Background: Previous studies have shown that patients with cerebrovascular disease (CVD) have a significantly increased risk of cognitive decline or dementia; however, the association between preoperative CVD and perioperative neurocognitive disorders (PNDs) remains unclear. This study aimed to explore the correlation between preoperative CVD and PNDs, as well as combine logistic regression and receiver operating characteristic (ROC) curves to construct a clinical prediction PND model. Materials and Methods: This retrospective cohort study evaluated 13 899 surgical patients of a large-scale comprehensive hospital between January 2021 and January 2022 to explore the association between preoperative CVD and PNDs, with follow-up to monitor postoperative survival until 28 February 2023, unless the patient died. The study participants comprised all inpatients from the Bone and Joint Surgery, Spine Surgery, Urology, Hepatobiliary Surgery, Gastrointestinal Surgery, and Thoracic Surgery departments. Patients were classified into two groups: the CVD group with a confirmed diagnosis and the noncerebrovascular disease group. The incidence of PNDs was measured, and potential associations between patient demographic information, preoperative comorbidities, and CVD, as well as the correlation between preoperative CVD and PNDs, were investigated by multivariate logistic regression analysis. Next, the authors constructed a clinical prediction PND model by drawing the ROC curve. The postoperative survival of all patients was tracked, and a survival curve was constructed and incorporated into the Cox proportional hazard regression model to analyze the relationship between preoperative CVD and the overall postoperative survival rate. Results: Of the included 13 899 patients, propensity score matching yielded 1006 patient pairs. Multivariate logistic regression analysis revealed that CVD was an independent risk factor for PNDs [odds ratio: 10.193; 95% CI: 7.454–13.938; P<0.001]. Subsequently, the authors developed a clinical prediction model for PNDs by multivariate logistic regression analysis. The area under the ROC curve was 0.798 (95% CI: 0.765–0.830). The survival of 11 702 patients was followed up. Multivariate Cox hazard ratio regression analysis revealed that CVD affected the overall postoperative survival rate (hazard ratio, 1.398; 95% CI: 1.112–1.758; P<0.001). Conclusion: CVD was an independent risk factor for PNDs and affected the overall postoperative survival rate of surgical patients with preoperative CVD.

This study aims to deliver a systematic review and meta-analysis to scrutinize the tolerability of different doses of oliceridine in acute pain patients. A comprehensive search was carried out in essential databases (PubMed, Embase, Cochrane Library, and Web of Science) for relevant studies up to the most recent available date.We included Randomized Controlled Trials (RCTs) that compared oliceridine with other interventions in acute pain management. Patients received an equi-analgesic dose of oliceridine relative to morphine, ensuring comparable Sum of Pain Intensity Differences (SPID-48 or SPID-24) or mean pain score reductions across groups. The initial loading dose was 1.5 mg for oliceridine and 4 mg for morphine, followed by demand doses via patient-controlled analgesia (PCA). For oliceridine, the demand doses were 0.1, 0.35, or 0.5 mg, while for morphine, it was 1 mg.Utilizing the Review Manager 5.4, data on nausea, vomiting, sedation, dizziness, pruritus, and hypoxemia were assembled and evaluated.We conducted sensitivity analyses to confirm the robustness of our findings. The preliminary search discovered 710 potential studies. Having gone through a careful screening process, a total of 7 RCTs met our inclusion benchmarks. Five distinct publications analyzed postoperative nausea and vomiting (PONV). According to our meta-analysis findings, patients assigned to the oliceridine group experienced a notably lower rate of postoperative nausea (PON) and postoperative vomiting (POV) compared to the morphine group (PON: RR = 0.55, 95% CI 0.41-0.74, P < 0.001; POV: RR = 0.36, 95% CI 0.28-0.47, P < 0.001). Data from 4 documents examined sedation and dizziness. Our findings demonstrate that oliceridine recipients had a significant decline in the incidence of both sedation and dizziness (sedation: RR = 0.64, 95% CI 0.45-0.91, P = 0.01; dizziness: RR = 0.71, 95% CI 0.57-0.88, P = 0.002).Moreover, the oliceridine group recorded a lower incident of hypoxemia showcasing a favorable safety profile (RR = 0.52, 95% CI 0.41-0.65, P < 0.001).Sensitivity analyses confirmed the robustness of these findings. The systematic review and meta-analysis reveal that oliceridine is a well-tolerated and safe intravenous analgesic for acute pain patients, often reducing the incidence of adverse events in comparison to morphine.

