小野 将平

OBJECTIVES: To operationalize and temporally validate an electronic medical record (EMR)-integrated machine learning system (Big data-driven Evaluation of Survival and Treatment in Acute Illness [BEST-AI]) that generates hourly predictions for multiple ICU outcomes, with emphasis on discrimination, calibration, and workflow integration. DESIGN: Single-center hybrid study with stepwise clinical deployment and forward-in-time temporal validation. SETTING: Thirty-bed tertiary mixed medical-surgical ICU in Japan. PATIENTS: All ICU admissions from August 2017 to March 2025. Exclusions: age younger than 16 years or ICU stay less than 4 hours. Development cohort (n = 11,176; from August 2017 to July 2024) and temporal validation cohort (n = 1,127; from August 2024 to March 2025). INTERVENTIONS: EMR-integrated deployment of BEST-AI providing hourly probabilistic predictions to clinicians within the EMR; no protocolized clinical interventions were mandated. MEASUREMENTS AND MAIN RESULTS: Six prediction tasks (in-hospital mortality, ICU mortality, ICU discharge ≤ 72 hr, intubation ≤ 72 hr, extubation ≤ 72 hr, tracheostomy at ICU discharge) were evaluated. In temporal validation, the area under the receiver operating characteristic curves ranged from 0.856 to 0.960, and the area under the precision-recall curves from 0.302 to 0.786. Decile-based calibration showed overall good agreement; hospital mortality was slightly overestimated at higher predicted probabilities, whereas ICU mortality remained well aligned. The intubation task had comparatively lower discrimination and greater deviation from perfect calibration, consistent with low event counts and heterogeneous timing. A 24-hour landmark sensitivity analysis (one prediction per patient at 24 hr after ICU admission) preserved discrimination and calibration relative to the main analysis, supporting robustness beyond repeated-measures evaluation. The system was successfully maintained with automated hourly updates and EMR-embedded patient- and unit-level visualizations, without prescriptive alerts. CONCLUSIONS: A continuously deployed, EMR-integrated ICU prediction system achieved strong temporal discrimination and generally good calibration. Embedding real-time predictions into routine workflow was feasible, and the system was maintained with automated hourly updates. Prospective multicenter studies are warranted to assess transportability and clinical impact.

IMPORTANCE: The optimal intensity of care for older patients (age ≥80 years) in intensive care units (ICUs) remains uncertain. Although institutional variation in critical care practice has been described, less is known about case-mix-adjusted variation in life-sustaining treatment use among older patients admitted to ICUs and whether greater institutional treatment intensity is associated with improved survival. OBJECTIVE: To quantify institutional variation in the use of life-sustaining treatments for older patients among ICUs and examine the association of treatment intensity with in-hospital mortality. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study used nationwide data from the Japanese Intensive Care Patient Database (JIPAD) for patients aged 80 years or older admitted to 127 ICUs at JIPAD-participating institutions in Japan between April 1, 2015, and March 31, 2023. EXPOSURES: Intensive care unit admission and age 80 years or older. MAIN OUTCOMES AND MEASURES: Institutional treatment intensity was quantified using standardized treatment ratio (STR), defined as the ratio of observed-to-expected life-sustaining treatment use after adjustment for patient-level characteristics. The association between STR category and in-hospital mortality was evaluated using both logistic regression and hierarchical bayesian multilevel logistic regression models. RESULTS: Among 60 713 patients (median age, 84 years [IQR, 82-87 years]; 32 302 male [53.2%]), the crude institutional rate of life-sustaining treatment use ranged from 4.8% (8 of 167 patients) to 38.0% (322 of 847 patients). After adjustment for patient case mix, the STR ranged from 0.24 (95% CI, 0.11-0.48) to 2.34 (95% CI, 1.91-2.85) across participating ICUs. In multilevel analyses adjusted for patient- and institution-level factors, higher institutional treatment intensity was not associated with in-hospital survival compared with intermediate treatment intensity (high STR category: odds ratio, 1.17; 95% credible interval, 0.91-1.39). CONCLUSIONS AND RELEVANCE: In this cohort study of older patients admitted to ICUs, institutional use of life-sustaining treatments varied substantially even after case-mix adjustment and higher institutional treatment intensity was not associated with better in-hospital survival. These findings suggest that increasing treatment intensity alone may not be associated with improved outcomes in this population and support the need for better approaches to identify patients most likely to benefit from intensive treatment.

