[Objective] To analyze the distribution of Rh blood group phenotype in some ethnic groups in China, so as to provide references for accurate blood transfusion. [Methods] The data of CNKI, Wanfang data and VIP were retrieved using "Rh blood group" and "nationality", and the search of PubMed database was conducted with the keywords "Rh blood group", "nationalities", "ethnic groups" and "China", with retrieval time until September 19, 2024 Data were extracted from eligible studies and the literature quality was evaluated using the criteria for cross-sectional studies in STROBE statement. Meta analysis was performed using Stata 11.0 software. [Results] A total of 350 relevant literature were retrieved, of which 26 were included. The total sample size for Rh phenotype distribution detection were 31 432, and the total population for RhD negative screening was 47 227, covering 26 ethnic groups. Meta-analysis revealed that the Rh blood groups phenotype distribution in certain ethnic populations in China was mainly CCDee 46.7% (95%CI=46.2%-47.2%), CcDEe 30.1% (95%CI=29.5%-30.6%), and CcDee 9.0% (95%CI=8.7%-9.3%). Analysis of the RhD-negative phenotype indicated an negative rate of RhD of 0.3% (95%CI=0.2%-0.3%), with the main phenotype distributions of ccdee at 0.2% (95%CI=0.1%-0.2%) and ccdEe at 0.2% (95%CI=0.0%-0.4%). The meta-analysis results of the distribution of common phenotypes among different ethnic groups showed that the CCDee phenotype was mainly distributed as Hani>Dong>Buyi>Miao>Tujia>Hui>Zang>Kazakh>Mongol>Uygur; the CcDEe phenotype: Zang>Mongol>Hui; the CcDee phenotype: Uygur>Kazakh>Mongol>Zang>Hui>Dong>Miao>Tujia>Buyi; the ccDEE phenotype: Zang>Hui=Mongol. The results of this study are similar to those of Qingdao population in China, but differ from studies conducted in North India, German individuals of European ancestry and Saudi Arabian populations. [Conclusion] The distribution of Rh blood group phenotypes in some ethnic groups in China shows no significant difference compared to the Han population, but there are differences when compared to populations in other countries and regions.

Objective:To investigate the value of Sequential Organ Failure (SOFA) score and its dynamics (ΔSOFA) in predicting mortality in hematology care unit (HCU) .Methods:A retrospective clinical study was conducted on 79 critically ill hematologic patients admitted to the Center for Critical Care Medicine, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences, between May and June 2024. SOFA scores and ΔSOFA were calculated within 2 days before and after HCU admission. The predictive value of SOFA and ΔSOFA in mortality was assessed using receiver operating characteristic (ROC) curve analysis.Results:Among the 79 patients, the HCU mortality rate was 54.4%. The SOFA scores on days 1–3 (D1, D2, and D3) and ΔSOFA on day 1 (ΔD_1) of all patients, leukemia patients and hematopoietic stem cell transplantation (HSCT) patients were significantly higher in the death group compared with the non-death group (all P<0.05). ROC curve analysis revealed that the D_1, D_2, D_3 scores, and ΔD_1 significantly predicted mortality ( P<0.001), with areas under the curve (AUCs) of 0.786, 0.866, 0.901, and 0.843, respectively. The sensitivity values were 74.36%, 57.89%, 62.85%, and 86.84%, while specificity values were 70%, 100%, 100%, and 67.65%, respectively. In the HSCT group, the D_-1, D_1, D_2, D_ 3, scores and ΔD_1 were predictive of HCU mortality, with AUCs of 0.833, 0.794, 0.871, 0.846, and 0.795, respectively. Sensitivity values for these scores were 100%, 85.71%, 71.43%, 57.14%, and 57.14%, while specificity values were 73.33%, 70.59%, 91.33%, 100%, and 100%, respectively. In the leukemia group, the D_1, D_2, D_3 scores, and ΔD_1 were predictive of HCU mortality, with AUCs of 0.760, 0.829, 0.846, and 0.756, respectively. Sensitivity values were 71.43%, 78.57%, 53.85%, and 71.43%, while specificity values were 76.19%, 78.95%, 100%, and 63.16%, respectively. For all patients, the D_3 score exhibited the highest specificity, while the ΔD_1 demonstrated the highest sensitivity. For patients in both the HSCT and leukemia groups, the sensitivity and specificity values of the D_1 and D_3 scores exceeded those of the ΔD_1. Conclusion:For patients with hematologic critical illness, including leukemia and those undergoing HSCT hospitalized in the HCU, D_1, D_2, D_ 3 scores and ΔD_1 are significantly associated with HCU mortality.

