To explore the potential of machine learning models in cardiovascular disease prediction for patients with type 2 diabetes, emphasizing the need to address challenges of clinical translation and equity.
Key Findings:
Machine learning models demonstrate superior discriminative performance in internal validations, with implications for clinical practice.
Current models are predominantly developed using populations from Europe and North America, lacking representativeness for Asian populations, which is critical for effective clinical application.
A systematic review highlighted high risk of bias and poor adherence to transparent reporting standards in existing models, necessitating improvements.
Interpretation:
The future of cardiovascular risk prediction in diabetic patients should prioritize equitable clinical implementation, focusing on practical strategies to address the limitations of existing machine learning models.
Limitations:
High prevalence of risk of bias in participant selection.
Inadequate independent external validation restricts model generalizability.
Current models may not be clinically useful across diverse populations, underscoring the need for studies that include varied demographic groups.
Conclusion:
Advancements in machine learning should focus on external validation, calibration-aware assessment, and subgroup-specific performance reporting, while also addressing bias in model development to bridge the gap between innovation and equitable clinical application.
