Objective:To analyze the clinical characteristics and risk factors of postoperative first bite syndrome（FBS） after parapharyngeal space surgery, and to provide evidence-based recommendations for the diagnosis and management of this complication. Methods:A retrospective analysis was conducted on 142 patients undergoing parapharyngeal space surgery from March 2016 to November 2024, including 12 cases of postoperative FBS. Univariate analysis compared differences in tumor location, pathological type, and surgical approach between FBS and non-FBS groups. Multivariate logistic regression identified independent risk factors. A systematic literature review evaluated the preventive value of transoral endoscopic surgery for FBS, followed by proposed clinical optimization strategies. Results:The incidence of FBS was 8.45%（12/142）. Univariate analysis revealed significant associations with: Superior parapharyngeal space tumors（OR=3.21, 95%CI 1.12-9.21, P=0.029）; Schwannoma pathology（OR=4.05, 95%CI 1.35-12.18, P=0.013）; Traditional lateral cervical approach（OR=5.67, 95%CI 1.89-17.02, P=0.002）. Multivariate analysis confirmed lateral cervical approach（aOR=4.98, 95%CI 1.62-15.31, P=0.005） and schwannoma（aOR=3.75, 95%CI 1.22-11.51, P=0.021） as independent risk factors. Literature review suggested lower FBS rates with transoral endoscopic approaches. The overall effect of the drug on FBS is poor. Conclusion:FBS is a frequent complication of parapharyngeal space surgery, significantly associated with tumor location, pathology, and surgical approach. Transoral endoscopic surgery can effectively reduce the risk of FBS through precise anatomical dissection that minimizes sympathetic nerve injury. Minimally invasive approaches are recommended for eligible cases.
Humans ; Retrospective Studies ; Postoperative Complications/etiology* ; Risk Factors ; Endoscopy ; Parapharyngeal Space/surgery* ; Male ; Female ; Adult ; Middle Aged ; Neurilemmoma/surgery*

OBJECTIVES: To investigate the risk factors of overall postoperative complications in elderly patients undergoing gastrointestinal surgeries. METHODS: This study was conducted among a total of 1388 elderly patients, who underwent elective gastrointestinal surgeries at 17 centers across China between April, 2020 and April, 2022. The primary outcome was the incidence of postoperative complications within 30 days, including procedure-related, neuropsychiatric, respiratory, cardiovascular, and gastrointestinal complications as well as acute kidney injury. Baseline characteristics, preoperative psychological and functional status, intraoperative anesthesia and surgical factors, intraoperative medication, use of nerve block, and postoperative analgesia methods were compared between the patients experiencing one or more postoperative complications and those without complications. Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors for postoperative complications. The relationship between postoperative acute pain and each type of complication were explored. RESULTS: The incidence of overall postoperative complications was 50.8% (705/1388) in these patients. Multivariate analysis showed that age (OR: 1.026; 95% CI: 1.006-1.046), prognostic nutritional index (OR: 0.998; 95% CI: 0.997-1.000), preoperative EuroQol-5 dimensions score (OR: 0.094; 95% CI: 0.018-0.500), blood loss (OR: 1.002; 95% CI: 1.001-1.003), and acute postoperative pain (OR: 1.308; 95% CI: 1.033-1.657) were significantly associated with the occurrence of postoperative complications. Specifically, patients experiencing severe postoperative pain had a significantly higher incidence of neuropsychiatric (27.2% vs 19.8%), procedure-related (17.3% vs 10.2%), and cardiovascular complications (3.6% vs 1.7%). CONCLUSIONS An advanced age, a low preoperative nutritional index, a poor quality of life score, a greater volume of intraoperative blood loss, and acute postoperative pain are independent risk factors for postoperative complications in elderly patients undergoing gastrointestinal surgeries. There is a significant association between acute postoperative pain and multi-system complications.
Humans ; Postoperative Complications/etiology* ; Aged ; Risk Factors ; Digestive System Surgical Procedures/adverse effects* ; Male ; Female ; China/epidemiology* ; Pain, Postoperative/epidemiology* ; Incidence ; Aged, 80 and over

