Dataset Information

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.

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.

SUBMITTER: Lip GYH

PROVIDER: S-EPMC8339094 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Left atrial (LA) and left ventricular (LV) remodeling are associated with atrial fibrillation (AF). The prospective associations of impairment in cardiac mechanical function, as assessed by speckle-tracking echocardiography, with incident AF are less clear. In the Cardiovascular Health Study, a community-based cohort of older adults, participants free of AF with echocardiograms of adequate quality for speckle tracking were included. We evaluated the associations of indices of cardiac mechanics (LA reservoir strain, LV longitudinal strain, and LV early diastolic strain rate) with incident AF. Of 4341 participants with strain imaging, participants with lower LA reservoir strain were older, had more cardiometabolic risk factors, and had lower renal function at baseline. Over a median follow-up of 10 years, 497 (11.4%) participants developed AF. Compared with the highest quartile of LA reservoir strain, the lowest quartile of LA reservoir strain was associated with higher risk of AF after covariate adjustment, including LA volume and LV longitudinal strain (heart rate [HR], 1.80; 95% CI, 1.31-2.45; P < 0.001). The association of LA reservoir strain and AF was stronger in subgroups with higher blood pressure, NT-proBNP, and LA volumes. There were no associations of LV longitudinal strain and LV early diastolic strain rate with incident AF after adjustment for LA reservoir strain. Lower LA reservoir strain was associated with incident AF, independent of LV mechanics, and with stronger associations in high-risk subgroups. These findings suggest that LA mechanical dysfunction precedes the development of AF. Therapies targeting LA mechanical dysfunction may prevent progression to AF. This research was supported by contracts HHSN268201200036C, HHSN268200800007C, HHSN268201800001C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, and N01HC85086 and grants KL2TR001424, R01HL107577, U01HL080295, and U01HL130114 from the NIH's National Center for Advancing Translational Sciences, and National Heart, Lung, and Blood Institute (NHLBI), with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided by R01AG023629 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org.