Factors associated with red blood cell transfusion among hospitalized patients: a cross-sectional study

Factors associated with red blood cell transfusion among hospitalized patients: a cross-sectional study 住院患者红细胞输注量相关因素的横断面研究 1 first-author 1 2 2 2 1 1 corresp Jue XIE, zyyyxj2011@zju.edu.cn Jue XIE, zyyyxj2011@zju.edu.cn zyyyxj2011@zju.edu.cn https://orcid.org/0000-0002-4270-671X Department of Blood Transfusion, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou310003, China Department of Blood Transfusion, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou310003, China Department of Epidemiology and Biostatistics, Zhejiang University School of Public Health, Hangzhou310058, China Department of Epidemiology and Biostatistics, Zhejiang University School of Public Health, Hangzhou310058, China 住院患者的红细胞输注量相关研究中，医院层面和患者层面的因素未得到充分认识。本研究从医院层面和患者层面这两个角度出发，将血液中心的供给量纳入考虑范畴，探讨影响患者红细胞输注数量的因素。我们从浙江大学医学院附属第一医院的医院信息系统中收集了从2013年1月至2020年12月期间病人红细胞的每日使用单位和相应的患者临床信息，基于每周红细胞总使用单位采用三重指数平滑法进行时间序列分析，并且以二元逻辑回归方法来追溯影响红细胞输注量的患者相关因素。研究结果表明，红细胞的给予单位数量与医院血液库存的季节性趋势和患者的整体状态有关。 输血 红细胞 三重指数平滑法 二元逻辑回归 Red blood cell (RBC) transfusion is a clinically effective therapy in anemia, for example in patients with malignancies ( Shander et al., 2020 Shander et al., 2020 ), bleeding ( Odutayo et al., 2017 Odutayo et al., 2017 ), and preoperative anemia ( Padmanabhan et al., 2019 Padmanabhan et al., 2019 ). The past few decades have witnessed a shortage of blood for transfusion due to limited health insurance coverage for blood use and the rapid expansion of hospitals ( Chen et al., 2011 Chen et al., 2011 ; Shi et al., 2014 Shi et al., 2014 ). Blood donation levels may easily be affected by general changes in the environment, policy, major events such as disasters, and public sentiment ( Hu et al., 2019 Hu et al., 2019 ). Meanwhile, the transfusion of allogeneic RBC is a double-edged sword, increasing the possibility of infectious and immunological complications, and also leading to higher morbidity and mortality after transfusion ( Frank et al., 2012 Frank et al., 2012 ). Considering that the continual shortfall has been increasingly prominent, identifying the factors associated with RBC transfusion could help blood transfusion departments to improve their supply of blood products as well as their inventory management ( O'Donnell et al., 2018 O'Donnell et al., 2018 ). Previous studies have considered blood shortages at either the hospital level or the patient level. Here, we analyzed the imbalance from both these perspectives, based on a large amount of data collected from the First Affiliated Hospital (Zhejiang University School of Medicine, Hangzhou, China). For this study, we collected data on patients' RBC requirements from January 1st, 2013 to December 31st, 2020. The daily total applied units of RBC were cumulative for all patient requirements during this period. We established a time series based on total weekly RBC applications for the entire hospital from 2013 to 2020 ( Fig. 1 Fig. 1 a). 1;2;3; We conducted a time-series analysis using the triple exponential smoothing method to decompose the factors from the hospital inventory which affected the amount of RBC given from 2013 to 2019 ( Figs. 1 Figs. 1 b‍–‍1e). Because of the impact of the coronavirus disease 2019 (COVID-19) in early 2020 ( Xiang et al., 2020 Xiang et al., 2020 ), there was a sizeable reduction in the total number of applications. Therefore, we did not include the data for this period in the analysis. Accordingly, the amounts of weekly RBC applications showed an obvious upward trend year by year, as well as a similar seasonality each year. The basic principle of the exponential smoothing method is to give different weights to observed values of a time-series sequence. Compared with earlier data, more recent data will be given greater weight, which helps to eliminate the influence of noise and produce more reasonable and reliable models. The triple exponential smoothing method, which applies exponential smoothing three times, allowed us to incorporate seasonal effects into the model. Holt-Winters multiplicative fitting and Holt-Winters additive fitting are the two main triple exponential smoothing methods. If a time series is stable, Holt-Winters additive fitting is