神尾 直

Critical care patients require continuous monitoring of vital signs and test results, yet efficiently collecting and using this data poses challenges in the intensive care unit (ICU). Usability limitations in electronic health records (EHRs) within critical care settings can delay access to essential information, potentially jeopardising patient safety. To address these issues, we developed a bedside display system that provides ICU staff with real-time, accurate access to critical data. Our system extracts and reorganises key ICU data from the existing EHR, thus avoiding costly and time-consuming upgrades. By automatically updating information such as laboratory results, blood gas analysis, lactate levels, ratio of partial pressure of arterial oxygen to fractional inspired oxygen, fluid balance and body temperature in real-time, the display allows rapid access to essential information for managing critically ill patients without the need for personal computer-based EHR logins. Post-implementation surveys with physicians, nurses and clinical engineers showed predominantly positive responses, recognising improvements in workflow and care quality. Survey results also highlighted the need for customising the display format to meet the unique requirements of each professional role, thereby maximising the system's effectiveness in critical care. This bedside display system offers four key benefits. It enhances data reliability during multidisciplinary rounds, enables physicians with busy schedules to access critical information efficiently, helps nurses detect changes in patient status early and allows a complete transition from paper-based to digital data collection. This approach offers a fresh perspective and has the potential to encourage further research into optimal information presentation methods in critical care settings.

Hematologic malignancies are a global public health concern, with high mortality rates in patients requiring critical care. The role of chemotherapy during intensive care unit (ICU) admission in this context remains unclear. This study aimed to analyze trends in survival rates based on chemotherapy timing and examine patient characteristics, ICU treatments, and clinical outcomes in each group. Using the Japanese Diagnosis Procedure Combination inpatient database, data from 21,837 patients aged ≥ 18 years who were hospitalized for hematologic malignancies and admitted to ICUs between April 1, 2012, and March 31, 2022, were analyzed. Patients were categorized based on chemotherapy timing as follows: no chemotherapy (NC), chemotherapy before ICU admission (CB), chemotherapy during ICU admission (CD), and chemotherapy after ICU discharge (CA). Mortality trends were assessed, with in-hospital mortality as the primary outcome variable. The CB group had the highest mortality rate, which decreased over time (61.2% in 2012 to 46.2% in 2021). The CD group had stable mortality rates (24.2% in 2012 and 22.6% in 2021), with a notable proportion of patients (55.4%) discharged home. These findings highlight the need for further investigation into the factors influencing ICU outcomes in patients receiving chemotherapy.

BACKGROUND: The increasing use of extracorporeal membrane oxygenation (ECMO) has highlighted challenges in managing bleeding complications. Optimal transfusion strategies remain uncertain for this diverse patient group, necessitating accurate predictive tools. This study developed and validated a machine learning (ML) algorithm to predict bleeding complications in patients with ECMO, using red blood cell (RBC) transfusion as a surrogate marker. METHODS: Data from the Tokushukai Medical Database (2018-2022), covering 71 hospitals, were used. An ML approach was employed to predict bleeding complications, using RBC transfusion events as a surrogate marker. Model performance was evaluated using precision, recall, F1 score, and accuracy. SHapley Additive exPlanations (SHAP) analysis was conducted to identify key factors influencing model predictions. RESULTS: Out of 470 ECMO-treated intensive care unit patients, 357 were included for model development. Forty-seven variables were used, with the light gradient boosting machine (LightGBM) and random forest models performing better than the other models, with receiver operating characteristic (ROC) area under the curve (AUC) above 0.7 for both (accuracy: 70.5%, ROC AUC: 0.703, recall: 0.784, and ROC AUC: 0.705, respectively). Models such as extreme gradient boosting performed similarly, while support vector classification had the lowest performance. SHAP analysis identified circulating blood volume, hemoglobin, and weight as the most important predictive factors. DISCUSSION: The LightGBM and Random Forest models effectively predict bleeding complications in patients with ECMO, using RBC transfusion as a surrogate marker. This tool can support early identification of high-risk patients and improve overall transfusion management.

Large coronary artery aneurysms (CAAs) with multiple arterial involvements are rare, and complications like coronary artery fistulae are extremely uncommon. Managing such cases presents a significant challenge. A 75-year-old female presented with a left inguinal mass and palpitations. Computed tomography revealed an abdominal aortic aneurysm and a left common iliac artery aneurysm. Coronary angiography identified a giant CAA and a coronary-to-pulmonary artery fistula. She underwent a two-stage surgical approach: first, an aortobiiliac Y-graft interposition, followed by open-heart surgery for aneurysmectomy and ligation of the pulmonary artery fistula 4 months later. Her postoperative course was uneventful, and she remained well at the 1-year follow-up. This case shows that prioritizing the aneurysm with the highest rupture risk, followed by staged treatment of CAAs, can lead to successful outcomes without major complications.