Objective To systematically evaluate the incidence and influencing factors of axillary web syndrome(AWS)in postoperative breast cancer patients,and to provide evidence for reducing the incidence of axillary web syndrome.Methods A computer search was performed in China National Knowledge Infrastructure(CNKI),VIP,Wanfang,SinoMed,PubMed,Medline,Scopus,The Cochrane Library,Web of Science,Embase,searched for articles on AWS influencing factors of breast cancer published from the establishment of the database to January 6th,2025.The articles were screened according to the inclusion and exclusion criteria.Revman5.4 and Stata17.0 were used for systematic review.Results Fifteen studies involving 3979 breast cancer patients and 1 156 patients with AWS were included.The results of the Meta-analysis showed that there was significant statistical heterogeneity among the included studies(I2=97.0%,P<0.0001).Using the random effects model,the incidence of AWS was 32.2%[95%CI(0.24,0.40),P<0.0001].The influencing factors for AWS after breast cancer surgery are age,body mass index(BMI),total mastectomy,lymph node metastasis,and neoadjuvant chemotherapy.NAC),axillary lymph node dissection(ALND),and the number of harvested axillary lymph nodes.Conclusion The incidence of AWS after breast cancer surgery was high.Clinicians should give early nursing to the influencing factors,reduce the incidence of AWS and improve patients'quality of life after surgery.

Objective To systematically evaluate the incidence and influencing factors of axillary web syndrome(AWS)in postoperative breast cancer patients,and to provide evidence for reducing the incidence of axillary web syndrome.Methods A computer search was performed in China National Knowledge Infrastructure(CNKI),VIP,Wanfang,SinoMed,PubMed,Medline,Scopus,The Cochrane Library,Web of Science,Embase,searched for articles on AWS influencing factors of breast cancer published from the establishment of the database to January 6th,2025.The articles were screened according to the inclusion and exclusion criteria.Revman5.4 and Stata17.0 were used for systematic review.Results Fifteen studies involving 3979 breast cancer patients and 1 156 patients with AWS were included.The results of the Meta-analysis showed that there was significant statistical heterogeneity among the included studies(I2=97.0%,P<0.0001).Using the random effects model,the incidence of AWS was 32.2%[95%CI(0.24,0.40),P<0.0001].The influencing factors for AWS after breast cancer surgery are age,body mass index(BMI),total mastectomy,lymph node metastasis,and neoadjuvant chemotherapy.NAC),axillary lymph node dissection(ALND),and the number of harvested axillary lymph nodes.Conclusion The incidence of AWS after breast cancer surgery was high.Clinicians should give early nursing to the influencing factors,reduce the incidence of AWS and improve patients'quality of life after surgery.

