A machine learning model for predicting congenital heart defects from administrative data.
congenital heart disease
machine learning
population health
Journal
Birth defects research
ISSN: 2472-1727
Titre abrégé: Birth Defects Res
Pays: United States
ID NLM: 101701004
Informations de publication
Date de publication:
01 11 2023
01 11 2023
Historique:
revised:
21
08
2023
received:
08
07
2023
accepted:
25
08
2023
medline:
3
11
2023
pubmed:
8
9
2023
entrez:
8
9
2023
Statut:
ppublish
Résumé
International Classification of Diseases (ICD) codes recorded in administrative data are often used to identify congenital heart defects (CHD). However, these codes may inaccurately identify true positive (TP) CHD individuals. CHD surveillance could be strengthened by accurate CHD identification in administrative records using machine learning (ML) algorithms. To identify features relevant to accurate CHD identification, traditional ML models were applied to a validated dataset of 779 patients; encounter level data, including ICD-9-CM and CPT codes, from 2011 to 2013 at four US sites were utilized. Five-fold cross-validation determined overlapping important features that best predicted TP CHD individuals. Median values and 95% confidence intervals (CIs) of area under the receiver operating curve, positive predictive value (PPV), negative predictive value, sensitivity, specificity, and F1-score were compared across four ML models: Logistic Regression, Gaussian Naive Bayes, Random Forest, and eXtreme Gradient Boosting (XGBoost). Baseline PPV was 76.5% from expert clinician validation of ICD-9-CM CHD-related codes. Feature selection for ML decreased 7138 features to 10 that best predicted TP CHD cases. During training and testing, XGBoost performed the best in median accuracy (F1-score) and PPV, 0.84 (95% CI: 0.76, 0.91) and 0.94 (95% CI: 0.91, 0.96), respectively. When applied to the entire dataset, XGBoost revealed a median PPV of 0.94 (95% CI: 0.94, 0.95). Applying ML algorithms improved the accuracy of identifying TP CHD cases in comparison to ICD codes alone. Use of this technique to identify CHD cases would improve generalizability of results obtained from large datasets to the CHD patient population, enhancing public health surveillance efforts.
Types de publication
Journal Article
Research Support, U.S. Gov't, P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
1693-1707Subventions
Organisme : CDC HHS
Pays : United States
Informations de copyright
© 2023 Wiley Periodicals LLC.
Références
Agarwal, S., Sud, K., & Menon, V. (2016). Nationwide hospitalization trends in adult congenital heart disease across 2003-2012. Journal of the American Heart Association, 5(1), e002330. https://doi.org/10.1161/JAHA.115.002330
Bhatt, A. B., Foster, E., Kuehl, K., Alpert, J., Brabeck, S., Crumb, S., Davidson, W. R., Earing, M. G., Ghoshhajra, B. B., Karamlou, T., Mital, S., Ting, J., & Tseng, Z. H. (2015). Congenital heart disease in the older adult: A scientific statement from the American Heart Association. Circulation, 131(21), 1884-1931. https://doi.org/10.1161/CIR.0000000000000204
Billett, J., Cowie, M. R., Gatzoulis, M. A., Vonder Muhll, I. F., & Majeed, A. (2008). Comorbidity, healthcare utilisation and process of care measures in patients with congenital heart disease in the UK: Cross-sectional, population-based study with case-control analysis. Heart, 94(9), 1194-1199. https://doi.org/10.1136/hrt.2007.122671
Botto, L. D., & Correa, A. (2003). Decreasing the burden of congenital heart anomalies: An epidemiologic evaluation of risk factors and survival. Progress in Pediatric Cardiology, 18(2), 111-121. https://doi.org/10.1016/S1058-9813(03)00084-5
Brida, M., & Gatzoulis, M. A. (2019). Adult congenital heart disease: Past, present and future. Acta Paediatrica, 108(10), 1757-1764. https://doi.org/10.1111/apa.14921
