Developing a prediction rule from automated clinical databases to identify high-risk patients in a large population with diabetes
Developing a prediction rule from automated clinical databases to identify high-risk patients in a large population with diabetes
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DOI：
10.2337/diacare.24.9.1547
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发表时间：
2001-09-01
期刊：
影响因子：
16.2
通讯作者：
Liu, J
中科院分区：
文献类型：
--
作者：
Selby, JV;Karter, AJ;Liu, J
目的 - 开发和验证一个预测规则，用于在大型管理式医疗组织中使用自动化数据来识别短期并发症高风险的糖尿病患者。 研究设计和方法 - 对北加州 Kaiser Permanente 的 57,722 名年龄大于或等于 19 岁的糖尿病患者进行回顾性队列分析，使用 1994 年至 1995 年的数据来模拟大血管和微血管并发症的风险（n = 1996 年期间，有 3,977 例）、感染性并发症（n = 1,580）和代谢并发症（n = 316），候选预测因子（n = 36）包括先前的住院和门诊诊断、实验室记录、药房记录、使用记录和调查数据。使用分割样本验证，在下半年评估了一半人口中逻辑回归模型得出的风险评分。敏感性、阳性预测值和接受者操作特征曲线用于比较从完整模型获得的分数与使用更简单方法获得的分数。结果 - 既往并发症史或相关门诊诊断是每个并发症组中最强的预测因子。对于既往没有事件的患者，单独使用胰岛素治疗、血清肌酐大于或等于 13 mg/dl、使用两种或多种抗高血压药物、HbA(1c) > 10% 和白蛋白尿/微量白蛋白尿是两种或全部三种并发症的独立预测因素。与简单地针对 HbA(1c) 水平升高的患者来识别高风险患者相比，从多变量模型得出的几种风险评分更有效。结论 - 基于自动化临床数据的简单预测规则对于规划糖尿病人群的护理管理非常有用。
OBJECTIVE - To develop and validate a prediction rule for identifying diabetic patients at high short-term risk of complications using automated data in a large managed care organization.RESEARCH DESIGN AND METHODS - Retrospective cohort analyses were performed in 57,722 diabetic members of Kaiser Permanente, Northern California, aged greater than or equal to 19 years, Data from 1994 to 1995 were used to model risk for macro- and microvascular complications (n = 3,977), infectious complications (n = 1,580), and metabolic complications (n = 316) during 1996, Candidate predictors (n = 36) included prior inpatient and outpatient diagnoses, laboratory records, pharmacy records, utilization records, and survey data. Using split-sample validation, the risk scores derived from logistic regression models in half of the population were evaluated in the second half. Sensitivity, positive predictive value, and receiver operating characteristics curves were used to compare scores obtained from full models to those derived using simpler approaches.RESULTS - History of prior complications or related outpatient diagnoses were the strongest predictors in each complications set. For patients without previous events, treatment with insulin alone, serum creatinine - greater than or equal to 13 mg/dl, use of two or more antihypertensive medications, HbA(1c) > 10%, and albuminuria/microalbuminuria were independent predictors of two or all three complications. Several risk scores derived from multivariate models were more efficient than simply targeting patients with elevated HbA(1c) levels for identifying high-risk patients.CONCLUSIONS - Simple prediction rules based on automated clinical data are useful in planning care management for populations with diabetes.