Improving difficult direct laryngoscopy prediction using deep learning and minimal image analysis: a single-center prospective study.

Accurate prediction of difficult direct laryngoscopy (DDL) is essential to ensure optimal airway management and patient safety. The present study proposed an AI model that would accurately predict DDL using a small number of bedside pictures of the patient's face and neck taken simply with a smartphone. In this prospective single-center study, adult patients scheduled for endotracheal intubation under general anesthesia were included. Patient pictures were obtained in frontal, lateral, frontal-neck extension, and open mouth views. DDL prediction was performed using a deep learning model based on the EfficientNet-B5 architecture, incorporating picture view information through multitask learning. We collected 18,163 pictures from 3053 patients. After under-sampling to achieve a 1:1 image ratio of DDL to non-DDL, the model was trained and validated with a dataset of 6616 pictures from 1283 patients. The deep learning model achieved a receiver operating characteristic area under the curve of 0.81-0.88 and an F1-score of 0.72-0.81 for DDL prediction. Including picture view information improved the model's performance. Gradient-weighted class activation mapping revealed that neck and chin characteristics in frontal and lateral views are important factors in DDL prediction. The deep learning model we developed effectively predicts DDL and requires only a small set of patient pictures taken with a smartphone. The method is practical and easy to implement.

Influence of Curved Video Laryngoscope Blade Sizes and Patient Heights on Video Laryngoscopic Views: A Randomized Controlled Trial.

This study aimed to compare the video laryngoscope views facilitated by curved blades 3 and 4 with an exploration of the relationship between these views and patient height. Conducted as a randomized controlled trial, this study enrolled adults scheduled for surgery under general anesthesia. Intubation procedures were recorded, and the percentage of glottic opening was measured before tube insertion. Multivariate analysis validated the impact of various factors, including blade size and patient height, on the percentage of glottic opening scores. A total of 192 patients were included. The median percentage of glottic opening scores for curved blades 3 and 4 were 100 and 83, respectively (p < 0.001). The unstandardized coefficient indicated a significant negative impact of blade 4 on the percentage of glottic opening scores (-13, p < 0.001). In the locally estimated scatterplot smoothing analysis, blade 3 exhibited a steady rise in glottic opening scores with increasing height, whereas blade 4 showed a peak followed by a decline around 185 cm. The unstandardized coefficient of height showed no significant association (0, p = 0.819). The study observed superior laryngoscopic views with blade 3 compared to blade 4. However, no significant association was found between laryngoscopic views and patient height.

Predictive Modeling and Integrated Risk Assessment of Postoperative Mortality and Pneumonia in Traumatic Brain Injury Patients through Clustering and Machine Learning: Retrospective Study.

This study harnessed machine learning to forecast postoperative mortality (POM) and postoperative pneumonia (PPN) among surgical traumatic brain injury (TBI) patients. Our analysis centered on the following key variables: Glasgow Coma Scale (GCS), midline brain shift (MSB), and time from injury to emergency room arrival (TIE). Additionally, we introduced innovative clustered variables to enhance predictive accuracy and risk assessment. Exploring data from 617 patients spanning 2012 to 2022, we observed that 22.9% encountered postoperative mortality, while 30.0% faced postoperative pneumonia (PPN). Sensitivity for POM and PPN prediction, before incorporating clustering, was in the ranges of 0.43-0.82 (POM) and 0.54-0.76 (PPN). Following clustering, sensitivity values were 0.47-0.76 (POM) and 0.61-0.77 (PPN). Accuracy was in the ranges of 0.67-0.76 (POM) and 0.70-0.81 (PPN) prior to clustering and 0.42-0.73 (POM) and 0.55-0.73 (PPN) after clustering. Clusters characterized by low GCS, small MSB, and short TIE exhibited a 3.2-fold higher POM risk compared to clusters with high GCS, small MSB, and short TIE. In summary, leveraging clustered variables offers a novel avenue for predicting POM and PPN in TBI patients. Assessing the amalgamated impact of GCS, MSB, and TIE characteristics provides valuable insights for clinical decision making.

Postoperative Nausea and Vomiting Prediction: Machine Learning Insights from a Comprehensive Analysis of Perioperative Data.