Broberg, C., McLarry, J., Mitchell, J., Winter, C., Doberne, J., Woods, P., Burchill, L., & Weiss, J. (2015). Accuracy of administrative data for detection and categorization of adult congenital heart disease patients from an electronic medical record. Pediatric Cardiology, 36(4), 719-725. https://doi.org/10.1007/s00246-014-1068-2
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 785-794). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939785
Dreiseitl, S., & Ohno-Machado, L. (2002). Logistic regression and artificial neural network classification models: A methodology review. Journal of Biomedical Informatics, 35(5-6), 352-359. https://doi.org/10.1016/s1532-0464(03)00034-0
Explaining machine learning models: A non-technical guide to interpreting SHAP analyses. (2021). Impromptu Engineer. Retrieved July 6, 2023, from https://www.aidancooper.co.uk/a-non-technical-guide-to-interpreting-shap-analyses/
Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting (with discussion and a rejoinder by the authors). The Annals of Statistics, 28(2), 337-407. https://doi.org/10.1214/aos/1016218223
Gilboa, S. M., Devine, O. J., Kucik, J. E., Oster, M. E., Riehle-Colarusso, T., Nembhard, W. N., Xu, P., Correa, A., Jenkins, K., & Marelli, A. J. (2016). Congenital heart defects in the United States: Estimating the magnitude of the affected population in 2010. Circulation, 134(2), 101-109. https://doi.org/10.1161/CIRCULATIONAHA.115.019307
Jabbar, H. K., & Khan, R. Z. (2014). Methods to avoid over-fitting and under-fitting in supervised machine learning (comparative study). Computer Science, Communication and Instrumentation Devices, 70(10.3850), 978-981. https://doi.org/10.3850/978-981-09-5247-1_017
Jahromi, A. H., & Taheri, M. (2017). A non-parametric mixture of Gaussian naive Bayes classifiers based on local independent features. Artificial Intelligence and Signal Processing Conference, 2017, 209-212. https://doi.org/10.1109/AISP.2017.8324083
Khan, A., Ramsey, K., Ballard, C., Armstrong, E., Burchill, L. J., Menashe, V., Pantely, G., & Broberg, C. S. (2018). Limited accuracy of administrative data for the identification and classification of adult congenital heart disease. Journal of the American Heart Association, 7(2), e007378. https://doi.org/10.1161/JAHA.117.007378
Liberman, R. F., Heinke, D., Lin, A. E., Nestoridi, E., Jalali, M., Markenson, G. R., Sekhavat, S., & Yazdy, M. M. (2023). Trends in delayed diagnosis of critical congenital heart defects in an era of enhanced screening, 2004-2018. The Journal of Pediatrics, 257, 113366. https://doi.org/10.1016/j.jpeds.2023.02.012
Logistic Regression Analysis-An overview | ScienceDirect Topics. (2020). Elsevier B. V. Retrieved September 24, 2022, from https://www.sciencedirect.com/topics/medicine-and-dentistry/logistic-regression-analysis
Lundberg, S. (2020). October 6, Interpretable Machine Learning with XGBoost. Medium. https://towardsdatascience.com/interpretable-machine-learning-with-xgboost-9ec80d148d27
Lundberg, S., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. arXiv, 30, 4768-4777. https://doi.org/10.48550/arXiv.1705.07874
Luo, Y., Li, Z., Guo, H., Cao, H., Song, C., Guo, X., & Zhang, Y. (2017). Predicting congenital heart defects: A comparison of three data mining methods. PLoS One, 12(5), e0177811. https://doi.org/10.1371/journal.pone.0177811
Marelli, A. J., Ionescu-Ittu, R., Mackie, A. S., Guo, L., Dendukuri, N., & Kaouache, M. (2014). Lifetime prevalence of congenital heart disease in the general population from 2000 to 2010. Circulation, 130(9), 749-756. https://doi.org/10.1161/CIRCULATIONAHA.113.008396
Massin, M. M., & Dessy, H. (2006). Delayed recognition of congenital heart disease. Postgraduate Medical Journal, 82(969), 468-470. https://doi.org/10.1136/pgmj.2005.044495
Müller, M. J., Norozi, K., Caroline, J., Sedlak, N., Bock, J., Paul, T., Geyer, S., & Dellas, C. (2022). Morbidity and mortality in adults with congenital heart defects in the third and fourth life decade. Clinical Research in Cardiology, 111(8), 900-911. https://doi.org/10.1007/s00392-022-01989-1
