OBJECTIVE To assess the correlation between the steady-state minimal concentration （cmin） and 24 h area under the drug concentration-time curve （AUC24）/minimal inhibitory concentration （MIC） ratio （AUC24/MIC） of vancomycin in pediatric patients， and analyze independent risk factors for treatment failure. METHODS Data of hospitalized children treated with vancomycin and receiving therapeutic drug monitoring in our hospital from January 2021 to July 2024 were retrospectively collected and divided into success group and failure group according to whether the treatment was successful or not. Spearman correlation analysis was used to analyze the correlation between cmin and AUC24/MIC of vancomycin， and one-way and multifactorial Logistic regression analyses were used to screen the independent risk factors for vancomycin treatment failure. RESULTS A total of 59 children were included， with 41 in the success group and 18 in the failure group. Compared with the failure group， AUC24/MIC of vancomycin was significantly higher in the success group （P＝0.038）， but there was no statistically significant difference in the cmin of the two groups （P＞0.05）； cmin of vancomycin was significantly positively correlated with AUC24/MIC （r＝0.499， P＜0.001）， but it has a certain efficacy in predicting the achievement of the AUC24/MIC standard （≥400） （area under the receiver operator characteristic curve＝0.696）， with an optimal cutoff value of 6.05 mg/L determined by the Youden index. The efficacy of AUC24/ MIC in predicting treatment failure was superior to cmin （areas under the receiver operator characteristic curve were 0.671 vs. 0.523， P were 0.038 vs. 0.684）， with higher sensitivity （83.3% vs. 66.7%）. Hypoproteinemia and AUC24/MIC≤369.1 were independent risk factors for vancomycin treatment failure （P＜0.05）. The incidence of nephrotoxicity was 3.4%. CONCLUSIONS There is a significant positive correlation between cmin and AUC24/MIC of vancomycin in pediatric patients； hypoproteinemia and AUC24/MIC≤369.1 are independent risk factors for vancomycin treatment failure in children.

OBJECTIVE To investigate the construction and practice of a drug traceability code management system in pediatric hospitals， providing a reference for promoting drug traceability code collection in healthcare institutions. METHODS Taking the outpatient pharmacy of our hospital as the research subject， a drug traceability code management system was constructed through the upgrade of the hospital information system （HIS）， process optimization， and human-machine collaboration mechanism. The PDCA （plan-do-check-act） cycle management method was applied to continuously optimize this system. Based on operational data from March 2024 to February 2025， the changes in the collection rate of drug traceability codes were analyzed， and the differences in the average patient pickup time， the average pharmacist dispensing time， and the dispensing error rate were compared before and after the implementation of the system. RESULTS In the initial period of trial operation of the drug traceability code management system（June 2024）， the collection rate of drug traceability codes was 57.17%， which subsequently improved to 93.52% by February 2025 following process optimization. Compared with the pre-implementation period （March-May 2024）， there was no significant difference （P＞0.05） in the average patient pickup time during the stable run-in period （August-October 2024）； the overall average pharmacist dispensing time increased significantly （P＜0.001）， but the clinical significance of this increase （0.42 s） was limited； stratified analyses showed a significant increase in the average pharmacist dispensing time for prescriptions involving chronic disease multidrug combinations （[ 23.29±6.83） s vs. （17.87±3.64 ） s， P＜0.001]； the dispensing error rate was reduced from 0.13‰ to 0.03‰ （P＝0.038）. CONCLUSIONS By adopting the strategy of “system reconstruction-process reengineering-human-machine collaboration”， our hospital has successfully established a drug traceability code management system. While complying with national regulatory requirements， we have maintained service efficiency and reduced the medication dispensing error rate.

