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Machine Learning to Predict Outcomes of Fetal Cardiac Disease: A Pilot Study

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Abstract

Prediction of outcomes following a prenatal diagnosis of congenital heart disease (CHD) is challenging. Machine learning (ML) algorithms may be used to reduce clinical uncertainty and improve prognostic accuracy. We performed a pilot study to train ML algorithms to predict postnatal outcomes based on clinical data. Specific objectives were to predict (1) in utero or neonatal death, (2) high-acuity neonatal care and (3) favorable outcomes. We included all fetuses with cardiac disease at Sunnybrook Health Sciences Centre, Toronto, Canada, from 2012 to 2021. Prediction models were created using the XgBoost algorithm (tree-based) with fivefold cross-validation. Among 211 cases of fetal cardiac disease, 61 were excluded (39 terminations, 21 lost to follow-up, 1 isolated arrhythmia), leaving a cohort of 150 fetuses. Fifteen (10%) demised (10 neonates) and 65 (48%) of live births required high acuity neonatal care. Of those with clinical follow-up, 60/87 (69%) had a favorable outcome. Prediction models for fetal or neonatal death, high acuity neonatal care and favorable outcome had AUCs of 0.76, 0.84 and 0.73, respectively. The most important predictors for death were the presence of non-cardiac abnormalities combined with more severe CHD. High acuity of postnatal care was predicted by anti Ro antibody and more severe CHD. Favorable outcome was most predicted by no right heart disease combined with genetic abnormalities, and maternal medications. Prediction models using ML provide good discrimination of key prenatal and postnatal outcomes among fetuses with congenital heart disease.

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Abbreviations

CHD:

Congenital heart disease

ML:

Machine learning

CV:

Cross-validation

References

  1. Oberije C et al (2014) A prospective study comparing the predictions of doctors versus models for treatment outcome of lung cancer patients: a step toward individualized care and shared decision making. Radiother Oncol 112(1):37–43

    Article  PubMed  PubMed Central  Google Scholar 

  2. Hatch S (2017) Uncertainty in medicine. BMJ 357:j2180

    Article  PubMed  Google Scholar 

  3. Donofrio MT et al (2014) Diagnosis and treatment of fetal cardiac disease: a scientific statement from the American Heart Association. Circulation 129(21):2183–2242

    Article  PubMed  Google Scholar 

  4. Pinto NM et al (2020) Prenatal cardiac care: goals, priorities & gaps in knowledge in fetal cardiovascular disease: perspectives of the Fetal Heart Society. Prog Pediatr Cardiol 59:101312

    Article  PubMed  PubMed Central  Google Scholar 

  5. Carvalho JS et al (2004) Clinical impact of first and early second trimester fetal echocardiography on high risk pregnancies. Heart 90(8):921–926

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Yu D, Sui L, Zhang N (2020) Performance of first-trimester fetal echocardiography in diagnosing fetal heart defects: meta-analysis and systematic review. J Ultrasound Med 39(3):471–480

    Article  PubMed  Google Scholar 

  7. Morgan CT et al (2019) Improving prenatal diagnosis of coarctation of the aorta. Can J Cardiol 35(4):453–461

    Article  PubMed  Google Scholar 

  8. Freud LR et al (2014) Fetal aortic valvuloplasty for evolving hypoplastic left heart syndrome: postnatal outcomes of the first 100 patients. Circulation 130(8):638–645

    Article  PubMed  PubMed Central  Google Scholar 

  9. MacColl CE et al (2012) Risk factors associated with in utero demise in fetuses with congenital heart disease: a case/control study. Cardiol Young 22:S74

    Google Scholar 

  10. Allan LD, Apfel HD, Printz BF (1998) Outcome after prenatal diagnosis of the hypoplastic left heart syndrome. Heart 79(4):371–373

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Sharland GK, Chan KY, Allan LD (1994) Coarctation of the aorta: difficulties in prenatal diagnosis. Br Heart J 71(1):70–75

