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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of a nomogram for predicting all-cause mortality in American adult hypertensive
Long Yang1, Xia Shen2, Zulihuma Seyiti1
1State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, College of Pediatrics, Xinjiang Medical University, Clinical Medical Research Institute of Xinjiang Medical University, Urumqi, China.
Insights
This study developed a nomogram to predict mortality in hypertensive adults. The model uses key health indicators to forecast all-cause mortality risk, aiding clinical decisions.
Area of Science:
- Cardiology
- Public Health
- Biostatistics
Background:
- Hypertension is a leading global cause of mortality.
- There is a lack of effective predictive models for mortality in hypertensive individuals.
- Accurate mortality prediction is crucial for timely intervention and improved patient outcomes.
Purpose of the Study:
- To develop and validate a nomogram model for predicting all-cause mortality in hypertensive populations.
- To identify key clinical indicators associated with mortality risk in hypertension.
- To provide clinicians with a tool for assessing prognosis in hypertensive patients.
Main Methods:
- Utilized data from nine cycles of the National Health and Nutrition Examination Survey (NHANES, 1999-2016).
- Applied Least Absolute Shrinkage and Selection Operator (LASSO) regression to select significant predictors.
- Constructed and validated a nomogram using training (70%) and validation (30%) datasets, assessing performance with concordance index, ROC, calibration, and DCA.
Main Results:
- Included 17,125 hypertensive participants (11,993 training, 5,132 validation).
- Identified nine key predictors: age, monocytes, neutrophils, serum albumin, serum potassium, cardiovascular disease, diabetes, serum creatinine, and HbA1C.
- Achieved high predictive accuracy with concordance indices of 0.800 (training) and 0.793 (validation), supported by ROC, calibration, and DCA.
Conclusions:
- Developed a robust nomogram for predicting all-cause mortality risk in American adults with hypertension.
- The nomogram demonstrates good predictive performance and clinical utility.
- Facilitates timely prognosis assessment and intervention planning for hypertensive patients.
Abstract:
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.
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