Background: Recent research has revealed the potential value of machine learning (ML) models in improving prognostic prediction for patients with trauma. ML can enhance predictions and identify which factors contribute the most to posttraumatic mortality. However, no studies have explored the risk factors, complications, and risk prediction of preoperative and postoperative traumatic coagulopathy (PPTIC) in patients with trauma. Objective: This study aims to help clinicians implement timely and appropriate interventions to reduce the incidence of PPTIC and related complications, thereby lowering in-hospital mortality and disability rates for patients with trauma. Methods: We analyzed data from 13,235 patients with trauma from 4 medical centers, including medical histories, laboratory results, and hospitalization complications. We developed 10 ML models in Python (Python Software Foundation) to predict PPTIC based on preoperative indicators. Data from 10,023 Medical Information Mart for Intensive Care patients were divided into training (70%) and test (30%) sets, with 3212 patients from 3 other centers used for external validation. Model performance was assessed with 5-fold cross-validation, bootstrapping, Brier score, and Shapley additive explanation values. Results: Univariate logistic regression identified PPTIC risk factors as (1) prolonged activated partial thromboplastin time, prothrombin time, and international normalized ratio; (2) decreased levels of hemoglobin, hematocrit, red blood cells, calcium, and sodium; (3) lower admission diastolic blood pressure; (4) elevated alanine aminotransferase and aspartate aminotransferase levels; (5) admission heart rate; and (6) emergency surgery and perioperative transfusion. Multivariate logistic regression revealed that patients with PPTIC faced significantly higher risks of sepsis (1.75-fold), heart failure (1.5-fold), delirium (3.08-fold), abnormal coagulation (3.57-fold), tracheostomy (2.76-fold), mortality (2.19-fold), and urinary tract infection (1.95-fold), along with longer hospital and intensive care unit stays. Random forest was the most effective ML model for predicting PPTIC, achieving an area under the receiver operating characteristic of 0.91, an area under the precision-recall curve of 0.89, accuracy of 0.84, sensitivity of 0.80, specificity of 0.88, precision of 0.88, F1-score of 0.84, and Brier score of 0.13 in external validation. Conclusions: Key PPTIC risk factors include (1) prolonged activated partial thromboplastin time, prothrombin time, and international normalized ratio; (2) low levels of hemoglobin, hematocrit, red blood cells, calcium, and sodium; (3) low diastolic blood pressure; (4) elevated alanine aminotransferase and aspartate aminotransferase levels; (5) admission heart rate; and (6) the need for emergency surgery and transfusion. PPTIC is associated with severe complications and extended hospital stays. Among the ML models, the random forest model was the most effective predictor. Trial Registration: Chinese Clinical Trial Registry ChiCTR2300078097; https://www.chictr.org.cn/showproj.html?proj=211051

Background: Severe acute pancreatitis (SAP) has high mortality. Early identification of high-risk factors that may progress to SAP and active intervention measures may improve the prognosis of SAP patients. Objective: Clinical data within 24 h after admission were retrospectively analyzed to provide an evidence for early screening of high-risk factors in patients with SAP. Methods: A review of clinical data of acute pancreatitis patients from January 1, 2018, to December 31, 2022, was conducted. We compared the clinical data of SAP and non-SAP patients, and a multivariable logistic regression model was used to identify the independent predictors of SAP. The receiver operating characteristic (ROC) curve of SAP was drawn for continuous numerical variables to calculate the optimal clinical cutoff value of each variable, and the predictive value of each variable was compared by the area under the ROC curve. Results: Based on the multivariate logistic regression analysis of Age (odds ratio (OR), 1.032;95% confident interval (CI),1.018-1.046, p < 0.001), body mass index (BMI) (OR, 1.181; 95% CI,1.083-1.288, p < 0.001), Non-HTGAP (nonhypertriglyceridemic acute pancreatitis) (OR, 2.098; 95% CI,1.276-3.45, p = 0.003), white blood cell count (WBC) (OR,1.072; 95% CI,1.034-1.111, p < 0.001), procalcitonin (PCT) (OR, 1.060; 95% CI, 1.027-1.095, p < 0.001), serum calcium (Ca) (OR,0.121; 95% CI, 0.050-0.292, p < 0.001), computed