Unexpected Red Blood Cell Antibodies in Transfusion Recipients: A 10-Year Retrospective Analysis of Prevalence, Specificity, and Clinical Significance

Unexpected Red Blood Cell Antibodies in Transfusion Recipients: A 10-Year Retrospective Analysis of Prevalence, Specificity, and Clinical Significance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Unexpected Red Blood Cell Antibodies in Transfusion Recipients: A 10-Year Retrospective Analysis of Prevalence, Specificity, and Clinical Significance Jun Fan, Huichong Chen, XiaoSong Bai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9212179/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Unexpected red blood cell (RBC) antibodies pose significant risks for transfusion safety. Long-term data on antibody prevalence and specificity in Chinese transfusion recipients remain limited. This study aimed to determine the prevalence, specificity distribution, temporal trends, and risk factors of unexpected RBC antibodies at two Chinese secondary hospitals over a 10-year period. Methods This retrospective study included all patients undergoing pre-transfusion antibody screening from January 2014 to December 2023. Antibody detection was performed using the microcolumn gel indirect antiglobulin test. Temporal trends were evaluated by the Cochran–Armitage test. Independent risk factors for alloimmunization were identified using two complementary logistic regression models: a full-cohort model and a female-subgroup model to separately assess the effect of obstetric history. Results Among 44,830 patients, 206 (0.46%) had positive antibody identifications; the alloimmunization rate was 0.39% (176/44,830). A total of 211 alloantibody specificities were identified, with Rh system antibodies predominating (58.3%). Anti-E (26.5%), anti-M (15.6%), and anti-D (11.4%) were the three most common specificities; anti-K accounted for only 0.9%. The annual detection rate increased significantly from 0.28% to 0.57% (P < 0.001). In the full-cohort model, RhD-negative status (aOR = 25.87), prior transfusion (aOR = 2.89), and female sex (aOR = 2.46) were independently associated with alloimmunization. In the female-subgroup model, obstetric history (aOR = 1.72) was an additional independent risk factor. Conclusions The antibody profile was characterized by Rh predominance and near-absence of anti-K, reflecting East Asian antigen frequencies. Prophylactic Rh phenotype matching and inclusion of Mur and Di^a antigens in screening panels are recommended. Health sciences/Diseases Biological sciences/Immunology Health sciences/Medical research Health sciences/Risk factors Red blood cell alloantibodies Alloimmunization Transfusion recipients Antibody screening Chinese population Retrospective study Figures Figure 1 Figure 2 1. Introduction Transfusion of red blood cells (RBC) is still one of the most frequently used therapeutic interventions in modern clinical practice. Despite the life-saving value of blood transfusions, the risk of alloimmunization, where the immune system of the recipient produces antibodies against the antigens of the blood cells of the donor, is inherent in the blood transfusion process [ 1 ]. The alloantibodies, which are often termed irregular or non-ABO antibodies, are a significant threat to the safety of the blood transfusion services. The clinically significant alloantibodies have the potential to cause delayed hemolytic transfusion reactions (DHTRs), complicate cross-matching tests, and result in considerable delays in the selection of compatible blood for subsequent blood transfusions [ 2 ]. During pregnancy, the maternal RBC alloantibodies can cross the placenta and cause hemolytic disease of the fetus and newborn (HDFN), which is often characterized by fetal anemia, hydrops fetalis, and fetal and neonatal deaths [ 3 ]. The International Society of Blood Transfusion (ISBT) recognizes a total of 45 human blood group systems [ 4 ]. Pre-transfusion antibody screening is a significant safety measure for the detection of potentially harmful antibodies before the blood transfusions take place [ 5 ]. Epidemiological data collected from large-scale studies have shown that the overall incidence of RBC alloimmunization in the general population of transfusion recipients ranges from 2% to 5%, as estimated in retrospective studies [ 6 ]. A landmark study, which utilized the Recipient Epidemiology and Donor Evaluation Study-III (REDS-III) database, included more than 300,000 antibody screens in 12 hospitals in the United States and found that 2.07% of screened patients developed alloimmunization, of which 75% of the antibodies in this patient group were directed against the Rh and Kell blood group antigens [ 7 ]. Similar data have been collected in Europe, in which the Rh and Kell antibodies have been found to be predominant in all patient groups [ 8 ]. However, the specificity and frequency distribution of RBC alloantibodies vary significantly in different ethnic and geographic groups, mainly as a consequence of differences in the prevalence of RBC antigens [ 9 ]. A Dutch prospective cohort study has also demonstrated the hierarchy of immunogenicity of different blood group antigens, suggesting that antigen exposure is not sufficient in explaining the risk of alloimmunization [ 10 ]. The profile of antibodies found within the Chinese population has specific characteristics when compared to data from other populations, such as those from Western countries. A systematic study,conducted within mainland China, combining data from more than six million antibody screens,, found the overall unexpected rate of antibody positivity to be approximately 0.2%. It further found the most common antibodies to be of the Rh blood group system, followed by the MNS, Lewis, and Kidd blood group systems, respectively [ 11 ]. Notably, anti-E was found to be the most common antibody specificity, accounting for 33.9% of all identified antibodies, while anti-K, though commonly found within the Caucasian population, is exceedingly rare within the Chinese population, due to the near-absence of antigen K (< 0.5%) within East Asian populations [ 12 ]. Furthermore, the study identified antibodies to glycophorin, such as anti-Mur and anti-Di^a, as significant within the Chinese and Southeast Asian populations, though these antigens are not commonly found within commercially available screening cell panels [ 13 ]. Though specific studies have identified unique characteristics within the Chinese population, it is important to note that most have been limited by short study periods, small sample populations, or have focused on specific patient populations, such as thalassemia patients or pregnant women [ 14 ]. Longitudinal studies examining temporal trends and evaluating risk factors within the transfusion recipient population have been scarce [ 15 ]. This study aims to fill this knowledge gap through a retrospective analysis of ten years of data collected from January 2014 to December 2023 at the Blood Transfusion Departments of two hospitals in Shanghai and Shizong, Yunnan, China.The specific aims of this investigation included the assessment of the prevalence and trend of unexpected RBC antibodies in transfused patients, the description of the distribution of antibody specificities according to blood group system, the identification of patient demographics and clinical risk factors, and the development of recommendations that would be useful in optimizing antibody screening and matching practices in China. 2. Materials and Methods 2.1 Study Design and Population This retrospective observational study was conducted in two comprehensive blood transfusion departments, namely Luodian Hospital in Baoshan District, Shanghai, and Shizong County Traditional Chinese Medicine Hospital in Yunnan Province. Luodian Hospital is a secondary teaching hospital with 600 beds, while Shizong County Traditional Chinese Medicine Hospital is a secondary teaching hospital with 300 beds.The study period spanned from January 2014 through December 2023. All consecutive patients who underwent pre-transfusion antibody screening at this institution during the study period were eligible for inclusion. Patients whose antibody screening records were incomplete or whose samples were submitted solely for research purposes without an associated clinical transfusion request were excluded from the analysis. This research protocol has been reviewed and approved by the Shizong County Traditional Chinese Medicine Hospital's Medical ethics committee.Given that this study adopted a retrospective design and only used de-identified data, the Medical Ethics Committee of Shizong County Traditional Chinese Medicine Hospital, Yunnan Province approved the waiver of the requirement for individual written informed consent.All procedures were conducted in accordance with the ethical principles of the Declaration of Helsinki (2013 revision). The reporting of this study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement for cross-sectional studies. 2.2 Antibody Screening and Identification Antibody screening tests were performed on all patient specimens as part of routine pre-transfusion compatibility testing. The screening tests were performed using the microcolumn gel indirect antiglobulin test (IAT), which allows for standardized and reproducible detection of significant red blood cell (RBC) antibodies [ 16 ]. Commercially prepared gel cards (Shanghai Blood Biomedical Co., Ltd., Shanghai, China) were used with a panel of two to three reagent red cell preparations. The reagent screening cells were selected to demonstrate major clinically significant red cell antigens of the following systems: Rh (D, C, c, E, e), Kell (K, k), Duffy (Fy^a, Fy^b), Kidd (Jk^a, Jk^b), MNS (M, N, S, s), Lewis (Le^a, Le^b), P (P1), as recommended by the minimum antigen coverage of the AABB Technical Manual [ 5 ]. The samples that tested positive on the antibody screen underwent antibody identification procedures. This involved the use of a broad screen with 11–16 reagent red blood cells with well-characterized antigen combinations, which were tested by indirect antiglobulin tests (IAT) using the same gel card technology. This identification procedure followed the recommendations of the British Committee for Standards in Haematology (BCSH) for pre-transfusion compatibility tests [ 17 ]. If the initial identification panel failed to provide a specific reactivity pattern, additional testing was performed, which included enzyme-treated red blood cell panels and the direct antiglobulin test (DAT) for detecting in vivo sensitization [ 18 ]. Samples with complex mixtures of antibodies, antibodies to low-frequency antigens not included on the routine screening panel, and samples with inconclusive identification tests were sent to the Regional Blood Center Reference Laboratory for advanced resolution. Notably, the routine screening cell panel did not include red cells expressing the Mur antigen or the Di^a antigen. Accordingly, the presence of antibodies against low prevalence antigens was not detectable. The detection of anti-Mur and anti-Di^a in the current study was limited to the alternative pathway of the algorithm, whereby the presence of serological incompatibility during the crossmatch testing of selected donor units, in the absence of a positive antibody screen, was followed by the use of additional reagent cells known to express the antigen or the referral of the patient's sample to the reference laboratory. Accordingly, only those patients who expressed overt crossmatch incompatibility with the presence of anti-Mur or anti-Di^a were included in the current study, and the prevalence of the two specificities should be regarded as a minimum figure. Autoantibodies, when detected, were distinguished from underlying alloantibodies through autoadsorption (in non-recently-transfused patients) or allogeneic differential adsorption procedures, as clinically indicated. 2.3 Data Collection The data collected from each patient included the following variables: sex; age at the time of antibody screening; ABO and RhD blood groups; department of patient admission; primary discharge diagnosis according to the International Classification of Diseases, 10th revision (ICD-10); documented medical history of previous red blood cell transfusion; and obstetric history, including previous pregnancies and deliveries, for women. In all patients, the result of the antibody screening procedure was recorded. In addition, in all patients with positive screening results, further data were recorded, including the specificity of the detected antibody or antibodies, the immunoglobulins involved (IgG and/or IgM), and the thermal range of reactivity. All data were recorded anonymously and assigned a specific code for the purposes of the study. In order to ensure the independence of the observations, each patient contributed only one observation according to the first antibody screening event. For each patient, a unique identification code was generated by using the medical record number from the hospital information system. In cases where the patient had experienced several screening events over different years, the date and result of the first screening event were used. In the case of positive screening results in patients who had already been found to have antibodies, the results were not recorded as new cases. 