Revisiting the dynamic risk profile of cardiovascular/non‐cardiovascular multimorbidity in incident atrial fibrillation patients and five cardiovascular/non‐cardiovascular outcomes: A machine‐learning approach

Journal of Arrhythmia

Ash Genaidy¹  Cara Estes¹  Patricia Marroquin¹  George Tran²  Gregory Y. H. Lip³ 

[1] Anthem Inc Indianapolis IN USA;IngenioRX Indianapolis IN USA;Liverpool Centre for Cardiovascular Science University of Liverpool and Liverpool Heart & Chest Hospital Liverpool UK;

关键词: atrial fibrillation;    cognitive impairment;    congestive heart failure;    machine learning;    major bleeding;    myocardial infarction;   

DOI  :  10.1002/joa3.12555

来源: DOAJ

【 摘 要 】

Abstract Background Patients with atrial fibrillation (AF) usually have a heterogeneous co‐morbid history, with dynamic changes in risk factors impacting on multiple adverse outcomes. We investigated a large prospective cohort of patients with multimorbidity, using a machine‐learning approach, accounting for the dynamic nature of comorbidity risks and incident AF. Methods Using machine‐learning, we studied a prospective US cohort using medical/pharmacy databases of 1 091 911 patients, with an incident AF cohort of 14 078 and non‐AF cohort of 1 077 833 enrolled in the 4‐year study. Five incident clinical outcomes (heart failure, stroke, myocardial infarction, major bleeding, and cognitive impairment) were examined in relationship to AF status (AF vs non‐AF), diverse multi‐morbid (conditions and medications) history, and demographic parameters (age and gender), with supervised machine‐learning techniques. Results Complex inter‐relationships of various comorbidities were uncovered for AF cases, leading to 6‐fold higher risk of heart failure relative to the non‐AF cohort (OR 6.02, 95% CI 5.72‐6.33), followed by myocardial infarction (OR=2.68), stroke (OR=2.19), and major bleeding (OR=1.36). Supervised machine learning algorithms on the original populations yielded comparable results for both neural network and logistic regression algorithms in terms of discriminant validity, with c‐indexes for incident adverse outcomes: heart failure (0.924, 95%CI 0.923‐0.925), stroke (0.871, 95%CI 0.869‐0.873), myocardial infarction (0.901, 95% CI 0.899‐0.903), major bleeding (0.700, 95%CI 0.697‐0.703), and cognitive impairment (0.919, 95% CI 0.9170.921). External calibration of all models demonstrated a good fit between the predicted probabilities and observed events. Decision curve analysis demonstrated that the obtained models were much more clinically useful than the “treat all” strategy. Conclusions Complex multimorbidity relationships uncovered using a machine learning approach for incident AF cases have major consequences for integrated care management, with implications for risk stratification and adverse clinical outcomes. This approach may facilitate automated approaches in the presence of multimorbidity, potentially helping decision making.

【 授权许可】

Unknown