Abstract

Introduction: Many randomized clinical trials (RCTs) have been conducted for subarachnoid hemorrhage (SAH), yet no treatment definitively improves outcome. Predictive ability of early phase trials is uncertain because outcome is strongly related to baseline severity factors, yet imbalances are common in these trials. We previously developed a method for ischemic stroke to identify promising treatments at early phase using a pooled model constructed from the control arms of RCTs and comparing trial outcomes at their own baselines. We applied this method to SAH. Methods: SAH predictive models for functional outcome and mortality were developed based on published variables (eg, WFNS, Fisher, age). The outcome model is the best fit 3-d surface bounded on either side by p=±0.1 prediction interval (PI) surfaces. These surfaces incorporate statistical variability to assess whether a treatment differs from expected outcome of a pooled sample. Treatment arms from RCTs and single arm trials for SAH are then visually compared against the pooled controlled arm to see if they surpassed the prediction surfaces at their own baseline conditions. Results: The best model fit was for good outcome (modified Rankin Score 0-2) based on WFNS grade 4-5% and age (26 RCTs, 5200 subjects; R 2 =0.54; p<0.001). To confirm the model can identify futility, 7 tirilazad trials were superimposed and all were on the prediction surface (Fig. Top Panel). 3 IV nicardipine trials were within the ±0.1 PI surfaces, while 2 case series using implanted prolonged release nicardipine (Middle: D&E) were above, suggesting promise. While SQ enoxaparin was below the surface (Bottom: A), low dose heparin infusion (B) was above the +p=0.1 surface showing promise. Mortality models did not find an increase with the most promising treatments. Conclusions: Outcome models based on percentage of high grade WFNS and age were successfully developed. This approach may be useful to prioritize treatments worthy of further study.

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