Early Identification of Delayed Cerebral Ischemia and Cerebral Vasospasm After Aneurysmal Subarachnoid Hemorrhage Through a Novel Dynamical Systems Approach
Bibliographic record
Abstract
Background A critical challenge after aneurysmal subarachnoid hemorrhage (aSAH) is early identification of delayed cerebral ischemia (DCI) and cerebral vasospasm (VSP). Transcranial doppler ultrasound (TCD) non-invasively records cerebral blood flow velocity (CBFV) and is used to identify DCI and VSP risk, but current methods extract mean CBFV velocity, ignoring CBFV shape and dynamics. We hypothesize early pathophysiologic changes are encoded in CBFV shape and dynamics but missed by current CBFV measures. By applying the attractor reconstruction method, we aim to transform qualitative features of CBFV waveforms into quantitative values that better predict DCI and VSP after aSAH. Methods Simultaneous EKG, arterial blood pressure (ABP), and bilateral CBFV recordings were collected from consecutive aSAH patients in the Neurological Intensive Care Unit. Angiographic VSP and DCI were adjudicated by neurointensivists. Artifact-free CBFV waveform segments were extracted manually. We used Takens’ theorem to generate attractors from the CBFV waveforms by means of a delay embedding. Features such as attractor concentration and width were calculated. Each patient’s latest recording prior to DCI/VSP or negative CT angiogram was included. Attractor features in patients with and without DCI/VSP were compared using two- tailed t-tests. To confirm the validity of our findings, we simulated CBFV waveforms and correlated attractor features with CBFV features. Results Fifty-three patients with aSAH from 2016-2019 were followed. Of these, 29 (54.7%) developed VSP, and 20 (37.7%) developed DCI. CBFV, but not ABP, attractor concentration was higher prior to DCI (DCI mean = 0.26±0.04, no DCI mean = 0.23±0.03, p=0.04) and VSP (VSP mean = 0.26±0.02, no VSP mean = 0.22±0.02, p≤0.01). Increasing upstroke convexity and heterogeneity of simulated CBFV recordings increased attractor concentration. CBFV, but not ABP, attractor width was increased before VSP (VSP mean = 66.93±15.65, no VSP mean = 49.37±14.46, p=0.01). Larger simulated CBFV amplitude increased attractor width. In these same recordings, there were no differences in number of patients with mean CBFV greater than 120 cm/s (DCI count = 1, no DCI count = 1, p=1.0, VSP count = 3, no VSP count = 1, p=0.32). Conclusion Attractor analysis of CBFV recordings identifies brain-specific, morphological, and dynamic CBFV changes prior to DCI and VSP where conventional flow velocity analysis does not. These metrics provide non-invasive and quantitative predictors of DCI and VSP, unlocking a new paradigm for TCD clinical utility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".