Objective:To investigate the value of Sequential Organ Failure (SOFA) score and its dynamics (ΔSOFA) in predicting mortality in hematology care unit (HCU) .Methods:A retrospective clinical study was conducted on 79 critically ill hematologic patients admitted to the Center for Critical Care Medicine, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences, between May and June 2024. SOFA scores and ΔSOFA were calculated within 2 days before and after HCU admission. The predictive value of SOFA and ΔSOFA in mortality was assessed using receiver operating characteristic (ROC) curve analysis.Results:Among the 79 patients, the HCU mortality rate was 54.4%. The SOFA scores on days 1–3 (D1, D2, and D3) and ΔSOFA on day 1 (ΔD_1) of all patients, leukemia patients and hematopoietic stem cell transplantation (HSCT) patients were significantly higher in the death group compared with the non-death group (all P<0.05). ROC curve analysis revealed that the D_1, D_2, D_3 scores, and ΔD_1 significantly predicted mortality ( P<0.001), with areas under the curve (AUCs) of 0.786, 0.866, 0.901, and 0.843, respectively. The sensitivity values were 74.36%, 57.89%, 62.85%, and 86.84%, while specificity values were 70%, 100%, 100%, and 67.65%, respectively. In the HSCT group, the D_-1, D_1, D_2, D_ 3, scores and ΔD_1 were predictive of HCU mortality, with AUCs of 0.833, 0.794, 0.871, 0.846, and 0.795, respectively. Sensitivity values for these scores were 100%, 85.71%, 71.43%, 57.14%, and 57.14%, while specificity values were 73.33%, 70.59%, 91.33%, 100%, and 100%, respectively. In the leukemia group, the D_1, D_2, D_3 scores, and ΔD_1 were predictive of HCU mortality, with AUCs of 0.760, 0.829, 0.846, and 0.756, respectively. Sensitivity values were 71.43%, 78.57%, 53.85%, and 71.43%, while specificity values were 76.19%, 78.95%, 100%, and 63.16%, respectively. For all patients, the D_3 score exhibited the highest specificity, while the ΔD_1 demonstrated the highest sensitivity. For patients in both the HSCT and leukemia groups, the sensitivity and specificity values of the D_1 and D_3 scores exceeded those of the ΔD_1. Conclusion:For patients with hematologic critical illness, including leukemia and those undergoing HSCT hospitalized in the HCU, D_1, D_2, D_ 3 scores and ΔD_1 are significantly associated with HCU mortality.

Objective:To understand the occupational stress and mental health status of hospital infection prevention and control practitioner (HIPCPs) in medical institutions, and analyze their main influencing factors.Methods:In November 2021, 550 nosocomial infection managers in Tianjin were randomly selected to conduct a questionnaire survey using the Concise Occupational Stress Questionnaire, Depression Screening Scale (PHQ-9) and Self-Rating Anxiety Scale (SAS). 497 valid questionnaires were obtained, and the total recovery efficiency was 90.36%. Single factor analysis and multivariate logistic regression method were used to analyze the main influencing factors of occupational stress and mental health status of psychiatric managers.Results:The detection rate of anxiety and depression among 497 HIPCPs was 22.73% (113/497) and 58.95% (293/497), respectively. Gender and major were the influencing factors of depression ( P=0.000, 0.001). Average working hours>52 hours per week and night shift days>1 days per week were the influencing factors of anxiety ( P=0.035, 0.014). Average working hours>52 h per week, night shift days >1 d per week and different majors were the influencing factors of occupational stress ( P=0.000, 0.025, 0.010). Multivariate logistic regression results showed that the risk of anxiety in those who worked more than 52 hours per week was 1.753 times that of those who worked less than 52 hours per week ( P=0.038), and the risk of depression in women was 3.071 times that of men ( P=0.006) . Conclusion:Working hours are an important influencing factor for occupational stress and anxiety among HIPCPs. In order to reduce the occurrence of occupational stress and mental health problems, it is necessary to strengthen psychological counseling for HIPCPs and balance work and rest.

