Chen Kuan-Fu, Liu Su-Hsun, Li Chih-Huang, Wu Chin-Chieh, Chaou Chung-Hsien, Tzeng I-Shiang, Hsieh Yu-Hsiang, Blaney Gerald N, Liu Zhen-Ying, Han Shih-Tsung, Chan Yi-Lin
Department of Emergency Medicine, Chang Gung Memorial Hospital, Keelung, Taiwan; Clinical Informatics and Medical Statistics Research Center, Chang Gung University, Taoyuan, Taiwan; Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung, Taiwan.
Department of Family Medicine, Chang Gung Memorial Hospital, Linkou, Taiwan; School of Medicine, Chang Gung University, Taoyuan, Taiwan.
Am J Emerg Med. 2017 Apr;35(4):640-646. doi: 10.1016/j.ajem.2016.10.075. Epub 2016 Nov 3.
We aimed to derive and validate a parsimonious and pragmatic clinical prediction rule using the concepts of Predisposition, Infection, Response, and Organ Dysfunction to predict in-hospital mortality; and to compare it with other prediction rules, as well as with conventional biomarkers for evaluating the mortality risk of patients with suspected sepsis in the emergency department (ED).
We conducted a pragmatic cohort study with consecutive ED patients aged 18 or older with documented diagnostic codes of infection and two sets of blood culture ordered by physicians between 2010 and 2012 in a tertiary teaching hospital.
7011 and 12,110 patients were included in the derivation cohort and the validation cohort for the final analysis. There were 479 deaths (7%) in the derivation cohort and 1145 deaths (9%) in the validation cohort. Independent predictors of death were absence of Chills (odds ratio: 2.28, 95% confidence interval: 1.75-2.97), Hypothermia (2.12, 1.57-2.85), Anemia (2.45, 1.97-3.04), wide Red cell Distribution Width (RDW) (3.27, 2.63-4.05) and history of Malignancy (2.00, 1.63-2.46). This novel clinical prediction rule (CHARM) performed well for stratifying patients into mortality risk groups (sensitivity: 99.4%, negative predictive value 99.7%, receiver operating characteristic area 0.77). The CHARM score also outperformed the other scores or biomarkers such as PIRO, SIRS, MEDS, CURB-65, C-reactive protein, procalcitonin and lactate (all p<.05).
In patients with suspected sepsis, this parsimonious and pragmatic model could be utilized to stratify the mortality risk of patients in the early stage of sepsis.
我们旨在运用易感性、感染、反应和器官功能障碍的概念，推导并验证一个简约实用的临床预测规则，以预测住院死亡率；并将其与其他预测规则以及用于评估急诊科疑似脓毒症患者死亡风险的传统生物标志物进行比较。
我们在一家三级教学医院开展了一项实用队列研究，研究对象为2010年至2012年间年龄在18岁及以上、有感染诊断代码记录且医生开具了两组血培养检查单的连续急诊科患者。
最终分析的推导队列和验证队列分别纳入了7011例和12110例患者。推导队列中有479例死亡（7%），验证队列中有1145例死亡（9%）。死亡的独立预测因素包括无寒战（比值比：2.28，95%置信区间：1.75 - 2.97）、体温过低（2.12，1.57 - 2.85）、贫血（2.45，1.97 - 3.04）、红细胞分布宽度（RDW）增宽（3.27，2.63 - 4.05）以及恶性肿瘤病史（2.00，1.63 - 2.46）。这个新的临床预测规则（CHARM）在将患者分层为死亡风险组方面表现良好（敏感性：99.4%，阴性预测值99.7%，受试者工作特征曲线下面积0.77）。CHARM评分也优于其他评分或生物标志物，如PIRO、SIRS、MEDS、CURB - 65、C反应蛋白、降钙素原和乳酸（所有P<0.05）。
对于疑似脓毒症患者，这个简约实用的模型可用于在脓毒症早期对患者的死亡风险进行分层。
