Objective: We aimed to identify the risk factors for ventilator-associated pneumonia in patients admitted to critical care after a torso injury. Methods: We retrospectively evaluated 178 patients with torso injury aged >15 years who were intubated in the emergency room and placed on a mechanical ventilator after intensive care unit (ICU) admission, survived for >48 hours, had thoracic and/or abdominal injuries, and had no end-stage renal disease. We compared clinico-laboratory variables between ventilator-associated pneumonia (n = 54, 30.3%) and non-ventilator-associated pneumonia (n = 124, 69.7%) groups. Risk factors for ventilator-associated pneumonia were assessed using multivariable logistic regression analysis. Results: Ventilator-associated pneumonia was associated with a significantly longer stay in the ICU (11.3 vs. 6.8 days) and longer duration of mechanical ventilation (7 vs. 3 days). Injury Severity Score (adjusted odds ratio [AOR]: 1.048; 95% confidence interval [CI]: 1.008–1.090), use of vasopressors (AOR: 2.541; 95% CI: 1.121–5.758), and insertion of a nasogastric tube (AOR: 6.749; 95% CI: 2.397–18.999) were identified as independent risk factors of ventilator-associated pneumonia. Conclusion: Ventilator-associated pneumonia in patients with torso injury who were admitted to the ICU was highly correlated with Injury Severity Score, use of vasopressors, and insertion of a nasogastric tube.

Acute kidney injury (AKI) is common in patients with trauma and is associated with poor outcomes. Therefore, early prediction of AKI in patients with trauma is important for risk stratification and the provision of optimal intensive care unit treatment. This study aimed to compare 2 models, machine learning (ML) techniques and logistic regression, in predicting AKI in patients with trauma. We retrospectively reviewed the charts of 400 patients who sustained torso injuries between January 2016 and June 2020. Patients were included if they were aged > 15 years, admitted to the intensive care unit, survived for > 48 hours, had thoracic and/or abdominal injuries, had no end-stage renal disease, and had no missing data. AKI was defined in accordance with the Kidney Disease Improving Global Outcomes definition and staging system. The patients were divided into 2 groups: AKI (n = 78) and non-AKI (n = 322). We divided the original dataset into a training (80%) and a test set (20%), and the logistic regression with stepwise selection and ML (decision tree with hyperparameter optimization using grid search and cross-validation) was used to build a model for predicting AKI. The models established using the training dataset were evaluated using a confusion matrix receiver operating characteristic curve with the test dataset. We included 400 patients with torso injury, of whom 78 (19.5%) progressed to AKI. Age, intestinal injury, cumulative fluid balance within 24 hours, and the use of vasopressors were independent risk factors for AKI in the logistic regression model. In the ML model, vasopressors were the most important feature, followed by cumulative fluid balance within 24 hours and packed red blood cell transfusion within 4 hours. The accuracy score showed no differences between the 2 groups; however, the recall and F1 score were significantly higher in the ML model (.94 vs 56 and.75 vs 64, respectively). The ML model performed better than the logistic regression model in predicting AKI in patients with trauma. ML techniques can aid in risk stratification and the provision of optimal care.

The Acute Physiology and Chronic Health Evaluation (APACHE) II model has been widely used in Korea. However, there have been few studies on the APACHE IV model in Korean intensive care units (ICUs). The aim of this study was to compare the ability of APACHE IV and APACHE II in predicting hospital mortality, and to investigate the ability of APACHE IV as a critical care triage criterion. The study was designed as a prospective cohort study. Measurements of discrimination and calibration were performed using the area under the receiver operating characteristic curve (AUROC) and the Hosmer-Lemeshow goodness-of-fit test respectively. We also calculated the standardized mortality ratio (SMR). The APACHE IV score, the Charlson Comorbidity index (CCI) score, acute respiratory distress syndrome, and unplanned ICU admissions were independently associated with hospital mortality. The calibration, discrimination, and SMR of APACHE IV were good (H = 7.67, P = 0.465; C = 3.42, P = 0.905; AUROC = 0.759; SMR = 1.00). However, the explanatory power of an APACHE IV score >93 alone on hospital mortality was low at 44.1%. The explanatory power was increased to 53.8% when the hospital mortality was predicted using a model that considers APACHE IV >93 scores, medical admission, and risk factors for CCI >3 coincidentally. However, the discriminative ability of the prediction model was unsatisfactory (C index <0.70). The APACHE IV presented good discrimination, calibration, and SMR for hospital mortality.

