Objective To investigate the influencing factors of antibiotic-associated diarrhea(AAD)following congenital heart disease(CHD)surgery in pediatric patients,develop a nomogram-based predictive model,and validate its efficacy.Methods A retrospective analysis was conducted on the clinical data of pediatric patients who underwent CHD surgery in the Pediatric Intensive Care Unit(PICU)of a tertiary hospital in Guang-dong Province from July 2022 to July 2024.Patients were categorized into an AAD group and a non-AAD group.Univariate and multivariate logistic regression analyses were performed to identify risk factors for AAD occurrence following CHD surgery.A risk prediction model was developed,and a nomogram was constructed.The predictive performance of the model was evaluated using the Receiver Operating Characteristic(ROC)curve to calculate the area under the curve(AUC),the Hosmer-Lemeshow goodness-of-fit test,calibration curves,and clinical decision curve analysis.External validation of the model was conducted using data from patients in the Surgical Intensive Care Unit(SICU).Results The incidence of AAD following CHD surgery was 48.52%(229 out of 472 cases).Risk factors for AAD included the combined use of antibiotics,mechanical ventilation,elevated C-reactive protein levels,prolonged surgical duration,and extended antibiotic usage time(all with OR>1,P<0.05).Conversely,probiotic administration was identified as a protective factor(OR<1,P<0.05).The predictive model demon-strated excellent discrimination,as evidenced by the ROC curve areas:0.922(95%CI:0.894～0.951)in the modeling group,0.886(95%CI:0.838～0.915)in the internal validation group,and 0.862(95%CI:0.784～0.941)in the external validation group.Additionally,the model exhibited satisfactory calibration,as indicated by the Hosmer-Lemeshow test results:χ2=7.96,P=0.538 in the modeling group;χ2=4.24,P=0.895 in the inter-nal validation group;and χ2=9.923,P=0.270 in the external validation group.Furthermore,the model provided significant clinical utility.Conclusions Combined antibiotic use,duration of antibiotic therapy,mechanical ventilation,surgical duration,C-reactive protein(CRP)levels,and probiotic administration are key factors influ-encing the occurrence of AAD.The risk prediction model developed based on these variables demonstrates robust predictive performance and can serve as a valuable reference for the development and implementation of preventive and therapeutic strategies in clinical practice.

Objective: To investigate the basic situation of pre-analytical quality management in blood station laboratories in North China, and to provide baseline data for promoting the homogenization and standardization of these pre-analytical processes in each blood station laboratory. Methods: A cross-sectional status survey was designed based on the quality management regulations of blood stations, ISO15189 standards and relevant quality management requirements. This survey covering various aspects including laboratory general situation, sample collection and temporary storage, transportation, reception, and quality continuous improvement situations. Data analysis was performed on the survey results of each laboratory. Results: All the 38 blood station laboratories in North China had established a pre-analytical quality management system framework and implemented basic pre-analytical quality control activities; however, there were differences in implementation. 1) Among the 12 basic quality items, 3 items were monitored by all the investigated laboratories (100%), 6 items were monitored by the vast majority of laboratories (about 90%), and 3 items were monitored by a portion of laboratories (about 60%). There were no significant differences in the monitoring index among the three regions and among different types of laboratories (P>0.05). 2) Among the total of 26 items in the three key processes before testing (sample collection and storage, transportation, reception and processing), 12 items were monitored by all laboratories (100%), 11 items were monitored by the vast majority of laboratories (about 90%), and 3 items were monitored by a portion of laboratories (about 75%). There were no significant differences in monitoring index among different regions and types of laboratories (P>0.05). Conclusion: This survey provides a reference and basis for the gap analysis of the pre-analytical process quality management in 38 blood station laboratories across North China. It facilitates laboratories in identifying pre-analytical quality problems, resolving problems, preventing errors, and ensuring that the quality of blood samples before testing meets the established requirements. It lays a foundation for the homogenization of pre-analytical quality management in regional blood stations.

