Association of malnutrition status with 30-day mortality in patients with sepsis using objective nutritional indices: a multicenter retrospective study in South Korea.

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, 33.846.4). Higher 30-day mortality rates were associated with individuals with moderate (CONUT score: 5-8; PNI: 35-38) or severe (CONUT: 9-12; PNI: <35) malnutrition compared with those with no malnutrition (CONUT: 0-1; 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.

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.

Comparison of mortality rates in patients with carbapenem-resistant Enterobacterales bacteremia according to carbapenemase production: a multicenter propensity-score matched study.

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.

Effect of sugammadex on postoperative complications in patients with severe burn who underwent surgery: a retrospective study.

This retrospective study investigated the association of sugammadex with postoperative pulmonary complication risk between 2013 and 2021 in patients with severe burn of five hospitals. Postoperative pulmonary complications included atelectasis, pulmonary edema, pulmonary effusion, pneumothorax, pneumonia, pulmonary thromboembolism, respiratory failure and acute respiratory distress. To identify whether sugammadex reduced the risk of postoperative pulmonary complication in patients with severe burn who underwent surgery, Kaplan-Meier curve were used to check the difference of incidence according to surgical cases and time-varying Cox hazard regression were used to calculate the hazard ratio. The study included 1213 patients with severe burn who underwent 2259 surgeries. Postoperative pulmonary complications were occurred in 313 (25.8%) patients. Among 2259 surgeries, sugammadex was used in 649 (28.7%) surgeries. Cumulative postoperative pulmonary complication were 268 (16.6%) cases in surgeries without sugammadex, and 45 (6.9%) cases in surgeries with sugammadex, respectively (P < 0.005). The postoperative pulmonary complications risk was reduced significantly in patients who use sugammadex than those who did not use sugammadex. (Adjusted hazard ratio, 0.61; 95% confidence interval, 0.42-0.89; P = 0.011). In conclusion, sugammadex reduced risk of postoperative pulmonary complications compared with nonuse of sugammadex in patients with severe burn who underwent surgery.

Stereotactic ablative radiotherapy for primary renal cell carcinoma.

Stereotactic ablative radiotherapy (SAbR) is an emerging non-invasive definitive treatment option for primary renal cell carcinoma (RCC), particularly when surgery is not ideal. Employing ablative doses, SAbR delivered in one to five fractions to the primary tumor has been shown to achieve high local control rates with favorable toxicity profile in multiple retrospective and prospective series, and has dispelled previous notions of RCC radio-resistance. Moreover, emerging evidence suggests possible immunomodulatory effects, leading to clinical investigations of SAbR in combination with systemic and surgical management in patients with metastatic disease. In this review, we summarize key evidence supporting SAbR delivered to the primary tumor including preclinical rationale, dose escalation studies, recent prospective trials, and outcomes from ongoing multi-institutional registries. We also discuss areas of active clinical investigation including the use of primary SAbR in combination with systemic therapies in patients with metastatic disease. The accumulated body of evidence supports SAbR as promising indication being increasingly incorporated into the multi-disciplinary management of primary RCC.

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.

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.

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.

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.

Neuromuscular blockade in mechanically ventilated pneumonia patients with moderate to severe hypoxemia: A multicenter retrospective study.

