Dataset Information

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.

ABSTRACT:

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.

SUBMITTER: You H

PROVIDER: S-EPMC10992723 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature

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Publications

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.

You Hongzhao H Zhang Dingyue D Liu Yilu Y Zhao Yanyan Y Xiao Ying Y Li Xiaojue X You Shijie S Wang Tianjie T Tian Tao T Xu Haobo H Zhang Rui R Liu Dong D Li Jing J Yuan Jiansong J Yang Weixian W

International journal of cardiology. Cardiovascular risk and prevention 20240327

<h4>Background</h4>The present study aimed to develop and validate a prediction nomogram model for 5-year all-cause mortality in diabetic patients with hypertension.<h4>Methods</h4>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 regre ...[more]

PMID: 38577011

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Project description:AimTo develop a personalized nomogram and risk score to predict the 5-year risk of diabetes among Chinese adults with prediabetes.MethodsThere were 26 018 participants with prediabetes at baseline in this retrospective cohort study. We randomly stratified participants into two cohorts for training (n = 12 947) and validation (n = 13 071). The least absolute shrinkage and selection operator (LASSO) model was applied to select the most significant variables among candidate variables. And we further established a stepwise Cox proportional hazards model to screen out the risk factors based on the predictors chosen by the LASSO model. We presented the model with a nomogram. The model's discrimination, clinical use and calibration were assessed using the area under the receiver operating characteristic (ROC) curve, decision curve and calibration analysis. The associated risk factors were also categorized according to clinical cut-points or tertials to create the diabetes risk score model. Based on the total score, we divided it into four risk categories: low, middle, high and extremely high. We also evaluated our diabetes risk score model's performance.ResultsWe developed a simple nomogram and risk score that predicts the risk of prediabetes by using the variables age, triglyceride, fasting blood glucose, body mass index, alanine aminotransferase, high-density lipoprotein cholesterol and family history of diabetes. The area under the ROC curve of the nomogram was 0.8146 (95% CI 0.8035-0.8258) and 0.8147 (95% CI 0.8035-0.8259) for the training and validation cohort, respectively. The calibration curve showed a perfect fit between predicted and observed diabetes risks at 5 years. Decision curve analysis presented the clinical use of the nomogram, and there was a wide range of alternative threshold probability spectrums. A total risk score of 0 to 2.5, 3 to 4.5, 5 to 7.5 and 8 to 13.5 is associated with low, middle, high and extremely high diabetes risk status, respectively.ConclusionsWe developed and validated a personalized prediction nomogram and risk score for 5-year diabetes risk among Chinese adults with prediabetes, identifying individuals at a high risk of developing diabetes. Doctors and other healthcare professionals can easily and quickly use our diabetes score model to assess the diabetes risk status in patients with prediabetes. In addition, the nomogram model and risk score we developed need to be validated in a prospective cohort study.

Project description:BackgroundPrimary hypertension (PH) poses significant risks to children and adolescents. Few prediction models for the risk of PH in children and adolescents currently exist, posing a challenge for doctors in making informed clinical decisions.ObjectiveThis study aimed to investigate the incidence and risk factors of PH in Chinese children and adolescents. It also aimed to establish and validate a nomogram-based model for predicting the next year's PH risk.MethodsA training cohort (n=3938, between January 1, 2008, and December 31, 2020) and a validation cohort (n=1269, between January 1, 2021, and July 1, 2023) were established for model training and validation. An independent cohort of 576 individuals was established for external validation of the model. The result of the least absolute shrinkage and selection operator regression technique was used to select the optimal predictive features, and multivariate logistic regression to construct the nomogram. The performance of the nomogram underwent assessment and validation through the area under the receiver operating characteristic curve, concordance index, calibration curves, decision curve analysis, clinical impact curves, and sensitivity analysis.ResultsThe PH risk factors that we have ultimately identified include gender (odds ratio [OR] 3.34, 95% CI 2.88 to 3.86; P<.001), age (OR 1.11, 95% CI 1.08 to 1.14; P<.001), family history of hypertension (OR 42.74, 95% CI 23.07 to 79.19; P<.001), fasting blood glucose (OR 6.07, 95% CI 4.74 to 7.78; P<.001), low-density lipoprotein cholesterol (OR 2.03, 95% CI 1.60 to 2.57; P<.001), and uric acid (OR 1.01, 95% CI 1.01 to 1.01; P<.001), while factor breastfeeding (OR 0.04, 95% CI 0.03 to 0.05; P<.001) has been identified as a protective factor. Subsequently, a nomogram has been constructed incorporating these factors. Areas under the receiver operating characteristic curves of the nomogram were 0.892 in the training cohort, 0.808 in the validation cohort, and 0.790 in the external validation cohort. Concordance indexes of the nomogram were 0.892 in the training cohort, 0.808 in the validation cohort, and 0.790 in the external validation cohort. The nomogram has been proven to have good clinical benefits and stability in calibration curves, decision curve analysis, clinical impact curves, and sensitivity analysis. Finally, we observed noteworthy differences in uric acid levels and family history of hypertension among various subgroups, demonstrating a high correlation with PH. Moreover, the web-based calculator of the nomogram was built online.ConclusionsWe have developed and validated a stable and reliable nomogram that can accurately predict PH risk within the next year among children and adolescents in primary care and offer effective and cost-efficient support for clinical decisions for the risk prediction of PH.

Dataset Information

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

Methods

Results

Conclusion

Publications

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.

Similar Datasets

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