Li Yan, Staley Benjamin, Henriksen Carl, Xu Dandan, Lipori Gloria, Winterstein Almut G
Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL.
Department of Pharmacy Services, UF Health Shands Hospital, University of Florida, Gainesville, FL.
Am J Health Syst Pharm. 2019 Feb 9;76(5):301-311. doi: 10.1093/ajhp/zxy051.
The purpose of this study was to develop a dynamic risk prediction model for inpatient hypokalemia and evaluate its predictive performance.
A retrospective cohort included all admissions aged 18 years and above from 2 large tertiary hospitals in Florida over a 22-month period. Hypokalemia was defined as a potassium value of less than 3 mmol/L, and subsequent initiation of potassium supplements. Twenty-five risk factors (RF) identified from literature were operationalized using discrete electronic health record (EHR) data elements. For each of the first 5 hospital days, we modeled the probability of developing hypokalemia at the subsequent hospital day using logistic regression. Predictive performance of our model was validated with 100 bootstrap datasets and evaluated by the C statistic and Hosmer-Lemeshow goodness-of-fit test.
A total of 4511 hypokalemia events occurred over 263 436 hospital days (1.71%). Validated C statistics of the prediction model ranged from 0.83 (Day 1 model) to 0.86 (Day 3), while p-values for the Hosmer-Lemeshow test spanned from 0.005 (Day 1) to 0.27 (Day 4 and 5). For the Day 3 prediction, 9.9% of patients with risk scores in the 90th percentile developed hypokalemia and accounted for 60.4% of all hypokalemia events. After controlling for baseline potassium values, strong predictors included diabetic ketoacidosis, increased mineralocorticoid activity, polyuria, use of kaliuretics, use of potassium supplements and watery stool.
This is the first risk prediction model for hypokalemia. Our model achieved excellent discrimination and adequate calibration ability. Once externally validated, this risk assessment tool could use real-time EHR information to identify individuals at the highest risk for hypokalemia and support proactive interventions by pharmacists.
本研究旨在开发一种用于住院患者低钾血症的动态风险预测模型，并评估其预测性能。
一项回顾性队列研究纳入了佛罗里达州2家大型三级医院在22个月期间所有年龄在18岁及以上的住院患者。低钾血症定义为血钾值低于3 mmol/L，以及随后开始补充钾剂。从文献中确定的25个风险因素通过离散电子健康记录（EHR）数据元素进行操作化处理。对于前5个住院日中的每一天，我们使用逻辑回归模型预测随后住院日发生低钾血症的概率。我们的模型的预测性能通过100个自助数据集进行验证，并通过C统计量和Hosmer-Lemeshow拟合优度检验进行评估。
在263436个住院日中共发生了4511例低钾血症事件（1.71%）。预测模型的验证C统计量范围从0.83（第1天模型）到0.86（第3天），而Hosmer-Lemeshow检验的p值范围从0.005（第1天）到0.27（第4天和第5天）。对于第3天的预测，风险评分处于第90百分位数的患者中有9.9%发生了低钾血症，占所有低钾血症事件的60.4%。在控制基线血钾值后，强预测因素包括糖尿病酮症酸中毒、盐皮质激素活性增加、多尿、使用排钾利尿剂、使用钾补充剂和水样便。
这是首个针对低钾血症的风险预测模型。我们的模型具有出色的区分能力和足够的校准能力。一旦经过外部验证，这种风险评估工具可以利用实时EHR信息识别低钾血症风险最高的个体，并支持药剂师进行积极干预。