Chronic obstructive pulmonary disease (COPD) and pulmonary hypertension (PH) are both chronic progressive respiratory diseases that cannot be completely cured. COPD is characterized by irreversible airflow limitation, chronic airway inflammation, and gradual decline in lung function, whereas PH is characterized by pulmonary vasoconstriction, remodeling, and infiltration of inflammatory cells. These diseases have similar pathological features, such as vascular hyperplasia, arteriolar contraction, and inflammatory infiltration. Despite these well-documented observations, the exact mechanisms underlying the occurrence and development of COPD and PH remain unclear. Evidence that mitochondrial dynamics imbalance is one major factor in the development of COPD and PH. Mitochondrial dynamics is precisely regulated by mitochondrial fusion proteins and fission proteins. When mitochondrial dynamics equilibrium is disrupted, it causes mitochondrial and even cell morphological dysfunction. Mitochondrial dynamics participates in various pathological processes for heart and lung disease. Mitochondrial dynamics may be different in the early and late stages of COPD and PH. In the early stages of the disease, mitochondrial fusion increases, inhibiting fission, and thereby compensatorily increasing adenosine triphosphate (ATP) production. With the development of the disease, mitochondria decompensation causes excessive fission. Mitochondrial dynamics is involved in the development of COPD and PH in a spatiotemporal manner. Based on this understanding, treatment strategies for mitochondrial dynamics abnormalities may be different at different stages of COPD and PH disease. This article will provide new ideas for the potential treatment of related diseases.
Humans ; Mitochondrial Dynamics/physiology* ; Pulmonary Disease, Chronic Obstructive/metabolism* ; Hypertension, Pulmonary/metabolism* ; Mitochondria/metabolism* ; Animals

OBJECTIVE: To develop and compare risk prediction models for in-hospital post-cardiac arrest brain injury (PCABI) in critically ill patients using nomograms and random forest algorithms, aiming to identify the optimal model for early identification of high-risk PCABI patients and providing evidence for precise treatment. METHODS: A retrospective cohort study was used to collect the first-time in-hospital cardiac arrest (IHCA) patients admitted to the intensive care unit (ICU) from 2008 to 2019 in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) as the study population, and the patients' age, gender, body mass, health insurance utilization, first vital signs and laboratory tests within 24 hours of ICU admission, mechanical ventilation, and critical care scores were extracted. Independent influencing factors of PCABI were identified through univariate and multivariate Logistic regression analyses. The included patients were randomly divided into a training cohort and an internal validation cohort in a 7:3 ratio, and the PCABI risk prediction model was constructed by the nomogram and random forest algorithm, respectively, and the model was evaluated by receiver operator characteristic curve (ROC curve), the calibration curve, and the decision curve analysis (DCA), and after the better model was selected, 179 patients admitted to Tianjin Medical University General Hospital as the external validation cohort for external evaluation were collected by using the same inclusion and exclusion criteria. RESULTS: A total of 1 419 patients with without traumatic brain injury who had their first-time IHCA were enrolled, including 995 in the training cohort (including 176 PCABI and 819 non-PCABI) and 424 in the internal validation cohort (including 74 PCABI and 350 non-PCABI). Univariate and multivariate analysis showed that age, potassium, urea nitrogen, sequential organ failure assessment (SOFA), acute physiology and chronic health evaluation III (APACHE III), and mechanical ventilation were independent influences on the occurrence of PCABI in patients with IHCA (all P < 0.05). Combining the above variables, we constructed a nomogram model and a random forest model for comparison, and the results show that the nomogram model has better predictive efficacy than the random forest model [nomogram model: area under the ROC curve (AUC) of the training cohort = 0.776, with a 95% credible interval (95%CI) of 0.741-0.811; internal validation cohort AUC = 0.776, with a 95%CI of 0.718-0.833; random forest model: AUC = 0.720, with a 95%CI of 0.653-0.787], and they performed similarly in terms of calibration curves, but the nomogram performed better in terms of decision curve analysis (DCA); at the same time, the nomogram model was robust in terms of external validation cohort (external validation cohort AUC = 0.784, 95%CI was 0.692-0.876). CONCLUSIONS A nomogram risk prediction model for the occurrence of PCABI in critically ill patients was successfully constructed, which performs better than the random forest model, helps clinicians to identify the risk of PCABI in critically ill patients at an early stage and provides a theoretical basis for early intervention.
Humans ; Critical Illness ; Retrospective Studies ; Heart Arrest/complications* ; Nomograms ; Brain Injuries/etiology* ; Intensive Care Units ; Algorithms ; Male ; Female ; Middle Aged ; ROC Curve ; Risk Factors ; Risk Assessment ; Logistic Models ; Aged