The occurrence of trauma is associated with various factors, including weather. We aimed to elucidate the relationship between local weather factors and the incidence of trauma to effectively manage and treat patients in a community setting. A retrospective study was conducted at a single center from January 2016 to December 2019. The study participants were trauma patients in the Cheongju area where the regional trauma center is located. Weather data including average daily temperature (°C), rainfall duration (hours), amount of rainfall (mm), average relative humidity (%), wind speed (m/s), and total sunlight hours per day were collected. One-way analysis of variance, correlation analysis, and linear regression analysis were performed. The average age of the participants (n = 3352) was 52.69 years. As regards seasonal difference in the incidence of trauma, there were more patients in spring than in winter (2.42/day vs 2.06/day, P = .05). The highest number of average daily trauma incidents occurred from April to June, and the difference between this value and that from January to February was significant (F = 2.20, P = .01). According to the distributed lag nonlinear model (DLnM), the relative risk is greater than 1 when the mean temperature is high (>15°C) compared to when the temperature is low (Ref. 8 hours), the trauma patient prevalence was relatively higher than the median value (lag = 0). DLnM analysis results showed that the relative risk of trauma patients increased as the amount of precipitation increased, and the incidence of trauma increased when the relative humidity was 40% to 50%. Multiple linear regression analysis revealed that high average daily temperatures and long average daily total sunlight hours resulted in an increased incidence of trauma (F = 6.605, P < .001). An increase in temperature, an increase in the daily sunlight hours, an increase in rainfall, high wind speed, and relative humidity of 40% to 50% are associated with a relatively high risk of trauma.

Rationale: Hypothermia, defined as a core body temperature below 35°C, is a common and serious complication in severe trauma patients, often worsened by hemorrhage and medical interventions. Dexmedetomidine, an α2-adrenergic agonist used for sedation in intensive care units, has known thermoregulatory effects; however, its association with hypothermia in trauma patients remains insufficiently explored. Patient concerns: A 40-year-old male with severe polytrauma from a motor vehicle accident presented in distress, with hypotension, tachycardia, and a baseline temperature of 35.8°C. Despite effective management, he developed profound hypothermia, with a recorded temperature dropping below 34.0°C after switching from midazolam to dexmedetomidine for sedation. Diagnoses: The patient had multiple bilateral rib fractures, a right-sided pneumothorax, and grade 3 liver and grade 5 splenic injuries, along with orthopedic fractures. His Injury Severity Score signified critical trauma, increasing the risk of complications like hypothermia. Interventions: Following stabilization, dexmedetomidine was administered for sedation. Continuous warming interventions were initiated to address hypothermia; however, the temperature continued to decline. Suspecting dexmedetomidine’s contribution, its administration was discontinued. Outcomes: After stopping dexmedetomidine, the patient’s temperature gradually recovered to 36.8°C within 5 hours. He demonstrated improved consciousness and stable vital signs, subsequently undergoing 2 successful orthopedic surgeries and discharging without further hypothermia-related issues. Lessons: This case highlights dexmedetomidine’s potential to induce hypothermia in critically ill trauma patients. It stresses the importance of careful temperature monitoring and proactive thermoregulation during sedative administration in intensive care. Further research is needed to explore the prevalence and mechanisms of dexmedetomidine-associated hypothermia in trauma populations.

We experienced 3 cases of manometry-induced colon perforation. A 75-year-old man (case 1) underwent anorectal manometry (ARM) 3 years after radiotherapy for prostate cancer and a laparoscopic intersphincteric resection for rectal cancer. A 70-year-old man (case 2) underwent ARM 3 months after conventional neoadjuvant chemoradiotherapy and a laparoscopic low anterior resection for rectal cancer. A 78-year-old man (case 3) underwent ARM 2 months after a laparoscopic intersphincteric resection for rectal cancer. In all cases, a colon perforation with fecal peritonitis occurred. All were treated successfully using prompt and active operations and were discharged without any complications. ARM with a balloon, as a measure of rectal compliance, should be performed 2 months or longer after surgery. If a perforation occurs, prompt and active surgical intervention is necessary due to the high possibility of extensive fecal peritonitis.