Development and validation of a nomogram for predicting all-cause mortality in American adult hypertensive populations - Yang, Long; Shen, Xia; Seyiti, Zulihuma; Tang, Jing; Kasimujiang, Abudushalamu; Dejite, Tuohutasheng; Zhao, Ling; Shan, Xue-Feng; Gao, Xiao-Ming (Frontiers in Pharmacology, 2023)
Backgrounds: Hypertension stands as the predominant global cause of mortality. A notable deficiency exists in terms of predictive models for mortality among individuals with hypertension. We aim to devise an effective nomogram model that possesses the capability to forecast all-cause mortality within hypertensive populations. Methods: The data for this study were drawn from nine successive cycles of the National Health and Nutrition Examination Survey (NHANES) spanning the years from 1999 to 2016. The dataset was partitioned into training and validation sets at a 7:3 ratio. We opted for clinical practice-relevant indicators, applied the least absolute shrinkage and selection operator (LASSO) regression to identify the most pertinent variables, and subsequently built a nomogram model. We also employed concordance index, receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA) to assess the model's validity, Results: A total of 17,125 hypertensive participants were included in this study with a division into a training set (11,993 individuals) and a validation set (5,132 individuals). LASSO regression was applied for the training set to obtain nine variables including age, monocytes, neutrophils, serum albumin, serum potassium, cardiovascular disease, diabetes, serum creatinine and glycated hemoglobin (HbA1C), and constructed a nomogram prediction model. To validate this model, data from the training and validation sets were used for validation separately. The concordance index of the nomogram model was 0.800 (95% CI, 0.792-0.808, P<0.001) based on the training set and 0.793 (95% CI, 0.781-0.805, P<0.001) based on the validation set. The ROC curves, calibration curves, and DCA curves all showed good predictive performance. Conclusion: We have developed a nomogram that effectively forecasts the risk of all-cause mortality among American adults in hypertensive populations. Clinicians may use this nomogram to assess patient’s prognosis and choose a proper intervention in a timely manner.