Background: The Controlling Nutritional Status (CONUT) score and the prognostic nutritional index (PNI) have emerged as important nutritional indices because they provide an objective assessment based on data. We aimed to investigate how these nutritional indices relate to outcomes in patients with sepsis.Methods: Data were collected retrospectively at five hospitals for patients aged ≥18 years receiving treatment for sepsis between January 1, 2017, and December 31, 2021. Serum albumin and total cholesterol concentrations, and peripheral lymphocytes were used to calculate the CONUT score and PNI. To identify predictors correlated with 30-day mortality, analyses were conducted using univariate and multivariate Cox proportional hazards models.Results: The 30-day mortality rate among 9,763 patients was 15.8% (n=1,546). The median CONUT score was 5 (interquartile range [IQR], 3–7) and the median PNI score was 39.6 (IQR, 33.8–46.4). Higher 30-day mortality rates were associated with individuals with moderate (CONUT score: 5–8; PNI: 35–38) or severe (CONUT: 9–12; PNI: 38). With CONUT scores, the hazard ratio (HR) associated with moderate malnutrition was 1.52 (95% confidence interval [CI], 1.24–1.87, P<0.001); for severe, HR=2.42 (95% CI, 1.95–3.02, P<0.001). With PNI scores, the HR for moderate malnutrition was 1.29 (95% CI, 1.09–1.53, P=0.003); for severe, HR=1.88 (95% CI, 1.67–2.12, P<0.001).Conclusions: The nutritional indices CONUT score and PNI showed significant associations with mortality of sepsis patients within 30 days.

Few data are available regarding factors that impact cricothyrotomy use and outcome in general hospital setting. The aim of the present study was to determine the incidence and outcomes of the patients underwent cricothyrotomy in a “cannot intubate, cannot oxygenate” (CICO) situation at university hospitals in Korea. This was a retrospective review of the electronic medical records of consecutive patients who underwent cricothyrotomy during a CICO situation between March, 2007, and October, 2018, at 2 university hospitals in Korea. Data regarding patient characteristics and outcomes were analyzed using descriptive statistics. During the study period, a total of 10,187 tracheal intubations were attempted and 23 patients received cricothyrotomy. Hospitalwide incidence of cricothyrotomy was 2.3 per 1000 tracheal intubations (0.23%). The majority of cricothyrotomy procedures (22 cases, 95.7%) were performed in the emergency department (ED); 1 cricothyrotomy was attempted in the endoscopy room. In the ED, 5663 intubations were attempted and the incidence of cricothyrotomy was 3.9 per 1000 tracheal intubations (0.39%). Survival rate at hospital discharge was 47.8% (11 of 23 cases). Except for cardiac arrest at admission, survival rate was 62.5% (10 of 16 cases). Successful cricothyrotomy was performed in 17 patients (73.9%) and 9 patients (52.9%) were survived. Among 6 patients of failed cricothyrotomy (26.1%), 2 patients (33.3%) were survived. After failure of cricothyrotomy, various methods of securing airway were established: 3 tracheal intubations, 1 nasotracheal intubation, and 1 tracheostomy. The success rate of cricothyrotomy and survival rate in the CICO situation were not high. After failure of cricothyrotomy, various methods of securing airway were performed.

The spread of carbapenem-resistant Enterobacterales (CRE) poses a public health threat worldwide. We aimed to compare the mortality rates between the carbapenemase-producing (CP) and non-CP CRE bacteremia. We conducted a retrospective cohort study in patients with CRE bacteremia after propensity score (PS) matching. We performed a Kaplan–Meier curve analysis to identify the cumulative hazard for 30-day mortality. There were 318 patients with CRE between January 1, 2018, and December 31, 2022. There were 252 patients with CP-CRE and 66 with non-CP-RE, respectively. Before PS matching, the 30-day mortality rates were 40.9% in the non-CP-CRE group and 53.2% in the CP-CRE group (p = 0.097). In patients in the intensive care unit (ICU), the mortality rates were 49.0% in the non-CP-CRE group and 57.1% in the CP-CRE group (p = 0.340). After PS matching, the hazard ratio (HR) for mortality in the CP-CRE group was 1.49 (95% confidence interval [CI] 0.74–3.03), p = 0.266). In ICU patients, the HR of CP-CRE was 1.11 (95% CI 0.36–3.39, p = 0.860). The Kaplan–Meier curve for 30-day mortality showed no difference in cumulative hazard. After PS matching, there was no difference in 30-day mortality between patients with CP-CRE and non-CP-CRE bacteremia.

