RT Journal Article
SR Electronic
T1 Development and validation of dynamic models to predict postdischarge mortality risk in patients with acute myocardial infarction: results from China Acute Myocardial Infarction Registry
JF BMJ Open
JO BMJ Open
FD British Medical Journal Publishing Group
SP e069505
DO 10.1136/bmjopen-2022-069505
VO 13
IS 3
A1 Lv, Junxing
A1 Wang, Chuangshi
A1 Gao, Xiaojin
A1 Yang, Jingang
A1 Zhang, Xuan
A1 Ye, Yunqing
A1 Dong, Qiuting
A1 Fu, Rui
A1 Sun, Hui
A1 Yan, Xinxin
A1 Zhao, Yanyan
A1 Wang, Yang
A1 Xu, Haiyan
A1 Yang, Yuejin
A1 
YR 2023
UL http://bmjopen.bmj.com/content/13/3/e069505.abstract
AB Objectives The risk of adverse events and prognostic factors are changing in different time phases after acute myocardial infarction (AMI). The incidence of adverse events is considerable in the early period after AMI hospitalisation. Therefore, dynamic risk prediction is needed to guide postdischarge management of AMI. This study aimed to develop a dynamic risk prediction instrument for patients following AMI.Design A retrospective analysis of a prospective cohort.Setting 108 hospitals in China.Participants A total of 23 887 patients after AMI in the China Acute Myocardial Infarction Registry were included in this analysis.Primary outcome measures All-cause mortality.Results In multivariable analyses, age, prior stroke, heart rate, Killip class, left ventricular ejection fraction (LVEF), in-hospital percutaneous coronary intervention (PCI), recurrent myocardial ischaemia, recurrent myocardial infarction, heart failure (HF) during hospitalisation, antiplatelet therapy and statins at discharge were independently associated with 30-day mortality. Variables related to mortality between 30 days and 2 years included age, prior renal dysfunction, history of HF, AMI classification, heart rate, Killip class, haemoglobin, LVEF, in-hospital PCI, HF during hospitalisation, HF worsening within 30 days after discharge, antiplatelet therapy, β blocker and statin use within 30 days after discharge. The inclusion of adverse events and medications significantly improved the predictive performance of models without these indexes (likelihood ratio test p<0.0001). These two sets of predictors were used to establish dynamic prognostic nomograms for predicting mortality in patients with AMI. The C indexes of 30-day and 2-year prognostic nomograms were 0.85 (95% CI 0.83–0.88) and 0.83 (95% CI 0.81–0.84) in derivation cohort, and 0.79 (95% CI 0.71–0.86) and 0.81 (95% CI 0.79–0.84) in validation cohort, with satisfactory calibration.Conclusions We established dynamic risk prediction models incorporating adverse event and medications. The nomograms may be useful instruments to help prospective risk assessment and management of AMI.Trial registration number NCT01874691.Data are available upon reasonable request. Data are available upon reasonable request from the corresponding authors (Haiyan Xu, xuhaiyan@fuwaihospital.org; Yuejin Yang, yangyjfw@126.com).