Year: 2026
Type: article
Study design: Observational Study
Abstract: Terson syndrome is defined as intraocular hemorrhage secondary to aneurysmal subarachnoid hemorrhage (aSAH). Despite clinical relevance, it remains under-recognized and is associated with poorer neurological and visual outcomes. Early detection is difficult in critically ill patients unable to report symptoms. We aimed to identify independent predictors and develop a clinically usable risk score for targeted ophthalmologic screening. We conducted a retrospective cohort study of 220 adult aSAH patients admitted to Odense University Hospital (Sept 1, 2018-Jan 21, 2025), of whom 89 underwent ophthalmologic screening. Terson syndrome was defined as any intraocular hemorrhage on examination. Predictors were selected using least absolute shrinkage and selection operator (LASSO) logistic regression, with multivariable analysis and internal validation via bootstrapping. A point-based risk score was derived and compared to a literature-based model using discrimination, calibration, information criteria, and decision curve analysis. Sensitivity analyses included Firth logistic regression and inverse probability weighting. Among 220 patients, 89 (40.5%) were screened; 31 had Terson syndrome (14.1% overall; 34.8% of screened). Three predictors were identified: male sex (OR 10.9), higher Hunt-Hess grade (OR 3.0 per grade), and anterior communicating artery aneurysm (OR 8.6; all p < 0.05). The model demonstrated strong discrimination (AUC 0.862; 95% CI, 0.787-0.937), good calibration, and outperformed the literature-based comparator (AUC 0.769). A 0-7 point-based score stratified risk from < 25% to > 80%, with superior net benefit. Sensitivity analyses confirmed robustness. This three-variable model offers a pragmatic tool for early Terson syndrome detection and may reduce preventable visual morbidity through targeted screening.
Source: Neurosurgical Review
Institutions: University of Southern Denmark, Odense University Hospital
Language: English
Subfield: Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,590
Funder: Syddansk Universitet
OpenAlex ID: W7124447423