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Published on: September 26, 2018
Multimodal data-driven, vertical visualization prediction model for early prediction of atherosclerotic
Jian Wang1, Yanan Xu2, Jiajun Zhu3
1Department of Cardiology.
Insights
A new model predicts atherosclerotic cardiovascular disease (ASCVD) risk in Chinese patients with new-onset hypertension. This tool uses four clinical variables to improve early detection and management of ASCVD risk.
Area of Science:
- Cardiology
- Preventive Medicine
- Data Science in Healthcare
Background:
- Hypertension is a major risk factor for atherosclerotic cardiovascular disease (ASCVD).
- Metabolic disorders are frequently associated with hypertension.
- No multimodal data-driven prediction model existed for ASCVD risk in Chinese individuals with new-onset hypertension.
Purpose of the Study:
- To develop and validate a multimodal data-driven prediction model for ASCVD risk.
- To identify key clinical variables for predicting ASCVD in Chinese patients with new-onset hypertension.
- To assess the model's discriminatory power, calibration, and clinical utility.
Main Methods:
- 514 patients with new-onset hypertension were randomly assigned to training (70%) and verification (30%) cohorts.
- Boruta feature selection and multivariate Cox regression identified predictive variables.
- Model performance was evaluated using C-index, calibration curves, and decision curve analysis (DCA).
Main Results:
- The prediction model incorporated 24-h SBP coefficient of variation, 24-h DBP coefficient of variation, urea nitrogen, and the triglyceride-glucose (TyG) index.
- The model demonstrated strong predictive performance with C-indexes of 0.731 (training) and 0.799 (verification).
- Calibration plots and DCA confirmed the model's accuracy and clinical utility, outperforming general ASCVD risk assessments.
Conclusions:
- A novel longitudinal prediction model effectively estimates ASCVD risk in individuals newly diagnosed with hypertension.
- The model, based on multimodal data, offers significant prognostic utility for ASCVD.
- This tool can aid in early risk stratification and management of ASCVD in the target population.
Background:
: Hypertension is an important contributing factor to atherosclerotic cardiovascular disease (ASCVD), and multiple risk factors, many of which are implicated in metabolic disorders, contribute to the cause of hypertension. Despite the promise of multimodal data-driven prediction model, no such prediction model was available to predict the risk of ASCVD in Chinese individuals with new-onset hypertension and no history of ASCVD.
Methods:
: A total of 514 patients were randomly allocated to training and verification cohorts (ratio, 7 : 3). We employed Boruta feature selection and conducted multivariate Cox regression analyses to identify variables associated with ASCVD in these patients, which were subsequently utilized for constructing the predictive model. The performance of prediction model was assessed in terms of discriminatory power (C-index), calibration (calibration curves), and clinical utility [decision curve analysis (DCA)].
Results:
: This model was derived from four clinical variables: 24-h SBP coefficient of variation, 24-h DBP coefficient of variation, urea nitrogen and the triglyceride-glucose (TyG) index. Bootstrapping with 500 iterations was conducted to adjust the C-indexes were C-index = 0.731, 95% confidence interval (CI) 0.620-0.794 and C-index: 0.799, 95% CI 0.677-0.892 in the training and verification cohorts, respectively. Calibration plots with 500 bootstrapping iterations exhibited a strong correlation between the predicted and observed occurrences of ASCVD in both the training and verification cohorts. DCA analysis confirmed the clinical utility of this prediction model. The constructed nomogram demonstrated significant additional prognostic utility for ASCVD, as evidenced by improvements in the C-index, net reclassification improvement, integrated discrimination improvement, and DCA compared with the overall ASCVD risk assessment.
Conclusion:
The developed longitudinal prediction model based on multimodal data can effectively predict ASCVD risk in individuals with an initial diagnosis of hypertension.
Trial Registration:
: The trial was registered in the Chinese Clinical Trial Registry (ChiCTR2300074392).
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