To visualise the non-linear correlation between age and poor outcome at discharge in patients with aneurysmal subarachnoid haemorrhage (SAH) while adjusting for covariates, and to address the heterogeneity of this correlation depending on disease severity by a registry-based design.
We extracted data from the Japanese Stroke Databank registry for patients with SAH treated via surgical clipping or endovascular coiling within 3 days of SAH onset between 2000 and 2017. Poor outcome was defined as a modified Rankin Scale Score ≥3 at discharge. Variable importance was calculated using machine learning (random forest) model. Correlations between age and poor outcome while adjusting for covariates were determined using generalised additive models in which spline-transformed age was fit to each neurological grade of World Federation of Neurological Societies (WFNS) and treatment.
In total, 4149 patients were included in the analysis. WFNS grade and age had the largest and second largest variable importance in predicting the outcome. The non-linear correlation between age and poor outcome was visualised after adjusting for other covariates. For grades I–III, the risk slope for unit age was relatively smaller at younger ages and larger at older ages; for grade IV, the slope was steep even in younger ages; while for grade V, it was relatively smooth, but with high risk even at younger ages.
The clear visualisation of the non-linear correlation between age and poor outcome in this study can aid clinical decision making and help inform patients with aneurysmal SAH and their families better.