基于数据挖掘的地震创伤患者入院后结局预测模型

Predictive model for estimating the death risk of in-hospitalized injured patients from a large scale disaster： a pattern recognition study based on patient data sets after the 2008 Wenchuan Earthquake, China

目的模式识别技术（PRT）是一种挖掘重要信息的新型工具，可以从海量数据中提取新的知识。基于汶川特大地震中创伤患者的数据，笔者采用PRT建立地震伤员结局预测模型，旨在为提高灾难医学救援水平提供一种新的方法。方法采用回顾性数据挖掘方法，数据来自于四川省医学科学院创伤数据中心2008年5月12日至20日收治的2316例住院地震伤患者病例信息。将患者资料按照生存与死亡、是否发生多器官功能不全综合征（multiple organ dysfunction syndrome，MODS）分组。根据正态性分布检验结果，计量资料以均数±标准差（x-±s）或者中位数（四分位数）表示，统计检验采用StudentT检验或者Wilcox检验；计数资料采用构成比表示，统计检验采用X^2检验或者Fisher检验。多元统计分析采用偏最小二乘法判别分析（partial least square-discriminant analysis, PLS-DA）。多元聚类图采用二维主成分的PLS的投影图，并采用重要性投影指标值（variable important projection, VIP）筛选与临床结局相关的重要变量，工效曲线（receiver operating characteristic curve, ROC）作变量灵敏性分析。结果经数据清理后1919例患者的病例资料纳入研究；筛选出31项人口学指标、生理一生化指标以及干预因素作为暴露参数；获得36例院内死亡病例和17例MODS病例。MODS相关病死率为47．1％。经过PLS．DA分析，二维主成分得分图可以辨识出生存、MODS和死亡模式。对病死率和MODS进行预测，ROC曲线下面积（areaunder curve，AUC）分别为0．882和0．979。PLS-DA的重要性投影指标值（VIP）确定了8项生理指标（pH，BE，PaCO2，PaO2，HCO3^-1，SBHC03，Cr和首日补液量）构成了与院内死亡和MODS发生的相关模型。结论研究建立了一项可以预测特大地震创伤入院患者预后模型（由人院接受创伤治疗的生理-生化指标集合和液体复苏干预构成）。基于该模型，将有助于开发帮助医务人员在特大灾难医学救援中早期预判高危患者的计算机辅助诊断系统。

Objective Massive earthquake is one of disasters resulting in huge numbers of heavy and serious casualties. Identifying risk factors that lead to organ failure and death is crucial for improving trauma service performance. Pattern recognition technique （PRT） is a new tool for mining important information and in turn can generate new knowledge from huge amount of data. Here we use PRT to identify patterns in the cause of deaths of trauma patients from a massive earthquake in the Wenchuan, China. Methods Weconducted a retrospectively data mining study. The data used is from a total of 2, 316 casualties ambulated to the Sichuan Academy of Medical Sciences （SAMS） Trauma Service from May 12 to 20, 2008 after a massive earthquake. Before analysis, data preprocessing and cleansing were conducted. We categorized patient data by survival/non-survival and MODS/non-MODS. According to the result of distribution test, quantitative data was described by mean ＋ standard deviation （SD） or median （ quartile）, Student t testing or Wilcox testing was employed. Qualitative data was described by ratio, X2 testing of fisher testing was employed. After mortality and multiple organ dysfunctional syndromes （MODS） related variables are acquired, partial least squares discriminant analysis （PLS-DA） algorithm was used to establish mortality and MODS correlation model. We adopted two principle components to establish PLS projection plotting, and used variable important projection （VIP） to screen variables that correlated with clinical outcome. Receiver operating characters （ROC） curve was used for sensitivity and specificity analysis. Results The records of 1919 patients were selected by data cleansing, and 31 demographical, physiological-biological parameters and intervention factors were acquired as exposure variables. There were 36 in-hospital death cases, and 17 MODS cases. MODS related mortality was 47. 1% （8/17）. In PLS-DA, the first two principal components in the scatter plot could distinguish survival, MODS and deceased patients. For predicting mortality and MODS, the AUC of ROC was 0. 882 and 0. 979, respectively. VIP indicator （ variable importance for the projection） of PLS-DA identified 8 physiological variables （pH, BE, PaCO2, PaO2, HCO3-1, SBHCO3, Cr, volume of fluid resuscitation at the first day in-hospital） comprised a pattern related to in-hospital death event and MODS. Condusions This study shows that a significant pattern （comprised by a set of physiological-biological and fluid resuscitation intervention when patient reach to trauma service） emerges which can predict survival probability for hospitalized casualties injured during a massive earthquake. Application of this model may provide a tool to help disaster health providers identify most-at-risk patients, especially after a massive disaster when limited medical resources have to cope with huge numbers of victims.