[Translated article] Therapeutic management, adherence, and clinical outcomes of heart failure in Andalucía. ANDALIC Protocol.

Heart failure is a prevalent syndrome with high mortality rates, representing a significant economic burden in terms of healthcare. The lack of systematic information about the treatment and adherence of patients with heart failure limits the understanding of these aspects and potentially the improvement of clinical outcomes. OBJECTIVE: To describe the clinical characteristics, therapeutic management, adherence, persistence, and clinical results, as well as the association between these variables, in a cohort of patients with heart failure in Andalusia. DESIGN: This study will be an observational, population-based, retrospective cohort study. Data of patients discharged from an Andalusian hospital with a diagnosis of heart failure between 2014 and 2023 will be extracted from the Andalusian population health database. ANALYSIS: The statistical analysis will incorporate the following strategies: (1) Descriptive analysis of the characteristics of the population cohort, adherence measures, and clinical outcomes. (2) Bivariate analyses to study the association of covariates with adherence, persistence, and clinical results. (3) Multivariate logistic regression and Cox regression analysis including relevant covariates. (4) To evaluate changes over time, multivariate Poisson regression models will be used. By conducting this comprehensive study, we aim to gain valuable insights into the clinical characteristics, treatment management, and adherence of heart failure patients in Andalusia, as well as to identify factors that may influence clinical outcomes. These findings could be critical both for the development of optimised strategies that improve medical care and quality of life of patients and for mitigating the health burden of HF in the region.

Therapeutic management, adherence, and clinical outcomes of heart failure in Andalucía. ANDALIC Protocol. / Manejo terapéutico, adherencia y resultados clínicos de la insuficiencia cardiaca en Andalucía. Protocolo ANDALIC.

Heart failure is a prevalent syndrome with high mortality rates, representing a significant economic burden in terms of healthcare. The lack of systematic information about the treatment and adherence of patients with heart failure limits the understanding of these aspects and potentially the improvement of clinical outcomes. OBJECTIVE: To describe the clinical characteristics, therapeutic management, adherence, persistence and clinical results, as well as the association between these variables, in a cohort of patients with heart failure in Andalusia. DESIGN: This study will be an observational, population-based, retrospective cohort study. Data of patients discharged from an Andalusian hospital with a diagnosis of heart failure between 2014 and 2023 will be extracted from the Andalusian population health database. ANALYSIS: The statistical analysis will incorporate the following strategies: 1) Descriptive analysis of the characteristics of the population cohort, adherence measures, and clinical outcomes. 2) Bivariate analyses to study the association of covariates with adherence, persistence and clinical results. 3) Multivariate logistic regression and Cox regression analysis including relevant covariates. 4) To evaluate changes over time, multivariate Poisson regression models will be used. By conducting this comprehensive study, we aim to gain valuable insights into the clinical characteristics, treatment management, and adherence of heart failure patients in Andalusia, as well as to identify factors that may influence clinical outcomes. These findings could be critical both for the development of optimized strategies that improve medical care and quality of life of patients and for mitigating the health burden of HF in the region.

Machine Learning-Based Prediction of Changes in the Clinical Condition of Patients With Complex Chronic Diseases: 2-Phase Pilot Prospective Single-Center Observational Study.

BACKGROUND: Functional impairment is one of the most decisive prognostic factors in patients with complex chronic diseases. A more significant functional impairment indicates that the disease is progressing, which requires implementing diagnostic and therapeutic actions that stop the exacerbation of the disease. OBJECTIVE: This study aimed to predict alterations in the clinical condition of patients with complex chronic diseases by predicting the Barthel Index (BI), to assess their clinical and functional status using an artificial intelligence model and data collected through an internet of things mobility device. METHODS: A 2-phase pilot prospective single-center observational study was designed. During both phases, patients were recruited, and a wearable activity tracker was allocated to gather physical activity data. Patients were categorized into class A (BI≤20; total dependence), class B (2060; moderate or mild dependence, or independent). Data preprocessing and machine learning techniques were used to analyze mobility data. A decision tree was used to achieve a robust and interpretable model. To assess the quality of the predictions, several metrics including the mean absolute error, median absolute error, and root mean squared error were considered. Statistical analysis was performed using SPSS and Python for the machine learning modeling. RESULTS: Overall, 90 patients with complex chronic diseases were included: 50 during phase 1 (class A: n=10; class B: n=20; and class C: n=20) and 40 during phase 2 (class B: n=20 and class C: n=20). Most patients (n=85, 94%) had a caregiver. The mean value of the BI was 58.31 (SD 24.5). Concerning mobility aids, 60% (n=52) of patients required no aids, whereas the others required walkers (n=18, 20%), wheelchairs (n=15, 17%), canes (n=4, 7%), and crutches (n=1, 1%). Regarding clinical complexity, 85% (n=76) met patient with polypathology criteria with a mean of 2.7 (SD 1.25) categories, 69% (n=61) met the frailty criteria, and 21% (n=19) met the patients with complex chronic diseases criteria. The most characteristic symptoms were dyspnea (n=73, 82%), chronic pain (n=63, 70%), asthenia (n=62, 68%), and anxiety (n=41, 46%). Polypharmacy was presented in 87% (n=78) of patients. The most important variables for predicting the BI were identified as the maximum step count during evening and morning periods and the absence of a mobility device. The model exhibited consistency in the median prediction error with a median absolute error close to 5 in the training, validation, and production-like test sets. The model accuracy for identifying the BI class was 91%, 88%, and 90% in the training, validation, and test sets, respectively. CONCLUSIONS: Using commercially available mobility recording devices makes it possible to identify different mobility patterns and relate them to functional capacity in patients with polypathology according to the BI without using clinical parameters.