方山 真朱

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: The optimal strategy for discontinuing arginine vasopressin and norepinephrine in patients recovering from shock remains uncertain. Although prior studies have suggested a higher risk of hypotension when arginine vasopressin is discontinued first, these findings may have been influenced by baseline imbalances and tapering practices. We conducted a retrospective study to evaluate whether the order of discontinuation between arginine vasopressin and norepinephrine was associated with the incidence of hypotension during the recovery phase of shock, with vasopressor end doses converted to norepinephrine equivalents for analysis. METHODS: This was a single-center retrospective cohort study of intensive care unit patients with shock who received both arginine vasopressin and norepinephrine from August 2017 to March 2024. Patients were categorized based on whether arginine vasopressin or norepinephrine was discontinued first. The primary outcome was the incidence of hypotension within 24 h of vasopressor cessation, defined as mean arterial pressure < 60 mmHg requiring a ≥ 25% increase in the remaining vasopressor, reinstitution of the stopped agent, or a bolus of ≥ 500 mL crystalloid or 25 g albumin. Overlap weighting using propensity scores was applied to adjust for baseline imbalances both in the overall cohort and in the septic shock subgroup. Propensity scores were estimated using logistic model, including baseline characteristics, hemodynamic parameters, and vasopressor end doses in norepinephrine equivalents. RESULTS: A total of 524 patients were analyzed, with 293 discontinuing AVP first and 231 discontinuing NE first. In the unadjusted cohorts, hypotension occurred in 19% of the AVP-first group and 26% of the NE-first group. After overlap weighting, all baseline covariates were balanced between the groups, and the incidence of hypotension was not significantly different (19% vs 21%, P = 0.59). In the septic shock subgroup (n = 267), the weighted analysis showed no significant difference in the incidence of hypotension between groups. CONCLUSIONS: In patients recovering from shock who received both arginine vasopressin and norepinephrine, discontinuing arginine vasopressin first was not associated with a higher risk of hypotension.

BACKGROUND: Polypharmacy, defined as the concurrent use of multiple medications, poses significant health risks, particularly among aging populations. While polypharmacy is a recognized concern, limited research has examined its spatial distribution or its association with demographic and socioeconomic factors. This study aimed to examine the spatial patterns of polypharmacy across Japan and identify regional characteristics associated with higher polypharmacy rates. This study could contribute to evaluating the effectiveness of current local and national policies and may also inform future policy initiatives. METHODS: An ecological study was conducted across 335 local health units in Japan using data from national health, demographic, and geographic databases. Polypharmacy was defined as prescriptions containing seven or more drugs, and the standardized polypharmacy ratio (SPR) was calculated by age-group population. Spatial autocorrelation of SPR was assessed using Moran's I statistic. Clustering analysis incorporating SPR and regional variables identified distinct high-risk areas. RESULTS: The prediction model for polypharmacy prescriptions achieved an R² of 0.98, indicating high accuracy, though SPR remained heterogeneous. Significant spatial autocorrelation was observed for both polypharmacy prescriptions (Moran's I = 0.4; P < 0.001) and SPR (Moran's I = 0.24; P < 0.001), highlighting regional clustering. Clustering analysis identified four groups by polypharmacy risk (critical, high, moderate, and low). High-SPR areas were associated with higher population density, a greater proportion of younger adults (ages 20 to 60), and increased levels of education, income, and tertiary industry workers. CONCLUSION: Polypharmacy in Japan exhibits significant spatial clustering, with higher rates in urbanized regions driven by demographic and socioeconomic factors. Region-specific interventions addressing these unique characteristics are essential for improving polypharmacy management.

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.