本文選題：創(chuàng)傷患者 + 大量輸血　； 參考：《南昌大學(xué)》2017年碩士論文

【摘要】：目的通過創(chuàng)傷輸血患者臨床資料的病例對照研究,探討創(chuàng)傷輸血患者臨床預(yù)后轉(zhuǎn)歸的影響因素;通過創(chuàng)傷輸血患者大量輸血影響因素分析,以建立大量輸血預(yù)測評分方案,并對其進行臨床應(yīng)用性能驗證。方法收集南昌大學(xué)第一附屬醫(yī)院2013年1月1日至2016年12月31日所有出院創(chuàng)傷患者的臨床病例資料,共納入年齡≥18歲且急診入院后24h內(nèi)有紅細胞(RBC)輸注的創(chuàng)傷輸血患者265例,根據(jù)患者急診入院后24h內(nèi)RBC輸注量,將患者分為非大量輸血組(24h內(nèi)RBC輸血量18u)和大量輸血組(24h內(nèi)RBC輸注量≥18u),其中非大量輸血組229例,大量輸血組患者36例;(1)分析比較兩組患者基本資料和臨床預(yù)后轉(zhuǎn)歸相關(guān)指標(biāo),并采用Logistic回歸分析患者院內(nèi)死亡的相關(guān)影響因素,以探討創(chuàng)傷輸血患者臨床預(yù)后轉(zhuǎn)歸的影響因素;(2)采用線性回歸分析創(chuàng)傷輸血患者大量輸血的影響因素,并篩選出創(chuàng)傷輸血患者大量輸血的獨立影響因素;(3)采用創(chuàng)傷輸血患者大量輸血獨立影響因素作為大量輸血預(yù)測評分指標(biāo),通過分層賦值建立大量輸血預(yù)測評分方案,并利用受試者工作特征曲線(ROC曲線)對其進行臨床應(yīng)用性能驗證。結(jié)果(1)非大量輸血與大量輸血組患者性別、年齡和致傷原因間差異均不具統(tǒng)計學(xué)意義(P0.05),兩組患者創(chuàng)傷嚴重度評分(ISS)、格拉斯哥昏迷評分(GCS)、心率(HR)、收縮壓(SBP)與血紅蛋白濃度(Hb)、凝血功能指標(biāo)間差異均具有統(tǒng)計學(xué)意義(P0.05);(2)大量輸血組患者60天死亡率和院內(nèi)總死亡率明顯高于非大量輸血組(P0.05),其院內(nèi)感染發(fā)生率、機械通氣時間、ICU住院時間、總住院時間亦均明顯高于非大量輸血組(P0.05),兩組患者均未發(fā)生輸血不良反應(yīng);(3)創(chuàng)傷輸血患者臨床預(yù)后轉(zhuǎn)歸影響因素分析:(1)Logistic單因素回歸分析顯示:ISS、GCS、RBC輸注量、新鮮冰凍血漿輸注量、院內(nèi)感染、ICU住院時間、機械通氣時間、總住院時間均為患者院內(nèi)死亡的危險因素(P0.05);(2)Logistic多因素回歸分析顯示:ISS、RBC輸注量和院內(nèi)感染是創(chuàng)傷輸血患者院內(nèi)死亡的獨立危險因素(P0.05);(3)其回歸模型診斷ROC曲線下面積(AUC)為0.80,顯示該回歸模型具有較高的診斷性能;(4)創(chuàng)傷輸血患者大量輸血影響因素及預(yù)測評分:(1)單因素回歸分析顯示:創(chuàng)傷類型、ISS、HR、SBP、Hb、凝血酶原時間(PT)、國際標(biāo)準(zhǔn)化比值(INR)、活化部分凝血活酶時間(APTT)、纖維蛋白原(Fbg)、剩余堿(BE)均為創(chuàng)傷輸血患者大量輸血的影響因素(P0.05);(2)多因素回歸分析顯示:創(chuàng)傷類型、ISS、HR、Hb、PT、Fbg、BE是創(chuàng)傷輸血患者大量輸血的獨立影響因素(P0.05);(3)通過對創(chuàng)傷輸血患者大量輸血獨立影響因素分層賦值,建立了大量輸血預(yù)測評分方案,其總分為0-8分,ROC曲線分析結(jié)果顯示AUC=0.91,靈敏度和特異度分別為88.9%和79.9%,認為當(dāng)評分≥4分時,需要大量輸血。結(jié)論大量輸血創(chuàng)傷患者的并發(fā)癥發(fā)生率與死亡率均明顯增高,ISS、RBC輸注量和院內(nèi)感染是影響創(chuàng)傷輸血患者院內(nèi)死亡的獨立危險因素。創(chuàng)傷類型、ISS、HR、Hb、PT、Fbg、BE是創(chuàng)傷輸血患者大量輸血的獨立影響因素,其分層賦值預(yù)測評分方案認為當(dāng)評分≥4分時需要大量輸血,且具備良好的臨床應(yīng)用性能。
[Abstract]:Objective a case-control study of the clinical data of patients with traumatic blood transfusion was studied to explore the influencing factors of the prognosis of the patients with traumatic blood transfusion, and to establish a large number of blood transfusion prediction scoring schemes through analysis of the influential factors of blood transfusion in patients with traumatic blood transfusion, and to verify the clinical application of them. Methods collect the First Affiliated Hospital of Nanchang University. From January 1, 2013 to December 31, 2016, the clinical data of all patients who were discharged from discharge were included in 265 cases of traumatic blood transfusion with red blood cell (RBC) infusion in 24h and 24h after emergency admission. The patients were divided into non large blood transfusion group (24h RBC 18u) and a large number of blood transfusion groups according to the amount of RBC infusion within 24h after emergency admission. (24h internal RBC infusion amount is more than 18u), including 229 cases of non large blood transfusion group and 36 patients in large blood transfusion group; (1) analysis and comparison of the basic data of the two groups and the related indexes of clinical prognosis, and the correlation factors of hospital mortality in patients with Logistic regression, in order to explore the influencing factors of the prognosis of the patients with trauma transfusion; (2) adopt The linear regression analysis of the influential factors of massive blood transfusion in patients with trauma blood transfusion, and screening out the independent influencing factors of massive blood transfusion in patients with traumatic blood transfusion; (3) the independent influence factors of massive blood transfusion in patients with