Effects of multimorbidity on incident COVID-19 events and its interplay with COVID-19 event status on subsequent incident myocardial infarction (MI).

BACKGROUND: With the spread of COVID-19 pandemic, there have been reports on its impact on incident myocardial infarction (MI) emanating from studies with small to modest sample sizes. We therefore examined the incidence of MI in a very large population health cohort with COVID-19 using a methodology which integrates the dynamicity of prior comorbid history. We used two approaches, i.e. main effect modelling and a machine learning (ML) methodology, accounting for the complex dynamic relationships among comorbidity and other variables. METHODS: We studied a very large prospective 18-90-year US population, including 4,289,481 patients from medical databases in a 12-month investigation of those with/without newly incident COVID-19 cases together with a 2-year comorbid profile in the baseline period. Incident MI outcomes were examined in relationship to diverse multimorbid conditions, COVID-19 status and demographic variables-with ML accounting for the dynamic nature of changing multimorbidity risk factors. RESULTS: Multimorbidity, defined as a composite of cardiometabolic/noncardiometabolic comorbid profile, significantly contributed to the onset of confirmed COVID-19 cases. Furthermore, a main effect model (C-index value 0.932; 95%CI 0.930-0.934) had medium to large effect sizes with incident MI outcomes in a COVID-19 cohort for the classic multimorbid conditions in medical history profile which includes prior coronary artery disease (OR 4.61 95%CI 4.49-4.73); hypertension (OR 3.55 95%CI 3.55-3.83); congestive heart failure (2.31 95%CI 2.24-2.37); valvular disease (1.43 95%CI 1.39-1.47); stroke (1.30 95%CI 1.26-1.34); and diabetes (1.26 95%CI 1.23-1.34). COVID-19 status (1.86 95%CI 1.79-1.93) contributed an independent large size risk effect for incident MI. The ML algorithm demonstrated better discriminatory validity than the main effect model (training: C-index 0.949, 95%CI 0.948-0.95; validation: C-index 0.949, 95%CI 0.948-0.95). Calibration of the ML-based formulation was satisfactory and better than the main effect model. Decision curve analysis demonstrated that the ML clinical utility was better than the 'treat all' strategy and the main effect model. The ML logistic regression model was better than the neural network algorithm. CONCLUSION: The very large investigation conducted herein confirmed the importance of cardiometabolic and noncardiometabolic multimorbidity in increasing vulnerabilities to a higher risk of COVID-19 infections. Furthermore, the presence of COVID-19 infections increased incident MI complications both in terms of independent effects and interactions with the multimorbid profile and age.

Incident atrial fibrillation and its risk prediction in patients developing COVID-19: A machine learning based algorithm approach.

BACKGROUND: The elderly multi-morbid patient is at high risk of adverse outcomes with COVID-19 complications, and in the general population, the development of incident AF is associated with worse outcomes in such patients. There is therefore the need to identify those patients with COVID-19 who are at highest risk of developing incident AF. We therefore investigated incident AF risks in a large prospective population of elderly patients with/without incident COVID-19 cases and baseline cardiovascular/non-cardiovascular multi-morbidities. We used two approaches: main effect modeling and secondly, a machine-learning (ML) approach, accounting for the complex dynamic relationships among comorbidity variables. METHODS: We studied a prospective elderly US cohort of 280,592 patients from medical databases in an 8-month investigation of with/without newly incident COVID19 cases. Incident AF outcomes were examined in relationship to diverse multi-morbid conditions, COVID-19 status and demographic variables, with ML accounting for the dynamic nature of changing multimorbidity risk factors. RESULTS: Multi-morbidity contributed to the onset of confirmed COVID-19 cases with cognitive impairment (OR 1.69; 95%CI 1.52-1.88), anemia (OR 1.41; 95%CI 1.32-1.50), diabetes mellitus (OR 1.35; 95%CI 1.27-1.44) and vascular disease (OR 1.30; 95%CI 1.21-1.39) having the highest associations. A main effect model (C-index value 0.718) showed that COVID-19 had the highest association with incident AF cases (OR 3.12; 95%CI 2.61-3.710, followed by congestive heart failure (1.72; 95%CI 1.50-1.96), then coronary artery disease (OR 1.43; 95%CI 1.27-1.60) and valvular disease (1.42; 95%CI 1.26-1.60). The ML algorithm demonstrated improved discriminatory validity incrementally over the statistical main effect model (training: C-index 0.729, 95%CI 0.718-0.740; validation: C-index 0.704, 95%CI 0.687-0.72). Calibration of the ML based formulation was satisfactory and better than the main-effect model. Decision curve analysis demonstrated that the clinical utility for the ML based formulation was better than the 'treat all' strategy and the main effect model. CONCLUSION: COVID-19 status has major implications for incident AF in a cohort with diverse cardiovascular/non-cardiovascular multi-morbidities. Our ML approach accounting for dynamic multimorbidity changes had good prediction for new onset AF amongst incident COVID19 cases.