OBJECTIVES: To investigate the effect of serum advanced glycation endproducts (AGEs) on stenosis after first autologous arteriovenous fistula (AVF) in patients with end-stage renal disease (ESRD). METHODS: Patients with ESRD undergoing standard native arteriovenous fistula (AVF) for the first time in the Department of Nephrology, Affiliated Hospital of Guilin Medical University from February to June 2022 were prospectively enrolled. The preoperative general data, clinical examination results and ultrasound data of the operated limbs were collected. The patients with and without stenosis within 2 months after the operation were compared for preoperative serum AGEs levels detected using ELISA and the clinical parameters. Logistic regression analysis was used to analyze the independent risk factors of AVF stenosis, and the sensitivity and specificity of AGEs for predicting postoperative stenosis were analyzed using receiver-operating characteristic (ROC) curve. RESULTS: Of the 94 patients enrolled, 34 had postoperative arteriovenous stenosis and 60 had no stenosis. The number of diabetic patients differed significantly between stenosis group and non-stenosis group (P<0.001). Serum AGEs levels, which were negatively correlated with serum phosphorus level (P<0.05), were significantly higher in stenosis group than in non-stenosis group (Z=-2.837, P=0.005). Serum AGE level was an independent risk factor for postoperative stenosis after AVF (OR=1.251, 95% CI:1.096-1.423, P<0.001). For predicting AVF stenosis, the area under the ROC curve (AUC) of AGEs was 0.677 (P=0.007, 95% CI: 0.572-0.770), with a specificity of 90.00% and a sensitivity of 52.94% at the optimal cut-off value of 8.43 µg/mL; AGEs combined with fibrinogen had an AUC of 0.763 (P<0.001, 95% CI: 0.664-0.844), with a specificity of 73.33% and a sensitivity of 70.59% at the optimal cut-off value of 0.30. CONCLUSIONS Elevated serum AGEs level is an independent risk factor for postoperative AVF stenosis, and its combination with fibrinogen has a better efficacy for predicting postoperative AVF stenosis.
Humans ; Glycation End Products, Advanced/blood* ; Risk Factors ; Arteriovenous Shunt, Surgical/adverse effects* ; Kidney Failure, Chronic/blood* ; Male ; Constriction, Pathologic/etiology* ; Female ; Middle Aged ; Postoperative Complications/etiology* ; Renal Dialysis ; Aged ; Prospective Studies ; ROC Curve ; Adult

OBJECTIVES: To develop risk prediction models for postoperative hypothermia after transurethral holmium laser enucleation of the prostate (HoLEP) using machine learning algorithms. METHODS: We retrospectively analyzed the clinical data of 403 patients from our center (283 patients in the training set and 120in the internal validation set) and 120 patients from Weifang People's Hospital (as the external validation set). The risk prediction models were built using logistic regression, decision tree and support vector machine (SVM), and model performance was evaluated in terms of accuracy, recall, precision, F1 score and AUC. RESULTS: Operation duration, prostate weight, intraoperative irrigation volume, and being underweight were identified as the predictors of postoperative hypothermia following HoLEP. Among the 3 algorithms, SVM showed the best precision rate and accuracy in all the 3 data sets and the best area under the ROC (AUC) in the training set and validation set, followed by logistic regression, which had a similar AUC in the two data sets. SVM outperformed logistic regression and decision tree models in the validation set in precision, accuracy, recall, F1 score, and AUC, and performed well in the external validation set with better precision rate and accuracy than logistic regression and decision tree models but slightly lower recall rate, F1 index, and AUC value than the decision tree model. SVM outperformed logistic regression and decision tree models in precision, accuracy, F1 score, and AUC in the training set, but had slightly lower recall rate than the decision tree. CONCLUSIONS Among the 3 models, SVM has the best performance and generalizability for predicting post-HoLEP hypothermia risk to provide support for clinical decisions.
Humans ; Male ; Retrospective Studies ; Machine Learning ; Transurethral Resection of Prostate/adverse effects* ; Hypothermia/etiology* ; Prostatic Hyperplasia/surgery* ; Algorithms ; Lasers, Solid-State ; Risk Assessment ; Postoperative Complications ; Decision Trees ; Logistic Models ; Aged ; Middle Aged ; Support Vector Machine