Objective: To determine the frequency and main reasons of unqualified samples by analyzing the quality of pre-analytical samples in blood stations in North China, thereby providing a reference and basis for gap analysis in the implementation of pre-analytical process quality management for participating laboratories and ensuring that only high-standard and high-quality blood samples proceed to testing. Methods: Data on the quality of pre-analytical samples from blood station laboratories in North China was collected via questionnaire. Statistical analysis were performed on: 1) the basic information of samples quality monitoring in the laboratories; 2) the distribution of the overall pre-analytical unqualified rate of samples and the pre-analytical unqualified rate of samples in each laboratory; 3) the distribution of reasons for sample disqualification. Results: 1) The overall pre-analytical unqualified rate of samples in blood station laboratories in North China was 4.55, with a total sigma level of 5.39σ. The 25th, 50th and 75th percentiles (P25, P50, P75) for the total unqualified rate were 0.00, 1.10 and 5.96, respectively. The corresponding percentiles for the Sigma level were 5.34σ, 5.71σ, and 6.00σ, respectively. The pre-analytical unqualified rate of serological and nucleic acid samples (4.89 vs 4.22) showed a significant difference (χ =9.575, P<0.05). 2) The average unqualified rate of samples in region A, B and C was 1.71, 9.50 and 12.64 (χ =1 590.721, P<0.05), and the sigma level was 5.66σ, 5.21σ and 5.16σ, respectively. 3) The main reasons for unqualified serological samples were chylous blood (72.65%), hemolysis (17.39%), abnormal hematocrit (5.80%), and insufficient volume (3.50%). The main reasons for the unqualified nucleic acid samples were chylous blood (78.26%), hemolysis (8.84%), failure to centrifuge as required (5.01%), abnormal hematocrit (4.66%), and insufficient volume (1.92%). Conclusion: In North China, the quality indicators for the pre-analytical processes in blood station laboratories are generally well-managed. Laboratories in region A outperformed the national average in pre-analytical specimen quality control. However, participating laboratories exhibit gaps in implementing pre-analytical quality management. Through effective analysis of pre-analytical process quality metrics and inter-laboratory comparisons, laboratories can identify discrepancies and address shortcomings. By establishing clear quality objectives, they can achieve continuous improvement and ensure the validity of test results.

Objective: To investigate the assessment criteria and subsequent handling practices of hemolytic and lipemic blood samples before testing in blood screening laboratories in North China, and to provide data to support the standardization of their management in blood station laboratories. Methods: Data on the preanalytical management of hemolytic and lipemic samples from 38 laboratories were collected. The details of management on the criteria and verificatioon for assessment, the assessment methods, and subsequent handling procedures of hemolytic and lipemic samples in blood station laboratories were analyzed. Results: 1) All 38 blood station laboratories monitored serological and nucleic acid samples for hemolysis and lipemia in pre-analytical phase. 2) The criteria and methods for assessing hemolytic and lipemic samples varied among the laboratories of the 38 blood stations. 15 laboratories (39.47%) followed manufacturer's instructions, 9 laboratories (23.68%) formulated their own criteria, and 14 laboratories (36.84%) referred to the criteria of other laboratories. 16 laboratories (42.11%) verified the criteria for assessing hemolytic and lipemic samples, with significant variations in verification rate across laboratories from different regions (P<0.05). For the assessment methods, visual inspection was used by 28 laboratories (73.68%) for hemolytic samples and by 27 laboratories (71.05%) for lipemic samples; the colorimetric card method was used by 10 laboratories (26.32%) for assessing both hemolytic and lipemic samples; the instrumental method was used by 1 laboratory (2.63%) for assessing lipemic samples.3) The handling procedures for hemolytic and lipemic samples varied significantly and followed a gradient distribution pattern among 38 laboratories (including accepting samples for testing, accepting samples for concession testing, re-collecting samples, and rejecting samples and halting testing). With increasing severity of hemolysis and lipemia, more laboratories halted testing, and relatively fewer laboratories accepted samples for normal testing. 5 laboratories (13.16%) applied different handling procedures on serological and nucleic acid samples. Conclusion: This survey provides a reference and basis for analyzing gaps in the management of hemolytic and lipemic samples during the preanalyical phase in blood station laboratories in North China. It enables laboratories to identify the problems and deficiencies in the management of hemolytic and lipemic samples, to ensure preanalytical samples quality meets the established requirements, and to lay a foundation for promoting the homogenization and standardization of the regional sample quality management mode.

