Background: New-onset postoperative atrial fibrillation (POAF) is the most common complication after cardiac surgery, occurring approximately in one-third of the patients. This study considered all-comer patients who underwent cardiac surgery to build a predictive model for POAF. Methods: A total of 3467 (Center 1) consecutive patients were used as a derivation cohort to build the model. The POLARIS score was then derived proportionally from the odds ratios obtained following multivariable logistic regression (MLR). The Brier Score, the area under the receiver operating characteristic curve, and the Hosmer–Lemeshow goodness-of-fit test were used to validate the model. Then, 2272 (Center 2) consecutive patients were used as an external validation cohort. Results: In the overall population (n = 5739), POAF occurred in 32.7% of patients. MLR performed in the derivation cohort showed that age, obesity, chronic renal failure, pulmonary hypertension, minimally invasive surgery, and aortic and mitral valve surgery were predictors of POAF. The derived POLARIS score was used to further stratify the population into four risk clusters: low (1.5–3), intermediate (3.5–5), high (5.5–7), and very high (7.5–9), each progressively showing an increase in POAF incidence. This was confirmed in a correlation analysis (Spearman’s rho: 0.636). Conclusions: The POLARIS score is a simple-to-use tool to stratify patients at higher risk of POAF. Precise identification of such patients might be used to implement clinical practice with the introduction of preoperative antiarrhythmic prophylaxis, further reducing the incidence of POAF and, potentially, its clinical sequelae, despite further investigations being warranted to test this model in prospective studies.

Background: Sarcopenia is prevalent in long-term care facilities (LTCFs) and is associated with falls, functional decline, and mortality. Aims: This proof-of-concept study, within the I-COUNT pilot randomized controlled trial, evaluated the feasibility and diagnostic performance of a non-invasive sarcopenia screening approach based on wearable surface electromyography (sEMG) combined with artificial intelligence. Results: Eighteen older adults residing in two Italian LTCFs (6 sarcopenic, 12 controls; 33.3% men, mean age 87 ± 5.4 years) underwent sEMG during three standardised motor tasks involving lower-limb muscles. Surface-EMG signals were transformed into time-frequency scalograms (Continuous Wavelet Transform) and classified as sarcopenic versus non-sarcopenic using deep transfer learning. The classification framework achieved 91% accuracy using Leave-One-Out Cross-Validation, with sensitivity of 84%, specificity of 93%, precision of 92%, and an F1-score of 91. Conclusions: These findings support wearable sEMG as a scalable tool for screening of sarcopenia in LTCFs and provide preliminary evidence for further validation in larger populations. Trial registration number: ClinicalTrials.gov ID NCT06820710.