Effect of pre-existing conditions and non-neurological medical complications on mortality in aneurysmal subarachnoid hemorrhage patients undergoing angiography or neurosurgical clipping
Bibliographic record
Abstract
Background: Aneurysmal subarachnoid hemorrhage (SAH) remains a devastating condition with a case-fatality of around 36% at 30 days. Established risk factors for mortality in SAH patients include patient demographics and the severity of the neurological injury. There is mounting evidence that pre-existing conditions, and non-neurological medical complications occurring during the index hospitalization may also be risk factors for mortality in SAH. The magnitude of their effect on mortality, however, is less well understood. In this study we aim to determine the effect of pre-existing conditions and medical complications on SAH mortality.Methods: For a 25% random sample of the Greater Montreal Region, we used discharge abstracts, physician billings and death certificate records, to identify adult patients with a new diagnosis of non-traumatic SAH who underwent cerebral angiography or surgical clipping of an aneurysm between 1997 and 2014. Patient demographic data, diagnostic codes and procedure codes were extracted to determine each patient's pre-existing conditions, medical complications, and severity of the neurological injury. Mortality was assessed at one year.Results: The overall one-year mortality rate was 14.76% (94/637). Compared to patients with no pre-existing conditions each additional pre-existing condition was associated with increased one-year mortality OR, 1.38 [95% CI, 1.11 – 1.72]. Compared to patients with no medical complications each additional complication was associated with increased one-year mortality OR, 1.28 [95% CI, 1.10 – 1.49]. Among specific pre-existing conditions, malignancy, diabetes, congestive heart failure, renal disease, and cerebrovascular disease were associated with increased mortality. As for specific in-hospital medical complications, sepsis, respiratory failure and cardiac arrhythmias were associated with increased mortality, when controlling for age, sex, pre-existing conditions and the severity of neurological injury. Lastly, pre-existing conditions were associated with increased non-neurological medical complications but not the severity of neurological injury.Conclusion: Pre-existing conditions and in-hospital non-neurological medical complications are associated with increased one-year mortality in SAH. Pre-existing conditions are associated with increased medical complications but not the severity of the neurological injury
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".