OBJECTIVE: To identify the risk factors for acute respiratory distress syndrome (ARDS) in geriatric patients following gastrointestinal perforation surgery, and constructed a model to validate its predictive value. METHODS: A retrospective analysis was conducted. The clinical data of geriatric patients (aged ≥ 60 years) after gastrointestinal perforation surgery admitted to the intensive care unit (ICU) of Hebei General Hospital from October 2017 to October 2024 were enrolled. Two groups were divided according to whether ARDS occurred postoperatively, and the differences in each index between the groups were compared. Lasso regression and multifactorial Logistic regression analyses were used to identify independent risk factors for the development of ARDS, and a prediction model was constructed based on these, which was presented using a nomogram. The receiver operator characteristic curve (ROC curve), calibration curve, and decision curve analysis (DCA) were plotted to evaluate the discrimination, accuracy, and clinical practicability of the model. RESULTS: A total of 155 geriatric patients following gastrointestinal perforation surgery were ultimately included in the analysis, among whom 43 developed ARDS, with an incidence rate of 27.7%. There were significantly differences in age, body mass index (BMI), acute kidney injury comorbidity, heart rate, onset time, the duration of surgery, the site of perforation, seroperitoneum, amount of bleeding, shock comorbidity, central venous pressure (CVP), C-reactive protein, and albumin between ARDS and non-ARDS groups. Lasso regression identified nine significant predictors: age, BMI, acute kidney injury comorbidity, onset time, seroperitoneum, shock comorbidity, CVP, hemoglobin, and albumin. Multivariate Logistic regression analysis identified BMI [odds ratio (OR) = 1.310, P < 0.001], hemoglobin (OR = 1.019, P = 0.045), seroperitoneum (OR = 1.001, P = 0.017), and albumin (OR = 0.871, P < 0.001) as independent risk factors for the occurrence of ARDS. A prediction model was constructed based on the above four independent risk factors, and the ROC curve showed that the area under the curve (AUC) of the model for predicting the occurrence of ARDS was 0.885 [95% confidence interval (95%CI) was 0.824-0.946], and internal validation was performed using bootstrap resampling (Bootstrap 500 times), which showed that the AUC value of the model was 0.886 (95%CI was 0.883-0.889). Calibration curves revealed excellent concordance between observed outcomes and model predictions. DCA indicated a high net benefit value for the model, which has good clinical utility. CONCLUSIONS BMI, hemoglobin, seroperitoneum, and albumin were identified as independent risk factors for ARDS in geriatric patients following gastrointestinal perforation surgery. The prediction model constructed using these four indicators facilitates early identification of high-risk individuals by clinicians.
Humans ; Respiratory Distress Syndrome/etiology* ; Retrospective Studies ; Aged ; Risk Factors ; Logistic Models ; Postoperative Complications ; Intestinal Perforation/surgery* ; Male ; ROC Curve ; Female ; Middle Aged ; Intensive Care Units ; Nomograms

OBJECTIVE: This study examines utilizes the advantages of machine learning algorithms to discern key determinants in prognosticate postoperative circulatory complications (PCCs) for older patients. METHODS: This secondary analysis of data from a randomized controlled trial involved 1,720 elderly participants in five tertiary hospitals in Beijing, China. Participants aged 60-90 years undergoing major non-cardiac surgery under general anesthesia. The primary outcome metric of the study was the occurrence of PCCs, according to the European Society of Cardiology and the European Society of Anaesthesiology diagnostic criteria. The analysis metrics contained 67 candidate variables, including baseline characteristics, laboratory tests, and scale assessments. RESULTS: Our feature selection process identified key variables that significantly impact patient outcomes, including the duration of ICU stay, surgery, and anesthesia; APACHE-II score; intraoperative average heart rate and blood loss; cumulative opioid use during surgery; patient age; VAS-Move-Median score on the 1st to 3rd day; Charlson comorbidity score; volumes of intraoperative plasma, crystalloid, and colloid fluids; cumulative red blood cell transfusion during surgery; and endotracheal intubation duration. Notably, our Random Forest model demonstrated exceptional performance with an accuracy of 0.9872. CONCLUSION We have developed and validated an algorithm for predicting PCCs in elderly patients by identifying key risk factors.
Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; Cardiovascular Diseases/etiology* ; Machine Learning ; Postoperative Complications/etiology* ; Risk Factors ; Randomized Controlled Trials as Topic ; Secondary Data Analysis

Pathological fractures after jaw cyst surgery are rare clinically but are a serious complication. Once a pathological fracture occurs, treatment time and economic costs increase, and doctors face difficulty in handling it. This article reports a case of a patient with mandibular pathological fractures after multiple odontogenic keratocyst surgery of the jaw. Mandibular lesions were located in the bilateral mandibular angles and had macrocystic changes. We adopted a conservative treatment plan, and the treatment effect was good. We also discussed and analyzed relevant literature to provide a reference for clinicians.
Humans ; Odontogenic Cysts/surgery* ; Conservative Treatment ; Postoperative Complications/therapy* ; Mandibular Fractures/etiology* ; Fractures, Spontaneous/etiology* ; Male ; Female