Postoperative nausea and vomiting (PONV) are common complications after surgery. This study aimed to present the utilization of machine learning for predicting PONV and provide insights based on a large amount of data. This retrospective study included data on perioperative features of patients, such as patient characteristics and perioperative factors, from two hospitals. Logistic regression algorithms, random forest, light-gradient boosting machines, and multilayer perceptrons were used as machine learning algorithms to develop the models. The dataset of this study included 106,860 adult patients, with an overall incidence rate of 14.4% for PONV. The area under the receiver operating characteristic curve (AUROC) of the models was 0.60-0.67. In the prediction models that included only the known risk and mitigating factors of PONV, the AUROC of the models was 0.54-0.69. Some features were found to be associated with patient-controlled analgesia, with opioids being the most important feature in almost all models. In conclusion, machine learning provides valuable insights into PONV prediction, the selection of significant features for prediction, and feature engineering.

Harnessing Machine Learning for Prediction of Postoperative Pulmonary Complications: Retrospective Cohort Design.

Postoperative pulmonary complications (PPCs) are significant causes of postoperative morbidity and mortality. This study presents the utilization of machine learning for predicting PPCs and aims to identify the important features of the prediction models. This study used a retrospective cohort design and collected data from two hospitals. The dataset included perioperative variables such as patient characteristics, preexisting diseases, and intraoperative factors. Various algorithms, including logistic regression, random forest, light-gradient boosting machines, extreme-gradient boosting machines, and multilayer perceptrons, have been employed for model development and evaluation. This study enrolled 111,212 adult patients, with an overall incidence rate of 8.6% for developing PPCs. The area under the receiver-operating characteristic curve (AUROC) of the models was 0.699-0.767, and the f1 score was 0.446-0.526. In the prediction models, except for multilayer perceptron, the 10 most important features were obtained. In feature-reduced models, including 10 important features, the AUROC was 0.627-0.749, and the f1 score was 0.365-0.485. The number of packed red cells, urine, and rocuronium doses were similar in the three models. In conclusion, machine learning provides valuable insights into PPC prediction, significant features for prediction, and the feasibility of models that reduce the number of features.

Association between Malnutrition and Migraine Risk Assessed Using Objective Nutritional Indices.

Dietary triggers are frequently linked to migraines. Although some evidence suggests that dietary interventions might offer a new avenue for migraine treatment, the connection between migraine and nutrition remains unclear. In this study, we explored the association between nutritional status and migraines. Clinical data spanning 11 years were sourced from the Smart Clinical Data Warehouse. The nutritional statuses of 6603 migraine patients and 90,509 controls were evaluated using the Controlling Nutrition Status (CONUT) score and the Prognostic Nutrition Index (PNI). The results showed that individuals with mild, moderate, and severe malnutrition were at a substantially higher risk of migraines than those with optimal nutrition, as determined by the CONUT score (adjusted odds ratio [aOR]: 1.72, 95% confidence interval [CI]: 1.63-1.82; aOR: 5.09, 95% CI: 4.44-5.84; aOR: 3.24, 95% CI: 2.29-4.59, p < 0.001). Similarly, moderate (PNI: 35-38) and severe (PNI < 35) malnutrition were associated with heightened migraine prevalence (aOR: 4.80, 95% CI: 3.85-5.99; aOR: 3.92, 95% CI: 3.14-4.89, p < 0.001) compared to those with a healthy nutritional status. These findings indicate that both the CONUT and PNI may be used as predictors of migraine risk and underscore the potential of nutrition-oriented approaches in migraine treatment.

Age-Related Variations in Postoperative Pain Intensity across 10 Surgical Procedures: A Retrospective Study of Five Hospitals in South Korea.

Age-related differences in pain perception have been reported in various contexts; however, their impact on postoperative pain intensity remains poorly understood, especially across different surgical procedures. Data from five hospitals were retrospectively analyzed, encompassing patients who underwent 10 distinct surgical procedures. Numeric rating scale scores were used to assess the worst postoperative pain intensity during the 24 h after surgery. The multivariate linear regression model analyzed the relationship between age and pain intensity. Subgroup analyses were performed according to sex and patient-controlled analgesia (PCA). This study included 41,187 patients. Among the surgeries studied, lumbar spine fusion (ß = -0.155, p < 0.001) consistently and significantly exhibited a decrease in worst postoperative pain with increasing age. Similar trends were observed in cholecystectomy (ß = -0.029, p < 0.001) and several other surgeries; however, the results were inconsistent across all analyses. Surgeries with higher percentages of PCA administration had lower median worst-pain scores. In conclusion, age may affect postoperative pain intensity after specific surgeries; however, a comprehensive understanding of the complex interplay between age, surgical intervention, and pain intensity is required. Pain management strategies should consider various factors, including age-related variations.

