Development and validation of a nomogram for predicting all-cause mortality in American adult hypertensive populations

Development and validation of a nomogram for arthritis: a cross-sectional study based on the NHANES

Previous epidemiological studies have associated various body-related indicators with arthritis; however, the results have been inconclusive. Therefore, this research aimed to develop and validate a nomogram model for predicting the risk of arthritis using easily available indicators and to assess the model's predictive performance. Cross-sectional data were collected from 3660 participants in the 2021-2023 National Health and Nutrition Examination Survey. The research conducted variable selection and model development using the Least Absolute Shrinkage and Selection Operator regression model and multivariate logistic regression analysis, and the performance of the nomogram was validated. The nomogram model incorporated nine independent predictors: age, sex, family poverty-income ratio, race, diabetes status, vitamin D level, systemic immunity-inflammation index, and waist-to-height ratio. After validation, it has been proven that the nomogram model has good performance. The nomogram model developed in this study effectively predicts the risk probability of arthritis in the general population of the United States. All variables included in this nomogram can be easily obtained from the population.

Construction of a nomogram for predicting the risk of all-cause mortality in patients with diabetic retinopathy

Background

Diabetic retinopathy (DR) not only leads to visual impairment but also increases the risk of death in type 2 diabetes patients. This study aimed to construct a nomogram to assess the risk of all-cause mortality in patients with DR.

Methods

This cross-sectional study included 1004 patients from the National Health and Nutrition Examination Survey database (NHANES) between 1999-2018. Participants were randomized in a 7:3 ratio into a training set and a test set. We selected predictors by LASSO regression and multifactorial Cox proportional risk regression analysis and constructed nomograms, guided by established clinical guidelines and expert consensus as the gold standard. We used the concordance index (C-index), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) to evaluate the nomogram's discriminative power, calibration quality, and clinical use.

Results

The training and test sets consisted of 703 and 301 participants with a median age of 64 and 63 years, respectively. The study identified seven predictors, including age, marital status, congestive heart failure (CHF), coronary heart disease (CHD), stroke, creatinine level, and taking insulin. The C-index of the nomogram model constructed from the training set was 0.738 (95% CI: 0.704-0.771), while the C-index of the test set was 0.716 (95% CI: 0.663-0.768). In the training set, the model's AUC values for predicting all-cause mortality risk at 3 years, 5 years, and 10 years were 0.739, 0.765, and 0.808, respectively. In the test set, these AUC values were 0.737, 0.717, and 0.732, respectively. The ROC curve, calibration curve, and DCA curve all demonstrated excellent predictive performance, confirming the model's effectiveness and reliability in clinical applications.

Conclusions

Our nomogram demonstrates high clinical predictive accuracy, enabling clinicians to effectively predict the overall mortality risk in patients with DR, thereby significantly improving their prognosis.

The association of lifestyle with cardiovascular and all-cause mortality based on machine learning: a prospective study from the NHANES

BACKGROUND

Lifestyle and cardiovascular mortality and all-cause mortality have been exhaustively explored by traditional methods, but the advantages of machine learning (ML) over traditional methods may lead to different or more precise conclusions. The aim of this study was to evaluate the effectiveness of machine learning-based lifestyle factors in predicting cardiovascular and all-cause mortality and compare the results obtained by traditional methods.

METHOD

A prospective cohort study was conducted using a nationally representative sample of adults aged 40 years or older, drawn from the US National Health and Nutrition Examination Survey from 2007 to 2010. The participants underwent a comprehensive in-person interview and medical laboratory examinations, and subsequently, their records were linked with the National Death Index for further analysis. Extreme gradient enhancement, random forest, support vector machine and other machine learning methods are used to build the prediction model.

RESULT

Within a cohort comprising 7921 participants, spanning an average follow-up duration of 9.75 years, a total of 1911 deaths, including 585 cardiovascular-related deaths, were recorded. The model predicted mortality with an area under the receiver operating characteristic curve (AUC) of 0.862 and 0.836. Stratifying participants into distinct risk groups based on ML scores proved effective. All lifestyle behaviors were associated with a reduced risk of all-cause and cardiovascular mortality. As age increases, the effects of dietary scores and sedentary time on mortality risk become more pronounced, while the influence of physical activity tends to diminish.

CONCLUSION

We develop a ML model based on lifestyle behaviors to predict all-cause and cardiovascular mortality. The developed model offers valuable insights for the assessment of individual lifestyle-related risks. It applies to individuals, healthcare professionals, and policymakers to make informed decisions.

Development and validation of a nomogram model for predicting the risk of hypertension in Bangladesh

Background and objectives

Hypertension (HTN) is a leading cause of non-communicable disease in low- and middle-income countries, including Bangladesh. Thus, the objectives of this study were to investigate the associated risk factors for HTN and develop with validate a monogram model for predicting an individual's risk of HTN in Bangladesh.

Materials and methods

This study exploited the latest nationally representative cross-sectional BDHS, 2017-18 data, which consisted of 6569 participants. LASSO and logistic regression (LR) analysis were performed to reduce dimensionality of data, identify the associated risk factors, and develop a nomogram model for predicting HTN risk in the training cohort. The discrimination ability, calibration, and clinical effectiveness of the developed model were evaluated using validation cohort in terms of area under the curve (AUC), calibration plot, decision curve analysis, and clinical impact curve analysis.

Results

The combined results of the LASSO and LR analysis demonstrated that age, sex, division, physical activity, family member, smoking, body mass index, and diabetes were the associated risk factors of HTN. The nomogram model achieved good discrimination ability with AUC of 0.729 (95 % CI: 0.685-0.741) for training and AUC of 0.715 (95 % CI: 0.681-0.729)] for validation cohort and showed strong calibration effects, with good agreement between the actual and predicted probabilities (p-value = 0.231).

Conclusion

The proposed nomogram provided a good predictive performance and can be effectively utilized in clinical settings to accurately diagnose hypertensive patients who are at risk of developing severe HTN at an early stage in Bangladesh.

Development and validation of a risk score nomogram model to predict the risk of 5-year all-cause mortality in diabetic patients with hypertension: A study based on NHANES data

Background

The present study aimed to develop and validate a prediction nomogram model for 5-year all-cause mortality in diabetic patients with hypertension.

Methods

Data were extracted from the National Health and Nutrition Examination Survey (NHANES). A total of 3291 diabetic patients with hypertension in the NHANES cycles for 1999-2014 were selected and randomly assigned at a ratio of 8:2 to the training cohort (n = 2633) and validation cohort (n = 658). Multivariable Cox regression was conducted to establish a visual nomogram model for predicting the risk of 5-year all-cause mortality. Receiver operating characteristic curves and C-indexes were used to evaluate the discriminant ability of the prediction nomogram model for all-cause mortality. Survival curves were created using the Kaplan-Meier method and compared by the log-rank test.

Results

The nomogram model included eight independent predictors: age, sex, education status, marital status, smoking, serum albumin, blood urea nitrogen, and previous cardiovascular disease. The C-indexes for the model in the training and validation cohorts were 0.76 (95% confidence interval: 0.73-0.79, p < 0.001) and 0.75 (95% confidence interval: 0.69-0.81, p < 0.001), respectively. The calibration curves indicated that the model had satisfactory consistency in the two cohorts. The risk of all-cause mortality gradually increased as the tertiles of the nomogram model score increased (log-rank test, p < 0.001).

Conclusion

The newly developed nomogram model, a readily useable and efficient tool to predict the risk of 5-year all-cause mortality in diabetic patients with hypertension, provides a novel risk stratification method for individualized intervention.