心源性脑栓塞型患者快速筛选的多因素预测模型构建
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  • 英文篇名:The construction of a multi-variable prediction model for rapid recognition of patients with cardioembolic stroke
  • 作者:徐文冲 ; 吴智鑫 ; 蔡卫东 ; 陈胜龙 ; 梁斯娜 ; 秦锋周 ; 陈景利 ; 李莹莹 ; 马明远 ; 何明丰
  • 英文作者:XU Wen-chong;WU Zhi-xin;CAI Wei-dong;CHEN Sheng-long;LIANG Si-na;QIN Feng-zhou;CHEN Jing-li;LI Ying-ying;MA Ming-yuan;HE Ming-feng;Foshan Hospital of TCM;
  • 关键词:心源性脑栓塞 ; 卒中 ; 筛选 ; 模型构建
  • 英文关键词:cardiogenic embolism;;cerebral apoplexy;;screening;;model construction
  • 中文刊名:GAYX
  • 英文刊名:Guangdong Medical Journal
  • 机构:佛山市中医院急诊科;佛山市中医院重症医学科;广东省人民医院(广东省医学科学院)急危重症医学部;清远市人民医院急诊科;佛山市中医院禅城高新区医院急诊科;佛山市中医院三水分院急诊科;
  • 出版日期:2019-03-25
  • 出版单位:广东医学
  • 年:2019
  • 期:v.40
  • 基金:广东省科技计划项目(编号:2014A020212002);; 佛山市医学类科技攻关项目(编号:2014AB00328,2015AB00354);; 佛山市科技局科技创新平台项目(编号:2017AG100061);佛山市科技局科技创新专项资金项目(编号:2014AG10002);; 佛山市医学重点专科培育项目(编号:Fspy2-2015004);; 佛山市十三五高水平重点专科(编号:FSGSPZD135025);; 佛山市十三五重点专科(编号:FSZDZK135016)
  • 语种:中文;
  • 页:GAYX201906007
  • 页数:5
  • CN:06
  • ISSN:44-1192/R
  • 分类号:39-43
摘要
目的构建急诊室快速筛选心源性脑栓塞(CE)型患者的多因素预测数学模型。方法对2015年6月至2016年5月期间,佛山市中医院及其附属医院急诊科收治的急性缺血性卒中患者进行前瞻性分析。参考中华医学会和美国AHA/ASA卒中指南对拟诊为卒中的患者进行诊疗。多学科专家诊断小组根据入选患者的诊疗资料和随访资料,构建快速筛选CE型患者的多因素预测数学模型。结果研究期间内,共有290例卒中患者纳入研究,平均(68.4±12.1)岁,男性达46.2%。参考TOAST分型:CE型17.9%;LAA型28.3%;SAO型42.8%,SOE和SUE型达11.0%。在校正可能的混杂因素后,B型脑钠肽(BNP)与D-二聚体(DD)是预测CE型的独立因素[BNP OR:1.04 (95%CI:1.03~1.06),P<0.01;DD OR:1.51(95%CI 1.02~2.24),P=0.04],并建立起快速筛选CE型的多因素预测数学模型。结论结合床边快速检测技术(POCT),该多因素预测数学模型可辅助用于CE型患者的快速筛选。
        Objective To construct a multi-variable prediction model for rapid recognition of patients with cardioembolic(CE) stroke. Methods This multi-center study was prospectively conducted in Foshan Hospital of TCM and its affiliated hospitals. From June 2015 to May 2016, patients(≥18 yrs) with suspected acute stroke were assessed on admission by emergency physicians. Patients′ neurological outcome and final diagnosis with TOAST subtypes were confirmed by neurologists. And a multi-variable prediction model for rapid recognition of CE stroke was analyzed according to the clinical data. Results A total of 290 stroke patients met this study criteria(mean age 68.4±12.1 years; 46.2% male). The final diagnosis at discharge was 17.9% with CE, 28.3% with LAA, 42.8% with SAO, and 11.0% with SOE or SUE. After adjustment for multiple possible clinical confounders, BNP and D-dimer were significantly associated with CE [BNP OR: 1.04(95%CI 1.03,1.06), P<0.01; D-dimer OR: 1.51(95% CI 1.02, 2.24), P=0.04]. A multi-variable prediction model with B-type natriuretic peptide was established for rapid recognition of CE. Conclusion Through this prediction model, CE stroke patients would be differentiated from other TOAST subtypes.
引文
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