OBJECTIVE: The aim of this study was to identify factors that predict admission to the intensive care unit (ICU) after activation of a rapid response system (RRS). METHODS: We conducted a retrospective observational study using data from 12,306 RRS activations recorded in the In-Hospital Emergency Registry in Japan database between November 2017 and September 2023. Patients aged under 18 years, noninpatients, and those who died or were transferred immediately after RRS activation were excluded. The primary outcome was ICU admission after RRS activation. Predictive factors were identified using multivariable logistic regression models: Model 1 included all available data, while model 2 was restricted to data available at the time of RRS activation. RESULTS: We analysed data from 8532 patients; 2298 (26.9%) were admitted to the ICU following RRS activation. Significant factors of ICU admission in model 1 included weekend activation (odds ratio [OR] = 1.17; 95% confidence interval [CI] = 1.02, 1.34), oxygen administration prior to activation (OR = 1.23; 95% CI = 1.08, 1.4), ICU discharge within 72 h before the index event (OR = 1.65; 95% CI = 1.28, 2.11), physician-initiated activation (OR = 2.16; 95% CI = 1.87, 2.50), and multiple abnormal vital signs. Model 2, which was limited to information available at the time of RRS activation, identified a similar pattern of associations. CONCLUSION: This study identified several important factors associated with ICU admission following RRS activation. These findings may support improved clinical decision-making regarding ICU transfers and provide a foundation for future work to develop and validate prediction models tailored to this setting.

BACKGROUND: Postoperative delirium is a common complication associated with prolonged hospitalization, cognitive decline, and increased mortality. Intraoperative hypotension (IOH) is a potential modifiable risk factor for postoperative delirium, but previous studies have shown inconsistent results due to methodological limitations. High-risk surgical patients, particularly those with comorbidities or advanced age, may be especially vulnerable. We evaluated the association between IOH and postoperative ICU delirium within 48 h. METHODS: We conducted a single-center retrospective study of high-risk adult patients who underwent surgery under general anesthesia without cardiopulmonary bypass and were admitted to the ICU between 2017 and 2024. IOH exposure was quantified using the cumulative area where mean arterial pressure (MAP) was below 65 mmHg (hypotension area) and total time under this threshold (hypotension time). Multivariable logistic regression was used to assess the association between IOH and postoperative ICU delirium, adjusting for preoperative comorbidities, intraoperative medications, and anesthetic depth. Subgroup and interaction analyses explored effect modifiers. RESULTS: Among 4798 patients, both hypotension area (OR 1.16, 95% CI 1.05-1.29, P = 0.003) and hypotension time (OR 3.42, 95% CI 1.21-9.65, P = 0.02) were significantly associated with postoperative ICU delirium within 48 h. Subgroup analyses suggested stronger associations in patients with advanced age, higher ASA-PS, inhalational anesthesia, neurosurgery, and intubation at ICU admission. CONCLUSIONS: IOH was significantly associated with postoperative ICU delirium. These findings underscore the importance of vigilant blood pressure management during surgery, particularly in high-risk patients. Interventional studies are needed to confirm these results and guide preventive strategies.

BACKGROUND: Clinically important gastrointestinal bleeding (CIGIB) is a serious complication in critically ill patients, contributing to prolonged ICU stays and increased mortality. Despite efforts to identify high-risk patients, no previous studies have employed machine learning models to predict CIGIB during ICU stay or identify key predictors in this context. METHODS: This single-center retrospective study included ICU patients aged 18 years or older admitted between 2017 and 2024. Patients with ICU stays of less than 24 hours or GIB within 24 hours of admission were excluded. Machine learning models, including XGBoost, Random Forest, and L1-regularized logistic regression, were trained using patient data from the first 24 hours of ICU admission. Model performance was assessed using AUROC, precision, recall, and F1 scores. Shapley Additive Explanations (SHAP) were employed to evaluate key predictors. RESULTS: A total of 7,357 ICU patients were included, of whom 171 (2.3%) experienced CIGIB. The XGBoost model demonstrated the highest predictive performance with an AUROC of 0.84. Key predictors included APACHE III scores, hematocrit levels, APTT, creatinine and respiratory rate, while invasive mechanical ventilation and stress ulcer prophylaxis within the first 24 hours of ICU admission did not rank among the top 20 predictors based on SHAP values. CONCLUSIONS: This study represents the first application of machine learning for predicting CIGIB in ICU patients, providing valuable insights into risk stratification. The model demonstrated high predictive accuracy and interpretability, highlighting its potential to guide early intervention and prophylaxis. Further multi-center studies and interventional trials are needed to validate these findings and refine clinical risk prediction strategies.

