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Updated: Aug 5, 2025
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Development and validation of dynamic models to predict postdischarge mortality risk in patients with acute
Junxing Lv1, Chuangshi Wang2, Xiaojin Gao1
1Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Insights
Dynamic risk prediction models were developed for patients after acute myocardial infarction (AMI). These models incorporate adverse events and medications to improve mortality prediction and guide post-discharge management.
Area of Science:
- Cardiology
- Clinical Prediction Models
- Public Health
Background:
- Prognostic factors and adverse event risks evolve over time following acute myocardial infarction (AMI).
- Effective post-discharge management of AMI patients necessitates dynamic risk assessment.
- Current prediction tools may not adequately capture temporal changes in patient risk.
Purpose of the Study:
- To develop a dynamic risk prediction instrument for patients post-AMI.
- To identify key prognostic factors and adverse events influencing mortality at different time points after AMI.
- To create nomograms for predicting mortality risk in AMI survivors.
Main Methods:
- Retrospective analysis of a prospective cohort of 23,887 patients from the China Acute Myocardial Infarction Registry.
- Multivariable analyses to identify predictors of 30-day and 2-year mortality.
- Development and validation of dynamic prognostic nomograms.
Main Results:
- Key predictors for 30-day mortality included age, prior stroke, heart rate, Killip class, LVEF, in-hospital PCI, recurrent ischemia/infarction, heart failure, antiplatelet, and statin use.
- Predictors for 2-year mortality involved age, renal dysfunction, prior HF, AMI classification, heart rate, Killip class, hemoglobin, LVEF, in-hospital PCI, HF, HF worsening, antiplatelet, beta-blocker, and statin use.
- Incorporating adverse events and medications significantly improved model predictive performance (p<0.0001), with C-indexes of 0.85/0.83 (30-day/2-year) in derivation and 0.79/0.81 in validation cohorts.
Conclusions:
- Dynamic risk prediction models incorporating adverse events and medications were successfully established for AMI patients.
- These nomograms offer a valuable tool for prospective risk assessment and management strategies.
- Improved prediction can facilitate tailored post-discharge care and potentially reduce mortality.
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.
