RESUMEN
BACKGROUND: There are few reports on transthoracic echocardiography (TTE) for the evaluation of valvular heart disease in a specific area or region. METHODS AND RESULTS: This cross-sectional questionnaire-based survey was conducted in 2023 in Kumamoto Prefecture, where 106 hospitals provide cardiology services. Ninety-three (88%) of the hospitals completed questionnaires regarding TTE. The severity of low flow/low gradient AS was evaluated by dobutamine stress echocardiography in only 7% of hospitals and exercise stress echocardiography for asymptomatic mitral regurgitation in only 5%. Multivariate logistic regression analysis revealed that participation in remote multi-institutional echocardiographic meetings and use of the Kumamoto Prefecture echocardiographic manual were significantly associated with the use of a multi-window approach (P < 0.05). CONCLUSIONS: In Kumamoto Prefecture, echocardiographic measurements are performed according to the recommendations at a relatively low rate. Dissemination of recommendations through remote meetings and the use of the echocardiographic manual may increase the likelihood of TTE being performed according to the recommendations.
RESUMEN
BACKGROUND: Although machine learning models demonstrate significant potential in predicting postoperative delirium, the advantages of their implementation in real-world settings remain unclear and require a comparison with conventional models in practical applications. OBJECTIVE: The objective of this study was to validate the temporal generalizability of decision tree ensemble and sparse linear regression models for predicting delirium after surgery compared with that of the traditional logistic regression model. METHODS: The health record data of patients hospitalized at an advanced emergency and critical care medical center in Kumamoto, Japan, were collected electronically. We developed a decision tree ensemble model using extreme gradient boosting (XGBoost) and a sparse linear regression model using least absolute shrinkage and selection operator (LASSO) regression. To evaluate the predictive performance of the model, we used the area under the receiver operating characteristic curve (AUROC) and the Matthews correlation coefficient (MCC) to measure discrimination and the slope and intercept of the regression between predicted and observed probabilities to measure calibration. The Brier score was evaluated as an overall performance metric. We included 11,863 consecutive patients who underwent surgery with general anesthesia between December 2017 and February 2022. The patients were divided into a derivation cohort before the COVID-19 pandemic and a validation cohort during the COVID-19 pandemic. Postoperative delirium was diagnosed according to the confusion assessment method. RESULTS: A total of 6497 patients (68.5, SD 14.4 years, women n=2627, 40.4%) were included in the derivation cohort, and 5366 patients (67.8, SD 14.6 years, women n=2105, 39.2%) were included in the validation cohort. Regarding discrimination, the XGBoost model (AUROC 0.87-0.90 and MCC 0.34-0.44) did not significantly outperform the LASSO model (AUROC 0.86-0.89 and MCC 0.34-0.41). The logistic regression model (AUROC 0.84-0.88, MCC 0.33-0.40, slope 1.01-1.19, intercept -0.16 to 0.06, and Brier score 0.06-0.07), with 8 predictors (age, intensive care unit, neurosurgery, emergency admission, anesthesia time, BMI, blood loss during surgery, and use of an ambulance) achieved good predictive performance. CONCLUSIONS: The XGBoost model did not significantly outperform the LASSO model in predicting postoperative delirium. Furthermore, a parsimonious logistic model with a few important predictors achieved comparable performance to machine learning models in predicting postoperative delirium.
