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 (hospital length-of-stay [LOS], mortality and morbidity) in PHTN patients (WHO Class I-V) undergoing anesthesia and surgery. METHODS AND FINDINGS: We reviewed 550 non-cardiac, non-obstetric procedures performed 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 (Pulmonary Hypertension Outcome Risk Score; PHORS) and determine its value in predicting post-procedure outcomes. In an adjusted model, functional status <4 METs was independently associated 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 hypertension with different etiologies. A risk assessment model predicts increased LOS with fair accuracy. A thorough evaluation of underlying etiologies of PHTN should be undertaken in every patient.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".