Characterizing the individual course of health-related quality of life after subarachnoid haemorrhage: Latent growth mixture modelling

Background: Survivors of aneurysmal subarachnoid haemorrhage (SAH) show heterogeneous profiles of health-related quality of life (HrQoL). The aim of this study was to characterize individual differences in the course of HrQoL following SAH using latent growth mixture modelling (LGMM). Methods: A longitudinal study with 113 incident cases of aneurysmal SAH was performed in order to evaluate clinical outcome (Hunt and Hess scale, Barthel-Index, Beck Depression Inventory) and HrQoL data (EQ-5D) at baseline, 6 and 12 months. The heterogeneity in HrQoL courses after SAH was analysed using LGMM. Results: Four subgroups (classes) of different patterns of HrQoL course after SAH were identified. Two of these classes (1 and 3) comprised patients with considerably reduced initial HrQoL, which was associated with more severe symptoms of SAH. Class 1 showing the worst EQ5Dindex values during the entire study period. Class 3 experiencing a considerable improvement in HrQoL values. In comparison to classes 1 and 3, class 2 and 4 were characterized by less severe SAH and better functional outcome. An important difference in the disease course between classes 2 and 4 was a temporary increase in depression scores at the 6-month time point in class 4, which was associated with a considerable reduction in HrQoL.The specific clinical parameters characterizing differences between classes, such as severity of SAH, functional outcome, cognitive impairment and post-stroke depression, were identified and the influence of their potential improvement on HrQoL was estimated. Conclusion: By means of LGMM we could classify the course of HrQoL after SAH in four different patterns, which are relevant for the clinical decisions. Clinical parameters, which can be modified in order to improve the course of HrQoL were identified and could help to develop individual therapeutic strategies for the rehabilitation after SAH.(c) 2022 Elsevier Inc. All rights reserved.