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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. [ABSTRACT FROM AUTHOR]

Background: Ischemic heart disease (IHD) has a high mortality in the population. Although serum creatinine (Cr) and serum total bilirubin (TBil) are rapid and readily available biomarkers in routine blood tests, there is a lack of literature on the prognostic value of combined Cr and TBil tests for IHD. This study aimed to evaluate a combined equation based on Cr and TBil to predict the long-term risk of death in IHD and to find indicators sensitive to the prognosis of IHD patients. Method: In this study, 2625 patients with IHD were included, and the combined value and combined equations of Cr and TBil were obtained by logistic regression analysis based on Cr and TBil collected at the time of admission. Patients were divided into four groups according to the quartiles of the combined value. COX proportional hazard regression model was used to analyze the risk factors for long-term death in IHD patients. Receiver operating characteristic (ROC) curves were used to evaluate the prognostic effect of Cr, TBil and combined value on long-term death events. Results: Logistic regression analysis was performed for long-term death events with Cr and TBil as independent variables, and the logit regression model was Logit(P) = 0.0129×TBil+0.007×Cr-0.417. Multifactorial Cox regression analysis showed that high values of the equation were independent risk factors for long-term death events (all-cause death: HR 1.457, 95% CI 1.256–1.689, P<0.001; cardiovascular death: HR 1.452, 95% CI 1.244–1.695, P<0.001). Combined Cr and TBil value are more valuable in predicting long-term death (AUC: 0.609, 95% CI 0.587–0.630, P<0.001). Conclusion: Combined Cr and TBil assay is superior to single biomarkers for predicting long-term death in patients with IHD. High values of the equation are independent predictors of long-term death and can be used to identify patients at high risk for IHD. [ABSTRACT FROM AUTHOR]

Background: The triglyceride-glucose (TyG) index is regarded as an independent predictor of cardiovascular (CV) consequences and a reliable surrogate measure of insulin resistance (IR). However, the predictive significance of the TyG index in patients with type 2 diabetes mellitus (T2DM) and ischemic cardiomyopathy (ICM) remains unknown. Methods: This study included 1514 consecutive subjects with ICM and T2DM. The tertile of the TyG index values was used to categorize these patients into three groups. Major adverse cardiac and cerebral events (MACCEs) were also noted. The TyG index was calculated using the [fasting triglycerides (mg/dL) × fasting plasma glucose (mg/dL)/2] equation. Results: After adjusting for age, BMI, and other potential confounders, the scores of multivariate Cox proportional hazards regression models for chest pain [9.056 (4.370 to 18.767), p< 0.001], acute myocardial infarction [4.437 (1.420 to 13.869), p=0.010], heart failure [7.334 (3.424 to 15.708), p< 0.001], cardiogenic shock [3.707 (1.207 to 11.384), p=0.022], malignant arrhythmia [5.309 (2.367 to 11.908), p< 0.001], cerebral infarction [3.127 (1.596 to 6.128), p< 0.001], gastrointestinal bleeding [4.326 (1.612 to 11.613), p=0.004], all-cause death [4.502 (3.478 to 5.827), p< 0.001] and cumulative incidence of MACCEs [4.856 (3.842 to 6.136), p< 0.001] increased significantly with an increase in TyG index levels (all p< 0.05). Time-dependent ROC analysis revealed that the area under the TyG index curve (AUC) reached 0.653 in the 3rd year, 0.688 in the 5th year, and 0.764 in the 10th year. The predictive efficiency of this model on MACCEs improved [net reclassification improvement (NRI): 0.361 (0.253 to 0.454); C-index: 0.678 (0.658 to 0.698); integrated discrimination improvement (IDI): 0.138 (0.098 to 0.175), all p< 0.05] following the incorporation of the TyG index into the base risk model. Conclusion: TyG index could be useful in predicting MACCEs and initiating preventive measures in subjects with ICM and T2DM. [ABSTRACT FROM AUTHOR]