Comparison of Machine Learning Techniques for Prediction of Hospitalization in Heart Failure Patients

The present study aims to compare the performance of eight Machine Learning Techniques (MLTs) in the prediction of hospitalization among patients with heart failure, using data from the Gestione Integrata dello Scompenso Cardiaco (GISC) study. The GISC project is an ongoing study that takes place in the region of Puglia, Southern Italy. Patients with a diagnosis of heart failure are enrolled in a long-term assistance program that includes the adoption of an online platform for data sharing between general practitioners and cardiologists working in hospitals and community health districts. Logistic regression, generalized linear model net (GLMN), classification and regression tree, random forest, adaboost, logitboost, support vector machine, and neural networks were applied to evaluate the feasibility of such techniques in predicting hospitalization of 380 patients enrolled in the GISC study, using data about demographic characteristics, medical history, and clinical characteristics of each patient. The MLTs were compared both without and with missing data imputation. Overall, models trained without missing data imputation showed higher predictive performances. The GLMN showed better performance in predicting hospitalization than the other MLTs, with an average accuracy, positive predictive value and negative predictive value of 81.2%, 87.5%, and 75%, respectively. Present findings suggest that MLTs may represent a promising opportunity to predict hospital admission of heart failure patients by exploiting health care information generated by the contact of such patients with the health care system

Networking and data sharing reduces hospitalization cost of heart failure: the experience of GISC study

Rationale, aims and objectives Heart failure (HF) is a concerning public health burden in Western society because, despite the improvement of medical treatments, it is still associated with adverse outcomes (high morbidity and mortality), resulting in one of the most expensive chronic disease in Western countries. Hospital admission particularly is the most expensive cost driver among the several resources involved in the management of HF. The aim of our study was to investigate the cost of hospitalization before and after the enrolment to a new strategy (GISC) in the management of patients with HF. Methods We enrolled a cohort of 90 patients. Patients were eligible to the study if they were hospitalized with a new diagnosis of HF or a diagnosis of decompensated HF. The enrolment to the study corresponded to the enrolment to the GISC intervention. We calculated the cost for every hospital admission at 6 and 12 months before and after the enrolment using the tariff paid for the diagnosis-related group. Results Comparing per-patient cumulative cost before and after the enrolment, we showed that patient's hospitalization was less expensive after the enrolment to the GISC intervention. The strategy resulted in an average cumulative estimated saving of \u20ac439\u2009322.00 (95% CI \u20ac413\u2009890.70; \u20ac464\u2009753.40) at 6 months and of \u20ac832\u2009276.80 (95% CI \u20ac786\u2009863.70; \u20ac877\u2009690.00) at 12 months after the enrolment. Conclusions We found out that the intervention was a cost-saving strategy for follow-up of the patients suffering from HF at 6 and 12 months after the enrolment compared with hospitalizations' cost before the recruitment

Remote monitoring for implantable defibrillators: A nationwide survey in italy

Background: Remote monitoring (RM) permits home interrogation of implantable cardioverter defibrillator (ICD) and provides an alternative option to frequent in-person visits. Objective: The Italia-RM survey aimed to investigate the current practice of ICD follow-up in Italy and to evaluate the adoption and routine use of RM. Methods: An ad hoc questionnaire on RM adoption and resource use during in-clinic and remote follow-up sessions was completed in 206 Italian implanting centers. Results: The frequency of routine in-clinic ICD visits was 2 per year in 158/206 (76.7%) centers, 3 per year in 37/206 (18.0%) centers, and 4 per year in 10/206 (4.9%) centers. Follow-up examinations were performed by a cardiologist in 203/206 (98.5%) centers, and by more than one health care worker in 184/206 (89.3%) centers. There were 137/206 (66.5%) responding centers that had already adopted an RM system, the proportion of ICD patients remotely monitored being 15% for single- and dual-chamber ICD and 20% for cardiac resynchronization therapy ICD. Remote ICD interrogations were scheduled every 3 months, and were performed by a cardiologist in 124/137 (90.5%) centers. After the adoption of RM, the mean time between in-clinic visits increased from 5 (SD 1) to 8 (SD 3) months (P<.001). Conclusions: In current clinical practice, in-clinic ICD follow-up visits consume a large amount of health care resources. The results of this survey show that RM has only partially been adopted in Italy and, although many centers have begun to implement RM in their clinical practice, the majority of their patients continue to be routinely followed-up by means of in-clinic visits