The accidental displacement of tooth roots into the mandibular canal is a serious complication during tooth extractions in oral and maxillofacial surgery, often resulting in direct damage to the structural and functional integrity of the inferior alveolar neurovascular bundle (IANB). This article reviews the anatomical features of the mandibular canal, the IANB, and adjacent tooth roots; identifies high-risk factors and anatomically vulnerable sites for root displacement; and outlines the clinical manifestations and radiographic characteristics of intraoperative root intrusion into the mandibular canal. Furthermore, management principles, surgical approaches and techniques, inferior alveolar nerve injury treatment, and prognostic considerations are discussed. The aim of this review is to provide a comprehensive clinical reference for improving surgical outcomes, and reducing postoperative complications.
Humans ; Tooth Extraction/adverse effects* ; Mandible/surgery* ; Tooth Root ; Mandibular Nerve/anatomy & histology* ; Postoperative Complications/prevention & control* ; Intraoperative Complications ; Mandibular Nerve Injuries/etiology*

Objective To investigate the effect of health failure mode and effect analysis(HFMEA)in optimizing the management process of postoperative diabetes insipidus in children undergoing neurosurgery.Methods Based on HFMEA,a management flowchart for postoperative diabetes insipidus in children undergoing neurosurgery was created.Brainstorming was adopted to identify failure modes in the workflow,analyze risk factors,and develop improvement measures,thereby refining the management flowchart.The amelioration and prognosis of diabetes insipidus in these children before(October 2022 to November 2023)and after(January 2024 to February 2025)implementation of the management flowchart were compared.Results The HFMEA-based management process for postoperative diabetes insipidus in children undergoing neurosurgery alleviated the symptoms of diabetes insipidus regarding the number of diabetes insipidus in the pediatric intensive care unit(P=0.006),the average daily urine output in the pediatric intensive care unit(P=0.001),the proportion of electrolyte abnormalities at discharge/transfer(P=0.037),the duration of mechanical ventilation(P=0.007),and the length of stay in the intensive care unit(P=0.001).Conclusion The HFMEA-based management process for postoperative diabetes insipidus in children undergoing neurosurgery is beneficial to the optimization of the management process,the alleviation of postoperative diabetes insipidus,and the improvement of prognosis in these children.
Humans ; Diabetes Insipidus/etiology* ; Neurosurgical Procedures/adverse effects* ; Child ; Postoperative Complications/therapy* ; Healthcare Failure Mode and Effect Analysis ; Intensive Care Units, Pediatric ; Risk Factors

BACKGROUND: Postoperative pulmonary complications (PPCs) are major adverse events in neurosurgical patients. This study aimed to develop and validate machine learning models predicting PPCs after neurosurgery. METHODS: PPCs were defined according to the European Perioperative Clinical Outcome standards as occurring within 7 postoperative days. Data of cases meeting inclusion/exclusion criteria were extracted from the anesthesia information management system to create three datasets: The development (data of Huashan Hospital, Fudan University from 2018 to 2020), temporal validation (data of Huashan Hospital, Fudan University in 2021) and external validation (data of other three hospitals in 2023) datasets. Machine learning models of six algorithms were trained using either 35 retrievable and plausible features or the 11 features selected by Lasso regression. Temporal validation was conducted for all models and the 11-feature models were also externally validated. Independent risk factors were identified and feature importance in top models was analyzed. RESULTS: PPCs occurred in 712 of 7533 (9.5%), 258 of 2824 (9.1%), and 207 of 2300 (9.0%) patients in the development, temporal validation and external validation datasets, respectively. During cross-validation training, all models except Bayes demonstrated good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.840. In temporal validation of full-feature models, deep neural network (DNN) performed the best with an AUC of 0.835 (95% confidence interval [CI]: 0.805-0.858) and a Brier score of 0.069, followed by Logistic regression (LR), random forest and XGBoost. The 11-feature models performed comparable to full-feature models with very close but statistically significantly lower AUCs, with the top models of DNN and LR in temporal and external validations. An 11-feature nomogram was drawn based on the LR algorithm and it outperformed the minimally modified Assess respiratory RIsk in Surgical patients in CATalonia (ARISCAT) and Laparoscopic Surgery Video Educational Guidelines (LAS VEGAS) scores with a higher AUC (LR: 0.824, ARISCAT: 0.672, LAS: 0.663). Independent risk factors based on multivariate LR mostly overlapped with Lasso-selected features, but lacked consistency with the important features using the Shapley additive explanation (SHAP) method of the LR model. CONCLUSIONS: The developed models, especially the DNN model and the nomogram, had good discrimination and calibration, and could be used for predicting PPCs in neurosurgical patients. The establishment of machine learning models and the ascertainment of risk factors might assist clinical decision support for improving surgical outcomes. TRIAL REGISTRATION ChiCTR 2100047474; https://www.chictr.org.cn/showproj.html?proj=128279 .
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Algorithms ; Lung Diseases/etiology* ; Machine Learning ; Neurosurgical Procedures/adverse effects* ; Postoperative Complications/diagnosis* ; Risk Factors ; ROC Curve