{"doi":"10.1101/2025.09.22.25335736","title":"Dual-outcome Prediction of Post-Ischemic Stroke Epilepsy and Mortality Using Multimodal Quantitative Biomarkers","abstract":"Abstract Background and Objectives Post-ischemic stroke epilepsy (PISE) reduces quality of life, and early risk prediction can guide prevention strategies and anti-epileptogenesis treatment trials. Stroke severity predicts both PISE and mortality, and ignoring mortality can overestimate epilepsy risk. We sought to enhance PISE risk stratification by modeling death as a competing outcome, integrating quantitative clinical, neuroimaging, and electroencephalography (EEG) biomarkers to distinguish shared and distinct predictors of epilepsy and mortality. Methods We developed a PISE prediction model using retrospective data from Yale-New Haven Hospital. The training cohort included patients from 2014–2020; the testing cohort from 2021–2022. Eligible patients were adults with acute ischemic stroke who underwent neuroimaging and EEG monitoring <7 days post-stroke and had follow-up >7 days. Results Of 280 patients, 53 developed PISE first, 104 died first, and the rest were censored. Quantitative PISE biomarkers included greater 72h stroke severity (HR Δ3 [95%CI], 1.2 [1.1-1.4]), infarct volume (HR Δ10mL , 1.06 [1.04-1.08]), EEG epileptiform abnormality burden (HR Δ10% , 1.2 [1.1-1.3]), and EEG power asymmetries (HR Δ10% , 2.0 [1.4-2.9]). Death predictors included older age (HR Δ10years , 1.7 [1.4-2.0]), worse pre-stroke functional status (HR, 1.4 [1.2-1.7]), atrial fibrillation history (HR, 2.4 [1.6-3.7]), cardioembolism etiology (HR, 1.9 [1.2-3.0]), anterior cerebral artery involvement (HR, 2.2 [1.2-3.7]), and greater EEG global theta-band powers (HR Δ10µV , 6.2 [2.3-17]). Our model, CRIME PISE , integrating these features, allows prediction of PISE-first and death-first risk scores with AUC of 0.72 (95%CI, 0.60-0.83) and 0.79 (0.72-0.85), respectively. Compared with the benchmark SeLECT model, CRIME PISE better predicted PISE in patients with ≥4 SeLECT points (AUC, 0.72 vs 0.58) but not those with <4 points (AUC, 0.33 vs 0.52). In the testing cohort, CRIME PISE identified a more selective group (n=18 vs 44 per SeLECT) with a higher PISE rate (39% vs 20%) and a lower mortality rate (22% vs 45%). Discussion CRIME PISE enhances PISE prediction by accounting for mortality as a competing outcome and incorporating multimodal quantitative biomarkers. Because its benefits over SeLECT are most pronounced in high-risk patients, a two-stage approach—SeLECT screening followed by CRIME PISE in SeLECT-positive cases—may better target candidates for anti-epileptogenesis trials by prioritizing patients likely to survive long-term and develop epilepsy.","journal":"medRxiv","year":2025,"id":576183,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9551,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":739993,"name":"Alexandria L. Soto","orcid":"0000-0003-1742-5251","position":1,"is_corresponding":false},{"id":557010,"name":"Tejaswi Sudhakar","orcid":"0000-0001-5628-3028","position":2,"is_corresponding":false},{"id":520478,"name":"Adeel Zubair","orcid":"0000-0002-9809-7888","position":3,"is_corresponding":false},{"id":305327,"name":"Haoqi Sun","orcid":"0000-0002-5041-8312","position":4,"is_corresponding":false},{"id":1484765,"name":"Jing Jin","orcid":"0000-0003-4996-5711","position":5,"is_corresponding":false},{"id":336251,"name":"Wendong Ge","orcid":"0000-0003-1557-5336","position":6,"is_corresponding":false},{"id":1485149,"name":"L. Anthony Loman","orcid":null,"position":7,"is_corresponding":false},{"id":671164,"name":"Adithya Sivaraju","orcid":"0000-0002-6027-9205","position":8,"is_corresponding":false},{"id":464922,"name":"Nils Petersen","orcid":"0000-0001-9711-3340","position":9,"is_corresponding":false},{"id":227172,"name":"Lawrence J. Hirsch","orcid":"0000-0002-6333-832X","position":10,"is_corresponding":false},{"id":686356,"name":"Hal Blumenfeld","orcid":"0000-0003-0812-8132","position":11,"is_corresponding":false},{"id":485320,"name":"Sahar F. Zafar","orcid":"0000-0001-5252-5376","position":12,"is_corresponding":false},{"id":306970,"name":"Aaron F. Struck","orcid":"0000-0002-9103-1798","position":13,"is_corresponding":false},{"id":256547,"name":"Kevin N. Sheth","orcid":"0000-0003-2003-5473","position":14,"is_corresponding":false},{"id":1226588,"name":"Emily Gilmore","orcid":null,"position":15,"is_corresponding":false},{"id":280809,"name":"M. Brandon Westover","orcid":"0000-0003-4803-312X","position":16,"is_corresponding":false},{"id":623212,"name":"Jennifer A. Kim","orcid":"0000-0003-3072-6198","position":17,"is_corresponding":false},{"id":1484764,"name":"Yilun Chen","orcid":"0000-0002-2348-7531","position":0,"is_corresponding":true}],"reference_count":31,"raw_metadata":null,"created_at":"2026-07-19T02:57:56.636458Z","pmid":"41040677","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":[]}