Objective To investigate status of internet addiction and circadian typology in college students and to explore the relationship between circadian typology and internet addiction. Methods A total of 3 572 college students including freshmen and juniors was enrolled in the study. Circadian typology and internet addiction were assessed by morningness-eveningness questionnaire(MEQ) and internet addiction test (IAT) respectively. All questionnaires were self-reported. Results The score of IAT was (38. 46±11. 84) points and incidence of internet addiction was 41. 7%. Freshmen and juniors were significantly different in distribution of circadian typology and IAT scores(χ2=10. 51,P<0. 05; t=5. 90,P<0. 01). Proportion of the evening type and scores of IAT of juniors(33. 0%,(39. 66±12. 71)points) were both higher than those of freshmen(28. 3%,(37. 32±10. 82)points). For freshmen,IAT scores of evening type students((40. 79± 11. 33)points) was higher than those of intermediate type students((36. 54±10. 27)points) and morning type students((32. 27±9. 66)points),and the differences were statistically significant(F=51. 81,P<0. 01). For juniors,IAT scores of evening type students((42. 90±13. 02)points) was higher than those of intermedi-ate type students((38. 61±12. 19)points) and morning type students((34. 06±11. 92)points),and the differences were statistically significant(F=37. 80,P<0. 01). After adjusting for gender,age,grade and par-ents’ educational level by analysis of covariance,IAT scores were still statistically different in circadian ty-pology(F=93. 53,P<0. 01). Results from ordinal logistic regression showed that grade(juniors compared to freshman,OR=1. 42,P<0. 01) and circadian typology (evening type compared to morning type,OR=3. 82,P<0. 01; intermediate type compared to morning type,OR=1. 97,P<0. 01) were independent factors influen- cing internet addiction. Conclusion Internet addiction of college students is at a high level and different in grades. Compared with junior students,senior students are at a higher level of internet addiction. College students with evening type are more likely to be internet addicted.

Objective:To estimate the predictive performance of the population pharmacokinetics software JPKD-vancomycin on predicting the vancomycin steady-state trough concentration, and to analyze the related factors affecting the predictive performance.Methods:The clinical data of patients who were treated with vancomycin and received therapeutic drug monitoring (TDM) admitted to Suzhou Hospital Affiliated to Nanjing Medical University from July 2013 to December 2018 were enrolled. All patients were designed an empirical vancomycin regimen (initial regimen) according to vancomycin medication guidelines. Steady-state trough concentrations of vancomycin were determined at 48 hours after the first dose and 0.5 hour before the next dose. Dosage regimen was adjusted when steady-state trough concentration was not in 10-20 mg/L (adjustment regimen), and then the steady-state trough concentration was determined again 48 hours after adjustment. First, the JPKD-vancomycin software was used to calculate the initial regimen and predict the steady-state trough concentration according to the results calculated by classic pharmacokinetic software Vancomycin Calculator. Second, the JPKD-vancomycin software was used to adjust the vancomycin dosage regime and predict the steady-state trough concentration of adjustment regimen. The weight residual (WRES) between the predicted steady-state trough concentration (C pre) and the measured steady-state trough concentration (C real) was used to evaluate the ability of the JPKD-vancomycin software for predicting the vancomycin steady-state trough concentration. The TDM results of initial regimen were divided into accurate prediction group (WRES < 30%) and the inaccurate prediction group (WRES ≥ 30%) according to the WRES value. Patient and disease characteristics including gender, age, weight, height, the length of hospital stay, comorbidities, vasoactive agent, mechanical ventilation, smoking history, postoperative, obstetric patients, trauma, laboratory indicators, vancomycin therapy and TDM results were collected from electronic medical records. Univariate and multivariate Logistic regression analysis was used to screen the related factors that influence the predictive performance of JPKD-vancomycin software, and the receiver operating characteristic (ROC) curve was drawn to evaluate its predictive value. Results:A total of 310 patients were enrolled, and 467 steady-state trough concentrations of vancomycin were collected, including 310 concentrations of initial regimen and 157 concentrations of adjustment regimen. Compared with the initial regimen, the WRES of adjusted regimen was significantly reduced [14.84 (6.05, 22.89)% vs. 20.41 (11.06, 45.76)%, P < 0.01], and the