BackgroundThe aim of this study was to evaluate the sedative effect of dexmedetomidine (DEX) added to ropivacaine for supraclavicular brachial plexus block (BPB) using the bispectral index (BIS).MethodsSixty patients (American Society of Anesthesiologists physical status 1 or 2, aged 20-65 years) undergoing wrist and hand surgery under supraclavicular BPB were randomly allocated to two groups. Ultrasound-guided supraclavicular BPB was performed with 40 ml of ropivacaine 0.5% and 1 µg/kg of DEX (Group RD) or 0.01 ml/kg of normal saline (Group R). The primary endpoint was the BIS change during 60 min after block. The secondary endpoint was the change in the mean arterial blood pressure (MAP), heart rate (HR), and SpO2 and the onset time and duration of the sensory and motor block.ResultsIn Group RD, the BIS decreased significantly until 30 min after the block (69.2 ± 13.7), but remained relatively constant to 60 min (63.8 ± 15.3). The MAP, HR and BIS were significantly decreased compared with Group R. The onset time of the sensory and motor block were significantly faster in Group RD than in Group R. The duration of the sensory and motor block were significantly increased in Group RD.ConclusionsDEX added to ropivacaine for brachial plexus block induced sedation that corresponds to a BIS value of 60 from which patients are easily awakened in a lucid state. In addition, perineural DEX shortened the onset time and prolonged the duration of the sensory and motor blocks.

BackgroundThe brain and gastrointestinal (GI) tract are strongly connected via neural, endocrine, and immune pathways. Previous studies suggest that headaches, especially migraines, may be associated with various GI disorders. However, upper GI endoscopy in migraineurs has shown a low prevalence of abnormal findings. Also, the majority of studies have not demonstrated an association between Helicobacter pylori (HP) infection and migraine, although a pathogenic role for HP infection in migraines has been suggested. Further knowledge concerning the relation between headaches and GI disorders is important as it may have therapeutic consequences. Thus, we sought to investigate possible associations between GI disorders and common primary headaches, such as migraines and tension-type headaches (TTH), using the Smart Clinical Data Warehouse (CDW) over a period of 10 years.MethodsWe retrospectively investigated clinical data using a clinical data analytic solution called the Smart CDW from 2006 to 2016. In patients with migraines and TTH who visited a gastroenterology center, GI disorder diagnosis, upper GI endoscopy findings, and results of HP infection were collected and compared to clinical data from controls, who had health checkups without headache. The time interval between headache diagnosis and an examination at a gastroenterology center did not exceed 1 year.ResultsPatients were age- and sex-matched and eligible cases were included in the migraine (n = 168), the TTH (n = 168), and the control group (n = 336). Among the GI disorders diagnosed by gastroenterologists, gastroesophageal reflux disorder was more prevalent in the migraine group, whereas gastric ulcers were more common in the migraine and TTH groups compared with controls (p < 0.0001). With regard to endoscopic findings, there were high numbers of erosive gastritis and chronic superficial gastritis cases in the migraine and TTH groups, respectively, and the severity of gastritis was significantly higher in patients with TTH compared with controls (p < 0.001). However, no differences were observed in the prevalence of HP infection between the groups.ConclusionThe observed association in this study may suggest that primary headache sufferers who experience migraines or TTH are more prone to GI disorders, which may have various clinical implications. Further research concerning the etiology of the association between headaches and GI disorders is warranted.

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).

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.

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.

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 &lt; 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 &lt; 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 &lt; 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.

Background and Objectives: For preventing postoperative delirium (POD), identifying the risk factors is important. However, the relationship between blood transfusion and POD is still controversial. The aim of this study was to identify the risk factors of POD, to evaluate the impact of blood transfusion in developing POD among people undergoing spinal fusion surgery, and to show the effectiveness of big data analytics using a clinical data warehouse (CDW). Materials and Methods: The medical data of patients who underwent spinal fusion surgery were obtained from the CDW of the five hospitals of Hallym University Medical Center. Clinical features, laboratory findings, perioperative variables, and medication history were compared between patients without POD and with POD. Results: 234 of 3967 patients (5.9%) developed POD. In multivariate logistic regression analysis, the risk factors of POD were as follows: Parkinson’s disease (OR 5.54, 95% CI 2.15–14.27; p < 0.001), intensive care unit (OR 3.45 95% CI 2.42–4.91; p < 0.001), anti-psychotics drug (OR 3.35 95% CI 1.91–5.89; p < 0.001), old age (≥70 years) (OR 3.08, 95% CI 2.14–4.43; p < 0.001), depression (OR 2.8 95% CI 1.27–6.2; p < 0.001). The intraoperative transfusion (OR 1.1, 95% CI 0.91–1.34; p = 0.582), and the postoperative transfusion (OR 0.91, 95% CI 0.74–1.12; p = 0.379) had no statistically significant effect on the incidence of POD. Conclusions: There was no relationship between perioperative blood transfusion and the incidence of POD in spinal fusion surgery. Big data analytics using a CDW could be helpful for the comprehensive understanding of the risk factors of POD, and for preventing POD in spinal fusion surgery.

The impact of migraine on postoperative nausea and vomiting (PONV) is controversial, and few studies have focused on their relationship. Thus, we investigated the impact of migraine, among other risk factors, on PONV in a large retrospective study. We analyzed 10 years of clinical data from the Smart Clinical Data Warehouse of Hallym University Medical Center. PONV was defined as nausea or vomiting within the first 24 h after surgery. Patients diagnosed by a neurologist and with a history of triptan use before surgery were enrolled into the migraine group. We enrolled 208,029 patients aged > 18 years who underwent general anesthesia (GA), among whom 19,786 developed PONV within 24 h after GA and 1982 had migraine. Before propensity score matching, the unadjusted and fully adjusted odds ratios (ORs) for PONV in subjects with versus without migraine were 1.52 (95% confidence interval (CI), 1.34–1.72; p < 0.001) and 1.37 (95% CI, 1.21–1.56; p < 0.001), respectively. The OR for PONV in patients with migraine was also high (OR, 1.37; 95% CI, 1.13–1.66; p = 0.001) after matching. Our findings suggest that migraine is a significant risk factor for PONV.