神尾 直

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.

OBJECTIVES: Predicting optimal coagulation control using heparin in intensive care units (ICUs) remains a significant challenge. This study aimed to develop a machine learning (ML) model to predict activated partial thromboplastin time (aPTT) in ICU patients receiving unfractionated heparin for anticoagulation and to identify key predictive factors. METHODS: Data were obtained from the Tokushukai Medical Database, covering six hospitals with ICUs in Japan, collected between 2018 and 2022. The study included 945 ICU patients who received unfractionated heparin. The dataset comprised both static and dynamic features, which were used to construct and train ML models. Models were developed to predict aPTT following initial and multiple heparin doses. Model performance was evaluated using the area under the receiver operating characteristic curve (ROC AUC), area under the precision-recall curve (PR AUC), precision, recall, F1 score, and accuracy. SHAP analysis was conducted to determine key predictive factors. RESULTS: The random forest model demonstrated the highest predictive performance, with ROC AUC values of 0.707 for the first infusion and 0.732 for multiple infusions. Corresponding PR AUC values were 0.539 and 0.551. Despite moderate overall predictive performance, the model exhibited high precision (0.585 for the first infusion and 0.589 for multiple infusions), indicating effectiveness in correctly identifying true positive cases. However, recall and F1 scores were lower, suggesting that some cases, particularly in sub-therapeutic and supra-therapeutic ranges, may have been missed. Incorporating time-series data, such as vital signs, provided only marginal improvements in performance. CONCLUSIONS: ML models demonstrated moderate performance in predicting aPTT following heparin infusion in ICU patients, with the random forest model achieving the highest classification accuracy. Although the models effectively identified true positive cases, their overall predictive performance remained limited, necessitating further refinement. The inclusion of static and dynamic features did not significantly enhance model accuracy. Future studies should explore additional factors to improve predictive models for optimizing individualized anticoagulation management in ICUs.

Optimal timing for intubating patients with coronavirus disease 2019 (COVID-19) has been debated throughout the pandemic. Early use of high-flow nasal cannula (HFNC) can help reduce the need for intubation, but delay can result in poorer outcomes. This study examines trends in laboratory parameters and serum severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA levels of patients with COVID-19 in relation to HFNC failure. Patients requiring HFNC within three days of hospitalization between July 1 and September 30, 2021 were enrolled. The primary outcome was HFNC failure (early failure ≤Day 3; late failure ≥Day 4), defined as transfer to intensive care just before/after intubation or in-hospital death. We examined changes in laboratory markers and SARS-CoV2-RNAemia on Days 1, 4, and 7, together with demographic data, oxygenation status, and therapeutic agents. We conducted a univariate logistic regression with the explanatory variables defined as 10% change rate in each laboratory marker from Day 1 to 4. We utilized the log-rank test to assess the differences in HFNC failure rates, stratified based on the presence of SARS-CoV2 RNAemia. Among 122 patients, 17 (13.9%) experienced HFNC failure (early: n = 6, late: n = 11). Seventy-five patients (61.5%) showed an initial SpO2/FiO2 ratio ≤243, equivalent to PaO2/FiO2 ratio ≤200, and the initial SpO2/FiO2 ratio was significantly lower in the failure group (184 vs. 218, p = 0.018). Among the laboratory markers, a 10% increase from Day 1 to 4 of lactate dehydrogenase (LDH) and interleukin (IL)-6 was associated with late failure (Odds ratio [OR]: 1.42, 95% confidence interval [CI]: 1.09-1.89 and OR: 1.04, 95%CI: 1.00-1.19, respectively). Furthermore, in patients with persistent RNAemia on Day 4 or 7, the risk of late HFNC failure was significantly higher (Log-rank test, p<0.01). In conclusion, upward trends in LDH and IL-6 levels and the persistent RNAemia even after treatment were associated with HFNC failure.