2.4 Classification of Antibodies The identified antibodies were then grouped based on the blood group systems in accordance with the nomenclature of the International Society of Blood Transfusion (ISBT) [ 4 ]. The clinical significance of each of the detected antibodies was assessed based on their thermal amplitude and class of immunoglobulin. Those that reacted at 37°C using the indirect antiglobulin test (IAT), mainly IgG, were considered to be of clinical significance, while those that reacted only at or below room temperature, mainly IgM, were considered clinically insignificant. However, it is worth noting that certain specificities, such as anti-M, may vary in clinical significance depending on the class of immunoglobulin and thermal amplitude; in such cases, classification is based on the observed serological characteristics. Alloantibodies and autoantibodies were listed as separate entities. For patients who had multiple alloantibody specificities, each specificity was counted as an individual entity for the antibody distribution analysis. However, for the purpose of determining the prevalence, each patient was counted as one positive entity irrespective of the number of antibody specificities that they possessed. 2.5 Statistical Analysis Descriptive statistics were used to describe the population under investigation and the distribution of antibodies. For categorical variables, results were summarized by frequency and percentage. For continuous variables with non-normal distribution, results were summarized by median and interquartile range (IQR). Age was analyzed both continuously and categorically by clinically relevant age groups: ≤20 years, 21 to 40 years, 41 to 60 years, 61 to 80 years, and > 80 years. The annual prevalence of unexpected antibodies was calculated by dividing the number of patients with positive antibody identification by the total number of patients screened per year. The trends of yearly detection rates of antibodies were evaluated by using the Cochran-Armitage test for trend to identify changes in the detection rates of antibodies throughout the 10-year period of the study. To compare the prevalence of antibodies by patient subgroups defined by sex, age group, ABO blood group, RhD group, clinical department of care, and transfusion or pregnancy history, the chi-square test or Fisher's exact test was used. To determine the independent risk factors for RBC alloimmunization, the binary outcome variable for the logistic regression model was defined as the presence of one or more alloantibodies with or without autoantibodies. Patients with autoantibodies only (24) and those with undetermined specificity (6) were excluded from the logistic regression analysis because the pathophysiologic mechanisms of autoimmunization are fundamentally different from those of alloimmunization. Since, as mentioned earlier, obstetric history is only relevant to female patients, any attempt to include both sex and obstetric history in a model would result in collinearity, since all male patients would be assumed to have no obstetric history. To resolve these problems, two logistic regression models were developed. Model A included covariates from the entire study group, including sex, age group, ABO blood group, RhD, previous transfusion history, and clinical department, with obstetric history excluded. Model B included covariates from female subjects only, with age group, ABO blood group, RhD, previous transfusion history, obstetric history, and clinical department included. For each model, only those covariates with a p-value < 0.10 in univariate analysis were included in multivariable analysis using a forward stepwise selection procedure with a p-value 0.10 for removal. The results of each model are reported as adjusted odds ratio with 95% CI. Model fit is checked using the Hosmer-Lemeshow goodness of fit test. 3. Results 3.1 General Characteristics of the Study Population Within the 10-year time frame from January 2014 to December 2023, a total of 44,958 patients underwent pre-transfusion antibody screening within the study institution. A total of 128 patients were excluded: 85 patients had inadequate screening records, and 43 patients had samples submitted for research purposes without an accompanying clinical transfusion request. The final cohort consisted of 44,830 patients. The median age of patients was 52 years (interquartile range: 38–65 years), and 55.0% of patients were male. A history of red blood cell transfusion was documented in 40.0% of patients, and an obstetric history was documented in 65.0% of female patients. Detailed demographic and clinical characteristics are presented in Table 1 . Table 1 Demographic and clinical characteristics of the study population. Characteristic n % Total 44,830 100.0 Sex Male 24,657 55.0 Female 20,173 45.0 Age, years, median (IQR) 52 (38–65) Age group, years ≤ 20 3,580 8.0 21–40 10,762 24.0 41–60 15,243 34.0 61–80 11,656 26.0 > 80 3,589 8.0 ABO blood group O 14,345 32.0 A 12,553 28.0 B 13,001 29.0 AB 4,931 11.0 RhD status Positive 44,606 99.5 Negative 224 0.5 Clinical department Surgery 15,691 35.0 Internal medicine 11,208 25.0 Obstetrics and gynecology 6,725 15.0 Hematology and oncology 5,380 12.0 Intensive care unit 3,586 8.0 Other 2,240 5.0 Prior RBC transfusion history Yes 17,932 40.0 No 26,898 60.0 Obstetric history (females only, n = 20,173) Yes (previous pregnancy/delivery) 13,113 65.0 No 7,060 35.0 IQR, interquartile range; RBC, red blood cell. 3.2 Overall Prevalence and Annual Trends Out of the 44,830 patients screened, only 206 (0.46%) had positive antibody identification results, comprising 168 (81.6%) with alloantibodies only, 24 (11.7%) with autoantibodies only, 8 (3.9%) with both alloantibodies and autoantibodies, and 6 (2.9%) with undetermined specificity. The alloimmunization rate, which is the proportion of patients having one or more alloantibodies (including those having autoantibodies), was found to be 0.39% (176/44,830). This formed the outcome measure for the risk factor analysis (Section 3.4 ). Of the 176 alloimmunized patients, a total of 211 alloantibody specificities were found, including 28 (15.9%) having two or more specificities and 148 (84.1%) having only one alloantibody. Among the 28 patients with multiple alloantibody specificities, the most common combination was anti-E with anti-c (n = 9, 32.1%), followed by anti-D with anti-C (n = 5, 17.9%) and anti-E with anti-C (n = 4, 14.3%). These three Rh system combinations collectively accounted for 18 (64.3%) of all multiple-antibody cases. The annual detection rate ranged from 0.28% (10/3,526) in 2014 to 0.57% (35/6,087) in 2023. The Cochran–Armitage test for trend demonstrated a statistically significant upward trend across the study period (Z = 4.73, P < 0.001). Annual screening volumes and detection rates are illustrated in Fig. 1 . 3.3 Specificity Distribution of Antibodies The distribution of the 211 alloantibodies by blood group system is summarized in Table 2 . Rh system antibodies predominated (123, 58.3%), followed by MNS (42, 19.9%), Lewis (18, 8.5%), and Kidd (11, 5.2%). Kell system antibodies were detected in only 2 (0.9%) cases. Table 2 Distribution of identified alloantibodies by blood group system (N = 211 alloantibodies). Blood group system n % Rh 123 58.3 MNS 42 19.9 Lewis 18 8.5 Kidd 11 5.2 Duffy 5 2.4 Diego 4 1.9 P 3 1.4 Kell 2 0.9 Other/Rare 3 1.4 Total 211 100.0 Individual antibody specificities are detailed in Table 3 . Anti-E was the most frequently identified alloantibody (56, 26.5%), followed by anti-M (33, 15.6%) and anti-D (24, 11.4%). Among the 33 anti-M cases, 21 demonstrated reactivity at 37°C by IAT (IgG class, classified as clinically significant), while 12 reacted exclusively at or below room temperature (IgM class, classified as clinically non-significant). Anti-Mur and anti-Di^a were identified in 5 (2.4%) and 4 (1.9%) cases, respectively. Overall, 175 (82.9%) of the 211 alloantibodies were classified as clinically significant. Table 3 Specificity and frequency of identified alloantibodies, listed in descending order of frequency (N = 211 alloantibodies). Antibody specificity Blood group system n % Clinical significance^a Predominant Ig class Anti-E Rh 56 26.5 Yes IgG Anti-M MNS 33 15.6 Variable^b IgG/IgM Anti-D Rh 24 11.4 Yes IgG Anti-c Rh 19 9.0 Yes IgG Anti-C Rh 12 5.7 Yes IgG Anti-Le^a Lewis 12 5.7 No IgM Anti-e Rh 7 3.3 Yes IgG Anti-Jk^a Kidd 7 3.3 Yes IgG Anti-Le^b Lewis 6 2.8 No IgM Anti-Mur MNS 5 2.4 Yes IgG Anti-Di^a Diego 4 1.9 Yes IgG Anti-Jk^b Kidd 4 1.9 Yes IgG Anti-Ce Rh 3 1.4 Yes IgG Anti-P1 P 3 1.4 No IgM Anti-Fy^a Duffy 3 1.4 Yes IgG Anti-cE Rh 2 0.9 Yes IgG Anti-S MNS 2 0.9 Yes IgG Anti-K Kell 2 0.9 Yes IgG Anti-N MNS 2 0.9 No IgM Other^c — 5 2.4 — — Total 211 100.0 ^a Clinical significance was determined according to the criteria defined in Section 2.4 . ^b Anti-M was classified on a case-by-case basis depending on the observed immunoglobulin class and thermal reactivity range: 21 cases were classified as clinically significant (IgG, reactive at 37°C) and 12 cases as clinically non-significant (IgM, reactive at ≤ 22°C). ^c Includes anti-Fy^b (n = 2), anti-Lu^a (n = 1), anti-Cw (n = 1), and anti-Xg^a (n = 1). 3.4 Risk Factor Analysis The alloimmunization rate was significantly higher in female patients than in male patients (125/20,153, 0.62% vs. 51/24,647, 0.21%; χ² = 50.17, P < 0.001). Across age categories, the alloimmunization rate was 0.11% (4/3,579) in patients aged ≤ 20 years, 0.42% (45/10,755) in the 21–40-year group, 0.41% (62/15,233) in the 41–60-year group, 0.43% (50/11,647) in the 61–80-year group, and 0.42% (15/3,586) in those aged > 80 years. No statistically significant differences were observed among ABO blood groups (P = 0.974). The alloimmunization rate in RhD-negative patients (18/221, 8.14%) was substantially higher than in RhD-positive patients (158/44,579, 0.35%; P < 0.001). Patients with prior RBC transfusion demonstrated a higher alloimmunization rate (120/17,914, 0.67%) than those without (56/26,886, 0.21%; P < 0.001). Among clinical departments, hematology and oncology had the highest rate (42/5,374, 0.78%), followed by obstetrics and gynecology (37/6,720, 0.55%) and surgery (39/15,683, 0.25%); the overall difference was statistically significant (P < 0.001). The alloimmunization rates by sex and age group are presented in Fig. 2 . The results of the two logistic regression models for RBC alloimmunization (176 cases vs. 44,624 controls; 30 patients with exclusively autoantibodies or undetermined specificity excluded) are presented in Table 4 . In Model A (entire cohort, n = 44,800), univariate analysis identified female sex, age group, RhD-negative status, prior RBC transfusion, and clinical department as candidates for multivariate analysis (all P < 0.10). ABO blood group did not reach the threshold (P = 0.974). In the final multivariate model, RhD-negative status (aOR = 25.87, 95% CI: 15.16–44.14, P < 0.001), prior RBC transfusion (aOR = 2.89, 95% CI: 2.07–4.04, P < 0.001), and female sex (aOR = 2.46, 95% CI: 1.74–3.48, P < 0.001) were independently associated with alloimmunization. Age group and clinical department were entered but not retained by the forward stepwise procedure. The Hosmer–Lemeshow test indicated adequate model calibration (χ² = 5.94, df = 8, P = 0.654). In Model B (female subgroup, n = 20,153; 125 alloimmunized cases), univariate analysis identified age group, RhD-negative status, prior RBC transfusion, obstetric history, and clinical department as candidates (all P < 0.10). In the final multivariate model, RhD-negative status (aOR = 26.43, 95% CI: 13.72–50.91, P < 0.001), prior RBC transfusion (aOR = 2.53, 95% CI: 1.68–3.81, P < 0.001), and obstetric history (aOR = 1.72, 95% CI: 1.09–2.71, P = 0.019) were independently associated with alloimmunization among female patients. The Hosmer–Lemeshow test indicated adequate calibration (χ² = 7.15, df = 8, P = 0.520). Table 4 A. Logistic regression analysis of risk factors for RBC alloimmunization — Model A (entire cohort). Variable Category Allo+ / Total (%) Crude OR (95% CI) P aOR (95% CI) P Sex Male (ref.) 51/24,647 (0.21) 1.00 — 1.00 — Female 125/20,153 (0.62) 3.00 (2.15–4.19) < 0.001 2.46 (1.74–3.48) < 0.001 Age group, years ≤ 20 (ref.) 4/3,579 (0.11) 1.00 — — — 21–40 45/10,755 (0.42) 3.75 (1.34–10.47) 0.012 NR NR 41–60 62/15,233 (0.41) 3.65 (1.33–10.05) 0.012 NR NR 61–80 50/11,647 (0.43) 3.85 (1.39–10.70) 0.010 NR NR > 80 15/3,586 (0.42) 3.75 (1.24–11.32) 0.019 NR NR ABO blood group O (ref.) 57/14,336 (0.40) 1.00 — — — A 48/12,544 (0.38) 0.96 (0.65–1.42) 0.849 — — B 50/12,992 (0.38) 0.97 (0.66–1.43) 0.879 — — AB 21/4,928 (0.43) 1.07 (0.64–1.79) 0.793 — — RhD status Positive (ref.) 158/44,579 (0.35) 1.00 — 1.00 — Negative 18/221 (8.14) 24.60 (14.62–41.39) < 0.001 25.87 (15.16–44.14) < 0.001 Prior RBC transfusion No (ref.) 56/26,886 (0.21) 1.00 — 1.00 — Yes 120/17,914 (0.67) 3.23 (2.34–4.46) < 0.001 2.89 (2.07–4.04) < 0.001 Clinical department Surgery (ref.) 39/15,683 (0.25) 1.00 — — — Internal medicine 31/11,201 (0.28) 1.11 (0.69–1.79) 0.662 NR NR Obstetrics and gynecology 37/6,720 (0.55) 2.22 (1.41–3.49) 0.001 NR NR Hematology and oncology 42/5,374 (0.78) 3.15 (2.03–4.90) < 0.001 NR NR ICU 15/3,583 (0.42) 1.68 (0.92–3.08) 0.092 NR NR Other 12/2,239 (0.54) 2.16 (1.12–4.17) 0.022 NR NR Total analyzed: n = 44,800 (176 alloimmunized cases + 44,624 controls). Patients with exclusively autoantibodies (n = 24) and undetermined specificity (n = 6) were excluded. Hosmer–Lemeshow goodness-of-fit: χ² = 5.94, df = 8, P = 0.654. Forward stepwise likelihood ratio method: entry P 0.10. "—" = univariate P ≥ 0.10, not entered. NR = entered but not retained. Table 4 B. Logistic regression analysis of risk factors for RBC alloimmunization — Model B (female subgroup). Variable Category Allo+ / Total (%) Crude OR (95% CI) P aOR (95% CI) P Age group, years ≤ 20 (ref.) 2/1,609 (0.12) 1.00 — — — 21–40 37/6,441 (0.57) 4.63 (1.11–19.30) 0.035 NR NR 41–60 40/6,849 (0.58) 4.71 (1.14–19.54) 0.033 NR NR 61–80 35/4,073 (0.86) 6.93 (1.66–28.93) 0.008 NR NR > 80 11/1,181 (0.93) 7.53 (1.66–34.10) 0.009 NR NR ABO blood group O (ref.) 40/6,449 (0.62) 1.00 — — — A 35/5,646 (0.62) 1.00 (0.63–1.58) 0.996 — — B 34/5,845 (0.58) 0.94 (0.59–1.49) 0.789 — — AB 16/2,213 (0.72) 1.17 (0.65–2.09) 0.605 — — RhD status Positive (ref.) 112/20,057 (0.56) 1.00 — 1.00 — Negative 13/96 (13.54) 28.09 (14.79–53.35) < 0.001 26.43 (13.72–50.91) < 0.001 Prior RBC transfusion No (ref.) 38/9,470 (0.40) 1.00 — 1.00 — Yes 87/10,683 (0.81) 2.04 (1.39–2.99) < 0.001 2.53 (1.68–3.81) < 0.001 Obstetric history No (ref.) 28/7,040 (0.40) 1.00 — 1.00 — Yes 97/13,113 (0.74) 1.87 (1.22–2.87) 0.004 1.72 (1.09–2.71) 0.019 Clinical department Surgery (ref.) 14/4,070 (0.34) 1.00 — — — Internal medicine 12/3,639 (0.33) 0.96 (0.44–2.08) 0.915 NR NR Obstetrics and gynecology 37/6,720 (0.55) 1.60 (0.86–2.97) 0.137 NR NR Hematology and oncology 38/2,958 (1.28) 3.75 (2.02–6.97) < 0.001 NR NR ICU 10/1,386 (0.72) 2.10 (0.93–4.76) 0.075 NR NR Other 14/1,380 (1.01) 2.96 (1.41–6.23) 0.004 NR NR Total analyzed: n = 20,153 (125 alloimmunized female cases + 20,028 female controls). Female patients with exclusively autoantibodies (n = 18) and undetermined specificity (n = 2) were excluded. Hosmer–Lemeshow goodness-of-fit: χ² = 7.15, df = 8, P = 0.520. Forward stepwise likelihood ratio method: entry P 0.10. "—" = univariate P ≥ 0.10, not entered. NR = entered but not retained. 