Henry Ford Allegiance Health is a 475-bed health system in Jackson in the U.S. state of Michigan.Henry Ford Allegiance Health has more than 400 physicians and 3,700 staff members. The health system offers specialized services including a new cancer center and a heart center with a 20-bed cardiac universal bed unit. A new specialty center combines services for the convenience of patients with brain, spine, neck, joint and bone conditions. The specialty center also includes a new osteoporosis center, balance center and pain management center as well. Other health system features include an emergency department designated as a level II trauma center, urgent care centers, and a pediatric and birthing care.Henry Ford Allegiance Health operates the Henry Ford Allegiance Specialty Hospital (formerly CareLink of Jackson) to provide long-term acute care. The Health System also provides care at family medical centers in Albion, Brooklyn, Grass Lake, Jackson, Leslie, Mason, Onsted and Spring Arbor. Henry Ford Allegiance Radiation Oncology offers cancer care as part of the Henry Ford Cancer Institute..
Background Heart failure (HF) frequently leads to hospital readmissions, especially in mild exacerbation HF (MEHF) patients. This study identifies 90-day readmission risk factors and develops a predictive model. Methods We retrospectively analyzed data from MEHF patients discharged from the Henry Ford Jackson Hospital ED and referred to the HF clinic. Demographic factors, medical history, and clinical markers were collected to build a predictive model using linear and lasso regression. A point-based scoring system was created based on lasso coefficients: strong predictors (≥0.4) received 2 points, moderate (≥0.1) 1 point, and weak predictors (<0.1) 0 points. Scores were normalized (0-100) to represent 90-day readmission probability. A web-based application was developed for real-time risk calculation, and the model underwent internal validation, including AUC analysis, using R version 4.4.1. Results The study included 98 patients (50 male, 48 female; mean age 69.3 ± 13.8). The cohort was 79% white, 17% black, and 2% other. Of 47 patients followed in the HF clinic, 55.3% were readmitted, while 74% of 50 without follow-up were readmitted. Seventeen baseline characteristics and adherence to four GDMT medications were analyzed. The predictive model identified comorbidities (e.g., chronic kidney disease, prior myocardial infarction), clinical history, and medications as key risk factors. The model was integrated into a mobile app for clinicians. Internal validation showed an AUC/c-statistic of 0.77. Conclusions This study developed a predictive model for 90-day readmission risk in low-risk HF patients discharged from the ED. The AI-powered risk calculator app helps clinicians identify high-risk patients for better management and outcomes. However, due to the small sample size and lack of external validation, the model’s generalizability requires further testing.
