OBJECTIVE To upgrade the drug traceability code management system for a pediatric hospital under the “payment by code” background， aiming to comprehensively enhance traceability integrity， efficiency， and compliance. METHODS Taking Xiamen Children’s Hospital as the implementation setting， a before-and-after control design was adopted to construct an intelligent drug traceability code management system through systematic upgrades involving the technology platform， core mechanisms， and coordination with medical insurance. Key interventions included： upgrading a traceability code management platform and designing a dynamic code pool； innovating differentiated traceability mechanisms for routine， split-dose， and special drugs； establishing a tiered early-warning and emergency response system； and constructing a data coordination and quality control system. The drug traceability code upload rate served as the primary outcome. Process indicators such as the root causes distribution of failed uploads and the duration of medication returns， and a comprehensive outcome （the number of insurance-flagged abnormal prescriptions） were also analyzed. The data between the baseline period （April 2025） and the observation period （June-August 2025） were compared and evaluated. RESULTS After the upgrade， the overall upload rate of drug traceability codes increased from 9.21% （baseline） to 99.86% （August 2025）. The upload rate of traceability codes in previously unmanaged areas， such as the inpatient pharmacy and pharmacy intravenous admixture services， soared from 0 to nearly 100%. The proportion of non-uploads due to system issues fell from 66.44% （June 2025） to 2.62% （August Additionally， the number of insurance-flagged） abnormal prescriptions dropped sharply from 2 275.00 in the first “payment by code” policy month （July 2025） to 212.00 by the end of the observation period （August 2025）， a 90.70% decrease. CONCLUSIONS The developed management system effectively addresses complex scenario challenges such as high-frequency drug splitting. It significantly enhances traceability code upload performance and ensures a high degree of compliance with medical insurance data requirements. These outcomes contribute to proactive risk mitigation against insurance claim denials and demonstrate a concurrent optimization of pharmacy operations.

Objective To aim at using machine learning algorithms to predict the risk of neonatal intensive care unit(ICU)mortality,providing clinicians with an early diagnosis and risk assessment tool to assist in decision-making.Methods Clinical data of preterm infants from the paediatric intensive care database retrospectively were collected.By using least absolute shrinkage and selection operator(LASSO)regression analysis and multivariate Logistic regression analysis,key clinical characteristics affecting preterm infant prognosis were screened.The study was balanced the data by using the synthetic minority oversampling technique,combined seven machine learning models to build a predictive model and evaluate its performance.The Shapley additive explanations(SHAP)was used for model interpretation.Results A total of 923 preterm infants were finally included,survival group comprised 886 infants,and death group comprised 37 infants.A total of 38 clinical characteristics were collected.LASSO screening identified 8 variables significantly associated with neonatal ICU mortality,including lactate,respiratory rate,chloride concentration,neutrophils,and red blood cell distribution width etc.Multivariate Logistic regression analysis revealed that lactate and respiratory rate were independent predictors of neonatal ICU outcomes.Internal testing and external validation showed that light gradient boosting machine model outperformed other models in terms of accuracy and precision etc.indicators.SHAP analysis indicated that respiratory rate and lactate levels had the largest predictive contribution to the risk of preterm infants mortality.Conclusion This study provides reliable tools for early identification and intervention in the prognosis of preterm infants,emphasizing the importance of key indicators.

Objective To explore the target and mechanism of Fengliao Changweikang in treating irritable bowel syndrome(IBS)based on network pharmacology and molecular docking technology.Methods The effective components of Fengliao Changweikang were screened by data mining,and the drug-target network was constructed to identify the key targets and their pathways related to IBS.The molecular docking verification of the main active ingredients and core target proteins was carried out,and the binding energy was evaluated and the results were visualized.Results Fengliao Changweikang contained 24 potential active ingredients,134 active ingredient targets,and 2353 disease-related targets.After intersecting these,70 potential target points were identified,involving 898 biological processes,96 molecular functions,and 66 cellular components.Kyoto Encyclopedia of Genes and Genomes enrichment analysis suggested that its mechanism of action in treating IBS may involve regulating key signaling pathways such as cancer pathways,PI3K/Akt signaling pathways,and phospholipase D pathways through interactions with core targets.Molecular docking analysis indicated that the core compounds in Fengliao Changweikang exhibit good binding activity with core receptor proteins.Conclusion Fengliao Changweikang works through multiple signaling pathways and multiple target mechanisms to treat IBS.