Objective To explore the efficacy of Yishen Yangyin Gujing Prescription[derived from Zuogui Wan(Bolus for Replenishing Kidney-Yin)]for the treatment of diabetic nephropathy in type 2 diabetes mellitus(T2DM)with qi and yin deficiency syndrome,and to observe its effects on inflammatory responses,pancreatic β-cell function and renal function.Methods A total of 162 T2DM patients with diabetic nephropathy of qi and yin deficiency syndrome who admitted to the Affiliated Hospital of Shaanxi University of Chinese Medicine from May 2020 to July 2023 were randomly divided into an observation group and a control group according to the random number table method,with 81 cases in each group.The control group was given conventional western medicine treatment for lowering blood pressure and renal protection,while the observation group was treated with Yishen Yangyin Gujing Prescription on the basis of treatment for the control group.The course of treatment for the two groups covered 12 weeks.Before and after the treatment,the changes in the outcomes of the two groups were observed,and the outcomes included the total scores of traditional Chinese medicine(TCM)syndrome,insulin sensitivity index,insulin secretion index,24-h urine total protein quantification(24hUTP),and serum levels of glycosylated hemoglobin(HbA1c),fasting insulin(FINS),fasting blood glucose(FBG),serum creatinine(SCr),blood urea nitrogen(BUN),transforming growth factor β1(TGF-β1),NLRP3 inflammasome,interleukin 6(IL-6),superoxide dismutase(SOD)and sphingosine-1-phosphate(S1P).After treatment,the clinical efficacy of the two groups of patients was compared.Results(1)After 12-week treatment,the total effective rate of the observation group was 93.83%(76/81),and that of the control group was 83.95%(68/81).The intergroup comparison showed that the efficacy of the observation group was significantly superior to that of the control group(χ2=9.163,P<0.05).(2)After treatment,the insulin sensitivity index and insulin secretion index in the two groups of patients were increased(P<0.05)and the total scores of TCM syndromes were decreased(P<0.05)when compared with those before treatment,and the increase of insulin sensitivity index and insulin secretion index as well as the decrease of the total scores of TCM syndrome in the observation group was significantly superior to that in the control group(P<0.05).(3)After treatment,the serum levels of HbA1c,FINS,FBG,SCr,BUN and 24hUTP in the two groups of patients were decreased when compared with those before treatment(P<0.05),and the decrease of the above indicators in the observation group was significantly superior to that in the control group(P<0.05).(4)After treatment,the serum TGF-β1,IL-6,and NLRP3 inflammasome levels in the two groups of patients were decreased(P<0.05)and the serum S1P and SOD levels were increased(P<0.05)when compared with those before treatment,and the decrease of serum TGF-β1,IL-6,and NLRP3 inflammasome levels as well as the increase of serum S1P and SOD levels in the observation group was significantly superior to that in the control group(P<0.05).Conclusion For T2DM patients with diabetic nephropathy of qi and yin deficiency syndrome,the combined use of western medicine and Yishen Yangyin Gujing Prescription treatment is helpful to alleviate inflammatory response,improve the function of pancreatic β-cells,regulate the blood glucose level,improve renal function,and enhance the clinical efficacy.

Objective:To construct a nomogram model for predicting the occurrence of acute kidney injury (AKI) in patients with cardiac arrest (CA) after cardiopulmonary resuscitation (CPR), and to verify its validity for early prediction.Methods:The study retrospectively included patients aged 18 years and older who received CPR for CA and were admitted to the emergency room of Tianjin Medical University General Hospital from February 2016 to September 2023. The general information, underlying diseases, resuscitation related indicators, and first laboratory test results of patients were collected. The patients were randomly divided into training and validation groups at a ratio of 7:3. AKI diagnosis was based on the diagnostic criteria of the Kidney Disease Improving Global Outcomes. Univariate and multivariate logistic regression models were used to identify independent risk factors for AKI in patients with cardiac arrest, and a nomogram was constructed on the basis of the independent risk factors. The predictive performance was evaluated by the area under the curve (AUC) of the receiver operating characteristic. The calibration curve, decision curve and clinical impact curve were used to evaluate the model. Bootstrap and cross validation methods were used for internal validation.Results:A total of 527 patients with cardiac arrest were included in the study, 230 patients developed AKI, with an AKI incidence of 43.6%. There was no statistically significant difference in clinical baseline data between the training and validation groups (all P>0.05), indicating comparability between the two groups of data. Multivariate logistic analysis revealed that age ( OR=1.346, 95% CI: 1.197-1.543, P<0.001), CA to CPR time ( OR=2.214, 95% CI: 1.512-3.409, P=0.016), adrenaline dosage ( OR=1.921, 95% CI: 1.383-2.783, P=0.004), APACHE-Ⅱ score ( OR=1.531, 95% CI: 1.316-1.820, P<0.001), baseline creatinine ( OR=1.137, 95% CI: 1.090-1.196, P<0.001), and lactate ( OR=2.558, 95% CI: 1.680-4.167, P<0.001) were the independent risk factors for AKI in patients with cardiac arrest. Initial defibrillable rhythm ( OR=0.214, 95% CI: 0.051-0.759, P=0.023) was a protective factor for AKI in patients with cardiac arrest. A nomogram prediction model was constructed based on the above variables. The AUC of the training group was 0.943 (95% CI: 0.921-0.965) and that of the validation group was 0.917 (95% CI: 0.874-0.960). This prediction model demonstrated good discrimination, calibration and clinical applicability. Conclusions:A nomogram predictive model was constructed on the basis of age, CA to CPR time, initial defibrillable rhythm, adrenaline dosage, the APACHE-Ⅱ score, and baseline creatinine and lactate levels. This nomogram has good predictive value for the early occurrence of AKI in patients with cardiac arrest after cardiopulmonary resuscitation, which can provide new strategies for the early identification of AKI and precise intervention.