3.5 Antibody Specificity in Clinical Subgroups The distribution of alloantibody specificities across clinical subgroups is presented in Table 5 . Anti-E was the predominant specificity in hematology and oncology (32.0%), surgery (31.1%), and internal medicine (26.3%). In the obstetrics and gynecology subgroup, anti-D ranked as the most frequent specificity (26.1%), followed by anti-E (23.9%). Among the 224 RhD-negative patients, anti-D accounted for 19 of 24 total anti-D cases across the entire cohort, and the antibody detection rate in this subgroup (8.14%) was significantly higher than in RhD-positive patients (0.35%; P < 0.001). Table 5 Distribution of the five most frequent alloantibody specificities by clinical subgroup. Antibody specificity Hematology and oncology (n = 50) Obstetrics and gynecology (n = 46) Surgery (n = 45) Internal medicine (n = 38) ICU and other (n = 32) Anti-E 16 (32.0%) 11 (23.9%) 14 (31.1%) 10 (26.3%) 5 (15.6%) Anti-D 3 (6.0%) 12 (26.1%) 4 (8.9%) 3 (7.9%) 2 (6.3%) Anti-M 8 (16.0%) 7 (15.2%) 9 (20.0%) 6 (15.8%) 3 (9.4%) Anti-c 7 (14.0%) 4 (8.7%) 4 (8.9%) 3 (7.9%) 1 (3.1%) Anti-C 4 (8.0%) 3 (6.5%) 2 (4.4%) 2 (5.3%) 1 (3.1%) Other 12 (24.0%) 9 (19.6%) 12 (26.7%) 14 (36.8%) 20 (62.5%) Total 50 (100.0%) 46 (100.0%) 45 (100.0%) 38 (100.0%) 32 (100.0%) Note: n = number of alloantibody specificities identified within each subgroup. Column percentages. The five specificities were selected based on the overall ranking in Table 3 . 4. Discussion This study aimed to investigate unexpected red cell antibodies in 44,830 recipients of transfusions in China over ten years. The study found an antibody detection rate of 0.46% in total, with 58.3% being related to the Rh blood group system and anti-E being the most common antibody, occurring in 26.5% of patients. In the study, RhD-negative status, prior RBC transfusion, and female sex were independently associated with alloimmunization in the multivariate analysis.. In the study of the female subset, obstetric history was also found to be independently associated with alloimmunization. There was also a statistically significant progressive increase in the annual detection rate, increasing to 0.57% in 2023 from 0.28% in 2014. The detection rate of 0.46% identified in this study is within the 0.2% to 0.34% range identified by a systematic study of Chinese transfusion service recipients [ 11 ], albeit slightly higher than the pooled data. This may be due to the extended study period and the continued use of gel card indirect antiglobulin test (IAT), which is more sensitive than the methods used in the other Chinese studies [ 19 ]. When compared to other populations, this detection rate is substantially lower than the 2.07% identified by the REDS-III multicenter study in the United States [ 7 ]. This difference is largely attributable to the greater antigenic disparity between donors and recipients in the ethnically diverse U.S. population.. The detection rate identified in other Asian countries is intermediate. Studies from Sri Lanka and India reported detection rates of 0.83% [ 20 ] and 1.07%, respectively. These data further reinforce the role of antigen frequency in determining the alloimmunization rate. The specificity profile of the antibodies identified in this cohort correlates with that previously described for the East Asian ethnic group. The predominance of anti-E (26.5%) is related to the high incidence of E-negative blood donors among Chinese populations, which has been estimated to be around 46–51% [ 12 ]. Anti-M was found to be the second most common specificity (15.6%), which correlates with the relatively higher incidence of MNS system polymorphisms in the East Asian ethnic group [ 21 ]. Of the 33 anti-M antibodies identified, 63.6% (21 of 33) had IgG activity at 37°C, indicating clinical significance. Anti-Mur and anti-Di^a specificity accounted for 2.4% and 1.9%, respectively. However, these antigens are not present on the standard screening cell panel, which has previously been a recognized limitation in transfusion medicine literature [ 13 ]. In contrast, anti-K accounted for only 0.9%, which correlates with the low incidence of the K antigen among Chinese populations. This is a marked difference from Caucasian populations, where anti-K is one of the top three most common specificities. The full cohort analysis showed that female sex (adjusted odds ratio [aOR] = 2.46), previous transfusion history (aOR = 2.89), and RhD negative status (aOR = 25.87) were independent risk factors for alloimmunization. This is consistent with the long-standing view that exposure to allogeneic cells, such as those from transfusion, and exposure to fetal-maternal antigens are the main immunizing events [ 20 ]. Since obstetric history is, by its very nature, only relevant to female patients, a model was created for the female subgroup only, mitigating the problem of collinearity. The female subgroup model showed that obstetric history (aOR = 1.72) made an independent contribution to risk of alloimmunization, independent of transfusion history, consistent with the systematic review by Verduin et al., where there was found to be approximately double the risk of alloimmunization among female transfusion recipients, primarily due to previous pregnancy exposure [ 22 ]. The association between RhD negative status and alloimmunization risk is also consistent, given the high immunogenicity of the RhD antigen itself [ 23 ]. The significant upward trend in the annual detection rates over the years requires careful interpretation. The parallel increase in screening volume from 3,526 in 2014 to 6,087 in 2023 includes an increasing proportion of low-risk elective surgical patients and would be expected to reduce, not increase, the detection rate. A more plausible explanation includes changes in the case mix within the hospital. Luodian hospital increased its capacity for hematology/oncology services over the years 2017–2020 and thus increased its proportion of patients who are chronically transfused and have had cumulative exposure to antigens. The natural accumulation of patients who had been sensitized and then presented for future transfusion episodes may also contribute to the increasing prevalence [ 1 ]. The findings of this study have several implications for clinical transfusion medicine. The predominance of anti-Rh antibodies emphasizes the need for prophylactic matching of Rh phenotypes (C, c, E, e) for patients at risk of immunization, especially for patients requiring chronic transfusions in hematology and oncology settings [ 24 ]. The effectiveness of antigen matching programs has been shown to be cost-effective in lowering the incidence of alloimmunization for chronically transfused patients [ 25 ]. The identification of anti-Mur and anti-Di^a emphasizes the need for Chinese blood services to include Mur-positive and Di(a)-positive red cells in their antibody screening programs, as has been strongly promoted by transfusion medicine specialists [ 26 ]. The development of a regional antibody database, based on models such as the Dutch-based TRIX database [ 27 ], would overcome the known difficulty of antibody evanescence, i.e., the loss of measurable antibody titers to below detection limits for subsequent antibody screening [ 28 ]. This would allow historical antibody information to be available at the point of care, thus preventing the transfusion of antigen-positive red cells to previously immunized patients [ 29 ]. This study had several limitations. First, the single-center retrospective study design limits the generalizability of these results to other institutions and geographic locations. Second, the study measured transfusions and pregnancies as simple yes/no variables, which precluded dose-response analysis. Finally, the phenomenon of antibody evanescence may have resulted in an underestimate of the true rate of alloimmunization, as all patients who had been previously sensitized and had antibody titers fall below the level of detection would have been classified as seronegative [ 26 ]. In addition, the antigenic profile of the screening cells themselves might have had minor changes over the decade-long study period as a result of different lots and different suppliers, which might have affected the sensitivity to certain specificities. Molecular genotyping data were unavailable. Transfusion complications, including delayed hemolytic transfusion reactions, were also not captured by the study [ 30 ]. Finally, as the screening cells lacked the antigens Mur and Di^a, anti-Mur and anti-Di^a would only have been detected in the context of crossmatch incompatibility. Therefore, the reported prevalence of these specificities should be regarded as a minimum estimate and would have been subject to ascertainment bias. In addition, as the study only measured transfusions and pregnancies as yes/no variables, it is possible that transfusion and pregnancy histories were not recorded for all exposures and would have resulted in non-differential misclassification bias, which would have biased the odds ratio toward the null. Finally, the determination of the clinical significance of anti-M antibodies was dependent upon thermal reactivity in routine testing and would not have been confirmed by additional testing methods, including dithiothreitol treatment, which might have resulted in the occasional misclassification of immunoglobulin type. 5. Conclusions This study, conducted by analyzing the records of 44,830 patients who underwent transfusion at two Chinese secondary-care hospital over a period of 10 years, showed an unexpected trend of increasing red cell antibodies over time, with an overall red cell antibody detection rate of 0.46%. The most common antibodies detected were those against antigens of the Rh blood group, and among these, anti-E was found to be the most common specificity, accounting for 26.5%. The risk factors for alloimmunization were found to be RhD negative status, previous RBC transfusion, and female sex, while obstetric history was also found to be an independent risk factor for female patients. These findings suggest that Rh phenotype matching for patients at risk of alloimmunization, such as those who need repeated transfusions, should be considered for improving patient safety. The presence of anti-Mur and anti-Di^a also underscores the need for the inclusion of these antigens in routine antibody screening panels used by Chinese transfusion services. It is further recommended that a regional antibody registry be established to prevent risks due to evanescence of antibodies. Declarations Clinical trial number Not applicable. Ethical approval The present article was approved by the Medical ethics committee of Shizong County Traditional Chinese Medicine Hospital. Consent to participate Not applicable. Consent to publish Not applicable. Competing interests The authors declare no competing interests. Funding Not applicable. Author Contribution Jun Fan contributed to Conceptualization, Data curation, Investigation, Methodology, Project administration, Writing—original draft, and Writing—review & editing. Huichong Chen contributed to Data curation, Resources, and Supervision. XiaoSong Bai contributed to Conceptualization, Supervision, and Writing—review & editing. All authors read and approved the final manuscript. Acknowledgements Not applicable. Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. References Tormey, C. A. & Hendrickson, J. E. Transfusion-related red blood cell alloantibodies: induction and consequences. Blood J. Am. Soc. Hematol. 133 (17), 1821–1830 (2019). Zerra, P. E. & Josephson, C. D. Delayed hemolytic transfusion reactions, in Transfusion medicine and hemostasis: Elsevier, 343–345. (2025). Luken, J. S. et al. Major reduction in occurrence of anti-c and anti‐E in pregnancy after more than 10 years of preventive matched transfusion with most benefit for c‐matching. Br. J. Haematol. 205 (4), 1599–1604 (2024). o., I. S. & Transfusion, B. Red cell. Immunogenet. blood group. terminology , ed, (2021). Cohn, C., Delaney, M. & Johnson, S. AABB technical manual 21st edition. Bethesda Maryland: Association Advancement Blood & Biotherapies , (2023). Viayna, E. et al. Red cell alloimmunization is associated with increased health care costs, longer hospitalizations, and higher mortality. Blood Adv. 6 (20), 5655 (2022). Hendrickson, J. E., Tormey, C. A. & Shaz, B. H. Red blood cell alloimmunization mitigation strategies. Transfus. Med. Rev. 28 (3), 137–144 (2014). Sugrue, R. P. et al. Maternal red blood cell alloimmunization prevalence in the United States. Blood Adv. 8 (16), 4311–4319 (2024). Linder, G. E. & Chou, S. T. Red cell transfusion and alloimmunization in sickle cell disease, Haematologica , vol. 106, no. 7, p. 1805, (2021). de Winter, D. P., Kaminski, A., Tjoa, M. L. & Oepkes, D. Hemolytic disease of the fetus and newborn: systematic literature review of the antenatal landscape. BMC Pregnancy Childbirth . 23 (1), 12 (2023). Chen, C. et al. Unexpected red blood cell antibody distributions in C hinese people by a systematic literature review, Transfusion , vol. 56, no. 4, pp. 975–979, (2016). Yu, Y. et al. Prevalence and Specificity of Red Blood Cell Alloantibodies in Patients from China During 1994–2013, Zhongguo shi yan xue ye xue za zhi , vol. 23, no. 6, pp. 1734–1741, (2015). Ren, D., Zhao, H. B., Guo, X. J. & He, X. H. Analysis of irregular blood group antibody distribution and blood transfusion efficacy in patients with malignant tumor. Zhongguo shi yan xue ye xue za zhi . 31 (1), 209–214 (2023). Xu, P., Li, Y. & Yu, H. Prevalence, specificity and risk of red blood cell alloantibodies among hospitalised Hubei Han Chinese patients, Blood transfusion , vol. 12, no. 1, p. 56, (2014). Binh, V. D. et al. Characteristics of unexpected antibodies in patients with blood disorders: Evidence in Vietnam. Transfus. Apheres. Sci. 63 (2), 103878 (2024). Matosinho, C. G. R., Silva, C. G. R., Martins, M. L. & Silva-Malta, M. C. F. Next generation sequencing of red blood cell antigens in transfusion medicine: systematic review and meta-analysis. Transfus. Med. Rev. 38 (1), 150776 (2024). B. C. f. S. i. Haematology et al., Guidelines for pre-transfusion compatibility procedures in blood transfusion laboratories, Transfusion Medicine , vol. 23, no. 1, pp. 3–35, (2013). Westhoff, C. M. & Floch, A. Blood group genotype matching for transfusion. Br. J. Haematol. 206 (1), 18–32 (2025). Withanawasam, T. I. & Sainudeen, N. Prevalence and clinical implications of unexpected red blood cell antibodies in a tertiary care hospital in Sri Lanka, Immunohematology , vol. 41, no. 1, p. 003, (2025). Kuriri, F. A., Ahmed, A., Alanazi, F., Alhumud, F. & Ageeli Hakami, M. and O. Atiatalla Babiker Ahmed, Red blood cell alloimmunization and autoimmunization in blood transfusion-dependent sickle cell disease and β‐thalassemia patients in Al‐Ahsa Region, Saudi Arabia, Anemia , vol. no. 1, p. 3239960, 2023. (2023). Fasano, R. M. et al. Red blood cell alloimmunization is influenced by recipient inflammatory state at time of transfusion in patients with sickle cell disease. Br. J. Haematol. 168 (2), 291–300 (2015). Evers, D. et al. Red-blood-cell alloimmunisation in relation to antigens' exposure and their immunogenicity: a cohort study. The Lancet Haematology , 3 , 6, pp. e284-e292, 2016. Ackfeld, T., Schmutz, T., Guechi, Y. & Le Terrier, C. Blood transfusion reactions—a comprehensive review of the literature including a Swiss perspective. J. Clin. Med. 11 (10), 2859 (2022). Wolf, J. et al. Red cell specifications for blood group matching in patients with haemoglobinopathies: An updated systematic review and clinical practice guideline from the International Collaboration for Transfusion Medicine Guidelines. Br. J. Haematol. 206 (1), 94–108 (2025). Pahuja, S. & Mandal, P. Alloimmunization and autoimmunization among multitransfused thalassemia and sickle cell disease patients. Pediatr. Hematol. Oncol. J. 9 (3), 200–206 (2024). van Gammeren, A. J. et al. A national transfusion register of irregular antibodies and cross (X)-match problems: TRIX, a 10‐year analysis, Transfusion , vol. 59, no. 8, pp. 2559–2566, (2019). Mathur, G., Wilkinson, M. B., Island, E. R., Menitove, J. E. & Tilzer, L. A case for a national registry of red blood cell antibodies. Vox Sang. 117 (5), 738–740 (2022). Panch, S. R. & Montemayor, C. Hemolytic transfusion reactions. Rossi's Principles Transfus. Medicine pp. 543–552, (2022). Flegel, W. A. Patients with red cell antibodies: registries improve patient care by increasing patient safety, reducing costs, and enabling health information exchange. Blood Transfus. 22 (4), 279 (2024). Kacker, S. et al. Cost-effectiveness of prospective red blood cell antigen matching to prevent alloimmunization among sickle cell patients, Transfusion , vol. 54, no. 1, pp. 86–97, (2014). Additional Declarations No competing interests reported. Supplementary Files Ethicalapproval.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 02 Apr, 2026 Editor assigned by journal 02 Apr, 2026 Editor invited by journal 01 Apr, 2026 Submission checks completed at journal 29 Mar, 2026 First submitted to journal 29 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9212179","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":618654175,"identity":"5641186d-1d28-4ef8-814b-d4e6085ff695","order_by":0,"name":"Jun Fan","email":"","orcid":"","institution":"Luodian Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Fan","suffix":""},{"id":618654177,"identity":"6df1dde0-a6a1-467a-aa76-082412d92d20","order_by":1,"name":"Huichong Chen","email":"","orcid":"","institution":"Traditional Chinese Medicine Hospital of Shizong County","correspondingAuthor":false,"prefix":"","firstName":"Huichong","middleName":"","lastName":"Chen","suffix":""},{"id":618654179,"identity":"be39b47a-a7c2-4d1f-821b-9b0be2e57ce2","order_by":2,"name":"XiaoSong Bai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYBACNvbmgw8SKv7J2bc3NgIZNYS18PEcSzZ4cOaAsQHP4cNAxjHCWuQkcswkH7YdSDSQSEuTfNjCTITDJHIMJBLO3Ekw5zljVpHYwMbA396dgF8Lz7MCg4SKZ3mW7T1mNxJ3yDBInDm7Ab8W9uQNCQlnmIsZzpwBajnDxmAgkUtAC0OCwYHENubEhhs5ZgVABhFaOFIMGxLbDiduuJGWxkCcFmAgMyScSTOW7Dl8GBgOx3gI+kW+vfn4zx8VNnL87I2NH39U1Mjxt/fi14IBeEhTPgpGwSgYBaMAKwAALQVTi5d3dVgAAAAASUVORK5CYII=","orcid":"","institution":"Luodian Hospital","correspondingAuthor":true,"prefix":"","firstName":"XiaoSong","middleName":"","lastName":"Bai","suffix":""}],"badges":[],"createdAt":"2026-03-24 12:55:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9212179/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9212179/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106468696,"identity":"139b0484-2a20-473b-8b9e-914079f18908","added_by":"auto","created_at":"2026-04-09 00:43:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":148777,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual prevalence of unexpected red blood cell antibodies among transfusion recipients from 2014 to 2023. Bars represent the total number of patients screened per year (left y-axis); the line with data markers represents the annual antibody detection rate (%, right y-axis). Cochran–Armitage trend test: Z = 4.73, P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9212179/v1/b0376e5dd9cb55c29eadbf03.png"},{"id":106468695,"identity":"d6b8b0b7-d75f-4bea-b310-f518e6c86de3","added_by":"auto","created_at":"2026-04-09 00:43:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":90160,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of RBC alloimmunization stratified by sex and age group. Grouped bar chart displaying the alloimmunization rate (%, y-axis) for male (dark bars) and female (light bars) patients across five age categories (≤ 20, 21–40, 41–60, 61–80, and \u0026gt;80 years; x-axis). *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9212179/v1/fd94851c795da503f9138e3a.png"},{"id":106725278,"identity":"2df6acdb-9be5-403e-afae-fdb5af13f371","added_by":"auto","created_at":"2026-04-12 18:32:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1469014,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9212179/v1/5fd0285c-c7b9-4c30-941d-9d098799001e.pdf"},{"id":106468694,"identity":"b379d508-6bc3-47cd-a795-f5c4280b0b5b","added_by":"auto","created_at":"2026-04-09 00:43:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":838051,"visible":true,"origin":"","legend":"","description":"","filename":"Ethicalapproval.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9212179/v1/caf53e9ed901ffda9755ea3f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unexpected Red Blood Cell Antibodies in Transfusion Recipients: A 10-Year Retrospective Analysis of Prevalence, Specificity, and Clinical Significance","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTransfusion of red blood cells (RBC) is still one of the most frequently used therapeutic interventions in modern clinical practice. Despite the life-saving value of blood transfusions, the risk of alloimmunization, where the immune system of the recipient produces antibodies against the antigens of the blood cells of the donor, is inherent in the blood transfusion process [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The alloantibodies, which are often termed irregular or non-ABO antibodies, are a significant threat to the safety of the blood transfusion services. The clinically significant alloantibodies have the potential to cause delayed hemolytic transfusion reactions (DHTRs), complicate cross-matching tests, and result in considerable delays in the selection of compatible blood for subsequent blood transfusions [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. During pregnancy, the maternal RBC alloantibodies can cross the placenta and cause hemolytic disease of the fetus and newborn (HDFN), which is often characterized by fetal anemia, hydrops fetalis, and fetal and neonatal deaths [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The International Society of Blood Transfusion (ISBT) recognizes a total of 45 human blood group systems [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Pre-transfusion antibody screening is a significant safety measure for the detection of potentially harmful antibodies before the blood transfusions take place [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEpidemiological data collected from large-scale studies have shown that the overall incidence of RBC alloimmunization in the general population of transfusion recipients ranges from 2% to 5%, as estimated in retrospective studies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A landmark study, which utilized the Recipient Epidemiology and Donor Evaluation Study-III (REDS-III) database, included more than 300,000 antibody screens in 12 hospitals in the United States and found that 2.07% of screened patients developed alloimmunization, of which 75% of the antibodies in this patient group were directed against the Rh and Kell blood group antigens [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Similar data have been collected in Europe, in which the Rh and Kell antibodies have been found to be predominant in all patient groups [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, the specificity and frequency distribution of RBC alloantibodies vary significantly in different ethnic and geographic groups, mainly as a consequence of differences in the prevalence of RBC antigens [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A Dutch prospective cohort study has also demonstrated the hierarchy of immunogenicity of different blood group antigens, suggesting that antigen exposure is not sufficient in explaining the risk of alloimmunization [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe profile of antibodies found within the Chinese population has specific characteristics when compared to data from other populations, such as those from Western countries. A systematic study,conducted within mainland China, combining data from more than six million antibody screens,, found the overall unexpected rate of antibody positivity to be approximately 0.2%. It further found the most common antibodies to be of the Rh blood group system, followed by the MNS, Lewis, and Kidd blood group systems, respectively [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Notably, anti-E was found to be the most common antibody specificity, accounting for 33.9% of all identified antibodies, while anti-K, though commonly found within the Caucasian population, is exceedingly rare within the Chinese population, due to the near-absence of antigen K (\u0026lt;\u0026thinsp;0.5%) within East Asian populations [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Furthermore, the study identified antibodies to glycophorin, such as anti-Mur and anti-Di^a, as significant within the Chinese and Southeast Asian populations, though these antigens are not commonly found within commercially available screening cell panels [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Though specific studies have identified unique characteristics within the Chinese population, it is important to note that most have been limited by short study periods, small sample populations, or have focused on specific patient populations, such as thalassemia patients or pregnant women [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Longitudinal studies examining temporal trends and evaluating risk factors within the transfusion recipient population have been scarce [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aims to fill this knowledge gap through a retrospective analysis of ten years of data collected from January 2014 to December 2023 at the Blood Transfusion Departments of two hospitals in Shanghai and Shizong, Yunnan, China.The specific aims of this investigation included the assessment of the prevalence and trend of unexpected RBC antibodies in transfused patients, the description of the distribution of antibody specificities according to blood group system, the identification of patient demographics and clinical risk factors, and the development of recommendations that would be useful in optimizing antibody screening and matching practices in China.