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Updated: Oct 28, 2026
Pre-Chiasmatic, Single Injection of Autologous Blood to Induce Experimental Subarachnoid Hemorrhage in a Rat Model
Published on: June 18, 2021
Silent Validation of a Longitudinal Model for Predicting Delayed Cerebral Ischemia in Real Time after Subarachnoid
Jan Willms1, Jenny Schmid1, Corinne Inauen2
1Neurocritical Care Unit, Department of Neurosurgery and Institute of Intensive Care Medicine, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
Background:
Longitudinal prediction of delayed cerebral ischemia (DCI) following subarachnoid hemorrhage (SAH) is crucial for timely interventions. Machine learning models for DCI risk prediction and stratification may support clinical decision-making, but safety concerns and regulatory constraints have precluded real-time bedside validation. We utilized the ICU (intensive care unit) Cockpit information technology infrastructure to establish a live data stream enabling real-time model predictions that were evaluated by independent research physicians in a silent validation framework, benchmarking algorithmic recommendations without influencing treatment decisions.
Methods:
We deployed a DCI algorithm validated on historical data in a live environment on running data streams of actual patients. Three independent physicians not involved in patient care reviewed real-time model outputs from day 2 through day 14 after SAH onset. With algorithm support, these physicians estimated DCI probability, decided on further diagnostic orders (computed tomography angiography with perfusion) and/or recommended the intervals for repetitive neurologic assessments (increased, 1-2 hourly; standard, ≥3 hourly). Researchers' decisions were compared with actual clinical decisions and the occurrence of DCI.
Results:
Out of 63 aneurysmal SAH patients enrolled, 17 developed DCI (27%). A total of 794 assessments were made. The number of increased neurologic assessment orders was 466 with algorithm support and 629 without (a reduction of 163; 25.91%). Although the number of true positives and false negatives for DCI prediction was similar with and without the algorithm, false positives were reduced by 164 (P<0.01). Additional diagnostic orders were 128 with algorithm support and 153 without (reduction of 25; 16.34%).
Conclusions:
This study demonstrates that continuous, real-time risk estimates can be generated without disrupting standard care, and that clinicians - when supported by an algorithm - may safely reduce unnecessary investigations without compromising DCI event detection. In addition, silent validation showed potential to optimize monitoring strategies and reduce clinician workload without reducing diagnostic accuracy. (Funded by the Swiss National Science Foundation [project nos. 407540-167195 and 325230_200568].).
