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

Background

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).

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Methods

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).

Outcomes

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

  • In men, the model with 2013 PCE variables with additional adjustment for former smoking, pack-years and YSQ was the best fit to the data with the highest likelihood, R², and Harrel C statistic, moderate NRI(>0) of 0.23 and a statistically and clinically meaningful rIDI of 0.19.
  • When smoking status, pack-years smoked and YSQ were added to the model with 2013 PCE variables in women, the likelihood ratio test and change in Harrell C statistic were not significant, but R² increased, the NRI(>0) was moderate (0.34) and rIDI was statistically significant and clinically meaningful (0.11).
  • The change in predicted probability of 10-year ASCVD incidence with addition of all 3 smoking variables in women was less pronounced than in men.
  • Models that included age, smoking variables and their interactions resulted in the highest Harrell C statistic and Nagelkerke R² compared with models with age and lipids, blood pressure or diabetes alone.

Reclassification

  • 14% of men who experienced ASCVD event within 10 years were classified as higher risk in the final model compared with the PCE. 6.7% Were incorrectly reclassified in a lower-risk category. A similar trend was observed in women.
  • It was estimated that ~3 million individuals in the US with ≥20 pack-years smoked would be correctly reclassified using models that include details of smoking exposure compared with the 2013 PCE.

Conclusion

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.

References

1. Lloyd-Jones DM, Huffman MD, Karmali KN, et al. Estimating longitudinal risks and benefits from cardiovascular preventive therapies among medicare patients: the Million Hearts Longitudinal ASCVD Risk Assessment Tool: a special report from the American Heart Association and American College of Cardiology. Circulation. 2017;135(13):e793-e813. doi:10.1161/CIR.0000000000000467

2. American College of Cardiology. ASCVD risk estimator plus. Accessed March 10, 2020. https:// tools.acc.org/ASCVD-Risk-Estimator Plus/#!/calculate/estimate/

3. Goff DC Jr, Lloyd-Jones DM, Bennett G, et al. 2013 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guideline.J Am Coll Cardiol. 2014;63(25 pt B):2935-2959. doi:10.1016/j.jacc.2013.11.005

4. Duncan MS, Freiberg MS, Greevy RA Jr, Kundu S, Vasan RS, Tindle HA. Association of smoking cessation with subsequent risk of cardiovascular disease.JAMA. 2019;322(7):642-650. doi:10.1001/jama.2019.10298

Find this article online at JAMA Cardiol

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