Zheng Wen, Ma Jingjing, Wu Shuo, Wang Guangmei, Zhang He, Zheng Jiaqi, Xu Feng, Wang Jiali, Chen Yuguo
Department of Emergency and Chest Pain Center, Qilu Hospital, Shandong University, Jinan, China.
Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Qilu Hospital, Shandong University, Jinan, China.
Clin Cardiol. 2019 Apr;42(4):467-475. doi: 10.1002/clc.23170. Epub 2019 Mar 19.
Symptom is still indispensable for the stratification of chest pain in the emergency department. However, it is a sophisticated aggregation of several aspects of characteristics and effective combination of those variables remains deficient. We aimed to develop and validate a chest pain symptom score (CPSS) to address this issue.
The CPSS may help stratifying acute undifferentiated chest pain in ED.
Patients with non-ST segment elevation chest pain and negative cardiac troponin (cTn) over 3 hours after symptom onset were consecutively recruited as the derivation cohort. Logistic regression analyses identified statistical predictors from all symptom aspects for 30-day acute myocardial infarction (AMI) or death. The performance of CPSS was compared with the symptom classification methods of the history variable in the history, electrocardiograph, age, risk factors, troponin (HEART) score. This new model was validated in a separated cohort of patients with negative cTn within 3 hours.
Seven predictors in four aspects of chest pain symptom were identified. The CPSS was an independent predictor for 30-day AMI or death (P < 0.001). In the derivation (n = 1434) and validation (n = 976) cohorts, the expected and observed event rates were well calibrated (Hosmer-Lemeshow test P > 0.30), and the c-statistics of CPSS were 0.72 and 0.73, separately, significantly better than the previous history classifications in HEART score (P < 0.001). Replacing the history variable with the CPSS improved the discrimination and risk classification of HEART score significantly (P < 0.001).
The effective combination of isolated variables was meaningful to make the most stratification value of symptoms. This model should be considered as part of a comprehensive strategy for chest pain triage.
症状对于急诊科胸痛分层而言仍然不可或缺。然而，它是多个特征方面的复杂集合，且这些变量的有效组合仍显不足。我们旨在开发并验证一种胸痛症状评分（CPSS）以解决这一问题。
CPSS可能有助于对急诊科急性未分化胸痛进行分层。
连续纳入症状发作3小时后非ST段抬高型胸痛且心肌肌钙蛋白（cTn）阴性的患者作为推导队列。逻辑回归分析从所有症状方面确定30天急性心肌梗死（AMI）或死亡的统计预测因素。将CPSS的表现与病史、心电图、年龄、危险因素、肌钙蛋白（HEART）评分中病史变量的症状分类方法进行比较。该新模型在另一组症状发作3小时内cTn阴性的患者中进行验证。
确定了胸痛症状四个方面的七个预测因素。CPSS是30天AMI或死亡的独立预测因素（P<0.001）。在推导队列（n = 1434）和验证队列（n = 976）中，预期和观察到的事件发生率校准良好（Hosmer-Lemeshow检验P>0.30），CPSS的c统计量分别为0.72和0.73，显著优于HEART评分中先前的病史分类（P<0.001）。用CPSS替代病史变量可显著改善HEART评分的辨别力和风险分类（P<0.001）。
孤立变量的有效组合对于使症状的分层价值最大化具有重要意义。该模型应被视为胸痛分诊综合策略的一部分。
