ACG risk stratification population-based high-risk heart failure patients’ identification

Introduction Heart failure (HF) is a leading cause of hospitalization and healthcare costs, with up to 50% of patients readmitted within six months after an index admission. Recurrent readmissions strongly predict mortality and reduced quality of life, threatening health system sustainability. In Portugal, telemonitoring pilots have reduced HF admissions by over 50% and emergency episodes by 90%, illustrating the potential of integrated, out-of-hospital models. However many HF programs remain hospital-centered, with limited risk stratification and insufficient primary-care involvement. Purpose To implement a risk-stratified, primary-care-led HF program, centered on family physicians and primary care nurses as main decision-makers, aiming to reduce 30- and 90-day readmissions, increase “home-time,” improve quality of life, and enhance professional empowerment, using the Johns Hopkins Adjusted Clinical Groups (ACG) System, a well-validated risk stratification tool, to identify high-complexity patients from our global population. Methods We designed a 12-month program combining population-based electronic risk stratification with a four-stage workflow (Fig.1): (1) identification of high-risk patients using the ACG system; (2) confirmation of ≥1 HF hospitalization in the previous year; (3) predefined inclusion/exclusion criteria; and (4) integrated multidimensional assessment (clinical, functional, social, family, behavioral) and personalized decision-making by family physicians and nurses, selecting one to three prioritized interventions from a standardized menu. Results Among a population of 338,586 individuals, ACG System identified 40,587 as high/very-high risk, including 5,316 with a primary diagnosis of HF; of those 2,708 had recent HF hospitalization. Across our primary care units (USF), each physician was allocated approximately 6-13 patients, demonstrating feasibility without excessive workload burden. A pilot analysis in 2 primary care units, identified a total of 154 patients, and of these, 10 patients with a primary admission diagnosis of HF (5 per unit) (Fig.2). Case review showed frequent GDMT under-use, gaps in early post-discharge follow-up, missed vaccinations, unaddressed social/family barriers, and inconsistent telemonitoring, allowing the creation of a multidimensional assessment grid, a decision matrix, a prioritized interventions menu, and follow-up checklists. Conclusions This program shifts HF management from hospital-centered, protocol-driven care to a risk-stratified, primary-care-led model supported by structured tools and specialist backup. By limiting each patient to a small number of prioritized, personalized interventions, it seeks to otimize hollistic HF care and reduce avoidable readmissions while improving “home-time,” quality of life, and professional empowerment, offering a scalable blueprint for HF and other chronic conditions.