Prevalence, demographics and clinical characteristics of Internet addiction among Chinese adolescents with schizophrenia.

Introduction: Several studies have reported that Internet addiction (IA) is more prevalent in the psychiatric disorder population. However, the prevalence of IA and its relationship with clinical variables among Chinese adolescents with schizophrenia is unclear. This study sought to investigate the prevalence of IA and its clinical correlates in Chinese adolescents with schizophrenia, which has not yet been reported. Methods: Seven hundred and six inpatient adolescents with schizophrenia were recruited. All patients underwent Young's Internet Addiction Test (IAT) to measure Internet addiction, as well as the Positive and Negative Syndrome Scale (PANSS) for psychopathology. Results: Our results showed that 186 patients had a total IAT score of 50 or more, bringing the prevalence of IA to 26.3%. Girls (21.7%, 92/424) were less likely to have combined IA than boys (33.3%, 94/282). Compared to those patients without IA, patients with IA had better socioeconomic status, higher probability of living in the city, higher levels of depressive symptoms, excited symptoms, and lower levels of concrete symptoms and PANSS total scores (all p<0.05). Further binary logistic regression analysis indicated that good socioeconomic status, living in the city and PANSS total scores were significantly associated with IA. In addition, correlation analysis showed significant correlations between IA total score and the following parameters: good socioeconomic status, living in the city, negative subscore, concrete subscore, depression subscore, excited subscore and PANSS total score (Bonferroni corrected all p <0.05). Conclusion: The results of this study indicate that the prevalence of IA in Chinese adolescents with schizophrenia is higher than that in the general population. Several demographic and clinical variables are risk factors for IA in adolescents with schizophrenia.

Sex differences in prevalence and clinical correlates of internet addiction among Chinese adolescents with schizophrenia.

BACKGROUND: Patients with schizophrenia (SCZ) exhibit sex differences in various aspects, and patients with SCZ have a high prevalence of internet addiction (IA). However, sex differences in IA among patients with SCZ mostly remain unstudied, particularly in Chinese adolescent patients with SCZ. This study investigated sex differences in prevalence, risk factors, and clinical correlates of IA among Chinese adolescent patients with SCZ. METHODS: A total of 706 adolescent patients with SCZ were enrolled in this study using a cross-sectional design and a convenience sampling method. Demographics and clinical data of the patients were collected using a standardized clinical assessment form. The Positive and Negative Syndrome Scale (PANSS) and the Young's Internet Addiction Test were used to evaluate psychopathological symptoms and IA respectively. RESULTS: Overall, the prevalence of IA among Chinese adolescent patients with SCZ was 26.30% (95% CI: 23.09-29.60%). In Chinese adolescents with SCZ, there was a sex difference in the comorbidity of IA (males: 33.33% vs. females: 21.69%). Binary logistic regression analysis showed that IA was significantly predicted by good socioeconomic status in male and female patients with SCZ. City of living and PANSS total score were associated with IA in male patients with SCZ. In contrast, hospitalization rate and depression score were associated with IA in female patients with SCZ. CONCLUSION: Our study suggests sex differences in clinical correlates of IA in Chinese adolescent patients with SCZ. An additional longitudinal study is required to confirm the findings of the present study.

Development and validation of a clinical prediction model for prognostic factors in patients with primary pontine hemorrhage.

We aimed to develop a prognostic model for primary pontine hemorrhage (PPH) patients and validate the predictive value of the model for a good prognosis at 90 days. A total of 254 PPH patients were included for screening of the independent predictors of prognosis, and data were analyzed by univariate and multivariable logistic regression tests. The cases were then divided into training cohort (n=219) and validation cohort (n=35) based on the two centers. A nomogram was developed using independent predictors from the training cohort to predict the 90-day good outcome and was validated from the validation cohort. Glasgow Coma Scale score, normalized pixels (used to describe bleeding volume), and mechanical ventilation were significant predictors of a good outcome of PPH at 90 days in the training cohort (all P<0.05). The U test showed no statistical difference (P=0.892) between the training cohort and the validation cohort, suggesting the model fitted well. The new model showed good discrimination (area under the curve=0.833). The decision curve analysis of the nomogram of the training cohort indicated a great net benefit. The PPH nomogram comprising the Glasgow Coma Scale score, normalized pixels, and mechanical ventilation may facilitate predicting a 90-day good outcome.

Development and validation of a clinical prediction model for prognostic factors in patients with primary pontine hemorrhage

Abstract We aimed to develop a prognostic model for primary pontine hemorrhage (PPH) patients and validate the predictive value of the model for a good prognosis at 90 days. A total of 254 PPH patients were included for screening of the independent predictors of prognosis, and data were analyzed by univariate and multivariable logistic regression tests. The cases were then divided into training cohort (n=219) and validation cohort (n=35) based on the two centers. A nomogram was developed using independent predictors from the training cohort to predict the 90-day good outcome and was validated from the validation cohort. Glasgow Coma Scale score, normalized pixels (used to describe bleeding volume), and mechanical ventilation were significant predictors of a good outcome of PPH at 90 days in the training cohort (all P<0.05). The U test showed no statistical difference (P=0.892) between the training cohort and the validation cohort, suggesting the model fitted well. The new model showed good discrimination (area under the curve=0.833). The decision curve analysis of the nomogram of the training cohort indicated a great net benefit. The PPH nomogram comprising the Glasgow Coma Scale score, normalized pixels, and mechanical ventilation may facilitate predicting a 90-day good outcome.