富田 啓介

BACKGROUND: Interleukin-6 (IL-6) is a cytokine that predicts clinical outcomes in critically ill patients, including those with sepsis. Elderly patients have blunted and easily dysregulated host responses to infection, which may influence IL-6 kinetics and alter the association between IL-6 levels and clinical outcomes. METHODS: This retrospective observational study included patients aged ≥ 16 years who were admitted to the intensive care unit at Chiba University Hospital. The patients were categorized into two groups: non-elderly (< 70 years) and elderly (≥ 70 years). Associations between log-transformed blood IL-6 levels and 28-day in-hospital mortality (primary outcome) and multiple organ dysfunction (MOD) on days 3 and 7 (secondary outcomes) were examined. RESULTS: The non-elderly and elderly groups included 272 and 247 patients, respectively. There were no significant differences in the Sequential Organ Failure Assessment score, components of the APACHE II score (Acute physiology score and Chronic health points), MOD at baseline, or any of the outcome measures between the groups. In the non-elderly group, univariate Cox regression analysis showed a significant association between IL-6 levels and mortality (hazard ratio [HR] 1.71, 95% confidence interval [CI] 1.25-2.37, P < 0.001). This association remained significant after adjusting for sex, body mass index, steroid use prior to sepsis onset, and number of chronic organ dysfunctions (HR 1.66, 95% CI 1.20-2.32, P = 0.002). However, no significant association was observed in the elderly group in either the univariate (P = 0.69) or multivariable analyses (P = 0.77). Multivariable logistic regression analysis of MOD on days 3 and 7 revealed significant associations between MOD and IL-6 levels in both groups. CONCLUSIONS: Blood IL-6 levels were significantly associated with mortality in non-elderly patients with sepsis, but not in elderly patients. IL-6 levels were associated with MOD in both groups. Therefore, IL-6 levels should be interpreted with caution when predicting mortality in elderly patients with sepsis. TRIAL REGISTRATION: Not applicable.

This study investigated the accuracy of a machine learning algorithm for predicting mortality in patients receiving rapid response system (RRS) activation. This retrospective cohort study used data from the In-Hospital Emergency Registry in Japan, which collects nationwide data on patients receiving RRS activation. The missing values in the dataset were replaced using multiple imputations (mode imputation, BayseRidge sklearn. linear model, and K-nearest neighbor model), and the enrolled patients were randomly assigned to the training and test cohorts. We established prediction models for 30-day mortality using the following four types of machine learning classifiers: Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting, random forest, and neural network. Fifty-two variables (patient characteristics, details of RRS activation, reasons for RRS initiation, and hospital capacity) were used to construct the prediction algorithm. The primary outcome was the accuracy of the prediction model for 30-day mortality. Overall, the data from 4,997 patients across 34 hospitals were analyzed. The machine learning algorithms using LightGBM demonstrated the highest predictive value for 30-day mortality (area under the receiver operating characteristic curve, 0.860 [95 % confidence interval, 0.825-0.895]). The SHapley Additive exPlanations summary plot indicated that hospital capacity, site of incidence, code status, and abnormal vital signs within 24 h were important variables in the prediction model for 30-day mortality.

To efficiently allocate medical resources at disaster sites, medical workers perform triage to prioritize medical treatments based on the severity of the wounded or sick. In such instances, evaluators often assess the severity status of the wounded or sick quickly, but their measurements are qualitative and rely on experience. Therefore, we developed a wearable device called Medic Hand in this study to extend the functionality of a medical worker's hand so as to measure multiple biometric indicators simultaneously without increasing the number of medical devices to be carried. Medic Hand was developed to quantitatively and efficiently evaluate "perfusion" during triage. Speed is essential during triage at disaster sites, where time and effort are often spared to attach medical devices to patients, so the use of Medic Hand as a biometric measurement device is more efficient for collecting biometric information. For Medic Hand to be handy during disasters, it is essential to understand and improve upon factors that facilitate its public acceptance. To this end, this paper reports on the usability evaluation of Medic Hand through a questionnaire survey of nonmedical workers.

AIM: This study investigated the correlation between the number of emergency medical service (EMS) dispatches and response time extension. In addition, we conducted a simulation to assess the potential for reducing response times by relocating the ambulance based on the number of dispatches. METHODS: This retrospective observational study analyzed data on patients treated with EMS between May 1 and June 25, 2021, in an urban area (Chiba City, Japan). Spearman's rank correlation tests were used to analyze the correlations among the number of dispatches, response time extension, and ambulance distance. We created a heat map to visualize the number of dispatches and distribution of emergency case occurrences, and simulated the relocation of the EMS team with the lowest number of dispatches to the closest EMS team with the highest number of dispatches. RESULTS: In total, 7915 emergency cases were included. The median response time across all dispatches was 9 min, whereas that for the response time extension cases was 12 min. There was a significant positive correlation between the increased number of dispatches, response time extension (r = 0.94, p < 0.0001), and ambulance distance (r = 0.95, p < 0.0001). The relocation simulation significantly shortened the average response time from 13 min and 30 s to 12 min and 11 s (9.9% decrease, p < 0.0001). CONCLUSION: An increased number of dispatches significantly increased the response time extension cases and ambulance distance. Our simulation suggests that EMS relocation can potentially shorten the response time. While increased dispatches influence the response time extension, optimal EMS allocation may improve response times.

Increased fluid overload (FO) is associated with poor outcomes in critically ill patients, especially in acute kidney injury (AKI). However, the exact timing from when FO influences outcomes remains unclear. We retrospectively screened intensive care unit (ICU) admitted patients with AKI between January 2011 and December 2015. Logistic or linear regression analyses were performed to determine when hourly %FO was significant on 90-day in-hospital mortality (primary outcome) or ventilator-free days (VFDs). In total, 1120 patients were enrolled in this study. Univariate analysis showed that a higher %FO was significantly associated with higher mortality from the first hour of ICU admission (odds ratio 1.34, 95% confidence interval 1.15-1.56, P < 0.001), whereas multivariate analysis adjusted with age, sex, APACHE II score, and sepsis etiology showed the association was significant from the 27th hour. Both univariate and multivariate analyses showed that a higher %FO was significantly associated with shorter VFDs from the 1st hour. The significant associations were retained during all following observation periods after they showed significance. In patients with AKI, a higher %FO was associated with higher mortality and shorter VFDs from the early phase after ICU admission. FO should be administered with a physiological target or goal in place from the initial phase of critical illness.