ArticleBMJ open2023
Development and validation of dynamic models to predict postdischarge mortality risk in patients with acute myocardial infarction: results from China Acute Myocardial Infarction Registry.
Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01874691 (China Acute Myocardial Infarction Registry), which is not on this map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
China Acute Myocardial Infarction Registry
Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Development and validation of a risk prediction model for in-hospital mortality among patients with acute myocardial infarction complicated by ventricular arrhythmia.BMC cardiovascular disorders · 2026Article
- Environmental and ethnic differences in short-term risk of first acute myocardial infarction: a prospective multiethnic cohort protocol.Frontiers in public health · 2026Article
- Diagnostic accuracy, treatment and prognosis of myocardial infarction: an 11-year follow-up of a community-based cohort of 0.5 million Chinese adults.BMJ public health · 2026Article
- Age shock index and age-modified shock index are valuable bedside prognostic tools for postdischarge mortality in ST-elevation myocardial infarction patients.Annals of medicine · 2024Article
- Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study.Journal of medical Internet research · 2024Article
- Clinical Variables Influence the Ability of miR-101, miR-150, and miR-21 to Predict Ventricular Remodeling after ST-Elevation Myocardial Infarction.Biomedicines · 2023Article
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Authors and funding
15 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe 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.
designA retrospective analysis of a prospective cohort.
setting108 hospitals in China.
participantsA 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.
resultsIn 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.
conclusionsWe 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.
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