Objective To construct a risk prediction model for admission sepsis in preterm infants,providing a basis for early clinical identification and intervention of admission sepsis.Methods Data of preterm infants admitted to Xiamen Children's Hospital from January 2020 to December 2023 were retrospectively collected and used for model construction.According to whether sepsis occurred after admission,they were divided into sepsis group(n=65)and non-sepsis group(n=394).LASSO regression combined with multivariate Logistic regression were used to screen risk factors,and a nomogram prediction model was constructed.External validation of the model was performed with 174 preterm infants admitted from January to December 2024.Results Gestational age,Apgar score ≤7 points at 10 minutes,total bilirubin,respiratory failure,and respiratory rate were identified as independent risk factors for admission sepsis in preterm infants.The area under the curve(AUC)of the training set was 0.853,and the external validation AUC was 0.937.The calibration results in the calibration curve were close to the ideal curve(Hosmer-Lemeshow test x2=6.599,P=0.580).Conclusion The prediction model developed based on seven bedside indicators demonstrates excellent performance,enabling rapid risk stratification and antimicrobial decision-making without the need for microbiological culture support.

Objective To compare the efficacy and safety of injectable azithromycin generics and originator in the treatment of mycoplasma pneumoniae pneumonia(MPP)in children,aiming to provide scientific evidence for clinical drug selection.Methods A retrospective collection of data from Xiamen Children's Hospital on children with MPP from January to July 2024,divided into domestic and original drug groups based on medication type.Comparing time indicators and infection indicators between two groups.Kaplan-Meier curves were used to analyze cumulative efficacy.A multivariable logistic regression model(stepwise adjustment of confounding factors)was used to calculate OR and corresponding 95％CI with effective outcomes as the dependent variable.Results After baseline correction,a total of 322 children were included.There were no significant differences in baseline characteristics between two groups(P＞0.05).The generic drug group had longer hospital stays,cough durations,and blood test recovery times compared to original drug group(P＜0.05),but there were no statistically significant differences in total treatment time,fever duration,or lung symptom improvement(P＞0.05).The recovery rates for infection indicators and cumulative clinical efficacy rates were similar in both groups(P＞0.05).Furthermore,there was no statistically significant difference in adverse event rates between domestic and original drug groups(P＞0.05).Conclusion The generic azithromycin is as effective as original drug in treating children with MPP and has good safety,making it an economically superior alternative medication.This study provides evidence to support the rational clinical use of generic drugs and expands the methodological reference value of real-world data in the consistency evaluation of pediatric drugs.

Objective To evaluate the effect of glucocorticoid(GC)treatment on the prognosis of patients with severe viral pneumonia,and to screen for related influencing factors and optimal beneficiary groups,providing reference for clinical medication decisions.Methods Based on the MIMIC-Ⅳ database,eligible patients with severe viral pneumonia were screened and divided into GC group and non GC group.Baseline differences were balanced using propensity score matching(PSM).Kaplan-Meier survival curves were used to analyze the cumulative survival rate of two groups of patients at 30 d,and Cox regression models were used to evaluate the association between GC use and the 30 d mortality risk in patients.Results A total of 518 severe viral pneumonia patients were included,including 43 in the GC group and 475 in the non-GC group.After PSM,there were 43 cases in the GC group and 86 cases in the non-GC group.The Kaplan-Meier survival curves showed that the 30 d cumulative survival rate of patients in the GC group was significantly higher than that in the non-GC group(P＜0.05).The results of multivariate Cox regression analysis showed that GC treatment significantly reduced the 30 d mortality risk[HR=0.35,95％CI(0.154,0.793),P=0.012],especially for patients older than 54 years,receiving mechanical ventilation,and with acute kidney injury.GC use,age＞54 years,and acute kidney injury were independent predictors of patient mortality risk(C-index=0.718 1).Subgroup analysis showed that for specific indications(age＞54 years,mechanical ventilation,no myocardial infarction,no hypertension,no hyperlipidemia,no heart failure,complicated by acute kidney failure),GC use could effectively reduce the 30 d mortality risk.Conclusion GC could effectively improve the prognosis of severe viral pneumonia patients,but individualized patient characteristics and treatment risks need to be considered comprehensively to optimize the medication regimen.

Objective To construct a risk prediction model for admission sepsis in preterm infants,providing a basis for early clinical identification and intervention of admission sepsis.Methods Data of preterm infants admitted to Xiamen Children's Hospital from January 2020 to December 2023 were retrospectively collected and used for model construction.According to whether sepsis occurred after admission,they were divided into sepsis group(n=65)and non-sepsis group(n=394).LASSO regression combined with multivariate Logistic regression were used to screen risk factors,and a nomogram prediction model was constructed.External validation of the model was performed with 174 preterm infants admitted from January to December 2024.Results Gestational age,Apgar score ≤7 points at 10 minutes,total bilirubin,respiratory failure,and respiratory rate were identified as independent risk factors for admission sepsis in preterm infants.The area under the curve(AUC)of the training set was 0.853,and the external validation AUC was 0.937.The calibration results in the calibration curve were close to the ideal curve(Hosmer-Lemeshow test x2=6.599,P=0.580).Conclusion The prediction model developed based on seven bedside indicators demonstrates excellent performance,enabling rapid risk stratification and antimicrobial decision-making without the need for microbiological culture support.