Background: Predicting difficult airway is challengeable in patients with limited airway evaluation. The aim of this study is to develop and validate a model that predicts difficult laryngoscopy by machine learning of neck circumference and thyromental height as predictors that can be used even for patients with limited airway evaluation. Methods: Variables for prediction of difficulty laryngoscopy included age, sex, height, weight, body mass index, neck circumference, and thyromental distance. Difficult laryngoscopy was defined as Grade 3 and 4 by the Cormack-Lehane classification. The preanesthesia and anesthesia data of 1677 patients who had undergone general anesthesia at a single center were collected. The data set was randomly stratified into a training set (80%) and a test set (20%), with equal distribution of difficulty laryngoscopy. The training data sets were trained with five algorithms (logistic regression, multilayer perceptron, random forest, extreme gradient boosting, and light gradient boosting machine). The prediction models were validated through a test set. Results: The model’s performance using random forest was best (area under receiver operating characteristic curve = 0.79 [95% confidence interval: 0.72–0.86], area under precision-recall curve = 0.32 [95% confidence interval: 0.27–0.37]). Conclusions: Machine learning can predict difficult laryngoscopy through a combination of several predictors including neck circumference and thyromental height. The performance of the model can be improved with more data, a new variable and combination of models.

Background: The aim of this study was to compare morning surgery (Group A), characterized by high cortisol levels, with afternoon surgery (Group B), characterized by low cortisol levels, with respect to cortisol, inflammatory cytokines (interleukin [IL]-6, IL-8), and postoperative hospital days (POHD) after hip surgery. Methods: The study was conducted in a single center, prospective, randomized (1:1) parallel group trial. Patients undergoing total hip replacement or hemiarthroplasty were randomly divided into two groups according to the surgery start time: 8 am (Group A) or 1–2 pm (Group B). Cortisol and cytokine levels were measured at 7:30 am on the day of surgery, before induction of anesthesia, and at 6, 12, 24, and 48 hours (h) after surgery. Visual analogue scale (VAS) and POHD were used to evaluate the clinical effect of surgery start time. VAS was measured at 6, 12, 24, and 48 h postoperatively, and POHD was measured at discharge. Results: In total, 44 patients completed the trial. The postoperative cortisol level was significantly different between the two groups. (24 h, P < .001; 48 h, P < .001). The percentage of patients whose level returned to the initial level was higher in Group B than in Group A (P < .001). Significant differences in IL-6 levels were observed between the two groups at 12, 24, and 48 h after surgery (P = .015; P = .005; P = .002), and in IL-8 levels at 12 and 24 h after surgery (P = .002, P < .001). There was no significant difference between the two groups in VAS and POHD. However, only three patients in Group A were inpatients for more than 3 weeks (P = .233). Conclusions: Afternoon surgery allowed for more rapid recovery of cortisol to the baseline level than morning surgery, and IL-6 and IL-8 were lower at 1–2 days postoperatively. The results of this study suggest that afternoon surgery may be considered in patients with postoperative delayed wound healing or inflammation because of the difference in cortisol, IL-6 and 8 in according to surgery start time. Clinical trial registration number: NCT03076827 (ClinicalTRrial.gov).

Previous scoring models, such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) score, do not adequately predict the mortality of patients receiving mechanical ventilation in the intensive care unit. Therefore, this study aimed to apply machine learning algorithms to improve the prediction accuracy for 30-day mortality of mechanically ventilated patients. The data of 16,940 mechanically ventilated patients were divided into the training-validation (83%, n = 13,988) and test (17%, n = 2952) sets. Machine learning algorithms including balanced random forest, light gradient boosting machine, extreme gradient boost, multilayer perceptron, and logistic regression were used. We compared the area under the receiver operating characteristic curves (AUCs) of machine learning algorithms with those of the APACHE II and ProVent score results. The extreme gradient boost model showed the highest AUC (0.79 (0.77–0.80)) for the 30-day mortality prediction, followed by the balanced random forest model (0.78 (0.76–0.80)). The AUCs of these machine learning models as achieved by APACHE II and ProVent scores were higher than 0.67 (0.65–0.69), and 0.69 (0.67–0.71)), respectively. The most important variables in developing each machine learning model were APACHE II score, Charlson comorbidity index, and norepinephrine. The machine learning models have a higher AUC than conventional scoring systems, and can thus better predict the 30-day mortality of mechanically ventilated patients.