• Joshua S Catapano, Kavelin Rumalla, Stefan W Koester, Ethan A Winkler, Robert F Rudy, Tyler S Cole, Jacob F Baranoski, Christopher S Graffeo, Visish M Srinivasan, Ruchira M Jha, Ashutosh P Jadhav, Andrew F Ducruet, Felipe C Albuquerque, and Michael T Lawton.
• Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona, USA.
• World Neurosurg. 2023 Mar 1; 171: e206e212e206-e212.

ObjectiveThe incidence and risk factors for chronic depression after aneurysmal subarachnoid hemorrhage (aSAH) are described.MethodsPatients with aSAH treated at a single institution (January 1, 2003-December 31, 2019) and a modified Rankin Scale score ≤3 at follow-up who were evaluated for chronic depression were analyzed. Chronic depression was defined using a depression screening questionnaire as ≥5 positive answers for symptoms lasting >2 weeks. A predictive model was designed for the primary outcome of depression.ResultsAmong 1419 patients with aSAH, 460 patients were analyzed; 130 (28%) had major depressive disorder. Mean follow-up was >6 years. Higher depression rates were associated with tobacco smoking (odds ratio [OR] = 2.64, P < 0.001), illicit drug use (OR = 2.35, P = 0.007), alcohol use disorder (1.92, P = 0.04), chronic obstructive pulmonary disease (COPD) (OR=2.68, P = 0.03), and vasospasm requiring angioplasty (OR=2.09, P = 0.048). The predictive model included tobacco smoking, illicit drug use, liver disease, COPD, diabetes, nonsaccular aneurysm type, anterior communicating artery or anterior cerebral artery aneurysm location, refractory spasm requiring angioplasty, and a modified Rankin Scale score at discharge of >1 (P ≤ 0.03). The model performed with appropriate goodness of fit and an area under the receiver operator curve of 0.70 for depression. Individual independent predictors of depression were tobacco smoking, COPD, diabetes, and nonsaccular aneurysm.ConclusionsA substantial percentage of patients had symptoms of depression on follow-up. The proposed predictive model for depression may be a useful clinical tool to identify patients at high risk for developing depression who warrant early screening and evaluation.Copyright © 2022 Elsevier Inc. All rights reserved.

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