Predictive Modeling of Resistant Hypertension Risk: Incorporating the TyG Index and Clinical Factors

Abstract

AbstractBackgroundResistant hypertension (RH), a form of high blood pressure that remains uncontrolled despite maximum medication, poses a significant cardiovascular risk. This paper introduces a novel predictive model, combining the triglyceride-glucose (TyG) index with traditional clinical factors, to anticipate the development of RH in patients with newly diagnosed primary hypertension.MethodsThe study included hospitalized patients with newly diagnosed primary hypertension and stable blood pressure after medication treatment from August 2019 to early August 2021. After screening, a total of 1635 cases were finally included and divided into development and validation cohorts. The least absolute shrinkage and selection operator (LASSO) regression was applied to select potential risk factors. Multivariate Cox regression analysis was used to identify independent hazard factors constructed by the predictive nomogram. Receiver operating characteristic curve analysis (ROC), calibration curve, and C-index were used to evaluate the performance of the nomogram.ResultsA total of 1227 patients were assigned to the development queue, while 408 patients were assigned to the validation queue. The constructed column line chart includes five clinical variables: age, apnea-hypopnea index (AHI), uric acid, fasting blood glucose, and TyG index. Multivariate Cox regression analysis revealed that compared to the other four risk factors, TyG index (HR=3.97, 95% CI: 2.81 - 5.62, P < 0.01) was significantly associated with RH. ROC curve analysis showed prediction values of 0.895 and 0.837 for RH in the development cohort and prediction values of 0.854 and 0.832 in the validation cohort respectively. The C-index was found to be 0.76 in the development cohort and 0.66 in the validation cohort. Furthermore, Kaplan-Meier analysis indicated that compared to the low-risk group, there was a higher likelihood of developing RH in the high-risk group.ConclusionsBased on the TyG index and electronic health record data, a model can be constructed to accurately and reliably predict the occurrence of RH in patients with stable blood pressure after initial diagnosis of primary hypertension and drug treatment.