Quantification of Early Neonatal Oxygen Exposure as a Risk Factor for Retinopathy of Prematurity Requiring Treatment
© 2021 by the American Academy of Ophthalmology..
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.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2021 |
|---|---|
| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:1 |
|---|---|
| Enthalten in: |
Ophthalmology science - 1(2021), 4 vom: 15. Dez., Seite 100070 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Chen, Jimmy S [VerfasserIn] |
|---|
| Links: |
|---|
| Anmerkungen: |
Date Revised 25.10.2022 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.1016/j.xops.2021.100070 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM347897886 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM347897886 | ||
| 003 | DE-627 | ||
| 005 | 20231226034925.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.xops.2021.100070 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1159.xml |
| 035 | |a (DE-627)NLM347897886 | ||
| 035 | |a (NLM)36275192 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Chen, Jimmy S |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Quantification of Early Neonatal Oxygen Exposure as a Risk Factor for Retinopathy of Prematurity Requiring Treatment |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 25.10.2022 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2021 by the American Academy of Ophthalmology. | ||
| 520 | |a 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 | ||
| 520 | |a Design: Retrospective cohort study | ||
| 520 | |a Participants: Two hundred forty-four infants screened for ROP at a single academic center | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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) | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a A-ROP, aggressive retinopathy of prematurity | |
| 650 | 4 | |a AUC, area under the receiver operating characteristic curve | |
| 650 | 4 | |a AUPRC, area under the precision-recall curve | |
| 650 | 4 | |a BW, birth weight | |
| 650 | 4 | |a CI, confidence interval | |
| 650 | 4 | |a EHR, electronic health record | |
| 650 | 4 | |a Electronic health records | |
| 650 | 4 | |a FiO2, fraction of inspired oxygen | |
| 650 | 4 | |a GA, gestational age | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a NICU, neonatal intensive care unit | |
| 650 | 4 | |a Oxygen exposure | |
| 650 | 4 | |a PMA, postmenstrual age | |
| 650 | 4 | |a ROC, receiver operating characteristic | |
| 650 | 4 | |a ROP, retinopathy of prematurity | |
| 650 | 4 | |a Retinopathy of prematurity | |
| 650 | 4 | |a TR-ROP, treatment-requiring retinopathy of prematurity | |
| 700 | 1 | |a Anderson, Jamie E |e verfasserin |4 aut | |
| 700 | 1 | |a Coyner, Aaron S |e verfasserin |4 aut | |
| 700 | 1 | |a Ostmo, Susan |e verfasserin |4 aut | |
| 700 | 1 | |a Sonmez, Kemal |e verfasserin |4 aut | |
| 700 | 1 | |a Erdogmus, Deniz |e verfasserin |4 aut | |
| 700 | 1 | |a Jordan, Brian K |e verfasserin |4 aut | |
| 700 | 1 | |a McEvoy, Cynthia T |e verfasserin |4 aut | |
| 700 | 1 | |a Dukhovny, Dmitry |e verfasserin |4 aut | |
| 700 | 1 | |a Schelonka, Robert L |e verfasserin |4 aut | |
| 700 | 1 | |a Paul Chan, R V |e verfasserin |4 aut | |
| 700 | 1 | |a Singh, Praveer |e verfasserin |4 aut | |
| 700 | 1 | |a Kalpathy-Cramer, Jayashree |e verfasserin |4 aut | |
| 700 | 1 | |a Chiang, Michael F |e verfasserin |4 aut | |
| 700 | 1 | |a Campbell, J Peter |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Ophthalmology science |d 2021 |g 1(2021), 4 vom: 15. Dez., Seite 100070 |w (DE-627)NLM329259180 |x 2666-9145 |7 nnns |
| 773 | 1 | 8 | |g volume:1 |g year:2021 |g number:4 |g day:15 |g month:12 |g pages:100070 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.xops.2021.100070 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 1 |j 2021 |e 4 |b 15 |c 12 |h 100070 | ||