病或痴呆症）。多变量逻辑回归被用来开发一种算法来预测12个月内的再入院。使用灵敏度、特异性和似然比（阳性和阴性）针对记录的再入院测试算法的性能。确定了预测再入院的几个因素（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（ROC）曲线下面积（c = 0.65）确定，该模型的区分能力适中。在风险评分阈值为50时，该算法仅识别出44.7%（95% CI：42.5%，46.9%）在未来12个月内因参考疾病入院的患者; 37.5%（95% CI：35.0%，40.0%）的患者被错误标记（他们没有后续入院）。一种基于昆士兰州医院住院患者数据的统计算法，在识别有再入院风险的患者方面仅表现出中等程度。主要的问题是有太多的假阴性，这意味着许多可能受益的患者不会得到病例管理。
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.
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影响因子：
11.4
作者：
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