基于凝血指标、血栓弹力图构建急性再生障碍性贫血患者输血不良反应发生风险预测模型及其验证-东南大学学报医学版

基于凝血指标、血栓弹力图构建急性再生障碍性贫血患者输血不良反应发生风险预测模型及其验证

单位：1. 梧州市红十字会医院输血科, 广西梧州 543002;
2. 梧州市红十字会医院血液内科, 广西梧州 543002;
3. 梧州市工人医院输血科, 广西梧州 543001;
4. 广西壮族自治区桂东人民医院输血科, 广西梧州 543001

关键词：急性再生障碍性贫血输血不良反应影响因素预测模型

分类号：R556.5

出版年·卷·期（页码）：2023·42·第五期（673-680）

摘要：

目的:探讨急性再生障碍性贫血患者输血不良反应的影响因素,构建预测模型,为临床实践提供最佳评估工具,以便及时准确制定防治措施。方法:选取2021年6月至2022年6月梧州市红十字会医院、梧州市工人医院、广西壮族自治区桂东人民医院3家医院388例急性再生障碍性贫血患者作为研究对象,按照7:3比例随机分为建模人群(n=271)和验证人群(n=117),统计输血不良反应、人口学特征、凝血指标、血栓弹力图,采用Logistic回归方程分析输血不良反应的影响因素,构建输血不良反应发生风险列线图预测模型,绘制受试者工作特征(ROC)曲线分析模型区分度,校准曲线分析模型准确度,临床决策曲线(DCA)评价模型有效性。结果:(1) 急性再生障碍性贫血患者输血不良反应发生率为9.02%(35/388);(2) Logistic回归方程显示,输血史、过敏史、血小板储存时间、纤维蛋白原(FIB)、活化部分凝血酶时间(APTT)、凝血酶原时间(PT)、凝血最终强度(MA)是急性再生障碍性贫血患者输血不良反应的影响因素(P<0.05);(3) 根据Logistic回归结果建立急性再生障碍性贫血患者输血不良反应列线图预测模型,在建模人群和验证人群中曲线下面积(AUC)分别为0.914、0.914,与实际观察结果之间有很好相关性,且净获益率较好;(4) 依据模型中位评分(4分)将建模人群和验证人群分为高危人群(≥ 4分)和低危人群(<4分),高危人群输血不良反应发生率(17.39%)高于低危人群(4.97%,P<0.05)。结论:基于输血史、过敏史、血小板储存时间、FIB、APTT、PT、MA构建的列线图模型可有效预测急性再生障碍性贫血患者输血不良反应发生风险,帮助医护人员实现风险预警前移,采取个体化精准干预。

Objective: To investigate the factors influencing adverse reactions to blood transfusion in patients with acute aplastic anemia and construct a prediction model to provide the best assessment tool for clinical practice and to formulate timely and accurate prevention and treatment measures. Methods: A total of 388 patients with acute aplastic anemia in Wuzhou Red Cross Hospital, Wuzhou Worker's Hospital and Guidong People's Hospital of Guangxi Zhuang Autonomous Region from June 2021 to June 2022 were selected as study subjects, randomly divided into modeling population(n=271) and validation population(n=117) according to the ratio of 7:3. Transfusion adverse reactions, demographic characteristics, coagulation indexes, thromboelastography were collected.Logistic regression equation was used to analyze the factors influencing transfusion adverse reactions. We constructed a prediction model for the risk of transfusion adverse reactions by column line graphs,receiver operating characteristic(ROC) curve was used to analyze model discrimination, calibration curves was used to analyze model accuracy, and decision curve analysis(DCA) was used to evaluate model validity. Results: (1) The incidence of adverse transfusion reactions in patients with acute aplastic anemia was 9.02%(35/388); (2) Logistic regression equation showed that transfusion history, allergy history, platelet storage time,fibrinogen(FIB), activated partial thromboplastin time(APTT),(prothrombin time)PT, and maxium amplitude(MA)were factors affecting adverse transfusion reactions in patients with acute aplastic anemia(P<0.05).(3) Based on the Logistic regression equation, the area under the ROC curve(AUC) prediction model for adverse blood transfusion reactions in patients with acute aplastic anemia was established. The AUC values were 0.914 and 0.914 respectively in the modeling population and the verification population. There was a good correlation with the actual observation results, and the net benefit rate was good.(4) Based on the median score of the model(4 points), the patients in the modeling population and the validation population were classified into high-risk population(≥ 4 points) and low-risk population(<4 points), and the incidence of adverse blood transfusion reactions was higher in the high-risk population(17.39%) than in the low-risk population(4.97%, P<0.05). Conclusion: The neagram model based on blood transfusion history, allergy history, platelet storage time, FIB, APTT, PT and MA can effectively predict the risk of transfusion adverse reactions in patients with acute aplastic anemia, and help medical staff realize risk warning advance and take individualized and precise intervention.

