Cardiovascular disease (CVD) outcomes and associated risk factors in a medicare population without prior CVD history: an analysis using statistical and machine learning algorithms.

There is limited information on predicting incident cardiovascular outcomes among high- to very high-risk populations such as the elderly (≥ 65 years) in the absence of prior cardiovascular disease and the presence of non-cardiovascular multi-morbidity. We hypothesized that statistical/machine learning modeling can improve risk prediction, thus helping inform care management strategies. We defined a population from the Medicare health plan, a US government-funded program mostly for the elderly and varied levels of non-cardiovascular multi-morbidity.

Transient ischemic attack events and incident cardiovascular and non-cardiovascular complications: Observations from a large diversified multimorbid cohort.

Background: Transient ischemic attack (TIA) is a strong signal prompting the incidence of future cardiovascular and non-cardiovascular complications, in light of recent debate on the so-called "stroke-heart syndrome." We aimed to investigate the relation of TIAs to incident clinical events.

Methods: Patients were drawn from three health plans with a wide spectrum of age groups and a wide mix of socio-economic/disability status.

Medication non-adherence patterns and profiles for patients with incident myocardial infarction: Observations from a large multi-morbid US population.

Background: Consistent adherence levels to multiple long-term medications for patients with cardiovascular conditions are typically advocated in the range of 50% or higher, although very likely to be much lower in some populations. We investigated this issue in a large cohort covering a broad age and geographical spectrum, with a wide range of socio-economic disability status.

Methods: The patients were drawn from three different health plans with a varied mix of socio-economic/disability levels.

Adherence levels and patterns for multiple cardiac medications prescribed to patients with incident atrial fibrillation events.

Aims: Using advanced longitudinal analyses, this real-world investigation examined medication adherence levels and patterns for incident atrial fibrillation (AF) patients with significant cardiovascular and noncardiovascular multimorbid conditions for each of 5 medication classes (β-blockers, calcium channel blockers/digoxin, antiarrhythmics, anticoagulants, antiplatelets). The population was derived from a large cohort covering a wide age spectrum/diversified US geographical areas/wide range of socioeconomic-disability status.

Methods: The patients were drawn from 3 different health plans.

Incidence and Complications of Atrial Fibrillation in a Low Socioeconomic and High Disability United States (US) Population: A Combined Statistical and Machine Learning Approach.

Background: Poor socioeconomic status coupled with individual disability is significantly associated with incident atrial fibrillation (AF) and AF-related adverse outcomes, with the information currently lacking for US cohorts. We examined AF incidence/complications and the dynamic nature of associated risk factors in a large socially disadvantaged US population.

Methods: A large population representing a combined poor socioeconomic status/disability (Medicaid program) was examined from diverse geographical regions across the US continent.

Incident and recurrent myocardial infarction (MI) in relation to comorbidities: Prediction of outcomes using machine-learning algorithms.

Background: To date, incident and recurrent MI remains a major health issue worldwide, and efforts to improve risk prediction in population health studies are needed. This may help the scalability of prevention strategies and management in terms of healthcare cost savings and improved quality of care.

Methods: We studied a large-scale population of 4.

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.

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.

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.

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.

Improving dynamic stroke risk prediction in non-anticoagulated patients with and without atrial fibrillation: comparing common clinical risk scores and machine learning algorithms.

Aims: Diversified cardiovascular/non-cardiovascular multi-morbid risk and efficient machine learning algorithms may facilitate improvements in stroke risk prediction, especially in newly diagnosed non-anticoagulated atrial fibrillation (AF) patients where initial decision-making on stroke prevention is needed. Therefore the aims of this article are to study common clinical risk assessment for stroke risk prediction in AF/non-AF cohorts together with cardiovascular/ non-cardiovascular multi-morbid conditions; to improve stroke risk prediction using machine learning approaches; and to compare the improved clinical prediction rules for multi-morbid conditions using machine learning algorithms.

Methods And Results: We used cohort data from two health plans with 6 457 412 males/females contributing 14,188,679 person-years of data.

Improving Stroke Risk Prediction in the General Population: A Comparative Assessment of Common Clinical Rules, a New Multimorbid Index, and Machine-Learning-Based Algorithms.

Background: There are few large studies examining and predicting the diversified cardiovascular/noncardiovascular comorbidity relationships with stroke. We investigated stroke risks in a very large prospective cohort of patients with multimorbidity, using two common clinical rules, a clinical multimorbid index and a machine-learning (ML) approach, accounting for the complex relationships among variables, including the dynamic nature of changing risk factors.

Methods: We studied a prospective U.

Prevalence/incidence of atrial fibrillation based on integrated medical/pharmacy claims, and association with co-morbidity profiles/multi-morbidity in a large US adult cohort.

Background: Identification of published data on prevalent/incidence of atrial fibrillation/flutter (AF) often relies on inpatient/outpatient claims, without consideration to other types of healthcare services and pharmacy claims. Accurate, population-level data that can enable the ongoing monitoring of AF epidemiology, quality of care at affordable cost, and complications are needed. We hypothesised that prevalent/incidence data would vary via the use of integrated medical/pharmacy claims, and associated comorbidities would vary accordingly.