{"doi":"10.1038/s41433-024-03172-4","title":"Correlation of limited-early-response status with 12-month CST, BVA, and machine learning-quantified retinal fluid in diabetic macular oedema in routine clinical practice","abstract":"BACKGROUND/OBJECTIVES: Anti-VEGF treatment response in DMO has been measured by changes in the central subfield thickness (CST) and best visual acuity (BVA) outcomes at 3 months after initial treatment, termed early or limited early response (ER/LER). This study correlates LER with 12-month BVA, CST, and retinal fluid volumes quantified by a machine learning algorithm on optical coherence tomography (OCT). SUBJECTS/METHODS: The study included treatment naïve DMO patients ≥ 18 years with OCT scans at baseline (M0), M3, M6, and M12. The 220 patients were categorized as limited early responders (LER) if they had ≤ 10% CST reduction and/or < 5 ETDRS letter gain at M3. BVA, CST, and subretinal (SRF), intraretinal (IRF), and total retinal (TRF) fluid volumes quantified by a machine learning algorithm were compared between groups and across time. RESULTS: At M12, the anatomic LER (aLER), defined solely by CST, had significantly worse BVA and CST versus the anatomic ER (aER) group (p < 0.001). Retinal fluid M12 outcomes did not significantly vary between all LER and ER groups. No significant BVA, CST, TRF, and IRF variance across time for LER was found (p > 0.1). CONCLUSIONS: BVA and CST M12 outcomes vary by aLER/aER status indicating that CST may be a strong predictor of treatment outcomes, while retinal fluid volumes were not predicted by LER status.","journal":"Eye","year":2024,"id":465746,"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":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.95,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1297593,"name":"Scott W. Perkins","orcid":null,"position":1,"is_corresponding":false},{"id":741451,"name":"Aneesha Kalur","orcid":null,"position":2,"is_corresponding":false},{"id":323241,"name":"Rishi P. Singh","orcid":"0000-0001-5859-8162","position":3,"is_corresponding":false},{"id":1297592,"name":"Resya Sastry","orcid":null,"position":0,"is_corresponding":true}],"reference_count":24,"raw_metadata":null,"created_at":"2026-07-19T02:04:50.328230Z","pmid":"38890549","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":[]}