Can Spirometry Improve the Performance of Cardiovascular Risk Model in High-risk Eastern European Countries?

Overview

Journal Front Cardiovasc Med

Specialty Cardiology & Vascular Diseases

Date 2023 Sep 15

PMID 37711557

Authors

Tatyana Sarycheva

Nadezda capkova

Andrzej Pajak

Abdonas Tamosiunas

Martin Bobak

Hynek Pikhart

Affiliations

Soon will be listed here.

Abstract

Aims: Impaired lung function has been strongly associated with cardiovascular disease (CVD) events. We aimed to assess the additive prognostic value of spirometry indices to the risk estimation of CVD events in Eastern European populations in this study.

Methods: We randomly selected 14,061 individuals with a mean age of 59 ± 7.3 years without a previous history of cardiovascular and pulmonary diseases from population registers in the Czechia, Poland, and Lithuania. Predictive values of standardised -scores of forced expiratory volume measured in 1 s (FEV1), forced vital capacity (FVC), and FEV1 divided by height cubed (FEV1/ht) were tested. Cox proportional hazards models were used to estimate hazard ratios (HRs) of CVD events of various spirometry indices over the Framingham Risk Score (FRS) model. The model performance was evaluated using Harrell's C-statistics, likelihood ratio tests, and Bayesian information criterion.

Results: All spirometry indices had a strong linear relation with the incidence of CVD events (HR ranged from 1.10 to 1.12 between indices). The model stratified by FEV1/ht tertiles had a stronger link with CVD events than FEV1 and FVC. The risk of CVD event for the lowest vs. highest FEV1/ht tertile among people with low FRS was higher (HR: 2.35; 95% confidence interval: 1.96-2.81) than among those with high FRS. The addition of spirometry indices showed a small but statistically significant improvement of the FRS model.

Conclusions: The addition of spirometry indices might improve the prediction of incident CVD events particularly in the low-risk group. FEV1/ht is a more sensitive predictor compared to other spirometry indices.

Citing Articles

Pulmonary function and atherosclerosis in the general population: causal associations and clinical implications.

Engstrom G, Lampa E, Dekkers K, Lin Y, Ahlm K, Ahlstrom H Eur J Epidemiol. 2024; 39(1):35-49.

PMID: 38165527 PMC: 10811042. DOI: 10.1007/s10654-023-01088-z.

References

Powles J, Zatonski W, Vander Hoorn S, Ezzati M . The contribution of leading diseases and risk factors to excess losses of healthy life in Eastern Europe: burden of disease study. BMC Public Health. 2005; 5:116. PMC: 1298310. DOI: 10.1186/1471-2458-5-116. View

Movsisyan N, Vinciguerra M, Medina-Inojosa J, Lopez-Jimenez F . Cardiovascular Diseases in Central and Eastern Europe: A Call for More Surveillance and Evidence-Based Health Promotion. Ann Glob Health. 2020; 86(1):21. PMC: 7059421. DOI: 10.5334/aogh.2713. View

Damen J, Hooft L, Schuit E, Debray T, Collins G, Tzoulaki I . Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ. 2016; 353:i2416. PMC: 4868251. DOI: 10.1136/bmj.i2416. View

Steyerberg E, Vergouwe Y . Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Heart J. 2014; 35(29):1925-31. PMC: 4155437. DOI: 10.1093/eurheartj/ehu207. View

Vikhireva O, Pajak A, Broda G, Malyutina S, Tamosiunas A, Kubinova R . SCORE performance in Central and Eastern Europe and former Soviet Union: MONICA and HAPIEE results. Eur Heart J. 2013; 35(9):571-7. PMC: 3938861. DOI: 10.1093/eurheartj/eht189. View

Lee H, Le H, Lee B, Lopez V, Wong N . Forced vital capacity paired with Framingham Risk Score for prediction of all-cause mortality. Eur Respir J. 2010; 36(5):1002-6. DOI: 10.1183/09031936.00042410. View

Conroy R, Pyorala K, Fitzgerald A, Sans S, Menotti A, De Backer G . Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003; 24(11):987-1003. DOI: 10.1016/s0195-668x(03)00114-3. View

Chinn S, Gislason T, Aspelund T, Gudnason V . Optimum expression of adult lung function based on all-cause mortality: results from the Reykjavik study. Respir Med. 2006; 101(3):601-9. DOI: 10.1016/j.rmed.2006.06.009. View

Kuk D, Varadhan R . Model selection in competing risks regression. Stat Med. 2013; 32(18):3077-88. DOI: 10.1002/sim.5762. View

10.

Timmis A, Vardas P, Townsend N, Torbica A, Katus H, De Smedt D . European Society of Cardiology: cardiovascular disease statistics 2021. Eur Heart J. 2022; 43(8):716-799. DOI: 10.1093/eurheartj/ehab892. View

11.

DAgostino Sr R, Vasan R, Pencina M, Wolf P, Cobain M, Massaro J . General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008; 117(6):743-53. DOI: 10.1161/CIRCULATIONAHA.107.699579. View

12.

Gupta R, Strachan D . Ventilatory function as a predictor of mortality in lifelong non-smokers: evidence from large British cohort studies. BMJ Open. 2017; 7(7):e015381. PMC: 5734388. DOI: 10.1136/bmjopen-2016-015381. View

13.

. SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. Eur Heart J. 2021; 42(25):2439-2454. PMC: 8248998. DOI: 10.1093/eurheartj/ehab309. View

14.

Miller M, Pedersen O . New concepts for expressing forced expiratory volume in 1 s arising from survival analysis. Eur Respir J. 2009; 35(4):873-82. DOI: 10.1183/09031936.00025809. View

15.

Boudik F, Reissigova J, Hrach K, Tomeckova M, Bultas J, Anger Z . Primary prevention of coronary artery disease among middle aged men in Prague: twenty-year follow-up results. Atherosclerosis. 2005; 184(1):86-93. DOI: 10.1016/j.atherosclerosis.2005.02.011. View

16.

Royston P, Altman D . External validation of a Cox prognostic model: principles and methods. BMC Med Res Methodol. 2013; 13:33. PMC: 3667097. DOI: 10.1186/1471-2288-13-33. View

17.

Cook N, Ridker P . Calibration of the Pooled Cohort Equations for Atherosclerotic Cardiovascular Disease: An Update. Ann Intern Med. 2016; 165(11):786-794. DOI: 10.7326/M16-1739. View

18.

Piko P, Kosa Z, Sandor J, Adany R . Comparative risk assessment for the development of cardiovascular diseases in the Hungarian general and Roma population. Sci Rep. 2021; 11(1):3085. PMC: 7862257. DOI: 10.1038/s41598-021-82689-0. View

19.

Pate A, Emsley R, Ashcroft D, Brown B, van Staa T . The uncertainty with using risk prediction models for individual decision making: an exemplar cohort study examining the prediction of cardiovascular disease in English primary care. BMC Med. 2019; 17(1):134. PMC: 6636064. DOI: 10.1186/s12916-019-1368-8. View

20.

Syriopoulou E, Mozumder S, Rutherford M, Lambert P . Robustness of individual and marginal model-based estimates: A sensitivity analysis of flexible parametric models. Cancer Epidemiol. 2018; 58:17-24. PMC: 6363964. DOI: 10.1016/j.canep.2018.10.017. View