Objective To investigate the influencing factors of antibiotic-associated diarrhea(AAD)following congenital heart disease(CHD)surgery in pediatric patients,develop a nomogram-based predictive model,and validate its efficacy.Methods A retrospective analysis was conducted on the clinical data of pediatric patients who underwent CHD surgery in the Pediatric Intensive Care Unit(PICU)of a tertiary hospital in Guang-dong Province from July 2022 to July 2024.Patients were categorized into an AAD group and a non-AAD group.Univariate and multivariate logistic regression analyses were performed to identify risk factors for AAD occurrence following CHD surgery.A risk prediction model was developed,and a nomogram was constructed.The predictive performance of the model was evaluated using the Receiver Operating Characteristic(ROC)curve to calculate the area under the curve(AUC),the Hosmer-Lemeshow goodness-of-fit test,calibration curves,and clinical decision curve analysis.External validation of the model was conducted using data from patients in the Surgical Intensive Care Unit(SICU).Results The incidence of AAD following CHD surgery was 48.52%(229 out of 472 cases).Risk factors for AAD included the combined use of antibiotics,mechanical ventilation,elevated C-reactive protein levels,prolonged surgical duration,and extended antibiotic usage time(all with OR>1,P<0.05).Conversely,probiotic administration was identified as a protective factor(OR<1,P<0.05).The predictive model demon-strated excellent discrimination,as evidenced by the ROC curve areas:0.922(95%CI:0.894～0.951)in the modeling group,0.886(95%CI:0.838～0.915)in the internal validation group,and 0.862(95%CI:0.784～0.941)in the external validation group.Additionally,the model exhibited satisfactory calibration,as indicated by the Hosmer-Lemeshow test results:χ2=7.96,P=0.538 in the modeling group;χ2=4.24,P=0.895 in the inter-nal validation group;and χ2=9.923,P=0.270 in the external validation group.Furthermore,the model provided significant clinical utility.Conclusions Combined antibiotic use,duration of antibiotic therapy,mechanical ventilation,surgical duration,C-reactive protein(CRP)levels,and probiotic administration are key factors influ-encing the occurrence of AAD.The risk prediction model developed based on these variables demonstrates robust predictive performance and can serve as a valuable reference for the development and implementation of preventive and therapeutic strategies in clinical practice.

Deep sternal wound infection (DSWI) is complicated and infection might affect multiple layers. It is difficult to identify and locate the degree and range of infection accurately before the operation. Thus, complete debridement tends to be challenging because of potential above-mentioned issues. Then it would lead to recurrence and further deterioration of the disease postoperatively. Timely and efficient diagnostic methods could promote early diagnosis of DSWI, and locate the focus of infection accurately so as to deal with it in time. This paper compares and summarizes the existing diagnostic methods for DSWI at home and abroad, such as microbiological examination, imaging examination, clinical symptoms and signs, in order to improve the efficiency of diagnosis and treatment of DSWI.

Deep sternal wound infection (DSWI) is complicated and infection might affect multiple layers. It is difficult to identify and locate the degree and range of infection accurately before the operation. Thus, complete debridement tends to be challenging because of potential above-mentioned issues. Then it would lead to recurrence and further deterioration of the disease postoperatively. Timely and efficient diagnostic methods could promote early diagnosis of DSWI, and locate the focus of infection accurately so as to deal with it in time. This paper compares and summarizes the existing diagnostic methods for DSWI at home and abroad, such as microbiological examination, imaging examination, clinical symptoms and signs, in order to improve the efficiency of diagnosis and treatment of DSWI.

Deep sternal wound infection (DSWI) is complicated and infection might affect multiple layers. It is difficult to identify and locate the degree and range of infection accurately before the operation. Thus, complete debridement tends to be challenging because of potential above-mentioned issues. Then it would lead to recurrence and further deterioration of the disease postoperatively. Timely and efficient diagnostic methods could promote early diagnosis of DSWI, and locate the focus of infection accurately so as to deal with it in time. This paper compares and summarizes the existing diagnostic methods for DSWI at home and abroad, such as microbiological examination, imaging examination, clinical symptoms and signs, in order to improve the efficiency of diagnosis and treatment of DSWI.

Deep sternal wound infection (DSWI) is complicated and infection might affect multiple layers. It is difficult to identify and locate the degree and range of infection accurately before the operation. Thus, complete debridement tends to be challenging because of potential above-mentioned issues. Then it would lead to recurrence and further deterioration of the disease postoperatively. Timely and efficient diagnostic methods could promote early diagnosis of DSWI, and locate the focus of infection accurately so as to deal with it in time. This paper compares and summarizes the existing diagnostic methods for DSWI at home and abroad, such as microbiological examination, imaging examination, clinical symptoms and signs, in order to improve the efficiency of diagnosis and treatment of DSWI.

Wound healing is a complex pathophysiological process. Vitamin can promote wound healing from different aspects and in multiple stages. This paper reviews the role of vitamin A, B, C, D, E in promoting wound healing and the related mechanism.