Development and validation of a risk prediction model for in-hospital major cardiovascular events in patients hospitalised for acute myocardial infarction

Abstract

ObjectivesPatients admitted to hospital with acute myocardial infarction (AMI) have considerable variability in in-hospital risks, resulting in higher demands on healthcare resources. Simple risk-assessment tools are important for the identification of patients with higher risk to inform clinical decisions. However, few risk assessment tools have been built that are suitable for populations with AMI in China. We aim to develop and validate a risk prediction model, and further build a risk scoring system.DesignData from a nationally representative retrospective study was used to develop the model. Patients from a prospective study and another nationally representative retrospective study were both used for external validation.Setting161 nationally representative hospitals, and 53 and 157 other hospitals were involved in the above three studies, respectively.Participants8010 patients hospitalised for AMI were included as development sample, and 4485 and 11 223 other patients were included as validation samples in their corresponding studies.Primary and secondary outcome measuresThe in-hospital major adverse cardiovascular events (MACE) was defined as death from any cause, recurrent AMI, or ischaemic stroke.ResultsThe proportion of in-hospital MACE was 11.7%, 8.8% and 11.4% among the development sample and two external-validation samples, respectively. Nine predictors (ie, age, sex, left ventricular ejection fraction, Killip class, systolic blood pressure, creatinine, white blood cell count, heart rate and blood glucose) were independently associated with in-hospital MACE. The model performed well on both discrimination and calibration capability, with areas under the Receiver Operating Characteristic Curve (ROC) curve of 0.85, 0.74 and 0.80, and calibration slopes of 0.98, 0.84 and 0.97 in the development sample and two external validation samples, respectively. A point-based risk scoring system was built with good discrimination and reclassification ability.ConclusionsA prediction model using readily available clinical parameters was developed and externally validated to estimate risks of in-hospital MACE among patients with AMI, thereby better informing decision-making in improving clinical care.

In-hospital major adverse cardiovascular events after primary percutaneous coronary intervention in patients with acute ST-segment elevation myocardial infarction: a retrospective study under the China chest pain center (standard center) treatment system

“…Patients with a higher Killip class were found to have more severe coronary artery disease, higher incidence of ventricular dysfunction and recurrent ischemia, and larger myocardial infarctions [ 38 ]. Consistent with recent studies, our study found that Killip class III/IV was significantly associated with in-hospital MACE [ 19 , 20 ]. To reduce treatment delays, it is more practical to assess the Killip class at the FMC based on the patient’s signs and symptoms of heart failure and to perform an echocardiogram when the patient was stable after PCI.…”

“…A number of studies have demonstrated an association between a lower incidence of MACE and higher SBP and DBP at admission [ 10 , 11 , 19 ]. In our cohort, both SBP and DBP were lower in the MACE group, and SBP at admission independently predicted in-hospital MACE.…”

Prediction models for major adverse cardiovascular events after percutaneous coronary intervention: a systematic review

“…A total of 12 studies addressed prognostic models for patients with ST-segment elevation myocardial infarction (STEMI) ( 20 , 26 – 29 , 32 – 34 , 36 – 39 ). Six studies were centered on patients with myocardial infarction (MI) ( 11 , 12 , 21 , 22 , 24 , 35 ). Meanwhile, three studies specifically focused on patients with acute coronary syndrome ( 13 , 23 , 25 ).…”

“…The models developed in the eight studies ( 21 , 24 , 25 , 30 , 33 , 37 , 39 ) did not undergo either internal or external validation. Three studies performed external validation by enrolling patients at different times ( 11 , 31 , 35 ). The study sample sizes ranged from 124 ( 22 ) to 23,718 ( 35 ).…”

A Prediction Model Based on Systemic Immune-Inflammatory Index Combined with Other Predictors for Major Adverse Cardiovascular Events in Acute Myocardial Infarction Patients

“… 32 , 33 All-cause death was defined as a death during hospitalization or abandonment of treatments at discharge due to a terminal condition. In China, many severe patients are reluctant to die in the hospital, 5 , 34 , 35 and they choose to abandon their treatments at discharge if they are in terminal condition. These patients were grouped into the all-cause mortality group.…”