BACKGROUND/PURPOSE: The benefit of neuromuscular blockades (NMBs) in critically ill patients receiving mechanical ventilation remains uncertain. Therefore, we aimed to investigate whether NMB use is associated with improved survival of mechanically ventilated pneumonia patients with moderate to severe hypoxemia. METHODS: This retrospective multicenter study was conducted at five university-affiliated hospitals. Data of pneumonia patients aged 18 years and older who received mechanical ventilation between January 1, 2011, and December 31, 2020, were analyzed. RESULTS: In a total of 1,130 patients, the mean patient age was 73.1 years (SD±12.6), and the overall mortality rate at 30 d was 29.5% (n = 333). NMB users had a higher 30 d mortality rate than NMB nonusers (33.9% vs. 26.8%, P = 0.014). After PS matching, the 30 d mortality rate was not significantly different between NMB users and nonusers (33.4% vs. 27.8%, p = 0.089). However, 90 d mortality rate was significantly increased in NMB users (39.7% vs. 31.9%, p = 0.021). Univariable Cox proportional hazard regression analyses showed that NMB use ≥ 3 d was significant risk factor for the 90 d mortality than those with < 3 d use (90 d mortality HR 1.39 [95% CI: 1.01-1.91], P = 0.045). CONCLUSIONS: NMB use was not associated with lower 30 d mortality among mechanically ventilated pneumonia patients with moderate to severe hypoxemia. Rather, NMB users had higher 90 d mortality, furthermore, and NMB use ≥ 3 d was associated with a higher risk of long-term mortality compared to NMB use < 3 d. Therefore, care should be taken to avoid extended use of NMB in critically ill pneumonia patients during mechanical ventilation.

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.

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.

Predictive model for difficult laryngoscopy using machine learning: retrospective cohort study

Abstract Background Both predictions and predictors of difficult laryngoscopy are controversial. Machine learning is an excellent alternative method for predicting difficult laryngoscopy. This study aimed to develop and validate practical predictive models for difficult laryngoscopy through machine learning. Methods Variables for the prediction of difficult laryngoscopy included age, Mallampati grade, body mass index, sternomental distance, and neck circumference. Difficult laryngoscopy was defined as grade 3 and 4 by the Cormack-Lehane classification. Pre-anesthesia and anesthesia data of 616 patients who had undergone anesthesia at a single center were included. The dataset was divided into a base training set (n = 492) and a base test set (n = 124), with equal distribution of difficult laryngoscopy. Training data sets were trained with six algorithms (multilayer perceptron, logistic regression, supportive vector machine, random forest, extreme gradient boosting, and light gradient boosting machine), and cross-validated. The model with the highest area under the receiver operating characteristic curve (AUROC) was chosen as the final model, which was validated with the test set. Results The results of cross-validation were best using the light gradient boosting machine algorithm with Mallampati score x age and sternomental distance as predictive model parameters. The predicted AUROC for the difficult laryngoscopy class was 0.71 (95% confidence interval, 0.59-0.83; p= 0.014), and the recall (sensitivity) was 0.85. Conclusion Predicting difficult laryngoscopy is possible with three parameters. Severe damage resulting from failure to predict difficult laryngoscopy with high recall is small with the reported model. The model's performance can be further enhanced by additional data training.

Effect of the preoperative physical status on postoperative nausea and vomiting risk: a matched cohort study.

BACKGROUND: The American Society of Anesthesiologists Physical Status Classification System is commonly used for preoperative assessment. Patient physical status before surgery can play an important role in postoperative nausea and vomiting. However, the relationship between the physical status classification and postoperative nausea and vomiting has not been well defined. METHODS: Adults aged ≥ 18 years who underwent procedures under anesthesia between 2015 and 2020 were included in the study. We analyzed the relationship of postoperative nausea and vomiting with physical status classification score using propensity score matching and Cox hazard regression. Differences in intraoperative use of vasopressor and inotropes and invasive monitoring were investigated according to the classification. RESULTS: A total of 163,500 patients were included in the study. After matching, classification 1 versus 2 included 43,400 patients; 1 versus ≤ 3, 13,287 patients; 2 versus ≤ 3, 23,530 patients (absolute standardized difference, 0-0.06). Patients with physical status classification ≤ 3 had a significantly lower postoperative nausea and vomiting risk than those with classification 1-2 (physical status classification 1 vs. ≤ 3, hazard ratio 0.76 [0.71-0.82], P < 0.001; 2 versus ≤ 3, hazard ratio 0.86 [0.82-0.91], P < 0.001). Intraoperative use of vasopressor or inotrope and invasive monitoring were noted more in the high physical status classification than the low physical status classification (absolute standardized difference [0.19-1.25]). CONCLUSION: There were differences in intraoperative invasive monitoring and use of vasopressor or inotrope among the classifications, and a score of 3 or higher reduced the risk of postoperative nausea and vomiting more than a score of 1-2.

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.