Multimodal data-driven prognostic model for predicting new-onset ST-elevation myocardial infarction following emergency percutaneous coronary intervention

Multimodal data-driven, vertical visualization prediction model for early prediction of atherosclerotic cardiovascular disease in patients with new-onset hypertension

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).

Multimodal data integration for enhanced longitudinal prediction for cardiac and cerebrovascular events following initial diagnosis of obstructive sleep apnea syndrome

Background

Obstructive sleep apnea syndrome (OSAS), a prevalent condition, often coexists with intricate metabolic issues and is frequently associated with negative cardiovascular outcomes. We developed a longitudinal prediction model integrating multimodal data for cardiovascular risk stratification of patients with an initial diagnosis of OSAS.

Methods

We reviewed the data of patients with new-onset OSAS who underwent diagnostic polysomnography between 2018-19. Patients were treated using standard treatment regimens according to clinical practice guidelines.

Results

Over a median follow-up of 32 months, 98/729 participants (13.4%) experienced our composite outcome. At a ratio of 7:3, cases were randomly divided into development (n = 510) and validation (n = 219) cohorts. A prediction nomogram was created using six clinical factors - sex, age, diabetes mellitus, history of coronary artery disease, triglyceride-glucose index, and apnea-hypopnea index. The prediction nomogram showed excellent discriminatory power, based on Harrell's C-index values of 0.826 (95% confidence interval (CI) = 0.779-0.873) for the development cohort and 0.877 (95% CI = 0.824-0.93) for the validation cohort. Moreover, comparing the predicted and observed major adverse cardiac and cerebrovascular events in both development and validation cohorts indicated that the prediction nomogram was well-calibrated. Decision curve analysis demonstrated the good clinical applicability of the prediction nomogram.

Conclusions

Our findings demonstrated the construction of an innovative visualisation tool that utilises various types of data to predict poor outcomes in Chinese patients diagnosed with OSAS, providing accurate and personalised therapy.

Registration

Chinese Clinical Trial Registry ChiCTR2300075727.

Multimodal Prognostic Model for Predicting Chronic Coronary Artery Disease in Patients Without Obstructive Sleep Apnea Syndrome

BACKGROUND

Obstructive sleep apnea syndrome (OSAS), with metabolic disorders as a central feature, is closely correlated with coronary artery disease (CAD). Our goal was to develop a prediction nomogram that integrated multimodal data that could accurately predict the prognosis of patients with chronic coronary disease (CCD).

METHODS

We evaluated 393 patients with CCD with a low-to-intermediate pretest probability of OSAS based on polysomnography. A nomogram was constructed by means of least absolute shrinkage and selection operator (LASSO) and multiple Cox regression analyses to identify independent risk factors for major adverse cardiovascular events (MACEs).

RESULTS

Two hundred seventy-seven patients were randomly assigned to the training set, and 116 to the verification set. The constructed nomogram consisted of seven clinical variables: age, previous CAD, current alcohol consumption, neck circumference, apnea-hypopnea index (AHI), and triglyceride-glucose index (TyG). The nomogram showed good discriminatory power, as evidenced by Harrell's C-index values of 0.79 (95% confidence interval [CI] 0.731-0.849) in the training set and 0.78 (95% CI 0.678-0.882) in the verification set. Moreover, a high correlation was observed between the predicted and actual incidence of MACEs in both the training and verification sets. Decision curve analysis demonstrated excellent clinical utility of the nomogram based on net benefit and threshold probabilities.

CONCLUSIONS

We developed an integrated visualized prognostic nomogram that utilizes multi-modal data, including clinical characteristics, AHI, and TyG index, to predict MACEs in patients with CCD. This approach demonstrated excellent performance, highlighting the potential of combining different data sources to enhance prediction accuracy.