proportion of WRES < 30% increased significantly [82.80% (130/157) vs. 63.87% (198/310), P < 0.01]. These results indicated that JPKD-vancomycin software had a better accuracy prediction for steady-state trough concentration of the adjusted regimen than the initial regimen. There were 198 concentrations in the accurate prediction group and 112 in the inaccurate prediction group. Univariate Logistic regression analysis showed that women [odds ratio ( OR) = 0.466, 95% confidence interval (95% CI) was 0.290-0.746, P = 0.002], low body weight ( OR = 0.974, 95% CI was 0.953-0.996, P = 0.022), short height ( OR = 0.963, 95% CI was 0.935-0.992, P = 0.014), low vancomycin clearance (CL Van; OR < 0.001, 95% CI was 0.000-0.231, P = 0.023) and postoperative patients ( OR = 1.695, 95% CI was 1.063-2.702, P = 0.027) were related factors affecting the predictive performance of JPKD-vancomycin software. Multivariate Logistic regression analysis indicated that women ( OR = 0.449, 95% CI was 0.205-0.986, P = 0.046), low CL Van ( OR < 0.001, 95% CI was 0.000-0.081, P = 0.015) and postoperative patients ( OR = 2.493, 95% CI was 1.455-4.272, P = 0.001) were independent risk factors for inaccurate prediction of JPKD-vancomycin software. The ROC analysis indicated that the area under ROC curve (AUC) of the CL Van for evaluating the accuracy of JPKD-vancomycin software in predicting vancomycin steady-state trough concentration was 0.571, the sensitivity was 56.3%, and the specificity was 57.1%. The predictive performance of JPKD-vancomycin software was decreased when CL Van was lower than 0.065 L·h -1·kg -1. Conclusions:JPKD-vancomycin software had a better predictive performance for the vancomycin steady-state trough concentrations of adjustment regimen than initial regimen. JPKD-vancomycin software had a poor predictive performance when the patient was female, having low CL Van, and was postoperative. The predictive performance of JPKD-vancomycin software was decreased when CL Van was lower than 0.065 L·h -1·kg -1.

Objective To evaluate the predictive value and to verify the clinical effect of JPKD-vancomycin for the trough concentration of vancomycin in patients with augmented renal clearance (ARC), and to provide a reference for clinical individualized drug therapy. Methods A retrospective analysis was conducted. The clinical data of 48 adult patients with ARC using vancomycin and monitoring steady-state trough concentration of vancomycin admitted to Suzhou Hospital Affiliated to Nanjing Medical University from July 2013 to July 2017 were collected. A combination of classical Vancomycin Calculator software and JPKD-vancomycin software was used. Based on the individual conditions of patients [gender, age, height, weight, serum creatinine (SCr), disease status], Vancomycin Calculator software was used to obtain the recommended regimen and its steady-state trough concentration, and then JPKD-vancomycin software was used to predict the steady-state trough concentration of initial regimen. If the regimen was adjusted during the treatment, JPKD-vancomycin software was used to predict the steady-state trough concentration of the adjusted regimen. The measured values of steady-state trough concentration were recorded. The weight deviation between predicted concentration and measured concentration (WRES) was calculated. WRES < 30% was considered as good prediction, and the predictive value of JPKD-vancomycin software was evaluated for vancomycin trough concentration. Results Forty-eight patients with ARC were enrolled, of whom 24 patients had adjusted the dosing regimen during the treatment. The initial concentration of blood samples was 48, after adjusting the dosage regimen, 24 blood samples were collected. The initial and adjusted daily dose of vancomycin was (2 000±500) mg/d and (2 500±600) mg/d, respectively, and the initial trough concentrations and adjusted trough concentrations was (8.4±7.3) mg/L and (9.1±4.3) mg/L, respectively. Only 14.6% and 25.0% of initial and adjusted trough concentrations reached the target range (10-20 mg/L) without significant difference (P > 0.05). The WRES value of adjusted trough concentrations predicted by JPKD-vancomycin software was significantly lower than that of initial regimen [10.6% (3.0%, 16.4%) vs. 14.3% (10.5%, 38.2%), P < 0.05], and the percentage of WRES < 30% also tended to increase [95.8% (23/24) vs. 70.8% (34/48), P < 0.05]. The well predictive rate of JPKD-vancomycin software for vancomycin trough concentration was 79.2% (57/72), but there were 15 patients with WRES > 30%. Conclusions JPKD-vancomycin software has good predictive value for the vancomycin trough concentration of ARC patients, especially for the trough concentration after adjusting the treatment regimen. JPKD-vancomycin can provide a reference for the design of clinical individualized application of vancomycin.