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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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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
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author Hou, Xiaoxia
Du, Xin
Wang, Guohong
Zhao, Xiaoyan
Zheng, Yang
Li, Yingxue
Xia, Eryu
Qin, Yong
Dong, Jianzeng
Ma, Chang-Sheng
author_facet Hou, Xiaoxia
Du, Xin
Wang, Guohong
Zhao, Xiaoyan
Zheng, Yang
Li, Yingxue
Xia, Eryu
Qin, Yong
Dong, Jianzeng
Ma, Chang-Sheng
author_sort Hou, Xiaoxia
collection PubMed
description 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.
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spelling pubmed-82528822021-07-23 Readily accessible risk model to predict in-hospital major adverse cardiac events in patients with acute myocardial infarction: a retrospective study of Chinese patients Hou, Xiaoxia Du, Xin Wang, Guohong Zhao, Xiaoyan Zheng, Yang Li, Yingxue Xia, Eryu Qin, Yong Dong, Jianzeng Ma, Chang-Sheng BMJ Open Medical Management 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. BMJ Publishing Group 2021-07-01 /pmc/articles/PMC8252882/ /pubmed/34210722 http://dx.doi.org/10.1136/bmjopen-2020-044518 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Medical Management
Hou, Xiaoxia
Du, Xin
Wang, Guohong
Zhao, Xiaoyan
Zheng, Yang
Li, Yingxue
Xia, Eryu
Qin, Yong
Dong, Jianzeng
Ma, Chang-Sheng
Readily accessible risk model to predict in-hospital major adverse cardiac events in patients with acute myocardial infarction: a retrospective study of Chinese patients
title Readily accessible risk model to predict in-hospital major adverse cardiac events in patients with acute myocardial infarction: a retrospective study of Chinese patients
title_full Readily accessible risk model to predict in-hospital major adverse cardiac events in patients with acute myocardial infarction: a retrospective study of Chinese patients
title_fullStr Readily accessible risk model to predict in-hospital major adverse cardiac events in patients with acute myocardial infarction: a retrospective study of Chinese patients
title_full_unstemmed Readily accessible risk model to predict in-hospital major adverse cardiac events in patients with acute myocardial infarction: a retrospective study of Chinese patients
title_short Readily accessible risk model to predict in-hospital major adverse cardiac events in patients with acute myocardial infarction: a retrospective study of Chinese patients
title_sort readily accessible risk model to predict in-hospital major adverse cardiac events in patients with acute myocardial infarction: a retrospective study of chinese patients
topic Medical Management
url 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
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