参考文献：

[1] ELGOHARY G,El FAKIH R,DE LATOUR R,et al.Haploidentical hematopoietic stem cell transplantation in aplastic anemia:a systematic review and meta-analysis of clinical outcome on behalf of the severe aplastic anemia working party of the European group for blood and marrow transplantation(saawp of ebmt)[J].Bone Marrow Transplant,2020,55(10):1906-1917.
[2] MONCHARMONT P,QUITTANCON E,BARDAY G,et al.Adverse transfusion reactions in patients with aplastic anaemia or myelodysplastic syndromes[J].Vox Sang,2019,114(4):349-354.
[3] URBANOWICA I,NAHACZEWSKA W,CELUCH B,et al.Narrative review of aplastic anemia-the importance of supportive treatment[J].Ann Palliat Med,2021,10(1):694-699.
[4] VAN BAARLE F L F,DE BRUIN S,BULLE E B,et al.Aged versus fresh a utologous platelet transfusion in a two-hit healthy volunteer model of transfusion-related acute lung injury[J].Transfusion,2022,62(12):2490-2501.
[5] DEZERN A E,ZAHURAK M L,SYMONS H J,et al.Haploidentical bmt for severe aplastic anemia with intensive gvhd prophylaxis including posttrans plant cyclophosphamide[J].Blood Adv,2020,4(8):1770-1779.
[6] 刘璇,陈麟凤.临床常见输血不良反应发生时间的相关因素分析[J].实用临床医药杂志,2022,26(16):105-107.
[7] 李娅,党璇,张玲玲,等.某三甲医院133例临床输血不良反应回顾性调查分析[J].南京医科大学学报(自然科学版),2021,41(6):913-916.
[8] 中华医学会血液学分会红细胞疾病学组.再生障碍性贫血诊断与治疗中国专家共识(2017年版)[J].中华血液病杂志,2017,38(1):1-5.
[9] 牛庆,夏波,王芳,等.输血不良反应102例回顾性临床分析[J].中国药物与临床,2021,21(19):3318-3320.
[10] 王莉,邹玉,陈文珠,等.血液病患者发生急性输血不良反应的临床表现及输血效果分析[J].中国输血杂志,2021,34(4):354-358.
[11] 胡婷婷,王诗铭,王希文,等.肿瘤患者输注血小板发生不良反应影响因素分析[J].临床输血与检验,2021,23(4):464-468.
[12] RAVAL J S,GRIGGS J R,FLEG A,et al.Blood product transfusion in adults:indications,adverse reactions,and modifications[J].Am Fam Physician,2020,102(1):30-38.
[13] ZHANG X,ZHANG Y,QI C,et al.Analysis of adverse reactions of blood transfusion and discussion of influencing factors in linyi area from 2013 to 2020[J].Iran J Public Health,2021,50(7):1416-1420.
[14] 李宏科,雷鹏举,何红英,等.基于循证医学研究我国成人输血不良反应的临床特点及相关影响因素[J].中国输血杂志,2021,34(10):1117-1120.
[15] LIKER M,BOJANIC I,PLENKOVIC F,et al.Platelet transfusion practice and related transfusion reactions in a large teaching hospital[J].Transfus Clin Biol,2022,29(1):37-43.
[16] KUNDRAPU S,SRIVASTAVA S,GOOD C E,et al.Bacterial contamination and septic transfusion reaction rates associated with platelet components before and after introduction of primary culture:experience at a us academic medical center 1991 through 2017[J].Transfusion,2020,60(5):974-985.
[17] GOEL R,TOBIAN A A R,SHAZ B H,et al.Noninfectious transfusion-associated adverse events and their mitigation strategies[J].Blood,2019,133(17):1831-1839.
[18] WANG R,LUO T,LIU Z,et al.Intraoperative cell salvage is associated with reduced allogeneic blood requirements and has no significant impairment on coagulation function in patients undergoing cesarean delivery:a retrospective study[J].Arch Gynecol Obstet,2020,301(5):1173-1180.
[19] SHI H,SHI B,LU J,et al.Application value of thromboelastography in perioperative clinical blood transfusion and its effect on the outcome of patient[J].Exp Ther Med,2019,17(5):3483-3488.
[20] FAN G,YUAN M,NIU H,et al.The significance of thromboelastogram in predicting postpartum hemorrhage and guiding blood transfusion[J].Clin Lab,2022,68(2):116-120.
[21] YILMAZ R,YUSIFOV M,HACIBEYOGLU G,et al.Comparison of pre-operative platelet functions by thromboelastogram in patients selective serotonin reuptake inhibitors user and non-user[J].Ulus Travma Acil Cerrahi Derg,2022,28(3):276-280.
[22] PRESSLY M A,PARKER R S,NEAL M D,et al.Accelerating availability of clinically-relevant parameter estimates from thromboelastogram point-of-care device[J].J Trauma Acute Care Surg,2020,88(5):654-660.
[23] TAKEDA C,HIROTSU A,YASUHARA G,et al.Utility of thromboelastogram in cardiac surgery in Jacobsen syndrome associated with platelet dysfunction:a case report[J].JA Clin Rep,2022,8(1):67.
[24] CHENG L,BAI W H,YANG J J,et al.Construction and validation of mortality risk nomograph model for severe/critical patients with COVID-19[J].Diagnostics(Basel),2022,12(10):2562.
[25] ZHANG Z,ZHAO X,LI Z,et al.Development of a nomogram model to predict survival outcomes in patients with primary hepatic neuroendocrine tumors based on SEER database[J].BMC Cancer,2021,21(1):567.

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