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.

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.

It is not clear whether mortality is associated with body temperature (BT) in older sepsis patients. This study aimed to evaluate the mortality rates in sepsis patients according to age and BT and identify the risk factors for mortality. We investigated the clusters using a machine learning method based on a combination of age and BT, and identified the mortality rates according to these clusters. This retrospective multicenter study was conducted at five hospitals in Korea. Data of sepsis patients aged ≥ 18 years who were admitted to the intensive care unit between January 1, 2011 and April 30, 2021 were collected. BT was divided into three groups (hypothermia < 36 °C, normothermia 36‒38 °C, and hyperthermia > 38 °C), and age groups were divided using a 75-year age threshold. Kaplan‒Meier analysis was performed to assess the cumulative mortality over 90 days. A K-means clustering algorithm using age and BT was used to characterize phenotypes. During the study period, 15,574 sepsis patients were enrolled. Overall, 90-day mortality was 20.5%. Kaplan‒Meier survival analyses demonstrated that 90-day mortality rates were 27.4%, 19.6%, and 11.9% in the hypothermia, normothermia, and hyperthermia groups, respectively, in those ≥ 75 years old (Log-rank p < 0.001). Cluster analysis demonstrated three groups: Cluster A (relatively older age and lower BT), Cluster B (relatively younger age and wide range of BT), and Cluster C (relatively higher BT than Cluster A). Kaplan‒Meier curve analysis showed that the 90-day mortality rates of Cluster A was significantly higher than those of Clusters B and C (24.2%, 17.1%, and 17.0%, respectively; Log-rank p < 0.001). The 90-day mortality rate correlated inversely with BT groups among sepsis patients in either age group (< 75 and ≥ 75 years). Clustering analysis revealed that the mortality rate was higher in the cluster of patients with relatively older age and lower BT.

Background: Postoperative cognitive dysfunction (POCD) following anesthesia and surgery is a common and severe complication, especially in elderly patients. A pre-existing cognitive impairment may impart susceptibility to further cognitive dysfunction; the mechanism remains unclear. We hypothesized that the specific impacts of anesthesia and surgery on individuals with preclinical Alzheimer’s disease (AD) may render them more susceptible to an increase in the risk of cognitive impairment. The aim of this study was to compare the cognitive impairment between normal adult mice and those with preclinical AD after propofol anesthesia and surgery.Methods: We performed abdominal surgery in cognitively pre-symptomatic, 5-month-old male mice with sporadic AD (apolipoprotein E4 allele, ApoE4-KI) and age-matched (C57BL/6J) controls. Propofol anesthesia (170 mg/kg) was induced via retro-orbital injection over 2 h. Morris water maze (MWM) and Y-maze tests were conducted 2 days before and 2, 4, and 7 days after surgery. The mean escape latencies and spontaneous alternation percentages were the major outcomes. Neuronal apoptosis in hippocampal sections was evaluated using the terminal dUTP nick-end labeling (TUNEL) assay. Hippocampal amyloid beta (Aβ) levels were assessed via quantitative immunohistochemistry (IHC).Results: The control mice exhibited increased mean escape latencies of MWM at postoperative 2 and 4, but not at day 7; ApoE4-KI mice exhibited such increases at postoperative days 2, 4 and 7. Significant differences between ApoE4-KI and control mice in terms of the mean escape latencies were evident at days 2 and 7 (both P < 0.05). However, performance on a non-hippocampal memory tasks (Y-maze test) did not differ. More TUNEL-positive neurons were evident in the hippocampal CA3 region of ApoE4-KI mice at postoperative days 2 and 4, but not at day 7 compared to the control group (both P < 0.05). IHC revealed significantly elevated Aβ deposition in the hippocampal CA3 region of ApoE4-KI mice at postoperative days 4 and 7 compared to control mice (both P < 0.05).Conclusions: Propofol anesthesia followed by surgery induced persistent changes in cognition, and pathological hippocampal changes in pre-symptomatic, but vulnerable AD mice. It would be appropriate to explore whether preclinical AD patients are more vulnerable to POCD development.