A machine learning approach for the prediction of pulmonary hypertension

English version

Autoři: Andreas Leha ^aff001; Kristian Hellenkamp ^aff002; Bernhard Unsöld ^aff003; Sitali Mushemi-Blake ^aff004; Ajay M. Shah ^aff004; Gerd Hasenfuß ^aff002; Tim Seidler ^aff002
Působiště autorů: Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany ^aff001; Clinic for Cardiology and Pulmonology/Heart Center, University Medical Center Göttingen, Göttingen, Germany ^aff002; Department of Internal Medicine II, University of Regensburg, Regensburg, Germany ^aff003; King’s College London British Heart Foundation Centre, School of Cardiovascular Medicine & Sciences, London, England, United Kingdom ^aff004; DZHK (German Centre for Cardiovascular Research), Partner Site Göttingen, Göttingen, Germany ^aff005
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0224453

Souhrn

Background

Machine learning (ML) is a powerful tool for identifying and structuring several informative variables for predictive tasks. Here, we investigated how ML algorithms may assist in echocardiographic pulmonary hypertension (PH) prediction, where current guidelines recommend integrating several echocardiographic parameters.

Methods

In our database of 90 patients with invasively determined pulmonary artery pressure (PAP) with corresponding echocardiographic estimations of PAP obtained within 24 hours, we trained and applied five ML algorithms (random forest of classification trees, random forest of regression trees, lasso penalized logistic regression, boosted classification trees, support vector machines) using a 10 times 3-fold cross-validation (CV) scheme.

Results

ML algorithms achieved high prediction accuracies: support vector machines (AUC 0.83; 95% CI 0.73–0.93), boosted classification trees (AUC 0.80; 95% CI 0.68–0.92), lasso penalized logistic regression (AUC 0.78; 95% CI 0.67–0.89), random forest of classification trees (AUC 0.85; 95% CI 0.75–0.95), random forest of regression trees (AUC 0.87; 95% CI 0.78–0.96). In contrast to the best of several conventional formulae (by Aduen et al.), this ML algorithm is based on several echocardiographic signs and feature selection, with estimated right atrial pressure (RAP) being of minor importance.

Conclusions

Using ML, we were able to predict pulmonary hypertension based on a broader set of echocardiographic data with little reliance on estimated RAP compared to an existing formula with non-inferior performance. With the conceptual advantages of a broader and unbiased selection and weighting of data our ML approach is suited for high level assistance in PH prediction.

Klíčová slova:

Algorithms – Machine learning algorithms – Boosting algorithms – Machine learning – Echocardiography – Decision trees – Pulmonary hypertension

Zdroje

1. Bishop CM. Pattern recognition and machine learning. New York: Springer; 2006. xx, 738 p. p.

2. Tajik AJ. Machine Learning for Echocardiographic Imaging: Embarking on Another Incredible Journey. Journal of the American College of Cardiology. 2016 Nov 29;68(21):2296–8. doi: 10.1016/j.jacc.2016.09.915 27884248.

3. Sengupta PP, Huang YM, Bansal M, Ashrafi A, Fisher M, Shameer K, et al. Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy. Circulation Cardiovascular imaging. 2016 Jun;9(6). doi: 10.1161/CIRCIMAGING.115.004330 27266599. Pubmed Central PMCID: 5321667.

4. Johnson NP, Toth GG, Lai D, Zhu H, Acar G, Agostoni P, et al. Prognostic value of fractional flow reserve: linking physiologic severity to clinical outcomes. Journal of the American College of Cardiology. 2014 Oct 21;64(16):1641–54. doi: 10.1016/j.jacc.2014.07.973 25323250.

5. Henglin M, Stein G, Hushcha PV, Snoek J, Wiltschko AB, Cheng S. Machine Learning Approaches in Cardiovascular Imaging. Circulation Cardiovascular imaging. 2017 Oct;10(10). doi: 10.1161/CIRCIMAGING.117.005614 28956772. Pubmed Central PMCID: 5718356.