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Weber RW et al (2013) Foetal echocardiographic assessment of borderline small left ventricles can predict the need for postnatal intervention. Cardiol Young 23(1):99–107

    Article  PubMed  Google Scholar 

  13. O’Brien SM et al (2009) An empirically based tool for analyzing mortality associated with congenital heart surgery. J Thorac Cardiovasc Surg 138(5):1139–1153

    Article  PubMed  Google Scholar 

  14. Brown DW et al (2011) Prenatal diagnosis of congenital heart disease and association with morbidity among survivors of initial palliation for single ventricle heart disease: analysis of the national pediatric cardiology quality improvement collaborative database. Circulation. https://doi.org/10.1161/circ.124.suppl_21.A16774

    Article  PubMed  Google Scholar 

  15. Bernard O et al (2018) Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans Med Imaging 37(11):2514–2525

    Article  PubMed  Google Scholar 

  16. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  CAS  PubMed  Google Scholar 

  17. Ouyang D et al (2020) Video-based AI for beat-to-beat assessment of cardiac function. Nature 580(7802):252–256

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Arnaout R et al (2021) An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease. Nat Med 27(5):882–891

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Hoffman JI, Kaplan S (2002) The incidence of congenital heart disease. J Am Coll Cardiol 39(12):1890–1900

    Article  PubMed  Google Scholar 

  20. Fouad KM et al (2021) Advanced methods for missing values imputation based on similarity learning. PeerJ Comput Sci 7:e619

    Article  PubMed  PubMed Central  Google Scholar 

  21. Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. Association for Computing Machinery, San Francisco p, pp 785–794

    Chapter  Google Scholar 

  22. Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13:281–305

    Google Scholar 

  23. Snoek J, Larochelle H, Adams RP (2012) Practical Bayesian optimization of machine learning algorithms. Adv Neural Info Process Syst 2012:2951–2959

    Google Scholar 

  24. Lundberg SM et al (2020) From local explanations to global understanding with explainable AI for trees. Nat Mach Intell 2(1):56–67

    Article  PubMed  PubMed Central  Google Scholar 

  25. Athalye C, Arnaout R (2023) Domain-guided data augmentation for deep learning on medical imaging. PLoS ONE 18(3):e0282532

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Athalye C et al (2023) Deep learning model for prenatal congenital heart disease (CHD) screening can be applied to retrospective imaging from the community setting, outperforming initial clinical detection in a well-annotated cohort. Ultrasound Obstet Gynecol 2023:10

    Google Scholar 

  27. Truong VT et al (2022) Application of machine learning in screening for congenital heart diseases using fetal echocardiography. Int J Cardiovasc Imaging 38:1007

    Article  Google Scholar 

  28. Gardiner HM (2013) First-trimester fetal echocardiography: routine practice or research tool? Ultrasound Obstet Gynecol 42(6):611–612

    Article  CAS  PubMed  Google Scholar 

  29. Zidere V et al (2013) Comparison of echocardiographic findings in fetuses at less than 15 weeks’ gestation with later cardiac evaluation. Ultrasound Obstet Gynecol 42(6):679–686

    Article  CAS  PubMed  Google Scholar 

  30. MacColl CE et al (2014) Factors associated with in utero demise of fetuses that have underlying cardiac pathologies. Pediatr Cardiol 35(8):1403–1414

    Article  PubMed  Google Scholar 

  31. Jepson BM et al (2023) Pregnancy loss in major fetal congenital heart disease: incidence, risk factors and timing. Ultrasound Obstet Gynecol 62(1):75–87

    Article  CAS  PubMed  Google Scholar 

  32. Rudolph AM (2018) Circulatory changes during gestational development of the sheep and human fetus. Pediatr Res 84(3):348–351

    Article  PubMed  Google Scholar 

  33. Rudolph AM, Heymann MA (1968) The fetal circulation. Annu Rev Med 19:195–206

    Article  CAS  PubMed  Google Scholar 

  34. Sun L et al (2020) Understanding fetal hemodynamics using cardiovascular magnetic resonance imaging. Fetal Diagn Ther 47(5):354–362

    Article  PubMed  Google Scholar 

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Funding

This research study was not funded.

