Deep Learning for Improved Risk Prediction in Surgical Outcomes

Abstract The Norwood surgical procedure restores functional systemic circulation in neonatal patients with single ventricle congenital heart defects, but this complex procedure carries a high mortality rate. In this study we address the need to provide an accurate patient specific risk prediction fo...

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Autores principales:	Ali Jalali, Hannah Lonsdale, Nhue Do, Jacquelin Peck, Monesha Gupta, Shelby Kutty, Sharon R. Ghazarian, Jeffrey P. Jacobs, Mohamed Rehman, Luis M. Ahumada
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0c7a8961e216439b9698af470ff81f1b
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spelling	oai:doaj.org-article:0c7a8961e216439b9698af470ff81f1b2021-12-02T17:52:43ZDeep Learning for Improved Risk Prediction in Surgical Outcomes10.1038/s41598-020-62971-32045-2322https://doaj.org/article/0c7a8961e216439b9698af470ff81f1b2020-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-62971-3https://doaj.org/toc/2045-2322Abstract The Norwood surgical procedure restores functional systemic circulation in neonatal patients with single ventricle congenital heart defects, but this complex procedure carries a high mortality rate. In this study we address the need to provide an accurate patient specific risk prediction for one-year postoperative mortality or cardiac transplantation and prolonged length of hospital stay with the purpose of assisting clinicians and patients’ families in the preoperative decision making process. Currently available risk prediction models either do not provide patient specific risk factors or only predict in-hospital mortality rates. We apply machine learning models to predict and calculate individual patient risk for mortality and prolonged length of stay using the Pediatric Heart Network Single Ventricle Reconstruction trial dataset. We applied a Markov Chain Monte-Carlo simulation method to impute missing data and then fed the selected variables to multiple machine learning models. The individual risk of mortality or cardiac transplantation calculation produced by our deep neural network model demonstrated 89 ± 4% accuracy and 0.95 ± 0.02 area under the receiver operating characteristic curve (AUROC). The C-statistics results for prediction of prolonged length of stay were 85 ± 3% accuracy and AUROC 0.94 ± 0.04. These predictive models and calculator may help to inform clinical and organizational decision making.Ali JalaliHannah LonsdaleNhue DoJacquelin PeckMonesha GuptaShelby KuttySharon R. GhazarianJeffrey P. JacobsMohamed RehmanLuis M. AhumadaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-13 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ali Jalali Hannah Lonsdale Nhue Do Jacquelin Peck Monesha Gupta Shelby Kutty Sharon R. Ghazarian Jeffrey P. Jacobs Mohamed Rehman Luis M. Ahumada Deep Learning for Improved Risk Prediction in Surgical Outcomes
description	Abstract The Norwood surgical procedure restores functional systemic circulation in neonatal patients with single ventricle congenital heart defects, but this complex procedure carries a high mortality rate. In this study we address the need to provide an accurate patient specific risk prediction for one-year postoperative mortality or cardiac transplantation and prolonged length of hospital stay with the purpose of assisting clinicians and patients’ families in the preoperative decision making process. Currently available risk prediction models either do not provide patient specific risk factors or only predict in-hospital mortality rates. We apply machine learning models to predict and calculate individual patient risk for mortality and prolonged length of stay using the Pediatric Heart Network Single Ventricle Reconstruction trial dataset. We applied a Markov Chain Monte-Carlo simulation method to impute missing data and then fed the selected variables to multiple machine learning models. The individual risk of mortality or cardiac transplantation calculation produced by our deep neural network model demonstrated 89 ± 4% accuracy and 0.95 ± 0.02 area under the receiver operating characteristic curve (AUROC). The C-statistics results for prediction of prolonged length of stay were 85 ± 3% accuracy and AUROC 0.94 ± 0.04. These predictive models and calculator may help to inform clinical and organizational decision making.
format	article
author	Ali Jalali Hannah Lonsdale Nhue Do Jacquelin Peck Monesha Gupta Shelby Kutty Sharon R. Ghazarian Jeffrey P. Jacobs Mohamed Rehman Luis M. Ahumada
author_facet	Ali Jalali Hannah Lonsdale Nhue Do Jacquelin Peck Monesha Gupta Shelby Kutty Sharon R. Ghazarian Jeffrey P. Jacobs Mohamed Rehman Luis M. Ahumada
author_sort	Ali Jalali
title	Deep Learning for Improved Risk Prediction in Surgical Outcomes
title_short	Deep Learning for Improved Risk Prediction in Surgical Outcomes
title_full	Deep Learning for Improved Risk Prediction in Surgical Outcomes
title_fullStr	Deep Learning for Improved Risk Prediction in Surgical Outcomes
title_full_unstemmed	Deep Learning for Improved Risk Prediction in Surgical Outcomes
title_sort	deep learning for improved risk prediction in surgical outcomes
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/0c7a8961e216439b9698af470ff81f1b
work_keys_str_mv	AT alijalali deeplearningforimprovedriskpredictioninsurgicaloutcomes AT hannahlonsdale deeplearningforimprovedriskpredictioninsurgicaloutcomes AT nhuedo deeplearningforimprovedriskpredictioninsurgicaloutcomes AT jacquelinpeck deeplearningforimprovedriskpredictioninsurgicaloutcomes AT moneshagupta deeplearningforimprovedriskpredictioninsurgicaloutcomes AT shelbykutty deeplearningforimprovedriskpredictioninsurgicaloutcomes AT sharonrghazarian deeplearningforimprovedriskpredictioninsurgicaloutcomes AT jeffreypjacobs deeplearningforimprovedriskpredictioninsurgicaloutcomes AT mohamedrehman deeplearningforimprovedriskpredictioninsurgicaloutcomes AT luismahumada deeplearningforimprovedriskpredictioninsurgicaloutcomes
_version_	1718379152133849088

Deep Learning for Improved Risk Prediction in Surgical Outcomes

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