[Appropriateness of requests for urgent outpatient cardiological services and impact on waiting lists: the experience of the Azienda Socio-Sanitaria Territoriale of the Province of Lodi].

Background: The Cardiology Complex Structure of the Azienda Socio Sanitaria Territoriale (ASST) of Lodi, Italy, assists patients with clinical conditions ranging from emergency to chronicity. The model of Homogeneous Waiting Groups should guide the appropriateness of prescriptions categorized as U (urgent), B (brief), D (deferrable), and P (programmable). This study aims to describe and analyze the characteristics of prescription and delivery of clinical and instrumental cardiology outpatient services with U and B priority during the November 2023-January 2024 quarter, paying particular attention to the clinical appropriateness of prescribing.

Machine learning and LACE index for predicting 30-day readmissions after heart failure hospitalization in elderly patients.

Machine learning (ML) techniques may improve readmission prediction performance in heart failure (HF) patients. This study aimed to assess the ability of ML algorithms to predict unplanned all-cause 30-day readmissions in HF elderly patients, and to compare them with conventional LACE (Length of hospitalization, Acuity, Comorbidities, Emergency department visits) index. All patients aged ≥ 65 years discharged alive between 2010 and 2019 after a hospitalization for acute HF were included in this retrospective cohort study.