OBJECTIVE To construct a risk prediction model for nonattainment plasma concentration of zonisamide in children with epilepsy， and to provide evidence for individualized dosing.METHODS Children with epilepsy who received standardized zonisamide treatment orally in Kunming Children’s Hospital were retrospectively selected as research subjects. Those treated from May 2022 to May 2024 were included in the modeling cohort， and those treated from June 2024 to January 2025 were included in the external validation cohort. According to plasma concentration， children were divided into therapeutic group and nonattainment group. Independent influencing factors for nonattainment plasma concentration of zonisamide were screened by collinearity diagnosis， univariate analysis， Lasso regression and random forest， based on which a nomogram risk prediction model was constructed and validated. Finally， the Shapley additive explanations （SHAP） method was used to interpret the features of the nomogram risk prediction model.RESULTS A total of 945 children with epilepsy were included in this study. There was no statistically significant difference in baseline data between the modeling cohort （531 cases） and the external validation cohort （414 cases） （ P ＞0.05）， indicating comparability. Through multi-stage feature screening， seven influencing factors were finally included to construct the prediction model， namely gender， age， dosage， aspartate transferase， total bilirubin， uric acid and vitamin D. The corrected area under the curve of the model was 0.799 in the modeling cohort and 0.762 in the external validation cohort， showing good calibration and clinical applicability. SHAP analysis showed that gender， dosage， total bilirubin， age and aspartate transferase were the top five important variables affecting model prediction.CONCLUSIONS The nomogram risk prediction model for nonattainment plasma concentration of zonisamide constructed in this study has stable predictive performance. Clinically， the risk of nonattainment plasma concentration can be quickly assessed by combining the child’s gender， age， dosage， liver and kidney function， and vitamin D， and the dosing regimen can be adjusted in time.

OBJECTIVE To investigate the reference range of tacrolimus blood concentration in children with Henoch-Schonlein purpura nephritis(HSPN)and analyze the factors affecting the blood concentration,in order to provide a reference for rational use of the drug in clinic.METHODS Clinical data of children with HSPN who were treated with tacrolimus and regularly monitored for blood concentration at the Children's Hospital Affiliated to Kunming Medical University were retrospectively collected from January 2018 to January 2024.The threshold of effective concentration of tacrolimus was determined by the receiver operating characteristic curve of the subjects.The clinical efficacy of tacrolimus in different concentrations and the incidence of adverse drug reaction(ADR)were compared to determine the reference range of tacrolimus blood concentration.The factors influencing the blood concentration were analyzed by one-way and multiple linear regression.RESULTS A total of 97 pediatric patients were included,and their tacrolimus blood concentrations were monitored 203 times,the blood concentration was 4.26(2.47,6.34)ng/mL.The area under the receiver operating characteristic curve of the subjects was 0.723(95%CI:0.596-0.850,P＜0.01),which corresponded to an effective threshold of 2.19 ng/mL.The clinical efficacy in pediatric patients with tacrolimus blood concentrations of 3-＜5 ng/mL,5-＜10 ng/mL,and≥10 ng/mL was significantly higher than that of children with concentrations＜3 ng/mL(P＜0.05).Additionally,the overall incidence of ADR in children with concentrations of 5-＜10 ng/mL and≥10 ng/mL was significantly higher than that in children with concentrations＜3 ng/mL and 3-＜5 ng/mL(P＜0.05).The impact of body mass index and CYP3A5 genotype on the blood concentration of tacrolimus was statistically significant(P＜0.05).CONCLUSIONS When using tacrolimus to treat HSPN in children clinically,the reference range for blood concentration is 3 to 5 ng/mL;body mass index and CYP3A5 genotype are factors that influence the blood concentration of tacrolimus.

Objective To explore the accuracy of fluid responsiveness assessment by variability of peripheral arterial peak velocity and variability of inferior vena cava diameter (ΔIVC) in patients with septic shock. Methods A prospective study was conducted. The patients with septic shock undergoing mechanical ventilation (MV) admitted to intensive care unit (ICU) of Beijing Electric Power Hospital from January 2016 to December 2017 were enrolled. According to sepsis bundles of septic shock, volume expansion (VE) was conducted. The increase in cardiac index (ΔCI) after VE ≥ 10% was defined as liquid reaction positive (responsive group), ΔCI < 10% was defined as the liquid reaction negative (non-responsive group). The hemodynamic parameters [central venous pressure (CVP), intrathoracic blood volume index (ITBVI), stroke volume variation (SVV), ΔIVC, variability of carotid Doppler peak velocity (ΔCDPV), and variability of brachial artery peak velocity (ΔVpeak-BA)] before and after VE were monitored. The correlations between the hemodynamic parameters and ΔCI were explored by Pearson correlation analysis. Receiver operating characteristic (ROC) curve was plotted to analyze the predictive value of all hemodynamic parameters on fluid responsiveness. Results During the study, 74 patients with septic shock were included, of whom 9 were excluded because of peripheral artery stenosis, recurrent arrhythmia or abdominal distension influencing the ultrasound examination, and 65 patients were finally enrolled in the analysis. There were 31 patients in the responsive group and 34 in the non-responsive group. SVV, ΔIVC, ΔCDPV and ΔVpeak-BA before VE in responsive group were significantly higher than those of the non-responsive group [SVV: (12.3±2.4)% vs. (9.2±2.1)%, ΔIVC: (22.3±5.3)% vs. (15.5±3.7)%, ΔCDPV: (15.3±3.3)% vs. (10.3±2.4)%, ΔVpeak-BA: (14.5±3.3)% vs. (9.6±2.3)%, all P < 0.05]. There was no significant difference in CVP [mmHg (1 mmHg = 0.133 kPa): 7.5±2.5 vs. 8.2±2.6] or ITBVI (mL/m2: 875.2±173.2 vs. 853.2±192.0) between the responsive group and non-responsive group (both P > 0.05). There was no significant difference in hemodynamic parameter after VE between the two groups. Correlation analysis showed that SVV, ΔIVC, ΔCDPV, and ΔVpeak-BA before VE showed significant linearity correlation with ΔCI (r value was 0.832, 0.813, 0.854, and 0.814, respectively, all P < 0.05), but no correlation was found between CVP and ΔCI (r = -0.342, P > 0.05) as well as ITBVI and ΔCI (r = -0.338, P > 0.05). ROC curve analysis showed that the area under ROC curve (AUC) of SVV, ΔIVC, ΔCDPV, and ΔVpeak-BA before VE for predicting fluid responsiveness was 0.857, 0.826, 0.906, and 0.866, respectively, which was significantly higher than that of CVP (AUC = 0.611) and ITBVI (AUC = 0.679). When the optimal cut-off value of SVV for predicting fluid responsiveness was 11.5%, the sensitivity was 70.4%, and the specificity was 94.7%. When the optimal cut-off value of ΔIVC was 20.5%, the sensitivity was 60.3%, and the specificity was 89.7%. When the optimal cut-off value of ΔCDPV was 13.0%, the sensitivity was 75.2%, and the specificity was 94.9%. When the optimal cut-off value of ΔVpeak-BA was 12.7%, the sensitivity was 64.8%, and the specificity was 89.7%. Conclusions Ultrasound assessment of ΔIVC, ΔCDPV, and ΔVpeak-BA could predict fluid responsiveness in patients with septic shock receiving mechanical ventilation. ΔCDPV had the highest predictive value among these parameters.