Development and Validation of a Model for Predicting Inpatient Hospitalization
Development and Validation of a Model for Predicting Inpatient Hospitalization
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DOI：
10.1097/mlr.0b013e3182353ceb
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发表时间：
2012-02-01
期刊：
影响因子：
3
通讯作者：
Clark, Jeanne M.
中科院分区：
文献类型：
--
作者：
Lemke, Klaus W.;Weiner, Jonathan P.;Clark, Jeanne M.
背景资料：住院治疗是昂贵的健康保险公司和society.Objectives：开发和验证一个预测模型急性护理住院从行政索赔的人口，包括所有年龄组research Design：我们构建了一个回顾性队列研究，使用美国健康计划索赔数据库，包括每年的人级文件与人口统计学标记，发病率和利用措施。我们开发和验证的模型使用单独的data.Participants：验证样本包括470万人参加了至少6个月，在2006年和1个月或更多的2007年。措施：风险因素和结果变量获得的行政索赔数据使用调整后的临床组（ACG）系统。利用变量被添加，模型拟合与多变量logistic regression.Results：A 3.2%的患者在1年期间住院，20%的患者在前一年住院再住院。风险因素的效应大小适中，优势比= 80岁，3+既往住院，3+急诊室就诊，20种ACG发病率类别和40种疾病，包括高影响肿瘤，双相情感障碍，脑瘫，染色体异常，囊性纤维化和溶血性贫血。ACG住院模型的模型性能是好的（AUC = 0.80）和上级的先前住院模型（AUC = 0.75）和Charlson合并症住院模型（AUC = 0.78）。结论：一个有效的基于人群的预测模型的医院风险估计未来住院的个人风险。该模型可能对健康计划和护理管理人员有用。
Background: Hospitalizations are costly for health insurers and society.Objectives: To develop and validate a predictive model for acute care hospitalization from administrative claims for a population including all age groups.Research Design: We constructed a retrospective cohort study using a US health plan claims database, including annual person-level files with demographic markers, and morbidity and utilization measures. We developed and validated the model using separate data.Participants: The validation sample included 4.7 million persons enrolled for at least 6 months in 2006 and 1 or more months in 2007.Measures: Risk factors and outcome variables were obtained from administrative claims data using the Adjusted Clinical Group (ACG) system. Utilization variables were added, and models were fitted with multivariate logistic regression.Results: A 3.2% of patients had a hospitalization during a 1-year period, and 20% of patients who had been hospitalized during the previous year were rehospitalized. Effect sizes of risk factors were modest with odds ratios = 80 years, 3+ prior hospitalizations, 3+ emergency room visits, 20 ACG morbidity categories, and 40 diseases including high impact neoplasms, bipolar disorder, cerebral palsy, chromosomal anomalies, cystic fibrosis, and hemolytic anemia. Model performance of ACG hospitalization models was good (AUC = 0.80) and superior to a prior hospitalization model (AUC = 0.75) and a Charlson comorbidity hospitalization model (AUC = 0.78).Conclusions: A validated population-based predictive model for hospital risk estimates individual risk for future hospitalization. The model could be useful to health plans and care managers.