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Readily accessible risk model to predict in-hospital major adverse cardiac events in patients with acute myocardial infarction: a retrospective study of Chinese patients

OBJECTIVE: Rapid, accurate identification of patients with acute myocardial infarction (AMI) at high risk of in-hospital major adverse cardiac events (MACE) is critical for risk stratification and prompt management. This study aimed to develop a simple, accessible tool for predicting in-hospital MAC...

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Detalles Bibliográficos
Autores principales: Hou, Xiaoxia, Du, Xin, Wang, Guohong, Zhao, Xiaoyan, Zheng, Yang, Li, Yingxue, Xia, Eryu, Qin, Yong, Dong, Jianzeng, Ma, Chang-Sheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8252882/
https://www.ncbi.nlm.nih.gov/pubmed/34210722
http://dx.doi.org/10.1136/bmjopen-2020-044518
Descripción
Sumario:OBJECTIVE: Rapid, accurate identification of patients with acute myocardial infarction (AMI) at high risk of in-hospital major adverse cardiac events (MACE) is critical for risk stratification and prompt management. This study aimed to develop a simple, accessible tool for predicting in-hospital MACE in Chinese patients with AMI. DESIGN: Retrospective review of deidentified medical records. SETTING: 38 urban and rural hospitals across diverse economic and geographic areas in China (Beijing, Henan Province and Jilin Province). PARTICIPANTS: 15 009 patients discharged from hospital with a diagnosis of AMI. MAIN OUTCOME MEASURE: The primary outcome was MACE occurrence during index hospitalisation. A multivariate logistic regression model (China AMI Risk Model, CHARM) derived using patient data from Beijing (n=7329) and validated with data from Henan (n=4247) and Jilin (n=3433) was constructed to predict the primary outcome using variables of age, white cell count (WCC) and Killip class. C-statistics evaluated discrimination in the derivation and validation cohorts, with goodness-of-fit assessed using Hosmer-Lemeshow statistics. RESULTS: The CHARM model included age (OR: 1.06 per 1-year increment, 95% CI 1.05 to 1.07, p<0.001), WCC (OR per 10(9)/L increment: 1.10 (95% CI 1.07 to 1.13), p<0.001) and Killip class (class II vs class I: OR 1.34 (95% CI 0.99 to 1.83), p=0.06; class III vs class I: OR 2.74 (95% CI 1.86 to 3.97), p<0.001; class IV vs class I: OR 14.12 (95% CI 10.35 to 19.29), p<0.001). C-statistics were similar between the derivation and validation datasets. CHARM had a higher true positive rate than the Thrombolysis In Myocardial Infarction score and similar to the Global Registry of Acute Coronary Events (GRACE). Hosmer-Lemeshow statistics were 5.5 (p=0.703) for derivation, 41.1 (p<0.001) for Henan, and 103.2 for Jilin (p<0.001) validation sets with CHARM, compared with 119.6, 34.0 and 459.1 with GRACE (all p<0.001). CONCLUSIONS: The CHARM model provides an inexpensive, accurate and readily accessible tool for predicting in-hospital MACE in Chinese patients with AMI.