菅原 斉

ABSTRACT Background Despite the high prognostic value of D‐dimer in various clinical conditions, limited research has addressed short‐term fatality prediction across disease categories. This study aimed to develop and compare models predicting 72‐h fatality in patients with D‐dimer levels ≥ 2 μg/mL, using laboratory variables. This timeframe was chosen based on its clinical relevance for early triage and intervention across multiple acute conditions. Methods We retrospectively analyzed data from 5158 patients (241 deaths within 72 h). The primary outcome was 72‐h fatality; predictors included age, sex, and 40 routine hematologic, biochemical, and coagulation tests. Traditional multivariate logistic regression analysis (MLRA) was compared with four machine learning (ML) models: Prediction One, LightGBM, XGBoost, and CatBoost. External validation was performed using a separate dataset of 5550 patients (309 deaths). D‐dimer levels were recorded in any clinical setting despite limited patient medical information. Results The 72‐h fatality rate increased with increasing D‐dimer levels (overall 4.67%). Major causes of death were intracranial disease (24.9%), malignancy (17.0%), and sepsis (8.3%). MLRA identified five key predictors: advanced age, low total protein and cholesterol levels, and elevated aspartate aminotransferase and D‐dimer levels. Its performance (AUC 0.829, 95% CI 0.768–0.888; sensitivity 0.762; specificity 0.809) was exceeded by LightGBM (AUC 0.987; sensitivity 0.987; specificity 0.911), which outperformed Prediction One (0.814), XGBoost (0.981), and CatBoost (0.937). Conclusion ML models, particularly LightGBM, effectively identify high‐risk patients using routine laboratory tests. The model enables timely decision‐making and early risk stratification in patients with high D‐dimer values, even when clinical information is limited.

Aim This study aimed to clarify the current understanding/misunderstanding regarding the “do not attempt resuscitation (DNAR)” order among physicians and nurses in Japan as well as related factors. Methods We conducted a questionnaire survey of physicians and nurses working in three Japanese medical institutions. We established “misconception indicators” for DNAR orders and identified related factors using the Mann–Whitney U test, with multiple comparisons using the Dunn test. Differences in each misconception indicator were compared between physicians and nurses using the chi‐square test. Results We obtained survey responses from 134 physicians and 233 nurses. Among them, >70% of physicians and nurses responded that a DNAR order indicated withholding invasive medical care. Moreover, responses suggesting that DNAR prompted palliative care were more common among physicians and nurses working at hospitals without intensive care units or rapid response systems. Additionally, >40% of physicians responded that a DNAR order prompted them to limit the use of medical resources, including the intensive care unit and blood transfusions, with this proportion being higher than that among nurses. Further, physicians with longer clinical experience were more likely to limit the use of medical resources in cases of a DNAR order. Conclusions Many physicians and nurses misinterpreted a DNAR order as prompting palliative care. To facilitate support toward patient decision‐making and correct implementation of DNAR orders, it is important to establish internal guidelines, provide education regarding end‐of‐life care and medical terminology, and introduce specialized care teams.