{"doi":"10.1016/j.xops.2023.100417","title":"Oxygenation Fluctuations Associated with Severe Retinopathy of Prematurity","abstract":"PurposeRetinopathy of Prematurity (ROP) is one of the leading causes of blindness in children. Although the role of oxygen in the pathophysiology of ROP is well established, a precise understanding of the dynamic relationship between oxygen exposure ROP incidence and severity is lacking. The purpose of this study was to evaluate the correlation between time-dependent oxygen variables and the onset of retinopathy of premature (ROP).DesignRetrospective cohort studySubjectsTwo hundred thirty infants who were born at a single academic center and met the inclusion criteria. Infants are mainly born between January 2011 and October 2022.MethodsPatient data were extracted from electronic healthcare records (EHR), with sufficient time-dependent oxygen data. Clinical outcomes for ROP were recorded as none/mild or moderate/severe (defined as type II or worse). Mixed effects linear models were used to compare the two groups in terms of dynamic oxygen variables, such as daily average and the coefficient of variation (COV) fractional inhaled oxygen concentration (FiO2). Support vector machine and long short-term memory (LSTM) based multimodal models were trained with 5-fold cross-validation to predict which infants would develop moderate/severe ROP. Gestational age (GA), birth weight, and time-dependent oxygen variables were used to develop predictive models.Main Outcome MeasuresModel cross-validation performance was evaluated by computing the mean Area Under the Receiver Operating Characteristic Curve (AUROC), precision, recall, and F1 score.ResultsWe found that both daily average and COV of Fi were associated with more severe ROP (adjusted P = .001). With 5-fold cross-validation, the multimodal-LSTM models had higher performance than the best static models (SVM using GA and 3 average FiO2 features) and SVM models trained on GA alone (mean AUROC = 0.89±0.04 vs. 0.86±0.05 vs. 0.83±0.04).ConclusionThe development of severe ROP might not only be influenced by oxygen exposure but also by its fluctuation, which provides direction for future study of pathophysiological factors associated with severe ROP development. Additionally, we demonstrated that multimodal neural networks can be a method to extract useful information from time-series data, which may be a valuable methodology for the investigation of other diseases using EHR data.","journal":"Ophthalmology Science","year":2023,"id":358764,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9593,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":327167,"name":"Brian K. Jordan","orcid":"0000-0002-1553-8503","position":1,"is_corresponding":false},{"id":434705,"name":"Brian Scottoline","orcid":"0000-0003-0207-9624","position":2,"is_corresponding":false},{"id":309183,"name":"Susan Ostmo","orcid":"0000-0001-5219-0705","position":3,"is_corresponding":false},{"id":336863,"name":"Aaron S. Coyner","orcid":"0000-0003-3261-1909","position":4,"is_corresponding":false},{"id":256387,"name":"Praveer Singh","orcid":"0000-0001-6641-2030","position":5,"is_corresponding":false},{"id":14567,"name":"Jayashree Kalpathy–Cramer","orcid":"0000-0001-8906-9618","position":6,"is_corresponding":false},{"id":264211,"name":"Deniz Erdoğmuş","orcid":"0000-0002-1114-3539","position":7,"is_corresponding":false},{"id":288236,"name":"R.V. Paul Chan","orcid":"0000-0002-1971-2249","position":8,"is_corresponding":false},{"id":264173,"name":"Michael F. Chiang","orcid":"0000-0002-8172-7636","position":9,"is_corresponding":false},{"id":264208,"name":"J. Peter Campbell","orcid":"0000-0001-7964-9475","position":10,"is_corresponding":false},{"id":1049503,"name":"Wei-Chun Lin","orcid":"0000-0002-2226-4619","position":0,"is_corresponding":true}],"reference_count":47,"raw_metadata":null,"created_at":"2026-07-19T01:13:48.379546Z","pmid":"38059124","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}