OBJECTIVES: Subarachnoid hemorrhage (SAH) is a cerebrovascular disease leading to severe disability. SAH-patients show heterogeneous profiles of health-related quality of life (HrQoL). No methodological approaches to characterize the individual course of HrQoL following SAH have been developed. The objective was to characterize individual trajectories concerning HrQoL following SAH by using latent growth mixture modeling (LGMM). METHODS: A total of 113 incident patients with aneurysmal SAH treated in the University of Bonn between January 2004 and December 2005 were recruited in this longitudinal study. Clinical parameters (Hunt and Hess scale, Barthel-Index, Rankin Scale, Beck Depression Inventory) and HrQoL data (EQ-5D) were evaluated at baseline, 6 and 12 months. LGMM was applied to analyse the heterogeneity in individual courses of HrQoL after SAH. RESULTS: We identified four subgroups of patients (latent classes) with different patterns of HrQoL-course. Class 1 had the worst HrQoL-course with a low score of the EQ-5D index at baseline (0.33) and a non-significant change in scores over time. Patients in class 3 showed rapid recovery from initially low EQ-5D scores (0.37) during the first 6 months (D=0.47). Patients in classes 2 and 4 had 48-57% better initial HrQoL and similarly high HrQoL scores after 12 months. Patients in class 4 experienced a temporary reduction of HrQoL by 55%. The following clinical parameters were identified to characterize differences between classes: severity of SAH (Hunt and Hess scale), functional outcome, cognitive impairment and post-stroke depression. Treatment of post-stroke depression in classes 1 and 4 can improve HrQoL measures by factor 1.3-2.8. CONCLUSIONS: This methodological approach can be applied for more elaborated understanding of individual differences in long-term course of HrQoL after SAH. Identification of different patterns of disease course using LGMM may help to find subgroups of treatment responders and to assist the development of individual therapy regimes.