recommended; otherwise, Holt-Winters multiplicative fitting is preferred. Overall, we saw little difference between the two models in fitting the total weekly RBC applications. The horizontal parameter ( α ) was 0.072 vs. 0.097, the trend parameter ( β ) was 0 vs. 0, and the seasonal parameter ( γ ) was 0.454 vs. 0.383 (multiplicative vs. additive). Most donors in China are college students and migrant workers; thus during the summer college vacation and the traditional Chinese Spring Festival Holiday, the donation decreased dramatically. The amount peaked in August and September, decreased from October, and then gradually rose again in December. Information about patients receiving RBC included the number of units applied, patient age (years), sex, and body mass index (BMI, kg/m 2 ), as well as the results of a blood test (hemoglobin (HGB), platelet (PLT), prothrombin time (PT), hematocrit (HCT), and alanine aminotransferase (ALT)), ABO type, usage classification, history of blood transfusion ( Xiang et al., 2020 Xiang et al., 2020 ), and hospital department. We used a binary logistic regression model to estimate the odds ratio (OR) and 95% confidence interval (CI) for the effects of selected individual and seasonal variables on the number of transfusion units. The units of RBC given were categorized into a binary variable by the median of transfused RBC, which was 0 (for < 2 units) or 1 (for ≥2 units). We adjusted for all selected factors in the first model, including age (years), sex (male/female), BMI ( < 18.5 kg/m 2 , 18.5–23.9 kg/m 2 , 24.0–27.9 kg/m 2 , and ≥28.0 kg/m 2 ), HGB (g/L), PLT (10 9 L -1 ), PT (s), HCT (%), ALT (U/L), ABO blood type, usage classification (surgery, ward, and emergency), history of blood transfusion (Yes/No), departments (surgical department, internal medicine department, obstetrics and gynecology department, emergency department, and others), and season (spring, summer, autumn, or winter). We then tried the second model, using backward step-wise binary logistic regression based on the first model. In addition, to assist in translation of the generated statistical model, we created a risk assessment nomogram as a graphic model representation ( Fig. 1 Fig. 1 f). Subgroup analysis was performed to identify potential modifications by different seasons. Patients receiving RBC transfusion were aged (55.91±16.20) years and 56% male. Blood types A (33.6%) and O (36.3%) were in greater demand. Almost half of patients (48.1%) received RBC in connection with surgery and 59.8% of them had a history of blood transfusion (Table S1). We found that older age and higher PLT were related to less RBC transfusion. Patients with AB (OR (95% CI): 1.26 (1.04, 1.54)) or B (OR (95% CI): 1.38 (1.21, 1.57)) blood types were more likely to have RBC transfusions of >2 units compared to type A. Conversely, patients with type O (OR (95% CI): 0.87 (0.79, 0.97)) are unlikely to have RBC transfusions of >2 units. Patients who were having elective surgery (OR (95% CI): 2.70 (2.24, 3.27)) or were hospitalized (OR (95% CI): 1.23 (1.07, 1.41)) needed more RBC transfusion compared to those in the emergency department. Also, having a history of blood transfusion was a risk factor for greater RBC need. Compared with the internal medicine department (OR (95% CI): 2.23 (2.07, 2.41)), obstetrics and gynecology department (OR (95% CI): 0.83 (0.70, 0.97)) and emergency department (OR (95% CI): 0.75 (0.64, 0.88)) were less likely to use RBC transfusions over 2 units. In general, the amount of blood used in the internal medicine department was higher in autumn (Fig. S1). Furthermore, patients who received RBC in summer (OR (95% CI): 0.72 (0.66, 0.79)) were negatively correlated to higher units of RBC transfusion, while in winter and autumn (OR (95% CI): 1.11 (1.01, 1.22)), there was a positive correlation (Table S2). In subgroup analysis, factors affecting the units of RBC transfusion varied across the four seasons (Table S3). For all four seasons, the transfusion demand for surgery was steadily higher than that for emergency events. In spring, male and O-type patients were less likely to have more units of RBC transfusion. Meanwhile, in the other three seasons, younger and B-type patients were associated with more RBC transfusion