To investigate the relationship between antithrombin (AT) activity level and prognosis in patients requiring intensive care. Patients whose AT activity was measured within 24 h of intensive care unit (ICU) admission were enrolled for analysis. The primary endpoint was mortality at discharge. Prognostic accuracy was examined using receiver operating characteristic (ROC) curves and cox hazard regression analysis. Patients were divided into 6 groups based on predicted mortality, and a χ² independence test was performed on the prognostic value of AT activity for each predicted mortality; P < .05 was considered significant. A total of 281 cases were analyzed. AT activity was associated with mortality at discharge (AT% [interquartile range, IQR]): survivor group, 69 (56-86) versus nonsurvivor group, 56 (44-73), P = .0003). We found an increasing risk for mortality in both the lowest level of AT activity (<50%; hazard ratio [HR] 2.43, 95% confidence interval [CI] 1.20-4.89, P = .01) and the middle-low level of AT activity (≥ 50% and < 70%; HR 2.06, 95% CI 1.06-4.02, P = .03), compared with the normal AT activity level (≥ 70%). ROC curve analysis showed that the prediction accuracy of AT was an area under the curve (AUC) of 0.66 (cutoff 58%, sensitivity 61.4%, specificity 68.2%, P = .0003). AT activity was significantly prognostic in the group with 20% to 50% predicted mortality (AUC 0.74, sensitivity: 24.0%-55.5%, specificity: 83.3%-93.0%). An early decrease in AT activity level in ICU patients may be a predictor of mortality at discharge.

Hot springs, hot-water bathing, and saunas are widely practiced forms of acute heat exposure and are often perceived as health-promoting. However, emergency clinicians frequently encounter patients in whom these exposures precipitate syncope, hypotension, drowning/aspiration, heat-related illness, and renal or electrolyte disturbances. This narrative review integrates these modalities within a unified “acute heat exposure” framework and summarizes pathophysiology and clinical implications from an emergency medicine perspective. We searched PubMed/MEDLINE from inception to January 2026 using controlled vocabulary and free-text terms related to heat stress, thermoregulation, hot-water immersion, sauna exposure, and acute clinical outcomes; evidence was synthesized qualitatively. Across modalities, acute heat exposure induces shared physiological responses—peripheral vasodilation, relative hypovolemia, circulatory stress, and internal heat storage—that can trigger diverse emergency presentations. We classify acute heat exposure–related illness into four domains: (1) cardiovascular events, including syncope, hypotension, and arrhythmic/ischemic complications in vulnerable individuals; (2) the heat-illness spectrum from exhaustion to heat stroke with organ dysfunction; (3) renal and electrolyte disturbances related to dehydration and hypoperfusion; and (4) neurological and traumatic complications, including falls, drowning, and aspiration. This framework may support risk stratification, evaluation, management, and prevention after hot spring, hot bath, or sauna use.

Purpose: This study was performed to evaluate the changes in oxygen supply–demand balance during induction of general anesthesia using an indirect calorimeter capable of measuring oxygen consumption (VO₂) and carbon dioxide production (VCO₂). Methods: This study included patients scheduled for surgery in whom remimazolam was administered as a general anesthetic. VO₂ and VCO₂ were measured at different intervals: upon awakening (T1), 15 min after tracheal intubation (T2), and 1 h after T2 (T3). Oxygen delivery (DO₂) was calculated simultaneously with these measurements. VO₂ was ascertained using an indirect calorimeter and further calculated using vital signs, among other factors. DO₂ was derived from cardiac output and arterial blood gas analysis performed with an arterial pressure-based cardiac output measurement system. Results: VO₂, VCO₂, and DO₂ decreased significantly from T1 to T2 and T3 [VO₂/body surface area (BSA) (ml/min/m²): T1, 130 (122–146); T2, 107 (83–139); T3, 97 (93–121); p = 0.011], [VCO₂/BSA (ml/min/m²): T1, 115 (105–129); T2, 90 (71–107); T3, 81 (69–101); p = 0.011], [DO₂/BSA (ml/min/m²): T1, 467 (395–582); T2, 347 (286–392); T3, 382 (238–414); p = 0.0020]. Among the study subjects, a subset exhibited minimal reduction in VCO₂. Although the respiratory frequency was titrated on the basis of end-tidal CO₂ levels, there was no significant difference between the groups. Conclusion: General anesthetic induction with remimazolam decreased VO₂, VCO₂, and DO₂.

Agitation-related movements in intensive care units (ICUs), such as unintended tube removal and bed exit attempts, pose significant risks to patient safety. The wearable inertial measurement units (IMUs) offer a potential means of capturing such movements. However, the technical feasibility of discriminating ICU-relevant agitation motion patterns from multi-site IMU data remains insufficiently established. To evaluate the technical feasibility of using a convolutional neural network (CNN) applied to multi-site IMU signals to discriminate predefined ICU-relevant agitation-related motion patterns under controlled experimental conditions. Fifteen healthy volunteers performed six scripted movements designed to emulate ICU-relevant agitation-related behaviors while wearing seven IMU sensors on the limbs and waist. A CNN comprising three convolutional layers with kernel sizes of 3, 5, and 7 was trained using 1-s windows extracted from 8-s trials and evaluated using leave-one-subject-out cross-validation. The performance was summarized using macro-averaged accuracy, sensitivity, specificity, precision, and F1 score. Across 135 independent training runs, the CNN achieved a median macro-averaged accuracy of 77.0%, sensitivity of 77.0%, specificity of 95.4%, and F1 score of 77.4%. These results indicate stable window-level discrimination of the predefined motion classes under standardized conditions. This proof-of-concept study demonstrates that multi-site IMU signals combined with CNN-based modeling can technically discriminate ICU-relevant agitation-related motion patterns in a controlled laboratory setting. Although these findings do not establish clinical validity in ICU patients, they provide a methodological foundation for future studies aimed at patient-level validation and real-world critical care deployment.