traumatic blood transfusion were used as a large number of blood transfusion prediction scores, and a large number of blood transfusion prediction scoring schemes were established by stratified assignment, and the subjects were employed. The characteristic curve (ROC curve) was used to verify its clinical application. Results (1) there was no significant difference in gender, age and cause of injury (P0.05), two groups of trauma severity score (ISS), Glasgow coma score (GCS), heart rate (HR), systolic blood pressure (SBP) and hemoglobin concentration (Hb). The difference of the blood coagulation function was statistically significant (P0.05). (2) the 60 day mortality rate and total hospital mortality in the large blood transfusion group were significantly higher than that of non large blood transfusion group (P0.05). The incidence of nosocomial infection, the time of mechanical ventilation, the time of ICU hospitalization, and the total hospitalization were also significantly higher than those of non large blood transfusion group (P0.05), and all the two groups were not sent. Adverse reaction of blood transfusion; (3) analysis of factors affecting the prognosis of clinical prognosis in patients with trauma transfusion: (1) Logistic single factor regression analysis showed that ISS, GCS, RBC infusion, fresh frozen plasma infusion, hospital infection, ICU hospitalization time, mechanical ventilation time, total hospitalization time were all risk factors for patients' hospital death (P0.05); (2) Logistic multiple factors Regression analysis showed that ISS, RBC infusion and nosocomial infection were independent risk factors (P0.05) for hospital death in patients with traumatic blood transfusion (P0.05); (3) the area under the ROC curve (AUC) was 0.80, indicating that the regression model had a high diagnostic performance; (4) the influence factors and prediction scores of massive blood transfusion in patients with trauma blood transfusion: (1) single factor regression. Analysis showed that the type of trauma, ISS, HR, SBP, Hb, prothrombin time (PT), international normalized ratio (INR), activated partial thromboplastin time (APTT), fibrinogen (Fbg), and residual alkali (BE) were all the factors affecting massive blood transfusion in patients with traumatic blood transfusion (P0.05). (2) multiple regression analysis showed that the type of trauma, ISS, HR, insufficiency, and blood transfusion The independent influence factor of large blood transfusion (P0.05); (3) through the stratified assignment of the independent influence factors of blood transfusion in patients with traumatic blood transfusion, a large number of blood transfusion prediction scores were set up, the total score was 0-8, the results of ROC curve analysis showed AUC=0.91, the sensitivity and specificity were 88.9% and 79.9%. It was considered that when the score was more than 4, it was necessary to lose a lot. Conclusion the incidence of complications and mortality in patients with massive blood transfusion were significantly higher. ISS, RBC infusion and nosocomial infection were independent risk factors affecting hospital death in patients with traumatic transfusion. The type of trauma, ISS, HR, Hb, PT, Fbg, BE were independent factors affecting the massive transfusion of blood transfusion in patients with traumatic blood transfusion, and their stratified assignment prediction scoring scheme was recognized. A large amount of blood transfusion is needed when the score is more than 4 points, and has good clinical application performance.
【學(xué)位授予單位】：南昌大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R641;R457.1

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