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Low hematocrit on ICU admission is a risk factor of long term outcome in patients who required prolonged mechanical ventilation after cardiovascular surgery. A single center retrospective cohort study

2016· preprint· en· W4214495061 on OpenAlexaboutno aff
Akito Tsukinaga

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMechanical ventilationHematocritSingle CenterRetrospective cohort studyRisk factorVentilation (architecture)CohortCohort studyEmergency medicineSurgeryAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Background and Goal of Study: The Revised Cardiac Risk Index (RCRI) is a validated index that uses preoperative data to predict postoperative cardiac complications with moderate accuracy. The Surgical Apgar Score (SAS) is a validated prognostic index based on intraoperative factors (hypotension, bradycardia, estimated blood loss). We conducted a retrospective cohort study to determine if combining the 2 indices allows for more accurate estimation of perioperative cardiac risk. Materials and Methods: Following research ethics approval, we conducted a retrospective cohort study of adults (u226518 y) who underwent major elective noncardiac surgery from 2009-2014 at the Toronto General Hospital (Toronto, ON, Canada). The exposure variables were the RCRI (classified as 0, 1, 2 or u22653) and SAS (classified as 0-2, 3-4, 4-5, 6-7, 7-8, or 9-10). The primary outcome was myocardial injury, defined a postoperative elevation in troponin I above the 99th percentile. We assessed the correlation between the RCRI and SAS using the Spearman rho. The area under the curve (AUC) of the receiver-operating characteristic (ROC) curve was used to measure the association of the RCRI and SAS with myocardial injury. Finally, ROC curve and net risk reclassification analyses were used to assess if combining the RCRI and SAS resulted in more accurate prediction of myocardial injury. Four risk categories (< 1%, 1-5%, 5-10% and >10%) were used for reclassification analyses.Results and Discussion: The cohort included 16,841 patients, of whom 183 (1.1%) had myocardial injury. The proportions with RCRI scores of 0, 1, 2 and u22653 were 46.5%, 38.9%, 11.3% and 3.3% respectively. The proportions with SAS of 0-2, 3-4, 4-5, 6-7, 7-8, and 9-10 were 0.5%, 4.4%, 21.3%, 55.3% and 18.6% respectively. There was weak correlation between the RCRI and SAS (rho 0.13; 95% CI 0.12-0.15). As individual indices, the RCRI (AUC 0.73; CI 0.70-0.76) predicted myocardial injury better than the SAS (AUC 0.63; CI 0.59-0.67). The combination of the indices resulted in improved predictive accuracy (AUC 0.77; CI 0.74-0.80) than the RCRI (P< 0.001). The improvement was driven by better risk classification of the 16,658 patients without myocardial injury (net 1325 patients reclassified to lower risk categories).Conclusions: Supplementation of a preoperative index (RCRI) with data from an intraoperative index (SAS) results in improved estimation of perioperative cardiac risk. Further research is needed to confirm these findings.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.057
GPT teacher head0.317
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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