Author information

Authors and Affiliations

  1. Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada

    L. E. Nield, N. Melamed, O. Nevo, D. Weisz & S. Ronzoni

  2. Department of Pediatrics, Blalock-Taussig-Thomas Congenital Heart Center, Johns Hopkins University, Baltimore, MD, USA

    C. Manlhiot, B. Chinni & A. Ims

  3. University of Toronto, Toronto, Canada

    K. Magor

  4. The Hospital for Sick Children, Toronto, Canada

    L. Freud

  5. Department of Obstetrics and Gynaecology, Mount Sinai Hospital Toronto, University of Toronto, Toronto, Canada

    T. Van Mieghem

Authors
  1. L. E. Nield
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  2. C. Manlhiot
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  3. K. Magor
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  4. L. Freud
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  6. A. Ims
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  7. N. Melamed
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  8. O. Nevo
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  10. D. Weisz
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Contributions

LN, SR and CM wrote the main manuscript text. CM, BC and AI performed the ML analysis and prepared the Tables and Figures. KM performed the data collection with the aid of LF, TM, ON, NM, DW helped write the manuscript and were instrumental in the original REB study proposal. All authors have reviewed and approved the manuscript.

Corresponding author

Correspondence to L. E. Nield.

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Competing interests

The authors declare no competing interests.

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Appendix A

Appendix A

Hoffman Criteria for defining severity of congenital heart disease [19].

Severe Congenital Heart Disease

  1. A.

    All those with cyanotic heart disease

    1. a.

      d-transposition of the great arteries

    2. b.

      Tetralogy of Fallot, including pulmonary atresia and absent pulmonary valve

    3. c.

      Hypoplastic right heart

      1. i.

        Tricuspid atresia

      2. ii.

        Pulmonary atresia with an intact ventricular septum

      3. iii.

        Ebstein anomaly

    4. d.

      Hypoplastic left heart

      1. i.

        Aortic atresia

      2. ii.

        Mitral atresia

    5. e.

      SV (single ventricle)

    6. f.

      DORV (double outlet right ventricle)

    7. g.

      Truncus arteriosus

    8. h.

      Total anomalous pulmonary venous connection

    9. i.

      Critical pulmonary stenosis

    10. j.

      Miscellaneous uncommon lesions such as double outlet left ventricle, malpositions and some forms of l-transposition of the great arteries (congenitally corrected transposition)

  2. B.

    Acyanotic lesions

    1. a.

      Atrioventricular septal defect (AVSD)

    2. b.

      Large ventricular septal defect (VSD)

    3. c.

      Large patent ductus arteriosus (PDA)

    4. d.

      Critical or severe aortic stenosis

    5. e.

      Severe pulmonary stenosis

    6. f.

      Critical coarctation of the aorta

Moderate Congenital Heart Disease

  1. A.

    Mild or moderate aortic stenosis or aortic incompetence

  2. B.

    Moderate pulmonary stenosis or incompetence

  3. C.

    Non critical coarctation of the aorta

  4. D.

    Large ASD

  5. E.

    Complex forms of VSD

Mild Congenital Heart Disease

  1. A.

    Small VSD

  2. B.

    Small PDA

  3. C.

    Mild pulmonary stenosis

  4. D.

    Bicuspid aortic valve without aortic stenosis or incompetence

  5. E.

    Small or spontaneously closed ASD

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Nield, L.E., Manlhiot, C., Magor, K. et al. Machine Learning to Predict Outcomes of Fetal Cardiac Disease: A Pilot Study. Pediatr Cardiol (2024). https://doi.org/10.1007/s00246-024-03512-x

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