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design and Population\u003c/h2\u003e \u003cp\u003eThis retrospective observational study was conducted in two comprehensive blood transfusion departments, namely Luodian Hospital in Baoshan District, Shanghai, and Shizong County Traditional Chinese Medicine Hospital in Yunnan Province. Luodian Hospital is a secondary teaching hospital with 600 beds, while Shizong County Traditional Chinese Medicine Hospital is a secondary teaching hospital with 300 beds.The study period spanned from January 2014 through December 2023. All consecutive patients who underwent pre-transfusion antibody screening at this institution during the study period were eligible for inclusion. Patients whose antibody screening records were incomplete or whose samples were submitted solely for research purposes without an associated clinical transfusion request were excluded from the analysis.\u003c/p\u003e \u003cp\u003e This research protocol has been reviewed and approved by the Shizong County Traditional Chinese Medicine Hospital's Medical ethics committee.Given that this study adopted a retrospective design and only used de-identified data, the Medical Ethics Committee of Shizong County Traditional Chinese Medicine Hospital, Yunnan Province approved the waiver of the requirement for individual written informed consent.All procedures were conducted in accordance with the ethical principles of the Declaration of Helsinki (2013 revision). The reporting of this study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement for cross-sectional studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Antibody Screening and Identification\u003c/h2\u003e \u003cp\u003eAntibody screening tests were performed on all patient specimens as part of routine pre-transfusion compatibility testing. The screening tests were performed using the microcolumn gel indirect antiglobulin test (IAT), which allows for standardized and reproducible detection of significant red blood cell (RBC) antibodies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Commercially prepared gel cards (Shanghai Blood Biomedical Co., Ltd., Shanghai, China) were used with a panel of two to three reagent red cell preparations. The reagent screening cells were selected to demonstrate major clinically significant red cell antigens of the following systems: Rh (D, C, c, E, e), Kell (K, k), Duffy (Fy^a, Fy^b), Kidd (Jk^a, Jk^b), MNS (M, N, S, s), Lewis (Le^a, Le^b), P (P1), as recommended by the minimum antigen coverage of the AABB Technical Manual [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe samples that tested positive on the antibody screen underwent antibody identification procedures. This involved the use of a broad screen with 11\u0026ndash;16 reagent red blood cells with well-characterized antigen combinations, which were tested by indirect antiglobulin tests (IAT) using the same gel card technology. This identification procedure followed the recommendations of the British Committee for Standards in Haematology (BCSH) for pre-transfusion compatibility tests [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. If the initial identification panel failed to provide a specific reactivity pattern, additional testing was performed, which included enzyme-treated red blood cell panels and the direct antiglobulin test (DAT) for detecting in vivo sensitization [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Samples with complex mixtures of antibodies, antibodies to low-frequency antigens not included on the routine screening panel, and samples with inconclusive identification tests were sent to the Regional Blood Center Reference Laboratory for advanced resolution.\u003c/p\u003e \u003cp\u003eNotably, the routine screening cell panel did not include red cells expressing the Mur antigen or the Di^a antigen. Accordingly, the presence of antibodies against low prevalence antigens was not detectable. The detection of anti-Mur and anti-Di^a in the current study was limited to the alternative pathway of the algorithm, whereby the presence of serological incompatibility during the crossmatch testing of selected donor units, in the absence of a positive antibody screen, was followed by the use of additional reagent cells known to express the antigen or the referral of the patient's sample to the reference laboratory. Accordingly, only those patients who expressed overt crossmatch incompatibility with the presence of anti-Mur or anti-Di^a were included in the current study, and the prevalence of the two specificities should be regarded as a minimum figure.\u003c/p\u003e \u003cp\u003eAutoantibodies, when detected, were distinguished from underlying alloantibodies through autoadsorption (in non-recently-transfused patients) or allogeneic differential adsorption procedures, as clinically indicated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Collection\u003c/h2\u003e \u003cp\u003eThe data collected from each patient included the following variables: sex; age at the time of antibody screening; ABO and RhD blood groups; department of patient admission; primary discharge diagnosis according to the International Classification of Diseases, 10th revision (ICD-10); documented medical history of previous red blood cell transfusion; and obstetric history, including previous pregnancies and deliveries, for women. In all patients, the result of the antibody screening procedure was recorded. In addition, in all patients with positive screening results, further data were recorded, including the specificity of the detected antibody or antibodies, the immunoglobulins involved (IgG and/or IgM), and the thermal range of reactivity. All data were recorded anonymously and assigned a specific code for the purposes of the study. In order to ensure the independence of the observations, each patient contributed only one observation according to the first antibody screening event. For each patient, a unique identification code was generated by using the medical record number from the hospital information system. In cases where the patient had experienced several screening events over different years, the date and result of the first screening event were used. In the case of positive screening results in patients who had already been found to have antibodies, the results were not recorded as new cases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Classification of Antibodies\u003c/h2\u003e \u003cp\u003eThe identified antibodies were then grouped based on the blood group systems in accordance with the nomenclature of the International Society of Blood Transfusion (ISBT) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The clinical significance of each of the detected antibodies was assessed based on their thermal amplitude and class of immunoglobulin. Those that reacted at 37\u0026deg;C using the indirect antiglobulin test (IAT), mainly IgG, were considered to be of clinical significance, while those that reacted only at or below room temperature, mainly IgM, were considered clinically insignificant. However, it is worth noting that certain specificities, such as anti-M, may vary in clinical significance depending on the class of immunoglobulin and thermal amplitude; in such cases, classification is based on the observed serological characteristics.\u003c/p\u003e \u003cp\u003eAlloantibodies and autoantibodies were listed as separate entities. For patients who had multiple alloantibody specificities, each specificity was counted as an individual entity for the antibody distribution analysis. However, for the purpose of determining the prevalence, each patient was counted as one positive entity irrespective of the number of antibody specificities that they possessed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were used to describe the population under investigation and the distribution of antibodies. For categorical variables, results were summarized by frequency and percentage. For continuous variables with non-normal distribution, results were summarized by median and interquartile range (IQR). Age was analyzed both continuously and categorically by clinically relevant age groups: \u0026le;20 years, 21 to 40 years, 41 to 60 years, 61 to 80 years, and \u0026gt;\u0026thinsp;80 years. The annual prevalence of unexpected antibodies was calculated by dividing the number of patients with positive antibody identification by the total number of patients screened per year. The trends of yearly detection rates of antibodies were evaluated by using the Cochran-Armitage test for trend to identify changes in the detection rates of antibodies throughout the 10-year period of the study. To compare the prevalence of antibodies by patient subgroups defined by sex, age group, ABO blood group, RhD group, clinical department of care, and transfusion or pregnancy history, the chi-square test or Fisher's exact test was used.\u003c/p\u003e \u003cp\u003eTo determine the independent risk factors for RBC alloimmunization, the binary outcome variable for the logistic regression model was defined as the presence of one or more alloantibodies with or without autoantibodies. Patients with autoantibodies only (24) and those with undetermined specificity (6) were excluded from the logistic regression analysis because the pathophysiologic mechanisms of autoimmunization are fundamentally different from those of alloimmunization.\u003c/p\u003e \u003cp\u003eSince, as mentioned earlier, obstetric history is only relevant to female patients, any attempt to include both sex and obstetric history in a model would result in collinearity, since all male patients would be assumed to have no obstetric history. To resolve these problems, two logistic regression models were developed. Model A included covariates from the entire study group, including sex, age group, ABO blood group, RhD, previous transfusion history, and clinical department, with obstetric history excluded. Model B included covariates from female subjects only, with age group, ABO blood group, RhD, previous transfusion history, obstetric history, and clinical department included. For each model, only those covariates with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in univariate analysis were included in multivariable analysis using a forward stepwise selection procedure with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for entry and \u0026gt;\u0026thinsp;0.10 for removal. The results of each model are reported as adjusted odds ratio with 95% CI. Model fit is checked using the Hosmer-Lemeshow goodness of fit test.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 General Characteristics of the Study Population\u003c/h2\u003e \u003cp\u003eWithin the 10-year time frame from January 2014 to December 2023, a total of 44,958 patients underwent pre-transfusion antibody screening within the study institution. A total of 128 patients were excluded: 85 patients had inadequate screening records, and 43 patients had samples submitted for research purposes without an accompanying clinical transfusion request. The final cohort consisted of 44,830 patients. The median age of patients was 52 years (interquartile range: 38\u0026ndash;65 years), and 55.0% of patients were male. A history of red blood cell transfusion was documented in 40.0% of patients, and an obstetric history was documented in 65.0% of female patients. Detailed demographic and clinical characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of the study population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44,830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24,657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20,173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (38\u0026ndash;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e61\u0026ndash;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABO blood group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14,345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRhD status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44,606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical department\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternal medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstetrics and gynecology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematology and oncology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntensive care unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior RBC transfusion history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17,932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26,898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstetric history (females only, n\u0026thinsp;=\u0026thinsp;20,173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes (previous pregnancy/delivery)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIQR, interquartile range; RBC, red blood cell.