Prognostic Factors in Aneurysmal Subarachnoid Hemorrhage: Pooled Analyses of Individual Patient Data and Development of Novel Risk Scores in Large Cohorts of International Patients
Bibliographic record
Abstract
Primary studies reporting prognostic associations in aneurysmal subarachnoid hemorrhage (SAH) are often insufficiently powered, use data of limited representativeness and scarcely examine the added value of prognostic factors above those of other known factors. Hence, considerable knowledge gaps and conflicting results exist in the literature on the nature and extent of prognostic associations in SAH. Prognostic factors have been combined to develop prediction models and risk scores for early outcome prediction after SAH. None is routinely applied in clinical or research settings; some major constraints relate to lack of evidence on the predictive accuracy, reliability and generalizability of reported risk scores. The global aim of this research was to address these challenges and contribute to improved understanding of prognostic associations in SAH by analysing large cohorts of SAH patients reflecting a broad spectrum of settings. Pooled analyses of patient-level data from multiple randomized clinical trials and prospective hospital registries involving 10963 patients demonstrated a strong prognostic effect of admission neurologic status on 3-month outcome according to Glasgow outcome score. Age had a moderate effect on outcome; premorbid hypertension and subarachnoid clot burden on the Fisher scale were weak predictors of outcome. Patient's sex had no independent predictive value. Prognostic effect of aneurysm size and location depended on treatment modality. Novel prognostic scores were developed combining these predictors for early prediction of mortality and unfavorable outcomes at 3 months, and demonstrated adequate performance at bootstrap (AUC: 0.77 - 0.83) and at cross validation. Using 2 nationally representative administrative datasets, socioeconomic status and race/ethnicity were explored as latent prognostic factors in SAH. Multinomial logistic regression analysis demonstrated socioeconomic status, measured as neighborhood income status, was associated with inpatient mortality risk after admission for SAH. The extent of the association could be related to health care system under which treatment was provided. Race/ethnicity was independently associated with inpatient mortality. Patients of Hispanic ethnicity had the best outcomes and Asia/Pacific Islanders experienced the worst outcomes during the inpatient course. This research has provided higher level evidence than prior studies on studied prognostic factors and reliable tools for early prediction of outcome after hospitalization for SAH.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; a candidate call from one teacher head, not a consensus.
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".