Objective:To develop and validate an interactive network dynamic nomogram for early prediction of poor neurological prognosis in patients with post-cardiac arrest brain injury (PCABI).Methods:A retrospective study was conducted on hospitalized patients who achieved return of spontaneous circulation after cardiac arrest at Tianjin Medical University General Hospital between January 2020 and April 2024. Patients were classified into favorable and poor prognosis groups based on the Glasgow-Pittsburgh Cerebral Performance Category at discharge. Eligible patients were randomly assigned to a training cohort and an internal validation cohort in a 7:3 ratio. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of poor neurological outcomes in PCABI, which were subsequently used to develop a nomogram prediction model. The predictive performance of the nomogram was evaluated by comparing its area under the curve (AUC) of receiver operating characteristic with those of individual predictors using the DeLong test. Model calibration and clinical utility were assessed using calibration curves and decision curve analysis, respectively. Internal validation was conducted, and an interactive dynamic nomogram was developed using web-based visualization techniques.Results:A total of 276 PCABI patients were enrolled (training set: 196; validation set: 80), with 82 cases (29.7%) classified as poor prognosis. Multivariate logistic regression analysis identified age ( OR=1.071, 95% CI: 1.021-1.124, P=0.005), APACHEⅡ score ( OR=1.746, 95% CI: 1.393-2.190, P<0.001), initial shockable rhythm ( OR=0.142, 95% CI: 0.025-0.819, P=0.029), defibrillation ( OR=0.228, 95% CI: 0.060-0.869, P=0.030), cardiopulmonary resuscitation duration ( OR=2.116, 95% CI: 1.487-3.010, P<0.001), and lactate level ( OR=1.392, 95% CI: 1.005-1.927, P=0.047) as independent predictors of poor neurological outcomes in PCABI. A nomogram prediction model was developed based on these factors, achieving an AUC of 0.965 (95% CI: 0.939-0.989) in the training cohort and 0.987 (95% CI: 0.967-1.000) in the internal validation cohort. The nomogram demonstrated significantly superior predictive performance compared to individual predictors ( P<0.001) and exhibited excellent discrimination, calibration, and clinical net benefit. The interactive dynamic nomogram, developed through web-based visualization, further enhanced its applicability in clinical practice. Conclusions:The interactive network dynamic nomogram, developed based on age, APACHEⅡ score, initial shockable rhythm, defibrillation, cardiopulmonary resuscitation duration, and lactate level, demonstrated favorable predictive value for poor neurological outcomes in PCABI. This tool facilitates clinical application and offers a novel strategy for early identification and targeted interventions in high-risk patients.

