Background: Perioperative blood glucose level has shown an association with postoperative outcomes. We compared the incidences of myocardial injury after noncardiac surgery (MINS) and 30‐day mortality, according to preoperative blood glucose and hemoglobin A1c (HbA1c) levels. Methods and Results: The patients were divided according to blood glucose level within 1 day before surgery. The hyperglycemia group was defined with fasting glucose >140 mg/dL or random glucose >180 mg/dL. In addition, we compared the outcomes according to HbA1c >6.5% among patients with available HbA1c within 3 months before surgery. The primary outcome was MINS, and 30‐day mortality was also compared. A total of 12 304 patients were enrolled and divided into 2 groups: 8324 (67.7%) in the normal group and 3980 (32.3%) in the hyperglycemia group. After adjustment with inverse probability of weighting, the hyperglycemia group exhibited significantly higher incidences of MINS and 30‐day mortality (18.7% versus 27.6%; odds ratio, 1.29; 95% CI, 1.18–1.42; P <0.001; and 2.0% versus 5.1%; hazard ratio, 2.00; 95% CI, 1.61–2.49; P <0.001, respectively). In contrast to blood glucose, HbA1c was not associated with MINS or 30‐day mortality. Conclusions: Preoperative hyperglycemia was associated with MINS and 30‐day mortality, whereas HbA1c was not. Immediate glucose control may be more crucial than long‐term glucose control in patients undergoing noncardiac surgery. Registration: URL: https://www.cris.nih.go.kr ; Unique identifier: KCT0004244.

Myocardial injury after non-cardiac surgery (MINS) is strongly associated with postoperative outcomes. We developed a prediction model for MINS and have provided it online. Between January 2010 and June 2019, a total of 6811 patients underwent non-cardiac surgery with normal preoperative level of cardiac troponin (cTn). We used machine learning techniques with an extreme gradient boosting algorithm to evaluate the effects of variables on MINS development. We generated two prediction models based on the top 12 and 6 variables. MINS was observed in 1499 (22.0%) patients. The top 12 variables in descending order according to the effects on MINS are preoperative cTn level, intraoperative inotropic drug infusion, operation duration, emergency operation, operation type, age, high-risk surgery, body mass index, chronic kidney disease, coronary artery disease, intraoperative red blood cell transfusion, and current alcoholic use. The prediction models are available at https://sjshin.shinyapps.io/mins_occur_prediction/. The estimated thresholds were 0.47 in 12-variable models and 0.53 in 6-variable models. The areas under the receiver operating characteristic curves are 0.78 (95% confidence interval [CI] 0.77–0.78) and 0.77 (95% CI 0.77–0.78), respectively, with an accuracy of 0.97 for both models. Using machine learning techniques, we demonstrated prediction models for MINS. These models require further verification in other populations.

Background: Hyperglycemia has shown a negative association with cognitive dysfunction. We analyzed patients with high preoperative blood glucose level and hemoglobin A1c (HbA1c) level to determine the prevalence of postoperative delirium.Methods: We reviewed a database of 23,532 patients with diabetes who underwent non-cardiac surgery. Acute hyperglycemia was defined as fasting blood glucose > 140 mg/dl or random glucose > 180 mg/dl within 24 h before surgery. Chronic hyperglycemia was defined as HbA1c level above 6.5% within three months before surgery. The incidence of delirium was compared according to the presence of acute and chronic hyperglycemia.Results: Of the 23,532 diabetic patients, 21,585 had available preoperative blood glucose level within 24 h before surgery, and 18,452 patients reported levels indicating acute hyperglycemia. Of the 8,927 patients with available HbA1c level within three months before surgery, 5,522 had levels indicating chronic hyperglycemia. After adjustment with inverse probability weighting, acute hyperglycemia was related to higher incidence of delirium (hazard ratio: 1.33, 95% CI [1.10,1.62], P = 0.004 for delirium) compared with controls without acute hyperglycemia. On the other hand, chronic hyperglycemia did not correlate with postoperative delirium.Conclusions: Preoperative acute hyperglycemia was associated with postoperative delirium, whereas chronic hyperglycemia was not significantly associated with postoperative delirium. Irrespective of chronic hyperglycemia, acute glycemic control in surgical patients could be crucial for preventing postoperative delirium.

Background: Cardiac complications are associated with perioperative mortality, but perioperative adverse cardiac events (PACEs) that are associated with long‐term mortality have not been clearly defined. We identified PACE as a composite of myocardial infarction, coronary revascularization, congestive heart failure, arrhythmic attack, acute pulmonary embolism, cardiac arrest, or stroke during the 30‐day postoperative period and we compared mortality according to PACE occurrence. Methods and Results: From January 2011 to June 2019, a total of 203 787 consecutive adult patients underwent noncardiac surgery at our institution. After excluding those with 30‐day mortality, mortality during a 1‐year follow‐up was compared. Machine learning with the extreme gradient boosting algorithm was also used to evaluate whether PACE was associated with 1‐year mortality. After excluding 1203 patients with 30‐day mortality, 202 584 patients were divided into 7994 (3.9%) patients with PACE and 194 590 (96.1%) without PACE. After an adjustment, the mortality was higher in the PACE group (2.1% versus 7.7%; hazard ratio [HR], 1.90; 95% CI, 1.74–2.09; P <0.001). Results were similar for 7839 pairs of propensity‐score‐matched patients (4.9% versus 7.9%; HR, 1.64; 95% CI, 1.44–1.87; P <0.001). PACE was significantly associated with mortality in the extreme gradient boostingmodel. Conclusions: PACE as a composite outcome was associated with 1‐year mortality. Further studies are needed for PACE to be accepted as an end point in clinical studies of noncardiac surgery.

Introduction: The large-scale artificial intelligence (AI) language model chatbot, Chat Generative Pre-Trained Transformer (ChatGPT), is renowned for its ability to provide data quickly and efficiently. This study aimed to assess the medical responses of ChatGPT regarding anesthetic procedures.Methods: Two anesthesiologist authors selected 30 questions representing inquiries patients might have about surgery and anesthesia. These questions were inputted into two versions of ChatGPT in English. A total of 31 anesthesiologists then evaluated each response for quality, quantity, and overall assessment, using 5-point Likert scales. Descriptive statistics summarized the scores, and a paired sample t-test compared ChatGPT 3.5 and 4.0.Results: Regarding quality, “appropriate” was the most common rating for both ChatGPT 3.5 and 4.0 (40 and 48%, respectively). For quantity, responses were deemed “insufficient” in 59% of cases for 3.5, and “adequate” in 69% for 4.0. In overall assessment, 3 points were most common for 3.5 (36%), while 4 points were predominant for 4.0 (42%). Mean quality scores were 3.40 and 3.73, and mean quantity scores were − 0.31 (between insufficient and adequate) and 0.03 (between adequate and excessive), respectively. The mean overall score was 3.21 for 3.5 and 3.67 for 4.0. Responses from 4.0 showed statistically significant improvement in three areas.Conclusion: ChatGPT generated responses mostly ranging from appropriate to slightly insufficient, providing an overall average amount of information. Version 4.0 outperformed 3.5, and further research is warranted to investigate the potential utility of AI chatbots in assisting patients with medical information.