Physicians' Academy for Cardiovascular Education

Inclusion of three smoking variables improves ASCVD risk estimation

Inclusion of Smoking Data in Cardiovascular Disease Risk Estimation

Literature - Duncan MS, Greevy RA, Tindle HA et al., - JAMA Cardiol. 2022;7(2):195-203. doi:10.1001/jamacardio.2021.4990

Introduction and methods


The ASCVD Risk Estimator Plus is the current criterion standard risk assessment tool. It is based on sex- and race-specific pooled cohort equations (2013 PCE) for estimation of 10-years ASCVD risk [1-3] described in the ACC/AHA Guideline on the Assessment of CV risk.

Former smokers for the first 5 years after cessation are considered at excess ASCVD risk and pack-years smoked are not included in the 2013 PCE. However, former heavy smokers (≥20 pack-years) can have an excess ASCVD risk for up to 16 years after stopping [4]. These findings suggest that years since quitting (YSQ) and pack-years smoked may play an important role in ASCVD risk estimation.

Predictive utility of adding former smoking status, pack-years smoked and YSQ to the 2013 PCE was evaluated for ASCVD risk prediction using data of the Framingham Heart Study (FHS).



Data of the FHS offspring cohort were used of participants who had their first examination cycle from 1971-1975. Participants underwent quadrennial examinations cycles. First, participants at baseline with a history of MI, ischemic stroke, HF, CABG, PCI or AF or missing data on smoking history were excluded. Second, person examinations were excluded for reasons of: age younger than 40 or older than 79 years, history of MI, IS, HF, CABG, PCI or AF; missing data on predictors. The final sample consisted of 18,400 person examinations of 3908 individuals.

Smoking measures included 3-level (current/former/never) smoking status, pack-years and YSQ.

Goodness of fit, incremental value and clinical utility of the 3 smoking variables were assessed. Goodness of fit was evaluated via likelihood ratio and Nagelkerke R². Incremental value was determined via change in Harrell C statistic and continuous net reclassification improvement (NRI>0). Clinical utility of a variable refers to its ability to sufficiently move an individual across the risk spectrum when added to a model; this was quantified using the relative integrated discrimination improvement (rIDI).


Follow-up of FHS participants for this investigation was until December 31, 2016. Outcome was ASCVD events, including MI, fatal or nonfatal IS and coronary heart disease death.

Main results

Model fitting in men and women



Including former smoking status, pack-years and YSQ to the 2013 pooled cohort equations improved ASCVD risk prediction compared to the reference model with 2013 PCE variables, in participants in the FHS offspring cohort. Adding these 3 smoking variables produced moderate NRI(>0) values and clinically meaningful rIDI values. Smoking variables resulted in better discrimination of ASCVD risk than the variables lipids, blood pressure or diabetes. Moreover, inclusion of these smoking variables could reclassify the ASCVD risk of ~3 million US individuals with ≥20 pack-years smoked.


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Find this article online at JAMA Cardiol

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