Humans ; Aged ; Lung Diseases ; Pneumonia/drug therapy* ; Adrenal Cortex Hormones/therapeutic use* ; Pulmonary Disease, Chronic Obstructive/drug therapy* ; Administration, Inhalation ; Community-Acquired Infections/drug therapy*

Objective:To construct an early prediction model for post cardiopulmonary resuscitation-acute kidney injury (PCPR-AKI) by machine learning and provide a basis for early identification of acute kidney injury (AKI) high-risk patients and accurate treatment.Methods:It was a single-center retrospective study. The clinical data of patients admitted to Tianjin Medical University General Hospital after cardiopulmonary resuscitation following cardiac arrest from January 1, 2016 to October 31, 2023 were collected. The end-point event of the study was defined as AKI occurring within 48 hours after cardiopulmonary resuscitation. The patients were divided into AKI group and non-AKI group according to the AKI diagnostic criteria, and the differences of baseline clinical data between the two groups were compared. The patients who met the inclusion criteria were randomly (using the train_test_split function, set the random seeds to 1, 2, and 3) divided into training and validation sets at a ratio of 7∶3. Random forest (RF), support vector machine, decision tree, extreme gradient boosting and light gradient boosting machine algorithm were used to develop the early prediction model of PCPR-AKI. The receiver-operating characteristic curve and decision curve analysis were used to evaluate the performance and clinical practicality of the predictive models, and the importance of variables in the optimal model was screened and ranked.Results:A total of 547 patients were enrolled, with age of 66 (59, 70) years old and 282 males (51.6%). There were 238 patients (43.5%) having incidence of AKI within 48 hours after cardiopulmonary resuscitation. In the AKI group, 182 patients (76.5%) were in stage 1, 47 patients (19.7%) were in stage 2, and 9 patients (3.8%) were in stage 3. There were statistically significant differences in the age, time to reach resuscitation of spontaneous circulation, time from cardiac arrest to starting cardiopulmonary resuscitation, proportion of initial defibrillation rhythm, proportion of electric defibrillation, proportion of mechanical ventilation, adrenaline dosage, sodium bicarbonate dosage, proportion of coronary heart disease, proportion of hypertension, proportion of diabetes, serum creatinine, blood urea nitrogen, blood lactic acid, blood potassium, brain natriuretic peptide, troponin, D-dimer, neuron specific enolase, and 24 hours urine volume after cardiopulmonary resuscitation between AKI group and non-AKI group (all P<0.05). Among the five machine learning algorithms, RF model achieved the best performance and clinical practicality, with area under the curve of 0.875, sensitivity of 0.863, specificity of 0.956, and accuracy rate of 90.7%. In the variable importance ranking of RF model, the top 10 variables were as follows: time to reach resuscitation of spontaneous circulation, time from cardiac arrest to starting cardiopulmonary resuscitation, initial defibrillable rhythm, serum creatinine, mechanical ventilation, blood lactate acid, adrenaline dosage, brain natriuretic peptide, D-dimer and age. Conclusions:An early predictive model for PCPR-AKI is successfully constructed based on machine learning. RF model has the best predictive performance. According to the importance of the variables, it can provide clinical strategies for early identification and precise intervention for PCPR-AKI.

Objective To analyze and discuss the medication rule of professor Ruan Yan in the treatment of children with allergic rhinitis by using data mining method,and to provide reference for the clinical research and patented drugs development for the treatment of children with allergic rhinitis.Methods The outpatient medical records of professor Ruan Yan for the treatment of children with allergic rhinitis were collected.Microsoft Excel 2010 software was used for frequency statistics.SPSS Clementine 12.0 software was used for association rule analysis,cluster analysis and factor analysis to obtain the data.The frequency of use of various drugs and the association rules between drugs were obtained.Then the medication rules in professor Ruan Yan's prescription were analyzed.Results A total of 308 Chinese medicine compounds were included,involving 80 kinds of Chinese medicines,among which relieving drugs and qi-invigorating herbs were high-frequently used.The distribution of traditional Chinese medicine syndrome types was mainly characterized by lung-qi deficiency-cold syndrome and lung-spleen qi deficiency syndrome.The medicinal properties were mainly spicy,warm and sweet,and most of them belonged to the lung,spleen and stomach meridians.Five core prescriptions were extracted by factor analysis.Four drug combinations were obtained by systematic cluster analysis.Conclusion Ventilating lung and opening the orifices,expelling wind and removing cold,strengthening the spleen and replenishing qi are basic therapeutic principles for professor Ruan Yan in the treatment of children with allergic rhinitis.The treatment mainly focused on dispelling evil,ventilating lung and opening the orifices,expelling wind and removing cold during the acute stage of allergic rhinitis.In the remission period,according to the principle of"treating disease must be based on its origin",the treatment should enhance children's physical fitness,tonify lung and strengthen spleen,thereby reducing recurrence.