Objectives Chinese neurosurgery has made great progress during the past decades; yet, little is known about the working status of neurosurgeons. This study aimed to evaluate the difference between academic and non-academic neurosurgeons, focusing on their professional burnout, job satisfaction and work engagement. Design Cross-sectional nationwide survey. Study setting The survey was conducted in China between 2017 and 2018. Participants A total number of 823 academic neurosurgeons and 379 non-academic neurosurgeons participated in this study. Outcome measures Professional burnout, job satisfaction and work engagement were assessed using the Maslach Burnout Inventory, the Job Descriptive Index and the Utrecht Work Engagement Scale, respectively. Results The majority of respondents were male (92.93%), less than 45 years old (85.27%) and married (79.53%). Chinese neurosurgeons worked 63.91±11.04 hours per week, and approximately 45% experienced burnout. Compared with non-academic respondents, academic neurosurgeons had longer working hours (p<0.01), higher income (p<0.01) and were less willing to get married (p<0.01). In addition, they showed a lower degree of burnout (p<0.01), a higher level of job satisfaction (p<0.01) and were more enthusiastic at work (p=0.015). Multivariate regression analyses indicated that divorced (OR 7.02, 95% CI 2.37 to 15.08) and workplace violence (OR 1.52, 95% CI 1.18 to 2.24) were associated with burnout for both academic and non-academic respondents. Long working hours (≥71 hours per week) and low annual income (<1 00 000 RMB) were risk factors for burnout among academic neurosurgeons. For non-academic neurosurgical surgeons (age 36–45 years), working as attending doctors, serving in public hospitals and having the first house-living child were all closely related to the incidence of burnout. Conclusion Chinese neurosurgeons are under significant stress particularly for the non-academic neurosurgeons. Offering better opportunities for training, promotion, higher income and safer working environments could be solutions to relieve burnout and improve career satisfaction and engagement. Trial registration number ChiCTR1800014762. This article is not linked to a clinical trial.

Aims: Coagulation abnormality is one of the primary concerns for patients with spontaneous intracerebral hemorrhage admitted to ER. Conventional laboratory indicators require hours for coagulopathy diagnosis, which brings difficulties for appropriate intervention within the optimal window. This study evaluates the possibility of building efficient coagulopathy prediction models using data mining and machine learning algorithms.Methods: A retrospective cohort enrolled 1668 cases with acute spontaneous intracerebral hemorrhage from three medical centers, excluding those under antithrombotic therapies. Coagulopathy‐related clinical parameters were initially screened by univariate analysis. Two machine learning algorithms, the random forest and the support vector machine, were deployed via an approach of four‐fold cross‐validation to screen out the most important parameters contributing to the occurrence of coagulopathy. Model discrimination was assessed using metrics, including accuracy, precision, recall, and F1 score.Results: Albumin/globulin ratio, neutrophil count, lymphocyte percentage, aspartate transaminase, alanine transaminase, hemoglobin, platelet count, white blood cell count, neutrophil percentage, systolic and diastolic pressure were identified as major predictors to the occurrence of acute coagulopathy. Compared to support vector machine, the model based on the random forest algorithm showed better accuracy (93.1%, 95% confidence interval [CI]: 0.913‐0.950), precision (92.4%, 95% CI: 0.897‐0.951), F1 score (91.5%, 95% CI: 0.889‐0.964), and recall score (93.6%, 95% CI: 0.909‐0.964), and yielded higher area under the receiver operating characteristic curve (AU‐ROC) (0.962, 95% CI: 0.942‐0.982).Conclusion: The constructed models exhibit good prediction accuracy and efficiency. It might be used in clinical practice to facilitate target intervention for acute coagulopathy in patients with spontaneous intracerebral hemorrhage.

OBJECTIVE: In China, orthopedics and neurosurgery are among the most desired majors for medical students. However, little is known about the working and living status of specialists in these two fields. This study was aimed at evaluating job satisfaction, engagement, and burnout in the population of Chinese orthopedist and neurosurgeon trainees. METHODS: A nationwide online survey was administered in mainland China. Questionnaires were answered anonymously. Job satisfaction, engagement, and burnout were assessed using the Job Descriptive Index, the Utrecht Work Engagement Scale, and the Maslach Burnout Inventory, respectively. RESULTS: Data were collected from 643 orthopedist trainees and 690 neurosurgeon trainees. Orthopedists and neurosurgeons showed no statistical difference in terms of age, sex, job titles, and preference for working in tertiary hospitals. Orthopedists had a higher marriage rate (p < 0.01), a lower divorce rate (p = 0.017), relatively shorter working hours (p < 0.01), and a higher annual income (p = 0.023) than neurosurgeons. Approximately 40% of respondents experienced workplace violence in the last 5 years. Less than 10% of respondents were satisfied with their pay, and over 70% would not encourage their offspring to become a doctor. Orthopedists were more satisfied with their careers than neurosurgeons (p < 0.01) and had a higher level of work engagement (p < 0.01). In addition, a higher proportion of orthopedists were burnt out (p < 0.01) than neurosurgeons, though the difference between the two groups was not significant (p = 0.088). Multivariate regressions suggested that younger age (≤ 25 years old), being a senior trainee, getting divorced, working in a regional hospital, long working hours (≥ 71 hrs/wk), a low annual income (<¥100,000), sleeping < 6 hrs/day, and experience with workplace violence were significantly related to burnout for both groups. CONCLUSIONS: Chinese orthopedic surgical and neurosurgical trainees are under significant stress. Orthopedic surgeons showed relatively optimistic data in their assessments of job satisfaction, engagement, and burnout. This study may provide valuable information for orthopedic and neurosurgical candidates considering either specialty as a career.