Objective:To understand the occupational stress and mental health status of hospital infection prevention and control practitioner (HIPCPs) in medical institutions, and analyze their main influencing factors.Methods:In November 2021, 550 nosocomial infection managers in Tianjin were randomly selected to conduct a questionnaire survey using the Concise Occupational Stress Questionnaire, Depression Screening Scale (PHQ-9) and Self-Rating Anxiety Scale (SAS). 497 valid questionnaires were obtained, and the total recovery efficiency was 90.36%. Single factor analysis and multivariate logistic regression method were used to analyze the main influencing factors of occupational stress and mental health status of psychiatric managers.Results:The detection rate of anxiety and depression among 497 HIPCPs was 22.73% (113/497) and 58.95% (293/497), respectively. Gender and major were the influencing factors of depression ( P=0.000, 0.001). Average working hours>52 hours per week and night shift days>1 days per week were the influencing factors of anxiety ( P=0.035, 0.014). Average working hours>52 h per week, night shift days >1 d per week and different majors were the influencing factors of occupational stress ( P=0.000, 0.025, 0.010). Multivariate logistic regression results showed that the risk of anxiety in those who worked more than 52 hours per week was 1.753 times that of those who worked less than 52 hours per week ( P=0.038), and the risk of depression in women was 3.071 times that of men ( P=0.006) . Conclusion:Working hours are an important influencing factor for occupational stress and anxiety among HIPCPs. In order to reduce the occurrence of occupational stress and mental health problems, it is necessary to strengthen psychological counseling for HIPCPs and balance work and rest.

【Objective】 To establish a blood quality monitoring indicator system, in order to continuously improve blood quality and standardized management. 【Methods】 Based on the research of literature and standards, and guided by the key control points of blood collection and supply process, the blood quality monitoring indicator system was developed. Through two rounds of Delphi expert consultation, the indicator content was further revised and improved according to expert opinions after six months of trial implementation. The indicator weight was calculated by questionnaire and analytic hierarchy process. 【Results】 A blood quality monitoring indicator system covering the whole process of blood collection and supply was constructed, including five primary indicators, namely blood donation service, blood component preparation, blood testing, blood supply and quality control, as well as 72 secondary indicators, including definitions, calculation formulas, etc. Two rounds of expert consultation and two rounds of feasibility study meeting were held to revise 17 items and the weight of each indicator was obtained through the analytic hierarchy process. After partial adjustments, a blood quality monitoring indicator system was formed. 【Conclusion】 A blood quality monitoring indicator system covering the whole process of blood collection and supply has been established for the first time, which can effectively evaluate the quality management level of blood banks and coordinate blood quality control activities of blood banks in Shandong like pieces in a chess game, thus improving the standardized management level

【Objective】 To objectively evaluate the quality control level of blood testing process in blood banks through quantitative monitoring and trend analysis, and to promote the homogenization level and standardized management of blood testing laboratories in blood banks. 【Methods】 A quality monitoring indicator system covering the whole process of blood collection and supply, including blood donation service, blood component preparation, blood testing, blood supply and quality control was established. The questionnaire Quality Monitoring Indicators for Blood Collection and Supply Process with clear definition of indicators and calculation formulas was distributed to 17 blood banks in Shandong province. Quality monitoring indicators of each blood bank from January to December 2022 were collected, and 31 indicators in terms of blood testing were analyzed using SPSS25.0 software. 【Results】 The proportion of unqualified serological tests in 17 blood bank laboratories was 55.84% for ALT, 13.63% for HBsAg, 5.08% for anti HCV, 5.62% for anti HIV, 18.18% for anti TP, and 1.65% for other factors (mainly sample quality). The detection unqualified rate and median were (1.23±0.57)% and 1.11%, respectively. The ALT unqualified rate and median were (0.74±0.53)% and 0.60%, respectively. The detection unqualified rate was positively correlated with ALT unqualified rate (r=0.974, P<0.05). The unqualified rate of HBsAg, anti HCV, anti HIV and anti TP was (0.15±0.09)%, (0.05±0.04)%, (0.06±0.03)% and (0.20±0.05)% respectively. The average unqualified rate, average hemolysis rate, average insufficient volume rate and the abnormal hematocrit rate of samples in 17 blood bank laboratories was 0.21‰, 0.08‰, 0.01‰ and 0.02‰ respectively. There were differences in the retest concordance rates of four HBsAg, anti HCV and anti HIV reagents, and three anti TP reagents among 17 blood bank laboratories (P<0.05). The usage rate of ELISA reagents was (114.56±3.30)%, the outage rate of ELISA was (10.23±7.05) ‰, and the out of range rate of ELISA was (0.90±1.17) ‰. There was no correlation between the out of range rate, outrage rate and usage rate (all P>0.05), while the outrage rate was positively correlated with the usage rate (r=0.592, P<0.05). A total of 443 HBV DNA positive samples were detected in all blood banks, with an unqualified rate of 3.78/10 000; 15 HCV RNA positive samples were detected, with an unqualified rate of 0.13/10 000; 5 HIV RNA positive samples were detected, with an unqualified rate of 0.04/10 000. The unqualified rate of NAT was (0.72±0.04)‰, the single NAT reaction rate [(0.39±0.02)‰] was positively correlated with the single HBV DNA reaction rate [ (0.36±0.02) ‰] (r=0.886, P<0.05). There was a difference in the discriminated reactive rate by individual NAT among three blood bank laboratories (C, F, H) (P<0.05). The median resolution rate of 17 blood station laboratories by minipool test was 36.36%, the median rate of invalid batch of NAT was 0.67%, and the median rate of invalid result of NAT was 0.07‰. The consistency rate of ELISA dual reagent detection results was (99.63±0.24)%, and the median length of equipment failure was 14 days. The error rate of blood type testing in blood collection department was 0.14‰. 【Conclusion】 The quality monitoring indicator system for blood testing process in Shandong can monitor potential risks before, during and after the experiment, and has good applicability, feasibility, and effectiveness, and can facilitate the continuous improvement of laboratory quality control level. The application of blood testing quality monitoring indicators will promote the homogenization and standardization of blood quality management in Shandong, and lay the foundation for future comprehensive evaluations of blood banks.