units. In summer, the HGB (OR (95% CI): 0.99 (0.97, 1.00)) and HCT (OR (95% CI): 1.04 (1.00, 1.09)) of the patients were significantly related to the number of RBC transfusion units. A history of blood transfusion (OR (95% CI): 1.48 (1.10, 1.98)) was also a risk factor for more RBC transfusion, but only in summer. In autumn, overweight and AB-type patients were positively associated with higher demand for RBC, and the need in the emergency department was less than that in the internal medicine department. There were fewer factors significantly related to RBC transfusion in winter than in the other three seasons. In summary, we collected a substantial amount of data on RBC transfusion in a large general hospital in Zhejiang Province, China. We conducted a time-series analysis using the triple exponential smoothing method and found a seasonal trend in the demand for RBC. Then, we created a binary logistic model based on patients' selected factors as well as seasonal trends. Taken together, the results suggest that age, PLT, PT, ABO type, different usage classification, history of blood transfusion, and hospital department are the possible determinants, and that the effects of these factors vary seasonally. The units of RBC given are associated with seasonal trends in the blood inventory and overall state of patients in this large general hospital. Further studies involving more hospital-level indicators may give more insight into best practices in blood transfusion management. Materials and methods 1 Detailed methods are provided in the electronic supplementary materials of this paper. References Chen XF , Chen CL , Zhang Y , et al. , 2011 . The effect of health insurance reform on the number of cataract surgeries in Chongqing, China . 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Blood safety and availability: continuing challenges in China’s blood banking system . Transfusion , 54 ( 2 ): 471 - 482 . https://doi.org/10.1111/trf.12273 https://doi.org/10.1111/trf.12273 Blood safety and availability: continuing challenges in China’s blood banking system Xiang WL , Cheng JJ , Wu LP , et al. , 2020 . Clinical character Clinical character istics and plasma antibody titer of patients with COVID-19 in Zhejiang, China . J Zhejiang Univ-Sci B (Biomed & Biotechnol) , 21 ( 12 ): 955 - 960 . https://doi.org/10.1631/jzus.B2000593 https://doi.org/10.1631/jzus.B2000593 istics and plasma antibody titer of patients with COVID-19 in Zhejiang, China Peiwen ZHANG wrote the manuscript. Dandan XU collected the data. Xinhan ZHANG analyzed the hospital data using the triple exponential smoothing method. Mengyin WU analyzed the patient data with binary logistic regression model. Xuecheng YAO made the nomogram graph. Dawei CUI adjusted the scope of research and coordinated the needs of all participants. Jue XIE contributed to the study design and was responsible for the integrity of the study and final manuscript. All authors have read and approved the final manuscript, and therefore, have full access to all the data in the study and take responsibility for the integrity and security of the data. This work was supported by the National Natural Science Foundation of China (Nos. 91846103 and 81971994) and the Zhejiang Provincial Key Research and Development Program (No. 2020C03032), China. 10.1631/jzus.B2100491 1673-1581（2021）12-1060-05 This work was supported by the National Natural Science Foundation of China (Nos. 91846103 and 81971994) and the Zhejiang Provincial Key Research and Development Program (No. 2020C03032), China. 2021-06-03 2021-09-17 2021-12-15 2022-05-09 2021-11-22 Copyright © Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2021 2021 浙江大学出版社 Peiwen ZHANG, Dandan XU, Xinhan ZHANG, Mengyin WU, Xuecheng YAO, Dawei CUI, and Jue XIE declare that they have no conflict of interest. All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (the First Affiliate Hospital, Zhejiang University School of Medicine, Hangzhou, China) and with the Helsinki Declaration of 1975. Informed consent was obtained from all patients for being included in the study. Additional informed consent was obtained from all patients for whom identifying information is included in this article. Compliance with ethics guidelines 浙江大学学报（英文版）（B辑：生物医学和生物技术） 12 22