imaizumi kazuyoshi

OBJECTIVES: To develop a comprehensive machine learning model incorporating various clinical factors, including frailty and comorbidities, to predict 30-day readmission and mortality risk in patients with chronic obstructive pulmonary disease (COPD). METHODS: This retrospective cohort study used electronic health records (EHR) from Fujita Health University Hospital (2004-2019) for 1294 patients with COPD and 3499 hospitalization or death events. The EHR contained longitudinal patient data (demographics, diagnoses, test results, clinical records). We developed two eXtreme Gradient Boosting models, the comprehensive Top64 and practical 11-feature models. We compared these with the Comorbidity, Obstruction, Dyspnea, and Previous Exacerbations index (CODEX) model, a widely used tool for predicting hospital readmission or death in patients with COPD. The area under the receiver operating characteristic curve (AUC) with 95% confidence interval (CI), sensitivity, and specificity were used to evaluate the model performance. RESULTS: The Top64 (AUC: 0.769, 95% CI: 0.747-0.791) and practical 11-feature (AUC: 0.746, 95% CI: 0.730-0.762) models performed better than the CODEX model (AUC: 0.587, 95% CI: 0.563-0.611). The Top64 model showed 0.978 sensitivity and 0.341 specificity, and the practical 11-feature model achieved 0.955 sensitivity and 0.361 specificity. The calibration curves showed good agreement between the observed and predicted results for both models. CONCLUSIONS: A machine learning approach based on clinical data readily available from the EHR performed better than existing models in predicting 30-day readmission and mortality risks in patients with COPD. A comprehensive risk prediction tool may enhance individualized care strategies and improve patient outcomes in COPD management.

Bronchoscopic lung volume reduction (BLVR) with endobronchial valves is an established treatment for selected patients with advanced emphysema. A 74-year-old male patient with chronic obstructive pulmonary disease and severe dyspnea was scheduled to undergo BLVR targeting the right middle lobe bronchus based on high-resolution CT findings, which showed severe emphysematous changes with hyperinflation and fissure completeness of 98% in the right middle lobe. The physician conducted preoperative virtual reality (VR)-assisted planning using the patient's imaging data, enabling comprehensive visualisation of the bronchial tree, airway measurements, and procedural simulation. The Chartis system confirmed a 'no flow' pattern, supporting the absence of collateral ventilation. During the procedure, a size 5.5 valve was placed in the right B4/5 bronchus following VR and intraoperative assessments. The patient remained stable postoperatively without complications. VR enhanced procedural planning by improving airway assessment, optimising valve sizing, and reducing cognitive load, leading to increased efficiency and operator confidence. Further research is warranted to validate the utility of VR in bronchoscopic interventions.

The purpose of this study was to evaluate how the first oral administration of suvorexant affects PSG results in patients with severe obstructive sleep apnea (OSA). Single-center, prospective study conducted in a nonrandomized, uncontrolled, unblinded fashion. Undiagnosed 64 patients with suspected OSA underwent first-night PSG, and 30 patients with severe OSA (Apnea Hypopnea Index [AHI] ≥ 30 events/h) underwent second-night PSG testing after administration of 15 mg suvorexant. The change in AHI between the first and second nights was not significant, although the upper limit of the 95% confidence interval for the mean difference in AHI was high at 5.987.The mean duration of apnea on the second night was significantly prolonged compared to that on the first night, but there were no significant differences n 3% oxygen desaturation index, saturation of percutaneous oxygen<90% time. On the second night, total sleep time was significantly prolonged, mid-night awakenings decreased, REM sleep percentage increased, and REM latency was shorter. Because the environment for PSG testing is very different from the patient's home and many patients have difficulty sleeping, there are clinical cases in which PSG is performed with sleep medication. In this study, PSG after oral administration of 15 mg of suvorexant on the second night showed no significant difference or clear trend in AHI. However, the upper limit of the 95% confidence interval for the mean difference in AHI was greater than 5, suggesting that suvorexant may exacerbate AHI, even with the first administration.