The Impact of Blood Transfusion in Developing Postoperative Delirium in Patients with Hip Fracture Surgery.

BACKGROUND: Many studies have been conducted to explore the risk factors associated with postoperative delirium (POD) in order to understand its underlying causes and develop prevention strategies, especially for hip fracture surgery. However, the relationship between blood transfusion and POD has been heatedly debated. The purpose of this study was to evaluate the risk factors of POD and the relationship between blood transfusions and the occurrence of POD in hip fracture surgery through big data analysis. METHODS: Medical data (including medication history, clinical and laboratory findings, and perioperative variables) were acquired from the clinical data warehouse (CDW) of the five hospitals of Hallym University Medical Center and were compared between patients without POD and with POD. RESULTS: The occurrence of POD was 18.7% (228 of 2398 patients). The risk factors of POD included old age (OR 4.38, 95% CI 2.77-6.91; p < 0.001), American Society of Anesthesiology physical status > 2 (OR 1.84 95% CI 1.4-2.42; p < 0.001), dementia (OR 1.99, 95% CI 1.53-2.6; p < 0.001), steroid (OR 0.53 95% CI 0.34-0.82; p < 0.001), Antihistamine (OR 1.53 95% CI 1.19-1.96; p < 0.001), and postoperative erythrocyte sedimentation rate (mm/h) (OR 0.97 95% CI 0.97-0.98; p < 0.001) in multivariate logistic regression analysis. The postoperative transfusion (OR 2.53, 95% CI 1.88-3.41; p < 0.001) had a significant effect on the incidence of POD. CONCLUSIONS: big data analytics using a CDW was a good option to identify the risk factors of POD and to prevent POD in hip fracture surgery.

Clinical Implications of the Association between Respiratory and Gastrointestinal Disorders in Migraine and Non-Migraine Headache Patients.

Headaches, particularly migraine, are associated with gastrointestinal (GI) disorders. In addition to the gut-brain axis, the lung-brain axis is suspected to be involved in the relationship between pulmonary microbes and brain disorders. Therefore, we investigated possible associations of migraine and non-migraine headaches (nMH) with respiratory and GI disorders using the clinical data warehouse over 11 years. We compared data regarding GI and respiratory disorders, including asthma, bronchitis, and COPD, among patients with migraine, patients with nMH, and controls. In total, 22,444 patients with migraine, 117,956 patients with nMH, and 289,785 controls were identified. After adjustment for covariates and propensity score matching, the odds ratios (ORs) for asthma (1.35), gastroesophageal reflux disorder (1.55), gastritis (1.90), functional GI disorder (1.35), and irritable bowel syndrome (1.76) were significantly higher in patients with migraine than in controls (p = 0.000). The ORs for asthma (1.16) and bronchitis (1.33) were also significantly higher in patients with nMH than in controls (p = 0.0002). When the migraine group was compared with the nMH group, only the OR for GI disorders was statistically significant. Our findings suggest that migraine and nMH are associated with increased risks of GI and respiratory disorders.

Prediction of Postoperative Pulmonary Edema Risk Using Machine Learning.

Postoperative pulmonary edema (PPE) is a well-known postoperative complication. We hypothesized that a machine learning model could predict PPE risk using pre- and intraoperative data, thereby improving postoperative management. This retrospective study analyzed the medical records of patients aged > 18 years who underwent surgery between January 2011 and November 2021 at five South Korean hospitals. Data from four hospitals (n = 221,908) were used as the training dataset, whereas data from the remaining hospital (n = 34,991) were used as the test dataset. The machine learning algorithms used were extreme gradient boosting, light-gradient boosting machine, multilayer perceptron, logistic regression, and balanced random forest (BRF). The prediction abilities of the machine learning models were assessed using the area under the receiver operating characteristic curve, feature importance, and average precisions of precision-recall curve, precision, recall, f1 score, and accuracy. PPE occurred in 3584 (1.6%) and 1896 (5.4%) patients in the training and test sets, respectively. The BRF model exhibited the best performance (area under the receiver operating characteristic curve: 0.91, 95% confidence interval: 0.84-0.98). However, its precision and f1 score metrics were not good. The five major features included arterial line monitoring, American Society of Anesthesiologists physical status, urine output, age, and Foley catheter status. Machine learning models (e.g., BRF) could predict PPE risk and improve clinical decision-making, thereby enhancing postoperative management.

