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Using least absolute shrinkage and selection operator (LASSO) regression with 10\u2010fold cross\u2010validation, we identified key predictors for nomogram development. The model's performance was rigorously evaluated through discrimination (C\u2010index, AUC\u2010ROC), calibration, and decision curve analysis (DCA) in both training (70%) and validation (30%) cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our hemorrhage\u2010specific nomogram incorporated five independent predictors: (1) age at initial hemorrhage, (2) surgical timing (optimal 3\u20136\u2009month window post\u2010ictus), (3) preoperative functional status (modified Rankin Scale), (4) perioperative complications, and (5) recurrent hemorrhage. The model demonstrated excellent discrimination (C\u2010index: 0.889 training, 0.819 validation) and calibration. DCA confirmed superior clinical utility across 15%\u201380% probability thresholds compared to conventional approaches. Restricted cubic spline analysis validated the non\u2010linear relationship between surgical timing and outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study provides the first validated clinical decision tool for adult hemorrhagic MMD, establishing 3\u20136\u2009months post\u2010hemorrhage as the optimal surgical window for EDAS. The nomogram's robust performance metrics and RCS\u2010validated temporal risk stratification offer neurosurgeons an evidence\u2010based approach to optimize revascularization timing and improve functional outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cns.70917","type":"journal-article","created":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T10:15:31Z","timestamp":1778321731000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Optimal Surgical Timing and Outcome Prediction in Hemorrhagic Moyamoya Disease: A Retrospective Cohort Study"],"prefix":"10.1002","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6538-375X","authenticated-orcid":false,"given":"Qingbao","family":"Guo","sequence":"first","affiliation":[{"name":"Department of Neurosurgery XI'AN NO. 9 HOSPITAL  Xi'an Shaanxi 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