Regression Models to Study the Total LOS Related to Valvuloplasty

Background: Valvular heart diseases are diseases that affect the valves by altering the normal circulation of blood within the heart. In recent years, the use of valvuloplasty has become recurrent due to the increase in calcific valve disease, which usually occurs in the elderly, and mitral valve regurgitation. For this reason, it is critical to be able to best manage the patient undergoing this surgery. To accomplish this, the length of stay (LOS) is used as a quality indicator. Methods: A multiple linear regression model and four other regression algorithms were used to study the total LOS function of a set of independent variables related to the clinical and demographic characteristics of patients. The study was conducted at the University Hospital “San Giovanni di Dio e Ruggi d’Aragona” of Salerno (Italy) in the years 2010–2020. Results: Overall, the MLR model proved to be the best, with an R(2) value of 0.720. Among the independent variables, age, pre-operative LOS, congestive heart failure, and peripheral vascular disease were those that mainly influenced the output value. Conclusions: LOS proves, once again, to be a strategic indicator for hospital resource management, and simple linear regression models have shown excellent results to analyze it.