Search

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. Participants were screened for cardiovascular disease (CVD), coronary or peripheral artery disease (CAD or PAD), heart failure (HF), atrial fibrillation (AF), ischemic stroke (IS), transient ischemic attack (TIA), and myocardial infarction (MI) for a 3-yr period in the comorbid history. They were followed up for up to 45.2 months. Analyses included descriptive approaches in terms of incidence rates and density ratios, and inferential in terms of main effect statistical/complex machine learning modeling. The contemporary risk factors of interest spanned across the domains of comorbidity, lifestyle, and healthcare utilization history. The cohort consisted of 154,551 individuals (mean age 68.8 years; 62.2% female). The overall crude incidence rate of CVD events was 9.9 new cases per 100 person-years. The highest rates among its component outcomes were obtained for CAD or PAD (3.6 for each), followed by HF (2.2) and AF (1.8), then IS (1.3), and finally TIA (1.0) and MI (0.9).Model performance was modest in terms of discriminatory power (C index: 0.67, 95%CI 0.667-0.674 for training; and 0.668, 95%CI 0.663-0.673 for validation data), equal agreement between predicted and observed events for calibration purposes, and good clinical utility in terms of a net benefit of 15 true positives per 100 patients relative to the All-patient treatment strategy. Complex models based on machine learning algorithms yielded incrementally better discriminatory power and much improved goodness-of-fitness tests from those based on main effect statistical modeling. This Medicare population represents a highly vulnerable group for incident CVD events. This population would benefit from an integrated approach to their care and management, including attention to their comorbidities and lifestyle factors, as well as medication adherence., (© 2023. The Author(s), under exclusive licence to Società Italiana di Medicina Interna (SIMI).)

This study aims to determine the frequency and impact of concomitant cerebral infarction and acute myocardial infarction (AMI) and association with in-hospital outcomes. We analyzed the nationally representative data from National Inpatient Sample between 2016 and 2019. We used multiple logistic regressions to determine the impact of sustaining a cerebral infarction or AMI on in-hospital mortality and linear regression to evaluate length of stay and hospitalization costs. A total of 2,396,450 admissions for cerebral infarction, 4,098,904 admissions for AMI and 130,635 admissions for both conditions were identified. The in-hospital mortality was 7.2% overall: 5.6% for patients with cerebral infarction, 7.7% for patients with AMI and 19.9% for patients with both conditions (p < 0.001). The median length of stay was 4 days, 3 days and 7 days for cerebral infarction, AMI and when both occurred together, respectively (p < 0.001). The median total healthcare cost for cerebral infarction, AMI and when both occurred was US$10,647, $15,735 and $23,290, respectively (p < 0.001). After adjustments for potential confounders, in-hospital mortality [adjusted odds ratio (aOR) 4.07, 95% CI 3.93-4.21], length of stay (aOR 3.95 95% CI 3.85-4.04), and hospitalization cost (regression coefficient 15,480 95% CI 15,135-15,825) were greater for patients with AMI and cerebral infarction compared to admission for cerebral infarction only. Cerebral infarction and AMI during the same hospitalization occurs in 5% of admissions with cerebral infarction and 3% of admission with AMI and is associated with significant increase in mortality, length of stay and cost compared to admissions with either condition alone., (© 2023. The Author(s), under exclusive licence to Società Italiana di Medicina Interna (SIMI).)