Development of preoperative and postoperative models to predict recurrence in postoperative glioma patients: a longitudinal cohort study.

BACKGROUND: Glioma recurrence, subsequent to maximal safe resection, remains a pivotal challenge. This study aimed to identify key clinical predictors influencing recurrence and develop predictive models to enhance neurological diagnostics and therapeutic strategies. METHODS: This longitudinal cohort study with a substantial sample size (n = 2825) included patients with non-recurrent glioma who were pathologically diagnosed and had undergone initial surgical resection between 2010 and 2018. Logistic regression models and stratified Cox proportional hazards models were established with the top 15 clinical variables significantly influencing outcomes screened by the least absolute shrinkage and selection operator (LASSO) method. Preoperative and postoperative models predicting short-term (within 6 months) postoperative recurrence in glioma patients were developed to explore the risk factors associated with short- and long-term recurrence in glioma patients. RESULTS: Preoperative and postoperative logistic models predicting short-term recurrence had accuracies of 0.78 and 0.87, respectively. A range of biological and early symptomatic characteristics linked to short- and long-term recurrence have been pinpointed. Age, headache, muscle weakness, tumor location and Karnofsky score represented significant odd ratios (t > 2.65, p < 0.01) in the preoperative model, while age, WHO grade 4 and chemotherapy or radiotherapy treatments (t > 4.12, p < 0.0001) were most significant in the postoperative period. Postoperative predictive models specifically targeting the glioblastoma and IDH wildtype subgroups were also performed, with an AUC of 0.76 and 0.80, respectively. The 50 combinations of distinct risk factors accommodate diverse recurrence risks among glioma patients, and the nomograms visualizes the results for clinical practice. A stratified Cox model identified many prognostic factors for long-term recurrence, thereby facilitating the enhanced formulation of perioperative care plans for patients, and glioblastoma patients displayed a median progression-free survival (PFS) of only 11 months. CONCLUSION: The constructed preoperative and postoperative models reliably predicted short-term postoperative glioma recurrence in a substantial patient cohort. The combinations risk factors and nomograms enhance the operability of personalized therapeutic strategies and care regimens. Particular emphasis should be placed on patients with recurrence within six months post-surgery, and the corresponding treatment strategies require comprehensive clinical investigation.

The relationship between online learning and eye strain in college students during the COVID-19 outbreak / 中国学校卫生

Objective@#To describe online learning and eye strain situation of college students during the COVID-19 outbreak, to provide a scientific basis for guiding students eye health.@*Methods@#A self-filled electronic questionnaire survey through questionnaire star was administered to college students across China. Information about online learning and eye strain of 1 046 college students during the epidemic was collected in Hefei, Anhui Province from March 16 to 20, 2020. The univariate and multivariate Logistic regression analysis were performed to analyze the association between online learning and eye strain of college students.@*Results@#The rate of eye strain during online learning was 72.1%, totally of 68.4% in 421 male students and 74.6% in 625 female students. Boys with online learning time <6 h/d, slow internet access，difficulty in understanding online class reported higher rate of eye strain than girls( χ 2=17.36,8.72,7.02, P ＜0.05). Freshmen reported the highest rate of slow internet access occasionally and active online class( χ 2=15.26,16.11, P ＜0.05), junior students reported highest rate of online learning time <6 h/d, and easy understandable online class( χ 2=15.33,32.59, P ＜0.05), medical college students reported higher rate of slow internet access, inactive online class than non-medical college students( χ 2=11.79,11.03, P ＜0.05). Multivariate Logistic regression analysis showed that odds ratio( OR ) of eye strain in females was 1.40 (1.06-1.87), compared with males; the OR of eye strain were 1.43 (1.01-2.03) and 1.54 (1.10-2.15) in the groups with online learning time 6-<8 h/d and ≥8 h/d, respectively, compared with the group with online learning time <6 h/d, the OR of eye strain in the groups with slow internet access was 2.28 (1.25-4.14), compared with students without slow internet access, the OR of eye strain in the capable to understand and difficult to understand group were 2.54 (1.73-3.74) and 5.40 (2.70-10.80) respectively, compared with the easy to understand group.@*Conclusion@#Female students, online learing time ≥ 8 h/d, slow internet access, difficult to understand class content were positively related with college students eye strain. Attention should be paid to the eye health of college students to reduce the adverse effects of online learning on vision.during the COVID-19 epidemic.