Quantification of Early Neonatal Oxygen Exposure as a Risk Factor for Retinopathy of Prematurity Requiring Treatment

Purpose: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness related to oxygen exposure in premature infants. Since oxygen monitoring protocols have reduced the incidence of treatment-requiring ROP (TR-ROP), it remains unclear whether oxygen exposure remains a relevant risk fa...

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Autores principales:	Jimmy S. Chen, MD, Jamie E. Anderson, BS, Aaron S. Coyner, PhD, Susan Ostmo, MS, Kemal Sonmez, PhD, Deniz Erdogmus, PhD, Brian K. Jordan, MD, PhD, Cynthia T. McEvoy, MD, MCR, Dmitry Dukhovny, MD, MPH, Robert L. Schelonka, MD, R.V. Paul Chan, MD, Praveer Singh, PhD, Jayashree Kalpathy-Cramer, PhD, Michael F. Chiang, MD, MA, J. Peter Campbell, MD, MPH
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Electronic health records Machine learning Oxygen exposure Retinopathy of prematurity Ophthalmology RE1-994
Acceso en línea:	https://doaj.org/article/3512bd76c0314624885dea900dad53bd
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spelling	oai:doaj.org-article:3512bd76c0314624885dea900dad53bd2021-12-04T04:36:27ZQuantification of Early Neonatal Oxygen Exposure as a Risk Factor for Retinopathy of Prematurity Requiring Treatment2666-914510.1016/j.xops.2021.100070https://doaj.org/article/3512bd76c0314624885dea900dad53bd2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666914521000683https://doaj.org/toc/2666-9145Purpose: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness related to oxygen exposure in premature infants. Since oxygen monitoring protocols have reduced the incidence of treatment-requiring ROP (TR-ROP), it remains unclear whether oxygen exposure remains a relevant risk factor for incident TR-ROP and aggressive ROP (A-ROP), a severe, rapidly progressing form of ROP. The purpose of this proof-of-concept study was to use electronic health record (EHR) data to evaluate early oxygen exposure as a predictive variable for developing TR-ROP and A-ROP. Design: Retrospective cohort study. Participants: Two hundred forty-four infants screened for ROP at a single academic center. Methods: For each infant, oxygen saturations and fraction of inspired oxygen (FiO2) were extracted manually from the EHR until 31 weeks postmenstrual age (PMA). Cumulative minimum, maximum, and mean oxygen saturation and FiO2 were calculated on a weekly basis. Random forest models were trained with 5-fold cross-validation using gestational age (GA) and cumulative minimum FiO2 at 30 weeks PMA to identify infants who developed TR-ROP. Secondary receiver operating characteristic (ROC) curve analysis of infants with or without A-ROP was performed without cross-validation because of small numbers. Main Outcome Measures: For each model, cross-validation performance for incident TR-ROP was assessed using area under the ROC curve (AUC) and area under the precision-recall curve (AUPRC) scores. For A-ROP, we calculated AUC and evaluated sensitivity and specificity at a high-sensitivity operating point. Results: Of the 244 infants included, 33 developed TR-ROP, of which 5 developed A-ROP. For incident TR-ROP, random forest models trained on GA plus cumulative minimum FiO2 (AUC = 0.93 ± 0.06; AUPRC = 0.76 ± 0.08) were not significantly better than models trained on GA alone (AUC = 0.92 ± 0.06 [P = 0.59]; AUPRC = 0.74 ± 0.12 [P = 0.32]). Models using oxygen alone showed an AUC of 0.80 ± 0.09. ROC analysis for A-ROP found an AUC of 0.92 (95% confidence interval, 0.87–0.96). Conclusions: Oxygen exposure can be extracted from the EHR and quantified as a risk factor for incident TR-ROP and A-ROP. Extracting quantifiable clinical features from the EHR may be useful for building risk models for multiple diseases and evaluating the complex relationships among oxygen exposure, ROP, and other sequelae of prematurity.Jimmy S. Chen, MDJamie E. Anderson, BSAaron S. Coyner, PhDSusan Ostmo, MSKemal Sonmez, PhDDeniz Erdogmus, PhDBrian K. Jordan, MD, PhDCynthia T. McEvoy, MD, MCRDmitry Dukhovny, MD, MPHRobert L. Schelonka, MDR.V. Paul Chan, MDPraveer Singh, PhDJayashree Kalpathy-Cramer, PhDMichael F. Chiang, MD, MAJ. Peter Campbell, MD, MPHElsevierarticleElectronic health recordsMachine learningOxygen exposureRetinopathy of prematurityOphthalmologyRE1-994ENOphthalmology Science, Vol 1, Iss 4, Pp 100070- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electronic health records Machine learning Oxygen exposure Retinopathy of prematurity Ophthalmology RE1-994