Effect of Pregnancy on Postoperative Nausea and Vomiting in Female Patients Who Underwent Nondelivery Surgery: Multicenter Retrospective Cohort Study.

Pregnant women usually have several risk factors of postoperative nausea and vomiting (PONV) and physiologic changes that make them susceptible to PONV development. We investigated the risk of PONV and postoperative vomiting (PV) in pregnant women in nondelivery surgery compared to nonpregnant women. This study included female adult patients who underwent nondelivery surgery at five hospitals between January 2011 and March 2021. To identify the association between pregnancy and PONV, logistic regression was used to calculate the odds ratio and 95% confidence intervals (CIs), adjusting for covariates. A total of 60,656 (nonpregnant women = 57,363 and pregnant women = 3293) complete patient outcomes and perioperative data were eligible for analysis. Although there was no significant association between pregnancy and PONV, the risk of PV in the pregnant women was 3.9-fold higher (95% confidence interval (95% CI), 3.06-4.97) than in the nonpregnant women. In addition, increased pregnancy duration increased the risk of PV (odds ratio (95% CI), 1.05 (1.01-1.09)) and preoperative nausea, and vomiting increased the risk of PONV (odds ratio (95% CI), 2.68 (1.30-5.54)) and PV (odds ratio (95% CI), 4.52 (2.36-8.69)). Pregnancy increased the risk of PV in female patients who underwent nondelivery surgery, and pregnancy duration and preoperative nausea and vomiting also were associated with PONV or PV.

Role of Peripheral Inflammatory Markers in Patients with Acute Headache Attack to Differentiate between Migraine and Non-Migraine Headache.

Although the potential relationship between headaches, particularly migraine, and peripheral inflammatory markers (PIMs) has been investigated, it is unclear whether PIMs are involved in the pathogenesis of migraine or can differentiate it from non-migraine headaches (nMHs). Using 10 years of data from the Smart Clinical Data Warehouse, patients who visited the neurology outpatient department (OPD) within 30 days after visiting the emergency room (ER) for headaches were divided into migraine and nMH groups, the PIMs were compared including the neutrophil-to-lymphocyte (NLR), monocyte-to-lymphocyte (MLR), platelet-to-lymphocyte (PLR) ratios, and neutrophil-to-monocyte ratio (NMR). Of the 32,761 patients who visited the ER for headaches, 4005 patients visited the neurology OPD within 30 days. There were significant increases in the NLR, MLR, and NMR, but a lower PLR in the migraine and nMH groups than the controls. The NMR was significantly higher in the migraine than the nMH group. A receiver operating characteristic curve analysis showed that the ability of the NLR and NMR to differentiate between migraine and nMHs was poor, whereas it was fair between the migraine groups and controls. The elevated PIMs, particularly the NLR and NMR, during headache attacks in migraineurs suggest that inflammation plays a role in migraine and PIMs may be useful for supporting a migraine diagnosis.

The Influence of High Blood Pressure on Developing Symptomatic Lumbar Epidural Hematoma after Posterior Lumbar Spinal Fusion Surgery: Clinical Data Warehouse Analysis.

BACKGROUND: Determining the risk factors for symptomatic lumbar epidural hematoma (SLEH) is important for preventing postoperative SLEH. However, the relationship between blood pressure and SLEH is still debatable. The purpose of our study was to determine the risk factors for postoperative SLEH, to assess the influence of high blood pressure on developing SLEH after posterior lumbar spinal fusion surgery, and to evaluate the usefulness of big data analysis utilizing a clinical data warehouse (CDW). METHODS: The clinical data of patients who had undergone posterior lumbar spinal fusion surgery were acquired from the CDW of Hallym University Medical Center. The acquired clinical data were compared between patients without postoperative SLEH and with postoperative SLEH. RESULTS: Postoperative SLEH that required hematoma evacuation surgery within 72 h after posterior lumbar spinal fusion surgery occurred in 17 (1.3%) of 1313 patients. According to the multivariate logistic regression analysis, the risk factors for postoperative SLEH are platelet count difference (OR 1.28, p = 0.03), postoperative international normalized ratio (INR) difference (OR 31.4, p = 0.028), and postoperative systolic blood pressure (SBP) difference (≥10 mmHg) (OR 1.68, p = 0.048). An increase in postoperative SBP (OR 1.68, p = 0.048) had a statistically significant influence on the occurrence of postoperative SLEH. CONCLUSIONS: Big data analysis utilizing a CDW could be useful for extending our knowledge of the risk factors for postoperative SLEH and preventing postoperative SLEH after posterior lumbar spinal fusion surgery.