Objective To compare the efficacy and safety of injectable azithromycin generics and originator in the treatment of mycoplasma pneumoniae pneumonia(MPP)in children,aiming to provide scientific evidence for clinical drug selection.Methods A retrospective collection of data from Xiamen Children's Hospital on children with MPP from January to July 2024,divided into domestic and original drug groups based on medication type.Comparing time indicators and infection indicators between two groups.Kaplan-Meier curves were used to analyze cumulative efficacy.A multivariable logistic regression model(stepwise adjustment of confounding factors)was used to calculate OR and corresponding 95％CI with effective outcomes as the dependent variable.Results After baseline correction,a total of 322 children were included.There were no significant differences in baseline characteristics between two groups(P＞0.05).The generic drug group had longer hospital stays,cough durations,and blood test recovery times compared to original drug group(P＜0.05),but there were no statistically significant differences in total treatment time,fever duration,or lung symptom improvement(P＞0.05).The recovery rates for infection indicators and cumulative clinical efficacy rates were similar in both groups(P＞0.05).Furthermore,there was no statistically significant difference in adverse event rates between domestic and original drug groups(P＞0.05).Conclusion The generic azithromycin is as effective as original drug in treating children with MPP and has good safety,making it an economically superior alternative medication.This study provides evidence to support the rational clinical use of generic drugs and expands the methodological reference value of real-world data in the consistency evaluation of pediatric drugs.

Objective To evaluate the effect of glucocorticoid(GC)treatment on the prognosis of patients with severe viral pneumonia,and to screen for related influencing factors and optimal beneficiary groups,providing reference for clinical medication decisions.Methods Based on the MIMIC-Ⅳ database,eligible patients with severe viral pneumonia were screened and divided into GC group and non GC group.Baseline differences were balanced using propensity score matching(PSM).Kaplan-Meier survival curves were used to analyze the cumulative survival rate of two groups of patients at 30 d,and Cox regression models were used to evaluate the association between GC use and the 30 d mortality risk in patients.Results A total of 518 severe viral pneumonia patients were included,including 43 in the GC group and 475 in the non-GC group.After PSM,there were 43 cases in the GC group and 86 cases in the non-GC group.The Kaplan-Meier survival curves showed that the 30 d cumulative survival rate of patients in the GC group was significantly higher than that in the non-GC group(P＜0.05).The results of multivariate Cox regression analysis showed that GC treatment significantly reduced the 30 d mortality risk[HR=0.35,95％CI(0.154,0.793),P=0.012],especially for patients older than 54 years,receiving mechanical ventilation,and with acute kidney injury.GC use,age＞54 years,and acute kidney injury were independent predictors of patient mortality risk(C-index=0.718 1).Subgroup analysis showed that for specific indications(age＞54 years,mechanical ventilation,no myocardial infarction,no hypertension,no hyperlipidemia,no heart failure,complicated by acute kidney failure),GC use could effectively reduce the 30 d mortality risk.Conclusion GC could effectively improve the prognosis of severe viral pneumonia patients,but individualized patient characteristics and treatment risks need to be considered comprehensively to optimize the medication regimen.

Objective To aim at using machine learning algorithms to predict the risk of neonatal intensive care unit(ICU)mortality,providing clinicians with an early diagnosis and risk assessment tool to assist in decision-making.Methods Clinical data of preterm infants from the paediatric intensive care database retrospectively were collected.By using least absolute shrinkage and selection operator(LASSO)regression analysis and multivariate Logistic regression analysis,key clinical characteristics affecting preterm infant prognosis were screened.The study was balanced the data by using the synthetic minority oversampling technique,combined seven machine learning models to build a predictive model and evaluate its performance.The Shapley additive explanations(SHAP)was used for model interpretation.Results A total of 923 preterm infants were finally included,survival group comprised 886 infants,and death group comprised 37 infants.A total of 38 clinical characteristics were collected.LASSO screening identified 8 variables significantly associated with neonatal ICU mortality,including lactate,respiratory rate,chloride concentration,neutrophils,and red blood cell distribution width etc.Multivariate Logistic regression analysis revealed that lactate and respiratory rate were independent predictors of neonatal ICU outcomes.Internal testing and external validation showed that light gradient boosting machine model outperformed other models in terms of accuracy and precision etc.indicators.SHAP analysis indicated that respiratory rate and lactate levels had the largest predictive contribution to the risk of preterm infants mortality.Conclusion This study provides reliable tools for early identification and intervention in the prognosis of preterm infants,emphasizing the importance of key indicators.