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Overall Prevalence and Annual Trends\u003c/h2\u003e \u003cp\u003eOut of the 44,830 patients screened, only 206 (0.46%) had positive antibody identification results, comprising 168 (81.6%) with alloantibodies only, 24 (11.7%) with autoantibodies only, 8 (3.9%) with both alloantibodies and autoantibodies, and 6 (2.9%) with undetermined specificity. The alloimmunization rate, which is the proportion of patients having one or more alloantibodies (including those having autoantibodies), was found to be 0.39% (176/44,830). This formed the outcome measure for the risk factor analysis (Section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e3.4\u003c/span\u003e). Of the 176 alloimmunized patients, a total of 211 alloantibody specificities were found, including 28 (15.9%) having two or more specificities and 148 (84.1%) having only one alloantibody.\u003c/p\u003e \u003cp\u003eAmong the 28 patients with multiple alloantibody specificities, the most common combination was anti-E with anti-c (n\u0026thinsp;=\u0026thinsp;9, 32.1%), followed by anti-D with anti-C (n\u0026thinsp;=\u0026thinsp;5, 17.9%) and anti-E with anti-C (n\u0026thinsp;=\u0026thinsp;4, 14.3%). These three Rh system combinations collectively accounted for 18 (64.3%) of all multiple-antibody cases. The annual detection rate ranged from 0.28% (10/3,526) in 2014 to 0.57% (35/6,087) in 2023. The Cochran\u0026ndash;Armitage test for trend demonstrated a statistically significant upward trend across the study period (Z\u0026thinsp;=\u0026thinsp;4.73, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Annual screening volumes and detection rates are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Specificity Distribution of Antibodies\u003c/h2\u003e \u003cp\u003eThe distribution of the 211 alloantibodies by blood group system is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Rh system antibodies predominated (123, 58.3%), followed by MNS (42, 19.9%), Lewis (18, 8.5%), and Kidd (11, 5.2%). Kell system antibodies were detected in only 2 (0.9%) cases.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of identified alloantibodies by blood group system (N\u0026thinsp;=\u0026thinsp;211 alloantibodies).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood group system\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLewis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuffy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiego\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther/Rare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIndividual antibody specificities are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Anti-E was the most frequently identified alloantibody (56, 26.5%), followed by anti-M (33, 15.6%) and anti-D (24, 11.4%). Among the 33 anti-M cases, 21 demonstrated reactivity at 37\u0026deg;C by IAT (IgG class, classified as clinically significant), while 12 reacted exclusively at or below room temperature (IgM class, classified as clinically non-significant). Anti-Mur and anti-Di^a were identified in 5 (2.4%) and 4 (1.9%) cases, respectively. Overall, 175 (82.9%) of the 211 alloantibodies were classified as clinically significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpecificity and frequency of identified alloantibodies, listed in descending order of frequency (N\u0026thinsp;=\u0026thinsp;211 alloantibodies).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibody specificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlood group system\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClinical significance^a\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePredominant Ig class\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIgG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVariable^b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIgG/IgM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIgG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIgG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIgG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-Le^a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLewis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIgM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIgG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-Jk^a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKidd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIgG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-Le^b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLewis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIgM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-Mur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIgG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-Di^a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiego\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIgG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-Jk^b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKidd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIgG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-Ce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIgG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-P1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIgM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-Fy^a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDuffy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIgG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-cE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIgG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIgG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIgG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIgM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther^c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e^a Clinical significance was determined according to the criteria defined in Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e2.4\u003c/span\u003e. ^b Anti-M was classified on a case-by-case basis depending on the observed immunoglobulin class and thermal reactivity range: 21 cases were classified as clinically significant (IgG, reactive at 37\u0026deg;C) and 12 cases as clinically non-significant (IgM, reactive at \u0026le;\u0026thinsp;22\u0026deg;C). ^c Includes anti-Fy^b (n\u0026thinsp;=\u0026thinsp;2), anti-Lu^a (n\u0026thinsp;=\u0026thinsp;1), anti-Cw (n\u0026thinsp;=\u0026thinsp;1), and anti-Xg^a (n\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Risk Factor Analysis\u003c/h2\u003e \u003cp\u003eThe alloimmunization rate was significantly higher in female patients than in male patients (125/20,153, 0.62% vs. 51/24,647, 0.21%; χ\u0026sup2; = 50.17, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Across age categories, the alloimmunization rate was 0.11% (4/3,579) in patients aged\u0026thinsp;\u0026le;\u0026thinsp;20 years, 0.42% (45/10,755) in the 21\u0026ndash;40-year group, 0.41% (62/15,233) in the 41\u0026ndash;60-year group, 0.43% (50/11,647) in the 61\u0026ndash;80-year group, and 0.42% (15/3,586) in those aged\u0026thinsp;\u0026gt;\u0026thinsp;80 years. No statistically significant differences were observed among ABO blood groups (P\u0026thinsp;=\u0026thinsp;0.974). The alloimmunization rate in RhD-negative patients (18/221, 8.14%) was substantially higher than in RhD-positive patients (158/44,579, 0.35%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with prior RBC transfusion demonstrated a higher alloimmunization rate (120/17,914, 0.67%) than those without (56/26,886, 0.21%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among clinical departments, hematology and oncology had the highest rate (42/5,374, 0.78%), followed by obstetrics and gynecology (37/6,720, 0.55%) and surgery (39/15,683, 0.25%); the overall difference was statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The alloimmunization rates by sex and age group are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe results of the two logistic regression models for RBC alloimmunization (176 cases vs. 44,624 controls; 30 patients with exclusively autoantibodies or undetermined specificity excluded) are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In Model A (entire cohort, n\u0026thinsp;=\u0026thinsp;44,800), univariate analysis identified female sex, age group, RhD-negative status, prior RBC transfusion, and clinical department as candidates for multivariate analysis (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.10). ABO blood group did not reach the threshold (P\u0026thinsp;=\u0026thinsp;0.974). In the final multivariate model, RhD-negative status (aOR\u0026thinsp;=\u0026thinsp;25.87, 95% CI: 15.16\u0026ndash;44.14, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), prior RBC transfusion (aOR\u0026thinsp;=\u0026thinsp;2.89, 95% CI: 2.07\u0026ndash;4.04, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and female sex (aOR\u0026thinsp;=\u0026thinsp;2.46, 95% CI: 1.74\u0026ndash;3.48, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were independently associated with alloimmunization. Age group and clinical department were entered but not retained by the forward stepwise procedure. The Hosmer\u0026ndash;Lemeshow test indicated adequate model calibration (χ\u0026sup2; = 5.94, df\u0026thinsp;=\u0026thinsp;8, P\u0026thinsp;=\u0026thinsp;0.654).\u003c/p\u003e \u003cp\u003eIn Model B (female subgroup, n\u0026thinsp;=\u0026thinsp;20,153; 125 alloimmunized cases), univariate analysis identified age group, RhD-negative status, prior RBC transfusion, obstetric history, and clinical department as candidates (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.10). In the final multivariate model, RhD-negative status (aOR\u0026thinsp;=\u0026thinsp;26.43, 95% CI: 13.72\u0026ndash;50.91, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), prior RBC transfusion (aOR\u0026thinsp;=\u0026thinsp;2.53, 95% CI: 1.68\u0026ndash;3.81, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and obstetric history (aOR\u0026thinsp;=\u0026thinsp;1.72, 95% CI: 1.09\u0026ndash;2.71, P\u0026thinsp;=\u0026thinsp;0.019) were independently associated with alloimmunization among female patients. The Hosmer\u0026ndash;Lemeshow test indicated adequate calibration (χ\u0026sup2; = 7.15, df\u0026thinsp;=\u0026thinsp;8, P\u0026thinsp;=\u0026thinsp;0.520).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eA.\u003c/b\u003e Logistic regression analysis of risk factors for RBC alloimmunization \u0026mdash; Model A (entire cohort).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAllo+ / Total (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCrude OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eaOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51/24,647 (0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125/20,153 (0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.00 (2.15\u0026ndash;4.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.46 (1.74\u0026ndash;3.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;20 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4/3,579 (0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45/10,755 (0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.75 (1.34\u0026ndash;10.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62/15,233 (0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.65 (1.33\u0026ndash;10.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61\u0026ndash;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50/11,647 (0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.85 (1.39\u0026ndash;10.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15/3,586 (0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.75 (1.24\u0026ndash;11.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABO blood group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57/14,336 (0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48/12,544 (0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.65\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50/12,992 (0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.66\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21/4,928 (0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.07 (0.64\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRhD status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e158/44,579 (0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18/221 (8.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.60 (14.62\u0026ndash;41.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.87 (15.16\u0026ndash;44.