6. Mazzanti M, Shirka E, Gjergo H, Hasimi E. Imaging, Health Record, and Artificial Intelligence: Hype or Hope? Current cardiology reports. 2018 May 10;20(6):48. doi: 10.1007/s11886-018-0990-y 29749590.

7. Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial Intelligence in Precision Cardiovascular Medicine. Journal of the American College of Cardiology. 2017 May 30;69(21):2657–64. doi: 10.1016/j.jacc.2017.03.571 28545640.

8. Gandhi S, Mosleh W, Shen J, Chow CM. Automation, machine learning, and artificial intelligence in echocardiography: A brave new world. Echocardiography. 2018 Jul 5. doi: 10.1111/echo.14086 29974498.

9. Tsang W, Salgo IS, Medvedofsky D, Takeuchi M, Prater D, Weinert L, et al. Transthoracic 3D Echocardiographic Left Heart Chamber Quantification Using an Automated Adaptive Analytics Algorithm. JACC Cardiovascular imaging. 2016 Jul;9(7):769–82. doi: 10.1016/j.jcmg.2015.12.020 27318718.

10. Hellenkamp K, Unsold B, Mushemi-Blake S, Shah AM, Friede T, Hasenfuss G, et al. Echocardiographic Estimation of Mean Pulmonary Artery Pressure: A Comparison of Different Approaches to Assign the Likelihood of Pulmonary Hypertension. Journal of the American Society of Echocardiography: official publication of the American Society of Echocardiography. 2018 Jan;31(1):89–98. doi: 10.1016/j.echo.2017.09.009 29174340.

11. Rudski LG, Lai WW, Afilalo J, Hua L, Handschumacher MD, Chandrasekaran K, et al. Guidelines for the echocardiographic assessment of the right heart in adults: a report from the American Society of Echocardiography endorsed by the European Association of Echocardiography, a registered branch of the European Society of Cardiology, and the Canadian Society of Echocardiography. Journal of the American Society of Echocardiography: official publication of the American Society of Echocardiography. 2010 Jul;23(7):685–713; quiz 86–8. doi: 10.1016/j.echo.2010.05.010 20620859.

12. Galie N, Humbert M, Vachiery JL, Gibbs S, Lang I, Torbicki A, et al. 2015 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension: The Joint Task Force for the Diagnosis and Treatment of Pulmonary Hypertension of the European Society of Cardiology (ESC) and the European Respiratory Society (ERS): Endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC), International Society for Heart and Lung Transplantation (ISHLT). European heart journal. 2016 Jan 1;37(1):67–119. doi: 10.1093/eurheartj/ehv317 26320113.

13. Chang YC. Maximizing an ROC-type measure via linear combination of markers when the gold reference is continuous. Statistics in medicine. 2013 May 20;32(11):1893–903. doi: 10.1002/sim.5616 22972679.

14. Friedman JH, Hastie T, Tibshirani R. Regularization Paths for Generalized Linear Models via Coordinate Descent. 2010. 2010 2010-02-02;33(1):22. Epub 2010-02-02.

15. Quinlan JR. C4.5: programs for machine learning: Morgan Kaufmann Publishers Inc.; 1993. 302 p.

16. Breiman L. Random Forests. Machine Learning. 2001 October 01;45(1):5–32.

17. Pagès J. Analyse factorielle de données mixtes. Revue de Statistique Appliquée. 2004;52(4):93–111.

18. Audigier V, Fran, #231, Husson o, Josse J. A principal component method to impute missing values for mixed data. Adv Data Anal Classif. 2016;10(1):5–26.

19. LeDell E, Petersen M, van der Laan M. Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates. Electronic journal of statistics. 2015;9(1):1583–607. Pubmed Central PMCID: doi: 10.1214/15-EJS1035 26279737.

20. R Development Core Team. A Language and Environment for Statistical Computing Available online at http://www.R-project.org/2018.