Background Previous studies have demonstrated a correlation between management by intensivists and a decrease in hospital stay and mortality, yet the underlying reason remains unknown. Using open data from the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) and other databses, the present study aimed to explore the relationship between inotrope and vasoconstrictor use and the number of intensivists. Materials and methods Cardiovascular agonists listed in the 2020 NDB for which the total dose was known were included for analysis. Trends in cardiovascular agonist use over six years were then graphically assessed, and a linear regression model with the use of each target drug per prefecture as the objective variable in the 2020 data was created to analyze the impact of intensivists on drug use. Results A total of 61 drugs were classified into eight groups based on their composition, and drug use in each of the 47 prefectures was tabulated. Both the rate of use and cost showed a yearly decrease for dopamine but a yearly increase for norepinephrine. Multivariable analysis indicated that the number of intensivists was only significant for dopamine, which had a coefficient of -310 (95% CI: -548 to -72, p = 0.01) but that no such trend was evident for the other drugs. Conclusions The results demonstrated that an increasing number of intensivists in each prefecture correlated with decreasing use of dopamine, possibly explaining the improved outcomes observed in closed ICUs led by intensivists. Further research is warranted to establish causality.

Veno venous extracorporeal membrane oxygenation（VV-ECMO）でも過剰な自発呼吸が経肺圧上昇をもたらし，肺障害の悪化を招く可能性がある。症例1は25歳，女性。粟粒結核・重症呼吸不全に対しVV-ECMOを導入した。第6病日に覚醒下におくと吸気終末経肺圧（end inspiratory transpulmonary pressure, EIP_L）は24 cmH₂Oまで上昇したため，再度深鎮静管理とし，EIP_Lは7 cmH₂Oまで低下した。症例2は51歳，男性。インフルエンザ関連急性呼吸不全・重症air leak症候群に対しVV-ECMO導入となった。第4病日の覚醒下移行後もEIP_Lは20 cmH₂O未満であることから自発呼吸を温存した。今回の2例が新たな肺障害を生むことなく自己肺の改善を認めたことからも，VV-ECMO中の食道内圧測定は過剰な自発呼吸を早期に認知し，自発呼吸温存の可否を決める有効な一指標になり得ると考える。

<jats:title>要旨</jats:title><jats:p>Air leak症候群に伴う重症呼吸不全はvenovenous–extracorporeal membrane oxygenation（VV–ECMO）の適応疾患の一つとされている。症例は45歳，男性。H1N1インフルエンザ関連acute respiratory distress syndrome（ARDS）に対してVV–ECMOを導入し，第10病日からawake ECMOに移行した。その後心嚢気腫・縦隔気腫を合併し，緊急で左胸部小開胸および心膜切開術を施行した。ただちにvital signsの安定化が得られ，ECMOおよび人工呼吸器離脱を果たした。しかし，その後発作的に起こる咳嗽に伴い重症air leak症候群が再増悪し，呼吸不全に対してVV–ECMO再導入となった。ECMO中は深鎮静・筋弛緩持続投与による厳密な超肺保護換気を徹底し，咳嗽予防とリーク消失を図った。胸膜癒着術を施行し2回目のECMO離脱を果たした。H1N1インフルエンザ関連ARDSの治癒過程では，自発呼吸下であっても気胸・気腫症の合併には注意を払うべきであり，ECMO中も同様である。本症例のような続発性自然気胸は難治性であり，leak消失まで長期化が予想される。そのためECMO導入により酸素化を改善し，肺を休めてair leakの改善を図ることが有効である可能性がある。</jats:p>