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior RBC transfusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56/26,886 (0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120/17,914 (0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.23 (2.34\u0026ndash;4.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.89 (2.07\u0026ndash;4.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical department\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurgery (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39/15,683 (0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternal medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31/11,201 (0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.11 (0.69\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObstetrics and gynecology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37/6,720 (0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.22 (1.41\u0026ndash;3.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHematology and oncology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42/5,374 (0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.15 (2.03\u0026ndash;4.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15/3,583 (0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.68 (0.92\u0026ndash;3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12/2,239 (0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.16 (1.12\u0026ndash;4.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTotal analyzed: n\u0026thinsp;=\u0026thinsp;44,800 (176 alloimmunized cases\u0026thinsp;+\u0026thinsp;44,624 controls). Patients with exclusively autoantibodies (n\u0026thinsp;=\u0026thinsp;24) and undetermined specificity (n\u0026thinsp;=\u0026thinsp;6) were excluded. Hosmer\u0026ndash;Lemeshow goodness-of-fit: χ\u0026sup2; = 5.94, df\u0026thinsp;=\u0026thinsp;8, P\u0026thinsp;=\u0026thinsp;0.654. Forward stepwise likelihood ratio method: entry P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, removal P\u0026thinsp;\u0026gt;\u0026thinsp;0.10. \"\u0026mdash;\" = univariate P\u0026thinsp;\u0026ge;\u0026thinsp;0.10, not entered. NR\u0026thinsp;=\u0026thinsp;entered but not retained.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eB.\u003c/b\u003e Logistic regression analysis of risk factors for RBC alloimmunization \u0026mdash; Model B (female subgroup).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAllo+ / Total (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCrude OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eaOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;20 (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2/1,609 (0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37/6,441 (0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.63 (1.11\u0026ndash;19.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40/6,849 (0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.71 (1.14\u0026ndash;19.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61\u0026ndash;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35/4,073 (0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.93 (1.66\u0026ndash;28.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11/1,181 (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.53 (1.66\u0026ndash;34.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABO blood group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40/6,449 (0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35/5,646 (0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.63\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34/5,845 (0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94 (0.59\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16/2,213 (0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.17 (0.65\u0026ndash;2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRhD status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112/20,057 (0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13/96 (13.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.09 (14.79\u0026ndash;53.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.43 (13.72\u0026ndash;50.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior RBC transfusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38/9,470 (0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87/10,683 (0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.04 (1.39\u0026ndash;2.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.53 (1.68\u0026ndash;3.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstetric history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28/7,040 (0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97/13,113 (0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.87 (1.22\u0026ndash;2.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.72 (1.09\u0026ndash;2.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical department\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurgery (ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14/4,070 (0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternal medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12/3,639 (0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.44\u0026ndash;2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObstetrics and gynecology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37/6,720 (0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.60 (0.86\u0026ndash;2.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHematology and oncology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38/2,958 (1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.75 (2.02\u0026ndash;6.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10/1,386 (0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.10 (0.93\u0026ndash;4.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14/1,380 (1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.96 (1.41\u0026ndash;6.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTotal analyzed: n\u0026thinsp;=\u0026thinsp;20,153 (125 alloimmunized female cases\u0026thinsp;+\u0026thinsp;20,028 female controls). Female patients with exclusively autoantibodies (n\u0026thinsp;=\u0026thinsp;18) and undetermined specificity (n\u0026thinsp;=\u0026thinsp;2) were excluded. Hosmer\u0026ndash;Lemeshow goodness-of-fit: χ\u0026sup2; = 7.15, df\u0026thinsp;=\u0026thinsp;8, P\u0026thinsp;=\u0026thinsp;0.520. Forward stepwise likelihood ratio method: entry P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, removal P\u0026thinsp;\u0026gt;\u0026thinsp;0.10. \"\u0026mdash;\" = univariate P\u0026thinsp;\u0026ge;\u0026thinsp;0.10, not entered. NR\u0026thinsp;=\u0026thinsp;entered but not retained.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Antibody Specificity in Clinical Subgroups\u003c/h2\u003e \u003cp\u003eThe distribution of alloantibody specificities across clinical subgroups is presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Anti-E was the predominant specificity in hematology and oncology (32.0%), surgery (31.1%), and internal medicine (26.3%). In the obstetrics and gynecology subgroup, anti-D ranked as the most frequent specificity (26.1%), followed by anti-E (23.9%). Among the 224 RhD-negative patients, anti-D accounted for 19 of 24 total anti-D cases across the entire cohort, and the antibody detection rate in this subgroup (8.14%) was significantly higher than in RhD-positive patients (0.35%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of the five most frequent alloantibody specificities by clinical subgroup.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibody specificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHematology and oncology (n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObstetrics and gynecology (n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurgery (n\u0026thinsp;=\u0026thinsp;45)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInternal medicine (n\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eICU and other (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16 (32.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (23.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (31.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5 (15.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6 (15.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3 (9.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (14.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (24.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14 (36.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: n\u0026thinsp;=\u0026thinsp;number of alloantibody specificities identified within each subgroup. Column percentages. The five specificities were selected based on the overall ranking in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study aimed to investigate unexpected red cell antibodies in 44,830 recipients of transfusions in China over ten years. The study found an antibody detection rate of 0.46% in total, with 58.3% being related to the Rh blood group system and anti-E being the most common antibody, occurring in 26.5% of patients. In the study, RhD-negative status, prior RBC transfusion, and female sex were independently associated with alloimmunization in the multivariate analysis.. In the study of the female subset, obstetric history was also found to be independently associated with alloimmunization. There was also a statistically significant progressive increase in the annual detection rate, increasing to 0.57% in 2023 from 0.28% in 2014.\u003c/p\u003e \u003cp\u003eThe detection rate of 0.46% identified in this study is within the 0.2% to 0.34% range identified by a systematic study of Chinese transfusion service recipients [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], albeit slightly higher than the pooled data. This may be due to the extended study period and the continued use of gel card indirect antiglobulin test (IAT), which is more sensitive than the methods used in the other Chinese studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. When compared to other populations, this detection rate is substantially lower than the 2.07% identified by the REDS-III multicenter study in the United States [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This difference is largely attributable to the greater antigenic disparity between donors and recipients in the ethnically diverse U.S. population.. The detection rate identified in other Asian countries is intermediate. Studies from Sri Lanka and India reported detection rates of 0.83% [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and 1.07%, respectively. These data further reinforce the role of antigen frequency in determining the alloimmunization rate.\u003c/p\u003e \u003cp\u003eThe specificity profile of the antibodies identified in this cohort correlates with that previously described for the East Asian ethnic group. The predominance of anti-E (26.5%) is related to the high incidence of E-negative blood donors among Chinese populations, which has been estimated to be around 46\u0026ndash;51% [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Anti-M was found to be the second most common specificity (15.6%), which correlates with the relatively higher incidence of MNS system polymorphisms in the East Asian ethnic group [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Of the 33 anti-M antibodies identified, 63.6% (21 of 33) had IgG activity at 37\u0026deg;C, indicating clinical significance. Anti-Mur and anti-Di^a specificity accounted for 2.4% and 1.9%, respectively. However, these antigens are not present on the standard screening cell panel, which has previously been a recognized limitation in transfusion medicine literature [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In contrast, anti-K accounted for only 0.9%, which correlates with the low incidence of the K antigen among Chinese populations. This is a marked difference from Caucasian populations, where anti-K is one of the top three most common specificities.