Cost-effectiveness and diagnostic accuracy of focused cardiac ultrasound in the pre-participation screening of athletes: the SPORT-FoCUS study.

AIMS: The role of pre-participation screening (PPS) modalities in preventing sudden cardiac death (SCD) in athletes is debated due to a high false-positive rate. Focused cardiac ultrasound (FoCUS) has shown higher sensitivity and specificity, but its cost-effectiveness remains uncertain. This study aimed to determine the diagnostic performance and cost-effectiveness of FoCUS use in PPS. METHODS AND RESULTS: A total of 2111 athletes (77.4% male, mean age 24.9 ± 15.2years) underwent standardized family and medical history collection, physical examination, resting electrocardiography (ECG), FoCUS (10 min/5 views protocol), comprehensive echocardiography and exercise stress test. We prospectively evaluated three PPS incremental models: Model A, standardized medical history and physical examination Model B, Model A plus resting and stress ECG and Model C, Model B plus FoCUS (10 min/5 views protocol). We determined their incremental diagnostic accuracy and cost-effectiveness ratio. A total of 30 athletes were diagnosed with a cardiac condition associated with SCD: 3 were identified by Model A, 14 by Model B, and 13 athletes by Model C. The introduction of FoCUS markedly increased the sensitivity of PPS, compared with Model A and Model B (sensitivity 94% vs. 19% vs. 58% specificity 93% vs. 93% vs. 92%). The total screening costs were as follows: Model A 35.64 euros, Model B 87.68 euros, and Model C 120.89 euros. Considering the sole conditions at risk of SCD, the incremental cost-effectiveness ratio was 135.62 euros for Model B and 114.31 for Model C. CONCLUSIONS: The implementation of FoCUS into the PPS allows to identify a significantly greater number of athletes at risk of SCD and markedly lowers the false negative rate. Furthermore, the incorporation of FoCUS into the screening process has shown to be cost-effective.

A significant electrocardiography false-positive rate makes pre-participation screening (PPS) for sudden cardiac death (SCD) in athletes controversial. Focused cardiac ultrasound (FoCUS) may increase sensitivity and specificity however, its cost-effectiveness is unknown. This study evaluates the diagnostic performance and cost-effectiveness of FoCUS in PPS. Incorporating a simplified echocardiographic exam called FoCUS into PPS resulted in higher diagnostic reliability, with a lower rate of false negatives and a higher number of athletes at risk for SCD identified.The integration of FoCUS into the screening process resulted to be cost-effective in our athletes' cohort.

A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score.

BACKGROUND: Several models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity. Machine learning algorithms represent a novel approach for the data-driven prediction of clinical outcomes with advantages over statistical modeling. OBJECTIVE: We aimed to develop a machine learning-based score-the Piacenza score-for 30-day mortality prediction in patients with COVID-19 pneumonia. METHODS: The study comprised 852 patients with COVID-19 pneumonia, admitted to the Guglielmo da Saliceto Hospital in Italy from February to November 2020. Patients' medical history, demographics, and clinical data were collected using an electronic health record. The overall patient data set was randomly split into derivation and test cohorts. The score was obtained through the naïve Bayes classifier and externally validated on 86 patients admitted to Centro Cardiologico Monzino (Italy) in February 2020. Using a forward-search algorithm, 6 features were identified: age, mean corpuscular hemoglobin concentration, PaO2/FiO2 ratio, temperature, previous stroke, and gender. The Brier index was used to evaluate the ability of the machine learning model to stratify and predict the observed outcomes. A user-friendly website was designed and developed to enable fast and easy use of the tool by physicians. Regarding the customization properties of the Piacenza score, we added a tailored version of the algorithm to the website, which enables an optimized computation of the mortality risk score for a patient when some of the variables used by the Piacenza score are not available. In this case, the naïve Bayes classifier is retrained over the same derivation cohort but using a different set of patient characteristics. We also compared the Piacenza score with the 4C score and with a naïve Bayes algorithm with 14 features chosen a priori. RESULTS: The Piacenza score exhibited an area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI 0.74-0.84, Brier score=0.19) in the internal validation cohort and 0.79 (95% CI 0.68-0.89, Brier score=0.16) in the external validation cohort, showing a comparable accuracy with respect to the 4C score and to the naïve Bayes model with a priori chosen features; this achieved an AUC of 0.78 (95% CI 0.73-0.83, Brier score=0.26) and 0.80 (95% CI 0.75-0.86, Brier score=0.17), respectively. CONCLUSIONS: Our findings demonstrated that a customizable machine learning-based score with a purely data-driven selection of features is feasible and effective for the prediction of mortality among patients with COVID-19 pneumonia.