tomography severity index (CTSI) > 4 (OR,12.942;95% CI,7.267-23.049, p < 0.001) were identified as independent risk factors for SAP. The area under the ROC curve (AUC) and optimal CUT-OFF values of continuous numerical variables for predicting SAP were Age (0.6079,51.5), BMI (0.6,23.25), WBC (0.6701,14.565), PCT (0.7086, 0.5175), Ca (0.7787,1.965), respectively. Conclusion: Age, BMI, non-HTGAP, WBC, PCT, serum Ca and CTSI > 4 have good predictive value for SAP. (c) 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Importance: The optimal timing for fixation of extremity fractures after traumatic brain injury (TBI) remains controversial.Objective: To investigate whether patients who underwent extremity fixation within 24 hours of TBI experienced worse outcomes than those who had the procedure 24 hours or more after TBI.Design, Setting, and Participants: This cohort study used data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. Patients 16 years or older with TBI who underwent internal extremity fixation met inclusion criteria. To compare outcomes, patients who underwent the procedure within 24 hours were propensity score matched with those who underwent it 24 hours or later. Patients were treated from December 9, 2014, to December 17, 2017. Data analysis was conducted between August 1, 2022, and December 25, 2023.Main Outcomes and Measures: The primary outcome was an unfavorable functional status at 6 months (Glasgow Outcome Scale–Extended [GOSE] score ≤4).Results: A total of 253 patients were included in this study. The median age was 41 (IQR, 27-57) years, and 184 patients (72.7%) were male. The median Injury Severity Score (ISS) was 41 (IQR, 27-49). Approximately half of the patients (122 [48.2%]) had a mild TBI while 120 (47.4%) had moderate to severe TBI. Seventy-four patients (29.2%) underwent an internal extremity fixation within 24 hours, while 179 (70.8%) had the procedure 24 hours or later. At 6 months, 86 patients (34.0%) had an unfavorable functional outcome. After propensity score matching, there were no statistically significant differences in unfavorable functional outcomes at 6 months (odds ratio [OR], 1.12 [95% CI, 0.51-1.99]; P = .77) in patients with TBI of any severity. Similar results were observed in patients with mild TBI (OR, 0.71 [95% CI, 0.22-2.29]; P = .56) and moderate to severe TBI (OR, 1.08 [95% CI, 0.32-3.70]; P = .90).Conclusions and Relevance: The outcomes of extremity fracture fixation performed within 24 hours after TBI appear not to be worse than those of procedures performed 24 hours or later. This finding suggests that early fixation after TBI could be considered in patients with mild head injuries.

Background: Previous observational studies have reported that delirium has an association with an increased risk of Alzheimer's disease (AD), and that patients with AD have a higher risk of developing delirium. However, due to the limitations of observational study, it is challenging to confirm whether delirium has a causal effect on AD or reverse causation exists. Methods: A bidirectional two-sample Mendelian randomization (MR) was performed to investigate the relationship between delirium and AD. Summary statistics from genome-wide association studies of delirium and AD phenotypes were utilized. Inverse-variance weighted (IVW) method was used as the main analysis approach, and additional analyses were performed using MR Egger, weighted median, simple mode and weighted mode to ensure result accuracy. Heterogeneity and horizontal pleiotropy were assessed using Cochran's Q statistics and MR Egger intercept, separately. Results: The MR analyses showed that genetically predicted delirium was not associated with AD (IVW: odds ratio [OR] 0.98, 95% CI 0.91–1.05, P = 0.544; MR Egger: OR 0.98, 95% CI 0.83–1.15, P = 0.780; weighted median: OR 0.96, 95% CI 0.88–1.05, P = 0.323; simple mode: OR 0.91, 95% CI 0.80–1.04, P = 0.212; weighted mode: OR 0.93, 95% CI 0.83–1.05, P = 0.277). However, in the reverse direction, AD was associated with delirium (IVW: OR 1.32, 95% CI 1.13–1.54, P = 3.91E-04; MR Egger: OR 1.42, 95% CI 1.02–1.98, P = 5.60E-02; Weighted median: OR 1.39, 95% CI 1.18–1.63, P = 8.22E-05; Simple mode: OR 1.41, 95% CI 1.10–1.80, P = 1.41E−02; Weighted mode: OR 1.39, 95% CI 1.16–1.67, P = 3.23E-03). Conclusion: Based on the results of our MR study, there is no bidirectional causality between delirium and AD, delirium is not associated with an increased risk of AD, while genetically predicted AD is a potential causal risk factor for delirium.