A novel risk prediction model combining age ≥65 years, smoking history, high red blood cell distribution width to albumin ratio, and D-dimer achieved an AUC of 0.774 for predicting cancer incidence after acute coronary syndrome.
Cohort (n=552)
No
Does a prediction model adding age, smoking history, and high RAR to D-dimer improve the prediction of cancer incidence in patients with acute coronary syndrome?
A novel prediction model incorporating age, smoking, RAR, and D-dimer improves the prediction of incident cancer in patients following acute coronary syndrome.
Effect estimate: AUC 0.774 (95% CI 0.676-0.873)
Absolute Event Rate: 0.774% vs 0.619%
BACKGROUND: Cancer is a known prognostic factor in patients with acute coronary syndrome (ACS), but few risk assessments of cancer development after ACS have been established. METHODS AND RESULTS: Of the 573 consecutive ACS admissions between January 2015 and March 2018 in Nobeoka City, Japan, 552 were analyzed. Prevalent cancer was defined as a treatment history of cancer, and incident cancer as post-discharge cancer incidence. The primary endpoint was post-discharge cancer incidence, and the secondary endpoint was all-cause death during follow-up. All-cause death occurred in 9 (23.1%) patients with prevalent cancer, and in 17 (3.5%) without cancer. In the multivariable analysis, prevalent cancer was associated with all-cause death. To develop the prediction model for cancer incidence, 21 patients with incident cancer and 492 without cancer were analyzed. We compared the performance of D-dimer with that of the prediction model, which added age (≥65 years), smoking history, and high red blood cell distribution width to albumin ratio (RAR) to D-dimer. The areas under the receiver-operating characteristics curves of D-dimer and the prediction model were 0.619 (95% confidence interval: 0.512-0.725) and 0.774 (0.676-0.873), respectively. Decision curve analysis showed superior net benefits of the prediction model. CONCLUSIONS: By adding elderly, smoking, and high RAR to D-dimer to the prediction model it became clinically useful for predicting cancer incidence after ACS.
Ishii et al. (Mon,) conducted a cohort in Acute Coronary Syndrome (n=552). SARAD prediction model (age ≥65, smoking history, high RAR, high D-dimer) vs. D-dimer alone was evaluated on Post-discharge cancer incidence (model discrimination) (AUC 0.774, 95% CI 0.676-0.873). A novel risk prediction model combining age ≥65 years, smoking history, high red blood cell distribution width to albumin ratio, and D-dimer achieved an AUC of 0.774 for predicting cancer incidence after acute coronary syndrome.
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