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Record W2958565858 · doi:10.3171/2019.4.jns19103

Acute kidney injury after aneurysmal subarachnoid hemorrhage and its effect on patient outcome: an exploratory analysis

2019· article· en· W2958565858 on OpenAlexaff
Matthew E. Eagles, Maria Powell, Oliver G. S. Ayling, Michael K. Tso, R. Loch Macdonald

Bibliographic record

VenueJournal of neurosurgery · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of TorontoUniversity of British ColumbiaSt. Michael's HospitalUniversity of Calgary
Fundersnot available
KeywordsMedicineSubarachnoid hemorrhageModified Rankin ScaleAcute kidney injuryPost-hoc analysisInternal medicinePropensity score matchingKidney diseaseIschemia

Abstract

fetched live from OpenAlex

OBJECTIVE: Acute kidney injury (AKI) is associated with death in critically ill patients, but this complication has not been well characterized after aneurysmal subarachnoid hemorrhage (aSAH). The purpose of this study was to determine the incidence of AKI after aSAH and to identify risk factors for renal dysfunction. Secondary objectives were to examine what effect AKI has on patient mortality and functional outcome at 12 weeks post-aSAH. METHODS: The authors performed a post hoc analysis of the Clazosentan to Overcome Neurological Ischemia and Infarction Occurring After Subarachnoid Hemorrhage (CONSCIOUS-1) trial data set (clinical trial registration no.: NCT00111085, https://clinicaltrials.gov). The primary outcome of interest was the development of AKI, which was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. Secondary outcomes of interest were death and a modified Rankin Scale score greater than 2 at 12 weeks post-aSAH. Propensity score matching was used to assess for a significant treatment effect related to clazosentan administration and AKI. Univariate analysis, locally weighted scatterplot smoothing (LOWESS) curves, and stepwise logistic regression models were used to evaluate for associations between baseline or disease-related characteristics and study outcomes. RESULTS: One hundred fifty-six (38%) of the 413 patients enrolled in the CONSCIOUS-1 trial developed AKI during their ICU stay. A history of hypertension (p < 0.001) and the number of nephrotoxic medications administered (p = 0.029) were independent predictors of AKI on multivariate analysis. AKI was an independent predictor of death (p = 0.028) but not a poor functional outcome (p = 0.21) on multivariate testing. Unresolved renal dysfunction was the strongest independent predictor of death in this cohort (p < 0.001). CONCLUSIONS: AKI is a common complication following aSAH. Patients with premorbid hypertension and those treated with nephrotoxic medications may be at greater risk for renal dysfunction. AKI appears to confer an increased probability of death after aSAH.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.304
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2019
Admission routes1
Has abstractyes

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