Development and Validation of Severe Hypoxemia Associated Risk Prediction Model in 1,000 Mechanically Ventilated Patients*
Bibliographic record
Abstract
OBJECTIVES: Patients with severe, persistent hypoxemic respiratory failure have a higher mortality. Early identification is critical for informing clinical decisions, using rescue strategies, and enrollment in clinical trials. The objective of this investigation was to develop and validate a prediction model to accurately and timely identify patients with severe hypoxemic respiratory failure at high risk of death, in whom novel rescue strategies can be efficiently evaluated. DESIGN: Electronic medical record analysis. SETTING: Medical, surgical, and mixed ICU setting at a tertiary care institution. PATIENTS: Mechanically-ventilated ICU patients. MEASUREMENTS AND MAIN RESULTS: Mechanically ventilated ICU patients were screened for severe hypoxemic respiratory failure (Murray lung injury score of ≥ 3). Survival to hospital discharge was the dependent variable. Clinical predictors within 24 hours of onset of severe hypoxemia were considered as the independent variables. An area under the curve and a Hosmer-Lemeshow goodness-of-fit test were used to assess discrimination and calibration. A logistic regression model was developed in the derivation cohort (2005-2007). The model was validated in an independent cohort (2008-2010). Among 79,341 screened patients, 1,032 met inclusion criteria. Mortality was 41% in the derivation cohort (n = 464) and 35% in the validation cohort (n = 568). The final model included hematologic malignancy, cirrhosis, aspiration, estimated dead space, oxygenation index, pH, and vasopressor use. The area under the curve of the model was 0.85 (0.82-0.89) and 0.79 (0.75-0.82) in the derivation and validation cohorts, respectively, and showed good calibration. A modified model, including only physiologic variables, performed similarly. It had comparable performance in patients with acute respiratory distress syndrome and outperformed previous prognostic models. CONCLUSIONS: A model using comorbid conditions and physiologic variables on the day of developing severe hypoxemic respiratory failure can predict hospital mortality.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".