Data from: Self-reported functional status predicts post-operative outcomes in non-cardiac surgery patients with pulmonary hypertension
Bibliographic record
Abstract
BACKGROUND: Pulmonary hypertension (PHTN) is associated with increased post-procedure morbidity and mortality. Pre-procedure echocardiography (ECHO) is a widely used tool for evaluation of these patients, but its accuracy in predicting post-procedure outcomes is unproven. Self-reported exercise tolerance has not been evaluated for operative risk stratification of PHTN patients. OBJECTIVE: We analyzed whether self-reported exercise tolerance predicts outcomes (hospi-tal length-of-stay [LOS], mortality and morbidity) in PHTN patients (WHO Class I - V) under-going anesthesia and surgery. METHODS AND FINDINGS: We reviewed 550 non-cardiac, non-obstetric procedures per-formed on 370 PHTN patients at a single institution between 2007 and 2013. All patients had cardiac ECHO documented within 1 year prior to the procedure. Pre-procedure comorbidities and ECHO data were collected. Functional status (< or ? 4 metabolic equivalents of task [METs]) was assigned based on responses to standard patient interview questions during the pre-anesthesia clinic visit. Multiple logistic regression was used to develop a risk score model (Pul-monary Hypertension Outcome Risk Score; PHORS) and determine its value in predicting post-procedure outcomes. In an adjusted model, functional status <4 METs was independently associ-ated with a LOS >7 days (p < .003), as were higher ASA class (p < .002), open surgical approach (p < .002), procedure duration > 2 hours (p < .001), and the absence of systemic hypertension (p = .012). PHORS Score ?2 was associated with an increased 30-day major complication rate (28.7% vs. 19.2%; p < 0.001) and ICU admission rate (8.6% s 2.8%; p = .007), but no statistical difference in hospital readmissions rate (17.6% vs. 14.0%; p = .29), or mortality (3.5% vs. 1.4%; p = .75). Similar ECHO findings did not further improve outcome prediction. CONCLUSIONS: Poor functional status is associated with severe PHTN and predicts increased LOS and post-procedure complications in patients with moderate to severe pulmonary hyperten-sion with different etiologies. A risk assessment model predicts increased LOS with fair accura-cy. A thorough evaluation of underlying etiologies of PHTN should be undertaken in every pa-tient.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".