Preoperative Inflammatory Markers and the Risk of Postoperative Delirium in Patients Undergoing Lumbar Spinal Fusion Surgery.

We investigated the possible associations between postoperative delirium (POD) and routinely available preoperative inflammatory markers in patients undergoing lumbar spinal fusion surgery (LSFS) to explore the role of neuroinflammation and oxidative stress as risk factors for POD. We analyzed 11 years' worth of data from the Smart Clinical Data Warehouse. We evaluated whether preoperative inflammatory markers, such as the neutrophil-to-lymphocyte ratio (NLR), the monocyte-to-lymphocyte ratio (MLR), and the CRP-to-albumin ratio (CAR), affected the development of POD in patients undergoing LSFS. Of the 3081 subjects included, 187 (7.4%) developed POD. A significant increase in NLR, MLR, and CAR levels was observed in POD patients (p < 0.001). A multivariate analysis showed that the second, third, and highest quartiles of the NLR were significantly associated with the development of POD (adjusted OR (95% CI): 2.28 (1.25−4.16], 2.48 (1.3−4.73], and 2.88 (1.39−5.96], respectively). A receiver operating characteristic curve analysis showed that the discriminative ability of the NLR, MLR, and CAR for predicting POD was low, but almost acceptable (AUC (95% CI): 0.60 (0.56−0.64], 0.61 (0.57−0.65], and 0.63 (0.59−0.67], respectively, p < 0.001). Increases in preoperative inflammatory markers, particularly the NLR, were associated with the development of POD, suggesting that a proinflammatory state is a potential pathophysiological mechanism of POD.

Development and Validation of Deep Learning-Based Algorithms for Predicting Lumbar Herniated Nucleus Pulposus Using Lumbar X-rays.

Lumbar herniated nucleus pulposus (HNP) is difficult to diagnose using lumbar radiography. HNP is typically diagnosed using magnetic resonance imaging (MRI). This study developed and validated an artificial intelligence model that predicts lumbar HNP using lumbar radiography. A total of 180,271 lumbar radiographs were obtained from 34,661 patients in the form of lumbar X-ray and MRI images, which were matched together and labeled accordingly. The data were divided into a training set (31,149 patients and 162,257 images) and a test set (3512 patients and 18,014 images). Training data were used for learning using the EfficientNet-B5 model and four-fold cross-validation. The area under the curve (AUC) of the receiver operating characteristic (ROC) for the prediction of lumbar HNP was 0.73. The AUC of the ROC for predicting lumbar HNP in L (lumbar) 1-2, L2-3, L3-4, L4-5, and L5-S (sacrum)1 levels were 0.68, 0.68, 0.63, 0.67, and 0.72, respectively. Finally, an HNP prediction model was developed, although it requires further improvements.

Comparison of the Effect of Sugammadex and Pyridostigmine on Postoperative Catheter-Related Bladder Discomfort: A Retrospective Matched Cohort Analysis.

Background and Objectives: As the use of sugammadex for reversing neuromuscular blockade during general anesthesia increases, additional effects of sugammadex have been reported compared to cholinesterase inhibitors. Here, we compare the incidence of postoperative catheter-related bladder discomfort (CRBD) between sugammadex and pyridostigmine/glycopyrrolate treatments for reversing neuromuscular blockade. Materials and Methods: We retrospectively analyzed patients aged ≥ 18 years who underwent surgery under general anesthesia, received sugammadex or pyridostigmine with glycopyrrolate to reverse neuromuscular blockade, and had a urinary catheter in the post-anesthesia care unit between March 2019 and February 2021. After applying the exclusion criteria, 1179 patients were included in the final analysis. The incidence and severity of CRBD were collected from post-anesthesia recovery records. Results: The incidence was 13.7% in the sugammadex group (n = 211) and 24.7% in the pyridostigmine group (n = 968). Following propensity score matching, 211 patients each were included in the pyridostigmine and sugammadex matched group (absolute standardized difference (ASD), 0.01-0.05). Compared to the pyridostigmine group, the odds ratio for CRBD occurring in the sugammadex group was 0.568 (95% confidential interval, 0.316-1.021, p = 0.059). Conclusions: Sugammadex has a similar effect on the occurrence of postoperative CRBD compared with pyridostigmine.