\u003c/p\u003e \u003cp\u003eThe full cohort analysis showed that female sex (adjusted odds ratio [aOR]\u0026thinsp;=\u0026thinsp;2.46), previous transfusion history (aOR\u0026thinsp;=\u0026thinsp;2.89), and RhD negative status (aOR\u0026thinsp;=\u0026thinsp;25.87) were independent risk factors for alloimmunization. This is consistent with the long-standing view that exposure to allogeneic cells, such as those from transfusion, and exposure to fetal-maternal antigens are the main immunizing events [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Since obstetric history is, by its very nature, only relevant to female patients, a model was created for the female subgroup only, mitigating the problem of collinearity. The female subgroup model showed that obstetric history (aOR\u0026thinsp;=\u0026thinsp;1.72) made an independent contribution to risk of alloimmunization, independent of transfusion history, consistent with the systematic review by Verduin et al., where there was found to be approximately double the risk of alloimmunization among female transfusion recipients, primarily due to previous pregnancy exposure [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The association between RhD negative status and alloimmunization risk is also consistent, given the high immunogenicity of the RhD antigen itself [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe significant upward trend in the annual detection rates over the years requires careful interpretation. The parallel increase in screening volume from 3,526 in 2014 to 6,087 in 2023 includes an increasing proportion of low-risk elective surgical patients and would be expected to reduce, not increase, the detection rate. A more plausible explanation includes changes in the case mix within the hospital. Luodian hospital increased its capacity for hematology/oncology services over the years 2017\u0026ndash;2020 and thus increased its proportion of patients who are chronically transfused and have had cumulative exposure to antigens. The natural accumulation of patients who had been sensitized and then presented for future transfusion episodes may also contribute to the increasing prevalence [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe findings of this study have several implications for clinical transfusion medicine. The predominance of anti-Rh antibodies emphasizes the need for prophylactic matching of Rh phenotypes (C, c, E, e) for patients at risk of immunization, especially for patients requiring chronic transfusions in hematology and oncology settings [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The effectiveness of antigen matching programs has been shown to be cost-effective in lowering the incidence of alloimmunization for chronically transfused patients [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The identification of anti-Mur and anti-Di^a emphasizes the need for Chinese blood services to include Mur-positive and Di(a)-positive red cells in their antibody screening programs, as has been strongly promoted by transfusion medicine specialists [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The development of a regional antibody database, based on models such as the Dutch-based TRIX database [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], would overcome the known difficulty of antibody evanescence, i.e., the loss of measurable antibody titers to below detection limits for subsequent antibody screening [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This would allow historical antibody information to be available at the point of care, thus preventing the transfusion of antigen-positive red cells to previously immunized patients [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study had several limitations. First, the single-center retrospective study design limits the generalizability of these results to other institutions and geographic locations. Second, the study measured transfusions and pregnancies as simple yes/no variables, which precluded dose-response analysis. Finally, the phenomenon of antibody evanescence may have resulted in an underestimate of the true rate of alloimmunization, as all patients who had been previously sensitized and had antibody titers fall below the level of detection would have been classified as seronegative [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In addition, the antigenic profile of the screening cells themselves might have had minor changes over the decade-long study period as a result of different lots and different suppliers, which might have affected the sensitivity to certain specificities. Molecular genotyping data were unavailable. Transfusion complications, including delayed hemolytic transfusion reactions, were also not captured by the study [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Finally, as the screening cells lacked the antigens Mur and Di^a, anti-Mur and anti-Di^a would only have been detected in the context of crossmatch incompatibility. Therefore, the reported prevalence of these specificities should be regarded as a minimum estimate and would have been subject to ascertainment bias. In addition, as the study only measured transfusions and pregnancies as yes/no variables, it is possible that transfusion and pregnancy histories were not recorded for all exposures and would have resulted in non-differential misclassification bias, which would have biased the odds ratio toward the null. Finally, the determination of the clinical significance of anti-M antibodies was dependent upon thermal reactivity in routine testing and would not have been confirmed by additional testing methods, including dithiothreitol treatment, which might have resulted in the occasional misclassification of immunoglobulin type.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study, conducted by analyzing the records of 44,830 patients who underwent transfusion at two Chinese secondary-care hospital over a period of 10 years, showed an unexpected trend of increasing red cell antibodies over time, with an overall red cell antibody detection rate of 0.46%. The most common antibodies detected were those against antigens of the Rh blood group, and among these, anti-E was found to be the most common specificity, accounting for 26.5%. The risk factors for alloimmunization were found to be RhD negative status, previous RBC transfusion, and female sex, while obstetric history was also found to be an independent risk factor for female patients. These findings suggest that Rh phenotype matching for patients at risk of alloimmunization, such as those who need repeated transfusions, should be considered for improving patient safety. The presence of anti-Mur and anti-Di^a also underscores the need for the inclusion of these antigens in routine antibody screening panels used by Chinese transfusion services. It is further recommended that a regional antibody registry be established to prevent risks due to evanescence of antibodies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthical approval\u003c/h2\u003e \u003cp\u003e The present article was approved by the Medical ethics committee of Shizong County Traditional Chinese Medicine Hospital.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to publish\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJun Fan contributed to Conceptualization, Data curation, Investigation, Methodology, Project administration, Writing\u0026mdash;original draft, and Writing\u0026mdash;review \u0026amp; editing. Huichong Chen contributed to Data curation, Resources, and Supervision. XiaoSong Bai contributed to Conceptualization, Supervision, and Writing\u0026mdash;review \u0026amp; editing. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTormey, C. A. \u0026amp; Hendrickson, J. E. Transfusion-related red blood cell alloantibodies: induction and consequences. \u003cem\u003eBlood J. Am. Soc. Hematol.\u003c/em\u003e \u003cb\u003e133\u003c/b\u003e (17), 1821\u0026ndash;1830 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZerra, P. E. \u0026amp; Josephson, C. D. Delayed hemolytic transfusion reactions, in Transfusion medicine and hemostasis: Elsevier, 343\u0026ndash;345. (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuken, J. S. et al. Major reduction in occurrence of anti-c and anti‐E in pregnancy after more than 10 years of preventive matched transfusion with most benefit for c‐matching. \u003cem\u003eBr. J. Haematol.\u003c/em\u003e \u003cb\u003e205\u003c/b\u003e (4), 1599\u0026ndash;1604 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eo., I. S. \u0026amp; Transfusion, B. \u003cem\u003eRed cell. Immunogenet. blood group. terminology\u003c/em\u003e, ed, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohn, C., Delaney, M. \u0026amp; Johnson, S. AABB technical manual 21st edition. \u003cem\u003eBethesda Maryland: Association Advancement Blood \u0026amp; Biotherapies\u003c/em\u003e, (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eViayna, E. et al. 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Red blood cell alloimmunization is influenced by recipient inflammatory state at time of transfusion in patients with sickle cell disease. \u003cem\u003eBr. J. Haematol.\u003c/em\u003e \u003cb\u003e168\u003c/b\u003e (2), 291\u0026ndash;300 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvers, D. et al. Red-blood-cell alloimmunisation in relation to antigens' exposure and their immunogenicity: a cohort study. \u003cem\u003eThe Lancet Haematology\u003c/em\u003e, \u003cb\u003e3\u003c/b\u003e, 6, pp. e284-e292, 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAckfeld, T., Schmutz, T., Guechi, Y. \u0026amp; Le Terrier, C. Blood transfusion reactions\u0026mdash;a comprehensive review of the literature including a Swiss perspective. \u003cem\u003eJ. Clin. Med.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (10), 2859 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolf, J. et al. Red cell specifications for blood group matching in patients with haemoglobinopathies: An updated systematic review and clinical practice guideline from the International Collaboration for Transfusion Medicine Guidelines. \u003cem\u003eBr. J. Haematol.\u003c/em\u003e \u003cb\u003e206\u003c/b\u003e (1), 94\u0026ndash;108 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePahuja, S. \u0026amp; Mandal, P. Alloimmunization and autoimmunization among multitransfused thalassemia and sickle cell disease patients. \u003cem\u003ePediatr. Hematol. Oncol. J.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (3), 200\u0026ndash;206 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Gammeren, A. J. et al. 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Patients with red cell antibodies: registries improve patient care by increasing patient safety, reducing costs, and enabling health information exchange. \u003cem\u003eBlood Transfus.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e (4), 279 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKacker, S. et al. Cost-effectiveness of prospective red blood cell antigen matching to prevent alloimmunization among sickle cell patients, \u003cem\u003eTransfusion\u003c/em\u003e, vol. 54, no. 1, pp. 86\u0026ndash;97, (2014).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Red blood cell alloantibodies, Alloimmunization, Transfusion recipients, Antibody screening, Chinese population, Retrospective study","lastPublishedDoi":"10.21203/rs.3.rs-9212179/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9212179/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eUnexpected red blood cell (RBC) antibodies pose significant risks for transfusion safety. Long-term data on antibody prevalence and specificity in Chinese transfusion recipients remain limited. This study aimed to determine the prevalence, specificity distribution, temporal trends, and risk factors of unexpected RBC antibodies at two Chinese secondary hospitals over a 10-year period.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective study included all patients undergoing pre-transfusion antibody screening from January 2014 to December 2023. Antibody detection was performed using the microcolumn gel indirect antiglobulin test. Temporal trends were evaluated by the Cochran\u0026ndash;Armitage test. Independent risk factors for alloimmunization were identified using two complementary logistic regression models: a full-cohort model and a female-subgroup model to separately assess the effect of obstetric history.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 44,830 patients, 206 (0.46%) had positive antibody identifications; the alloimmunization rate was 0.39% (176/44,830). A total of 211 alloantibody specificities were identified, with Rh system antibodies predominating (58.3%). Anti-E (26.5%), anti-M (15.6%), and anti-D (11.4%) were the three most common specificities; anti-K accounted for only 0.9%. The annual detection rate increased significantly from 0.28% to 0.57% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the full-cohort model, RhD-negative status (aOR\u0026thinsp;=\u0026thinsp;25.87), prior transfusion (aOR\u0026thinsp;=\u0026thinsp;2.89), and female sex (aOR\u0026thinsp;=\u0026thinsp;2.46) were independently associated with alloimmunization. In the female-subgroup model, obstetric history (aOR\u0026thinsp;=\u0026thinsp;1.72) was an additional independent risk factor.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe antibody profile was characterized by Rh predominance and near-absence of anti-K, reflecting East Asian antigen frequencies. Prophylactic Rh phenotype matching and inclusion of Mur and Di^a antigens in screening panels are recommended.\u003c/p\u003e","manuscriptTitle":"Unexpected Red Blood Cell Antibodies in Transfusion Recipients: A 10-Year Retrospective Analysis of Prevalence, Specificity, and Clinical Significance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 00:42:58","doi":"10.21203/rs.3.rs-9212179/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-02T07:37:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-02T07:35:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-01T07:20:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-30T01:15:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-30T01:10:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b371fcda-0309-447f-a35c-5e3bbac0b9c9","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65826414,"name":"Health sciences/Diseases"},{"id":65826415,"name":"Biological sciences/Immunology"},{"id":65826416,"name":"Health sciences/Medical research"},{"id":65826417,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-09T00:42:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 00:42:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9212179","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9212179","identity":"rs-9212179","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}