Seizure is a common complication for severe traumatic brain injury (TBI). Valproic acid (VPA) is a first-line antiepileptic drug, though its metabolism is affected by genetic polymorphisms and varies between individuals. The aim of this study was to investigate such association and to explore its influence on the occurrence of early post-traumatic seizure. A prospective case control study was conducted from 2012 to 2016 recruiting adult patients with severe TBI. Electroencephalograph (EEG) monitoring was performed approximately 4 h for each patient from day 1 to day 7 after injury. If seizures were detected, EEG monitoring was extended until 12 h after seizures being controlled. Genetic polymorphisms in UGT1A6, UGT2B7, CYP2C9, and CYP2C19 were analyzed in association with daily VPA plasma concentrations, adjusted dosages, and occurrence of seizures. Among the 395 recruited patients, eighty-three (21%) had early post-traumatic seizure, of which 30 (36.14%) were non-convulsive. Most seizures were first detected on day 1 (34.94%) and day 2 (46.99%) after injury. Patients with seizure had longer ICU length of stay and relatively lower VPA plasma concentrations. Patients with UGT1A6_19T > G/541A > G/552A > C double heterozygosities or CYP2C9 extensive metabolizers (EMs) initially had lower adjusted VPA plasma concentrations (power >0.99) and accordingly require higher VPA dosages during later time of treatment (power >0.99). The odds ratio indicated a higher risk of early post-traumatic seizure occurrence in male patients (OR 1.96, 95% CI 1.01-3.81, p = 0.043), age over 65 (OR 2.13, 95% CI 1.01-4.48), and with UGT1A6_19T > G/541A > G/552A > C double heterozygosities (OR 2.38, 95% CI 1.11-5.10, p = 0.02), though the power of the difference was between 0.54 to 0.61. Due to limited facility, the actual frequency of non-convulsive seizures is suspected to be higher than identified. There has been discrepancy regarding to genetic polymorphisms and VPA metab olism between this study and some previous reports. This could be related to confounders such as sample size, race, and patient age. Another limitation is that the case numbers of certain genotypes are limited in this study. Continuous EEG monitoring is necessary to detect both convulsive and non-convulsive early post-traumatic seizures in severe TBI patients. UGT1A6/CYP2C9 polymorphisms have influence on VPA metabolism. UGT1A6_19T > G/541A > G/552A > C double heterozygositie is associated with occurrence of early post-traumatic seizures in addition to patients’ age and gender. Further investigations with larger sample size are required to confirm the difference. Retrospectively registered with Chinese Clinical Trail Registry on 1st Jan 2016 ( ChiCTR-OPC-16007687 )

Aims: Hypokalemia is a common complication following traumatic brain injury, which may complicate treatment and lead to unfavorable outcomes. Identifying patients at risk of hypokalemia on the first day of admission helps to implement prophylactic treatment, reduce complications, and improve prognosis.Methods: This multicenter retrospective study was performed between January 2017 and December 2020 using the electronic medical records of patients admitted due to traumatic brain injury. A propensity score matching approach was adopted with a ratio of 1:1 to overcome overfitting and data imbalance during subgroup analyses. Five machine learning algorithms were applied to generate a best‐performed prediction model for in‐hospital hypokalemia. The internal fivefold cross‐validation and external validation were performed to demonstrate the interpretability and generalizability.Results: A total of 4445 TBI patients were recruited for analysis and model generation. Hypokalemia occurred in 46.55% of recruited patients and the incidences of mild, moderate, and severe hypokalemia were 32.06%, 12.69%, and 1.80%, respectively. Hypokalemia was associated with increased mortality, while severe hypokalemia cast greater impacts. The logistic regression algorithm had the best performance in predicting decreased serum potassium and moderate‐to‐severe hypokalemia, with an AUC of 0.73 ± 0.011 and 0.74 ± 0.019, respectively. The prediction model was further verified using two external datasets, including our previous published data and the open‐assessed Medical Information Mart for Intensive Care database. Linearized calibration curves showed no statistical difference (p > 0.05) with perfect predictions.Conclusions: The occurrence of hypokalemia following traumatic brain injury can be predicted by first hospitalization day records and machine learning algorithms. The logistic regression algorithm showed an optimal predicting performance verified by both internal and external validation.