【Objective】 To establish an effective quality monitoring indicator system for blood quality control in blood banks, in order to analyze the quality control indicators for blood collection and supply, and evaluate blood quality control process, thus promoting continuous improvement and standardizing management of blood quality control in blood banks. 【Methods】 A quality monitoring indicator system covering the whole process of blood collection and supply, including blood donation services, component preparation, blood testing, blood supply and quality control was established. The Questionnaire of Quality Monitoring Indicators for Blood Collection and Supply Process was distributed to 17 blood banks in Shandong, which clarified the definition and calculation formula of indicators. The quality monitoring indicator data from January to December 2022 in each blood bank were collected, and 20 quality control indicators data were analyzed by SPSS25.0 software. 【Results】 The average pass rate of key equipment monitoring, environment monitoring, key material monitoring, and blood testing item monitoring of 17 blood banks were 99.47%, 99.51%, 99.95% and 98.99%, respectively. Significant difference was noticed in the pass rate of environment monitoring among blood banks of varied scales(P<0.05), and the Pearson correlation coefficient (r) between the total number of blood quality testing items and the total amount of blood component preparation was 0.645 (P<0.05). The average discarding rates of blood testing or non-blood testing were 1.14% and 3.36% respectively, showing significant difference among blood banks of varied scales (P<0.05). The average discarding rate of lipemic blood was 3.07%, which had a positive correlation with the discarding rate of non testing (r=0.981 3, P<0.05). There was a statistically significant difference in the discarding rate of lipemic blood between blood banks with lipemic blood control measures and those without (P<0.05). The average discarding rate of abnormal color, non-standard volume, blood bag damage, hemolysis, blood protein precipitation and blood clotting were 0.20%, 0.14%, 0.06%, 0.06%, 0.02% and 0.02% respectively, showing statistically significant differences among large, medium and small blood banks(P<0.05).The average discarding rates of expired blood, other factors, confidential unit exclusion and unqualified samples were 0.02%, 0.05%, 0.003% and 0.004%, respectively. The discarding rate of blood with air bubbles was 0.015%, while that of blood with foreign body and unqualified label were 0. 【Conclusion】 The quality control indicator system of blood banks in Shandong can monitor weak points in process management, with good applicability, feasibility, and effectiveness. It is conducive to evaluate different blood banks, continuously improve the quality control level of blood collection and supply, promote the homogenization and standardization of blood quality management, and lay the foundation for comprehensive evaluation of blood banks in Shandong.