21. Aduen JF, Castello R, Lozano MM, Hepler GN, Keller CA, Alvarez F, et al. An alternative echocardiographic method to estimate mean pulmonary artery pressure: diagnostic and clinical implications. Journal of the American Society of Echocardiography: official publication of the American Society of Echocardiography. 2009 Jul;22(7):814–9. doi: 10.1016/j.echo.2009.04.007 19505794.

22. Narula S, Shameer K, Salem Omar AM, Dudley JT, Sengupta PP. Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography. Journal of the American College of Cardiology. 2016 Nov 29;68(21):2287–95. doi: 10.1016/j.jacc.2016.08.062 27884247.

23. Chemla D, Castelain V, Humbert M, Hebert JL, Simonneau G, Lecarpentier Y, et al. New formula for predicting mean pulmonary artery pressure using systolic pulmonary artery pressure. Chest. 2004 Oct;126(4):1313–7. doi: 10.1378/chest.126.4.1313 15486398.

24. Friedberg MK, Feinstein JA, Rosenthal DN. A novel echocardiographic Doppler method for estimation of pulmonary arterial pressures. Journal of the American Society of Echocardiography: official publication of the American Society of Echocardiography. 2006 May;19(5):559–62. doi: 10.1016/j.echo.2005.12.020 16644441.

25. Syyed R, Reeves JT, Welsh D, Raeside D, Johnson MK, Peacock AJ. The relationship between the components of pulmonary artery pressure remains constant under all conditions in both health and disease. Chest. 2008 Mar;133(3):633–9. doi: 10.1378/chest.07-1367 17989160.

26. Dabestani A, Mahan G, Gardin JM, Takenaka K, Burn C, Allfie A, et al. Evaluation of pulmonary artery pressure and resistance by pulsed Doppler echocardiography. Am J Cardiol. 1987 Mar 1;59(6):662–8. doi: 10.1016/0002-9149(87)91189-1 3825910.

27. Granstam SO, Bjorklund E, Wikstrom G, Roos MW. Use of echocardiographic pulmonary acceleration time and estimated vascular resistance for the evaluation of possible pulmonary hypertension. Cardiovascular ultrasound. 2013;11:7. doi: 10.1186/1476-7120-11-7 23445525. Pubmed Central PMCID: 3600025.

28. Kitabatake A, Inoue M, Asao M, Masuyama T, Tanouchi J, Morita T, et al. Noninvasive evaluation of pulmonary hypertension by a pulsed Doppler technique. Circulation. 1983 Aug;68(2):302–9. doi: 10.1161/01.cir.68.2.302 6861308.

29. Tabassian M, Sunderji I, Erdei T, Sanchez-Martinez S, Degiovanni A, Marino P, et al. Diagnosis of Heart Failure With Preserved Ejection Fraction: Machine Learning of Spatiotemporal Variations in Left Ventricular Deformation. Journal of the American Society of Echocardiography: official publication of the American Society of Echocardiography. 2018 Aug 23. doi: 10.1016/j.echo.2018.07.013 30146187.

30. Sanchez-Martinez S, Duchateau N, Erdei T, Kunszt G, Aakhus S, Degiovanni A, et al. Machine Learning Analysis of Left Ventricular Function to Characterize Heart Failure With Preserved Ejection Fraction. Circulation Cardiovascular imaging. 2018 Apr;11(4):e007138. doi: 10.1161/CIRCIMAGING.117.007138 29661795.

31. Samad MD, Ulloa A, Wehner GJ, Jing L, Hartzel D, Good CW, et al. Predicting Survival From Large Echocardiography and Electronic Health Record Datasets: Optimization With Machine Learning. JACC Cardiovascular imaging. 2018 Jun 9. doi: 10.1016/j.jcmg.2018.04.026 29909114.

32. Goldstein BA, Navar AM, Carter RE. Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges. European heart journal. 2017 Jun 14;38(23):1805–14. doi: 10.1093/eurheartj/ehw302 27436868. Pubmed Central PMCID: 5837244.