In the Journals

Risk factors for CV events after MI identified

Researchers identified 19 risk factors for CV events within 1 year after MI and developed a risk-prediction model.

Yun Wang, PhD, from the department of biostatistics, Harvard T.H. Chan School of Public Health, and the Center for Outcomes Research and Evaluation at Yale-New Haven Hospital, and colleagues conducted a prospective cohort study of 4,227 patients (mean age, 61 years; 24% women) with acute MI in China between 2013 and July 2014.

Patients were divided into training, test and validation cohorts. Outcomes of interest included major CV events, defined as recurrent acute MI, stroke, HF and death within 1 year of discharge from the index MI hospitalization.

The most common comorbidities in the cohort were hypertension (55.8%), CHD (42.5%) and dyslipidemia (30.5%), according to the researchers.

At 1 year, major adverse CV events occurred in 8.1% (95% CI, 6.91-9.24) of the training cohort, 9% (95% CI, 7.22-10.7) of the test cohort and 6.4% (95% CI, 4.89-7.85) of the validation cohort, Wang and colleagues wrote.

The researchers identified 19 risk factors comprising 15 unique variables: age, education, prior acute MI, prior ventricular tachycardia or ventricular fibrillation, hypertension, angina, prearrival medical assistance, more than 4 hours from symptom onset to admission, ejection fraction, renal dysfunction, heart rate, systolic BP, white blood cell count, blood glucose and in-hospital complications.

The risk model developed by the researchers had a C-statistic of 0.79 in the training cohort (95% CI, 0.75-0.83), 0.73 in the test cohort (95% CI, 0.68-0.78) and 0.77 in the validation cohort (95% CI, 0.7-0.83).

The predictive range was 1.2% to 33.9% in the training cohort, 1.2% to 37.9% in the test cohort and 1.3% to 34.3% in the validation cohort.

The researchers conducted a latent class analysis based on assigning patients in the training cohort to one of 10 classes based on the combination of 19 risk factors. This analysis produced a C-statistic of 0.69 (95% CI, 0.65-0.74) for the model.

The model classified 11.3% of patients as high risk, 81% of patients as average risk and 7.7% of patients as low risk. The probabilities of 1-year events were 0.32 for the high-risk group, 0.06 for the average-risk group and 0.01 for the low-risk group.

The model “may aid clinicians in identifying high-risk patients who would benefit most from intensive follow-up and aggressive risk factor reduction,” Wang and colleagues wrote. – by Erik Swain

Disclosures: Wang reports no relevant financial disclosures. Please see the study for all other authors’ relevant financial disclosures.

Researchers identified 19 risk factors for CV events within 1 year after MI and developed a risk-prediction model.

Yun Wang, PhD, from the department of biostatistics, Harvard T.H. Chan School of Public Health, and the Center for Outcomes Research and Evaluation at Yale-New Haven Hospital, and colleagues conducted a prospective cohort study of 4,227 patients (mean age, 61 years; 24% women) with acute MI in China between 2013 and July 2014.

Patients were divided into training, test and validation cohorts. Outcomes of interest included major CV events, defined as recurrent acute MI, stroke, HF and death within 1 year of discharge from the index MI hospitalization.

The most common comorbidities in the cohort were hypertension (55.8%), CHD (42.5%) and dyslipidemia (30.5%), according to the researchers.

At 1 year, major adverse CV events occurred in 8.1% (95% CI, 6.91-9.24) of the training cohort, 9% (95% CI, 7.22-10.7) of the test cohort and 6.4% (95% CI, 4.89-7.85) of the validation cohort, Wang and colleagues wrote.

The researchers identified 19 risk factors comprising 15 unique variables: age, education, prior acute MI, prior ventricular tachycardia or ventricular fibrillation, hypertension, angina, prearrival medical assistance, more than 4 hours from symptom onset to admission, ejection fraction, renal dysfunction, heart rate, systolic BP, white blood cell count, blood glucose and in-hospital complications.

The risk model developed by the researchers had a C-statistic of 0.79 in the training cohort (95% CI, 0.75-0.83), 0.73 in the test cohort (95% CI, 0.68-0.78) and 0.77 in the validation cohort (95% CI, 0.7-0.83).

The predictive range was 1.2% to 33.9% in the training cohort, 1.2% to 37.9% in the test cohort and 1.3% to 34.3% in the validation cohort.

The researchers conducted a latent class analysis based on assigning patients in the training cohort to one of 10 classes based on the combination of 19 risk factors. This analysis produced a C-statistic of 0.69 (95% CI, 0.65-0.74) for the model.

The model classified 11.3% of patients as high risk, 81% of patients as average risk and 7.7% of patients as low risk. The probabilities of 1-year events were 0.32 for the high-risk group, 0.06 for the average-risk group and 0.01 for the low-risk group.

The model “may aid clinicians in identifying high-risk patients who would benefit most from intensive follow-up and aggressive risk factor reduction,” Wang and colleagues wrote. – by Erik Swain

Disclosures: Wang reports no relevant financial disclosures. Please see the study for all other authors’ relevant financial disclosures.

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