BACKGROUND & AIMS:
AIMS:To develop and externally validate a prediction model for the 6-month risk of a severe hypoglycemic event among individuals with pharmacologically treated diabetes.
METHODS:The development cohort consisted of 31,674 Kaiser Permanente Colorado members with pharmacologically treated diabetes (2007-2015). The validation cohorts consisted of 38,764 Kaiser Permanente Northwest members and 12,035 HealthPartners members. Variables were chosen that would be available in electronic health records. We developed 16-variable and 6-variable models, using a Cox counting model process that allows for the inclusion of multiple 6-month observation periods per person.
RESULTS:Across the three cohorts, there were 850,992 6-month observation periods, and 10,448 periods with at least one severe hypoglycemic event. The six-variable model contained age, diabetes type, HgbA1c, eGFR, history of a hypoglycemic event in the prior year, and insulin use. Both prediction models performed well, with good calibration and c-statistics of 0.84 and 0.81 for the 16-variable and 6-variable models, respectively. In the external validation cohorts, the c-statistics were 0.80-0.84.
CONCLUSIONS:We developed and validated two prediction models for predicting the 6-month risk of hypoglycemia. The 16-variable model had slightly better performance than the 6-variable model, but in some practice settings, use of the simpler model may be preferred.
背景与目标:
目的:建立并从外部验证在药物治疗的糖尿病患者中严重降血糖事件的6个月风险的预测模型。
方法:该开发队列由31,674名Kaiser Permanente Colorado成员进行药理治疗的糖尿病(2007-2015年)组成。验证队列由38,764位Kaiser Permanente Northwest成员和12,035位HealthPartners成员组成。选择了可以在电子健康记录中使用的变量。我们使用Cox计数模型过程开发了16变量和6变量模型,该过程允许每个人包含多个6个月的观察期。
结果:在这三个队列中,有850,992个6个月观察期和10,448个观察期,其中至少有一次严重的降血糖事件。六变量模型包含年龄,糖尿病类型,HgbA1c,eGFR,前一年发生降血糖事件的历史以及胰岛素的使用。两种预测模型均表现良好,对于16变量和6变量模型分别具有良好的校准和0.84和0.81的c统计量。在外部验证队列中,c统计量为0.80-0.84。
结论:我们开发并验证了两种用于预测6个月低血糖风险的预测模型。 16变量模型比6变量模型具有更好的性能,但是在某些实践设置中,可能更喜欢使用更简单的模型。