spellingShingle	Electronic health records Machine learning Oxygen exposure Retinopathy of prematurity Ophthalmology RE1-994 Jimmy S. Chen, MD Jamie E. Anderson, BS Aaron S. Coyner, PhD Susan Ostmo, MS Kemal Sonmez, PhD Deniz Erdogmus, PhD Brian K. Jordan, MD, PhD Cynthia T. McEvoy, MD, MCR Dmitry Dukhovny, MD, MPH Robert L. Schelonka, MD R.V. Paul Chan, MD Praveer Singh, PhD Jayashree Kalpathy-Cramer, PhD Michael F. Chiang, MD, MA J. Peter Campbell, MD, MPH Quantification of Early Neonatal Oxygen Exposure as a Risk Factor for Retinopathy of Prematurity Requiring Treatment
description	Purpose: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness related to oxygen exposure in premature infants. Since oxygen monitoring protocols have reduced the incidence of treatment-requiring ROP (TR-ROP), it remains unclear whether oxygen exposure remains a relevant risk factor for incident TR-ROP and aggressive ROP (A-ROP), a severe, rapidly progressing form of ROP. The purpose of this proof-of-concept study was to use electronic health record (EHR) data to evaluate early oxygen exposure as a predictive variable for developing TR-ROP and A-ROP. Design: Retrospective cohort study. Participants: Two hundred forty-four infants screened for ROP at a single academic center. Methods: For each infant, oxygen saturations and fraction of inspired oxygen (FiO2) were extracted manually from the EHR until 31 weeks postmenstrual age (PMA). Cumulative minimum, maximum, and mean oxygen saturation and FiO2 were calculated on a weekly basis. Random forest models were trained with 5-fold cross-validation using gestational age (GA) and cumulative minimum FiO2 at 30 weeks PMA to identify infants who developed TR-ROP. Secondary receiver operating characteristic (ROC) curve analysis of infants with or without A-ROP was performed without cross-validation because of small numbers. Main Outcome Measures: For each model, cross-validation performance for incident TR-ROP was assessed using area under the ROC curve (AUC) and area under the precision-recall curve (AUPRC) scores. For A-ROP, we calculated AUC and evaluated sensitivity and specificity at a high-sensitivity operating point. Results: Of the 244 infants included, 33 developed TR-ROP, of which 5 developed A-ROP. For incident TR-ROP, random forest models trained on GA plus cumulative minimum FiO2 (AUC = 0.93 ± 0.06; AUPRC = 0.76 ± 0.08) were not significantly better than models trained on GA alone (AUC = 0.92 ± 0.06 [P = 0.59]; AUPRC = 0.74 ± 0.12 [P = 0.32]). Models using oxygen alone showed an AUC of 0.80 ± 0.09. ROC analysis for A-ROP found an AUC of 0.92 (95% confidence interval, 0.87–0.96). Conclusions: Oxygen exposure can be extracted from the EHR and quantified as a risk factor for incident TR-ROP and A-ROP. Extracting quantifiable clinical features from the EHR may be useful for building risk models for multiple diseases and evaluating the complex relationships among oxygen exposure, ROP, and other sequelae of prematurity.
format	article
author	Jimmy S. Chen, MD Jamie E. Anderson, BS Aaron S. Coyner, PhD Susan Ostmo, MS Kemal Sonmez, PhD Deniz Erdogmus, PhD Brian K. Jordan, MD, PhD Cynthia T. McEvoy, MD, MCR Dmitry Dukhovny, MD, MPH Robert L. Schelonka, MD R.V. Paul Chan, MD Praveer Singh, PhD Jayashree Kalpathy-Cramer, PhD Michael F. Chiang, MD, MA J. Peter Campbell, MD, MPH
author_facet	Jimmy S. Chen, MD Jamie E. Anderson, BS Aaron S. Coyner, PhD Susan Ostmo, MS Kemal Sonmez, PhD Deniz Erdogmus, PhD Brian K. Jordan, MD, PhD Cynthia T. McEvoy, MD, MCR Dmitry Dukhovny, MD, MPH Robert L. Schelonka, MD R.V. Paul Chan, MD Praveer Singh, PhD Jayashree Kalpathy-Cramer, PhD Michael F. Chiang, MD, MA J. Peter Campbell, MD, MPH
author_sort	Jimmy S. Chen, MD
title	Quantification of Early Neonatal Oxygen Exposure as a Risk Factor for Retinopathy of Prematurity Requiring Treatment
title_short	Quantification of Early Neonatal Oxygen Exposure as a Risk Factor for Retinopathy of Prematurity Requiring Treatment
title_full	Quantification of Early Neonatal Oxygen Exposure as a Risk Factor for Retinopathy of Prematurity Requiring Treatment
title_fullStr	Quantification of Early Neonatal Oxygen Exposure as a Risk Factor for Retinopathy of Prematurity Requiring Treatment
title_full_unstemmed	Quantification of Early Neonatal Oxygen Exposure as a Risk Factor for Retinopathy of Prematurity Requiring Treatment
title_sort	quantification of early neonatal oxygen exposure as a risk factor for retinopathy of prematurity requiring treatment
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/3512bd76c0314624885dea900dad53bd
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Quantification of Early Neonatal Oxygen Exposure as a Risk Factor for Retinopathy of Prematurity Requiring Treatment

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