Momenzadeh Amanda, Cranney Caleb, Choi So Yung, Bresee Catherine, Tighiouart Mourad, Gianchandani Roma, Pevnick Joshua, Moore Jason H, Meyer Jesse G
Department of Computational Biomedicine; Cedars-Sinai; Los Angeles, CA USA.
Department of Computational Biomedicine; Cedars-Sinai; Los Angeles, CA.
medRxiv. 2024 Aug 2:2024.07.31.24311287. doi: 10.1101/2024.07.31.24311287.
A multitude of factors affect a hospitalized individual's blood glucose (BG), making BG difficult to predict and manage. Beyond medications well established to alter BG, such as beta-blockers, there are likely many medications with undiscovered effects on BG variability. Identification of these medications and the strength and timing of these relationships has potential to improve glycemic management and patient safety.
EHR data from 103,871 inpatient encounters over 8 years within a large, urban health system was used to extract over 500 medications, laboratory measurements, and clinical predictors of BG. Feature selection was performed using an optimized Lasso model with repeated 5-fold cross-validation on the 80% training set, followed by a linear mixed regression model to evaluate statistical significance. Significant medication predictors were then evaluated for novelty against a comprehensive adverse drug event database.
We found 29 statistically significant features associated with BG; 24 were medications including 10 medications not previously documented to alter BG. The remaining five factors were Black/African American race, history of type 2 diabetes mellitus, prior BG (mean and last) and creatinine.
The unexpected medications, including several agents involved in gastrointestinal motility, found to affect BG were supported by available studies. This study may bring to light medications to use with caution in individuals with hyper- or hypoglycemia. Further investigation of these potential candidates is needed to enhance clinical utility of these findings.
This study uniquely identifies medications involved in gastrointestinal transit to be predictors of BG that may not well established and recognized in clinical practice.
众多因素会影响住院患者的血糖(BG),使得血糖难以预测和管理。除了已明确能改变血糖的药物,如β受体阻滞剂外,可能还有许多药物对血糖变异性有尚未被发现的影响。识别这些药物以及这些关系的强度和时间,有可能改善血糖管理和患者安全。
使用来自一个大型城市医疗系统8年内103,871次住院病例的电子健康记录(EHR)数据,提取500多种药物、实验室测量数据以及血糖的临床预测指标。在80%的训练集上使用优化的套索模型并重复进行5折交叉验证进行特征选择,随后使用线性混合回归模型评估统计显著性。然后针对一个综合的药物不良事件数据库评估显著的药物预测指标的新颖性。
我们发现了29个与血糖有统计学显著关联的特征;其中24个是药物,包括10种先前未记录能改变血糖的药物。其余五个因素是黑人/非裔美国人种族、2型糖尿病病史、既往血糖(平均值和最后值)以及肌酐。
现有研究支持了所发现的影响血糖的意外药物,包括几种参与胃肠动力的药物。这项研究可能会揭示在高血糖或低血糖患者中需谨慎使用的药物。需要对这些潜在候选药物进行进一步研究,以提高这些发现的临床实用性。
本研究独特地识别出参与胃肠转运的药物是血糖的预测指标,而这些指标在临床实践中可能尚未得到充分确立和认可。