Mylotte, D., Pilote, L., Ionescu-Ittu, R., Abrahamowicz, M., Khairy, P., Therrien, J., Mackie, A. S., & Marelli, A. (2014). Specialized adult congenital heart disease care: The impact of policy on mortality. Circulation, 129(18), 1804-1812. https://doi.org/10.1161/CIRCULATIONAHA.113.005817
Nguyen, C., Wang, Y., & Nguyen, H. N. (2013). Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic. Journal of Biomedical Science and Engineering, 6(5), 551-560. https://doi.org/10.4236/jbise.2013.65070
Oster, M. E., Lee, K. A., Honein, M. A., Riehle-Colarusso, T., Shin, M., & Correa, A. (2013). Temporal trends in survival among infants with critical congenital heart defects. Pediatrics, 131(5), e1502-e1508. https://doi.org/10.1542/peds.2012-3435
Rani, S., & Masood, S. (2020). Predicting congenital heart disease using machine learning techniques. Journal of Discrete Mathematical Sciences and Cryptography, 23(1), 293-303. https://doi.org/10.1080/09720529.2020.1721862
Rodriguez, F. H., Ephrem, G., Gerardin, J. F., Raskind-Hood, C., Hogue, C., & Book, W. (2018). The 745.5 issue in code-based, adult congenital heart disease population studies: Relevance to current and future ICD-9-CM and ICD-10-CM studies. Congenital Heart Disease, 13(1), 59-64. https://doi.org/10.1111/chd.12563
Rodriguez, F. H., Raskind-Hood, C. L., Hoffman, T., Farr, S. L., Glidewell, J., Li, J. S., D'Ottavio, A., Botto, L., Reeder, M. R., Hsu, D., Lui, G. K., Sullivan, A. M., & Book, W. M. (2022). How well do ICD-9-CM codes predict true congenital heart defects? A Centers for Disease Control and Prevention-based multisite validation project. Journal of the American Heart Association, 11(15), e024911. https://doi.org/10.1161/JAHA.121.024911
Santur, Y., Karaköse, M., & Akin, E. (2016). Random forest based diagnosis approach for rail fault inspection in railways. In 2016 National Conference on Electrical, Electronics and Biomedical Engineering (ELECO). Institute of Electrical and Electronics Engineers (IEEE). (pp. 745-750).
Stout, K. K., Daniels, C. J., Aboulhosn, J. A., Bozkurt, B., Broberg, C. S., Colman, J. M., Crumb, S. R., Dearani, J. A., Fuller, S., Gurvitz, M., Khairy, P., Landzberg, M. J., Saidi, A., Valente, A. M., & Van Hare, G. F. (2019). 2018 AHA/ACC guideline for the management of adults with congenital heart disease: Executive summary: A report of the American College of Cardiology/American Heart Association task force on clinical practice guidelines. Circulation, 139(14), e637-e697. https://doi.org/10.1161/CIR.0000000000000602
Udholm, S., Rex, C., Eckerström, F., Onat, M., Nyboe, C., & Hjortdal, V. E. (2019). Small unrepaired atrial septal defects display impaired exercise capacity compared with healthy peers. Congenital Heart Disease, 14(3), 372-379. https://doi.org/10.1111/chd.12740
Wallace, F., Buratto, E., Schulz, A., d'Udekem, Y., Weintraub, R. G., Brizard, C. P., & Konstantinov, I. E. (2022). Long-term outcomes of primary aortic valve repair for isolated congenital aortic stenosis in children. The Journal of Thoracic and Cardiovascular Surgery, 164(5), 1263-1274.e1. https://doi.org/10.1016/j.jtcvs.2021.11.097
Warnes, C. A., Liberthson, R., Danielson, G. K., Dore, A., Harris, L., Hoffman, J. I., Somerville, J., Williams, R. G., & Webb, G. D. (2001). Task force 1: The changing profile of congenital heart disease in adult life. Journal of the American College of Cardiology, 37(5), 1170-1175. https://doi.org/10.1016/s0735-1097(01)01272-4
Yang, Q., Rasmussen, S. A., & Friedman, J. M. (2002). Mortality associated with Down's syndrome in the USA from 1983 to 1997: A population-based study. Lancet, 359(9311), 1019-1025. https://doi.org/10.1016/s0140-6736(02)08092-3
Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2017). Understanding deep learning requires rethinking generalization. arXiv, 64(3), 107-115. https://doi.org/10.48550/arXiv.1611.03530