The Relationship between Perioperative Blood Transfusion and Postoperative Delirium in Patients Undergoing Spinal Fusion Surgery: Clinical Data Warehouse Analysis.

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.

Risk of Vestibulocochlear Disorders in Patients with Migraine or Non-Migraine Headache.

Headaches, especially migraines, have been associated with various vestibular symptoms and syndromes. Tinnitus and hearing loss have also been reported to be more prevalent among migraineurs. However, whether headaches, including migraine or non-migraine headaches (nMH), are associated with vestibular and cochlear disorders remains unclear. Thus, we sought to investigate possible associations between headache and vestibulocochlear disorders. We analyzed 10 years of data from the Smart Clinical Data Warehouse. In patients with migraines and nMH, meniere's disease (MD), BPPV, vestibular neuronitis (VN) and cochlear disorders, such as sensorineural hearing loss (SNHL) and tinnitus, were collected and compared to clinical data from controls who had health check-ups without headache. Participants included 15,128 with migraines, 76,773 patients with nMH and controls were identified based on propensity score matching (PSM). After PSM, the odds ratios (OR) in subjects with migraine versus controls were 2.59 for MD, 2.05 for BPPV, 2.98 for VN, 1.74 for SNHL, and 1.97 for tinnitus, respectively (p < 0.001). The OR for MD (1.77), BPPV (1.73), VN (2.05), SNHL (1.40), and tinnitus (1.70) in patients with nMH was also high after matching (p < 0.001). Our findings suggest that migraines and nMH are associated with an increased risk of cochlear disorders in addition to vestibular disorders.

Risk of Dementia in Patients Who Underwent Surgery under Neuraxial Anesthesia: A Nationwide Cohort Study.

The incidence of dementia in patients with surgery under neuraxial anesthesia and the possibility of surgery under neuraxial anesthesia as a risk factor for dementia were investigated. We performed a retrospective matched cohort study with nationwide, representative cohort sample data of the Korean National Health Insurance Service in South Korea between 1 January 2003, and 31 December 2004. The participants were divided into control (n = 4488) and neuraxial groups (n = 1122) using propensity score matching. After 9 years of follow-up, the corresponding incidences of dementia were 11.5 and 14.8 cases per 1000 person-years. The risk of dementia in the surgery under neuraxial group was 1.44-fold higher (95% confidence interval [95%CI], 1.17-1.76) than that in the control group. In the subgroup analysis of dementia, the risk of Alzheimer's disease in those who underwent surgery under neuraxial anesthesia was 1.48-fold higher (95%CI, 1.17-1.87) than that in those who did not undergo surgery under anesthesia. Our findings suggest that patients who underwent surgery under neuraxial anesthesia had a higher risk of dementia and Alzheimer's disease than those who did not undergo surgery under neuraxial anesthesia.

Longitudinal Study of the Association between General Anesthesia and Increased Risk of Developing Dementia.

The association between exposure to general anesthesia (GA) and the risk of dementia is still undetermined. To investigate a possible link to the development of dementia in older people who have undergone GA, we analyzed nationwide representative cohort sample data from the Korean National Health Insurance Service. The study cohort comprised patients over 55 years of age who had undergone GA between January 2003 and December 2004 and consisted of 3100 patients who had undergone GA and 12,400 comparison subjects who had not received anesthesia. After the nine-year follow-up period, we found the overall incidence of dementia was higher in the patients who had undergone GA than in the comparison group (10.5 vs. 8.8 per 1000 person-years), with the risk being greater for women (adjusted HR of 1.44; 95% CI, 1.19-1.75) and those with comorbidities (adjusted HR of 1.39; 95% CI, 1.18-1.64). Patients who underwent GA showed higher risks for Alzheimer's disease and vascular dementia (adjusted HR of 1.52; 95% CI, 1.27-1.82 and 1.64; 95% CI, 1.15-2.33, respectively). This longitudinal study using a sample cohort based on a nationwide population sample demonstrated a significant positive association between GA and dementia, including Alzheimer's disease and vascular dementia.