Derivation and Validation of a Clinical Prediction Rule for Nosocomial Pneumonia after Coronary Artery Bypass Graft Surgery
Bibliographic record
Abstract
BACKGROUND: Nosocomial pneumonia is an important cause of morbidity and mortality among surgical patients in the United States. The emergence of effective but potentially costly or risky preventive interventions makes perioperative risk stratification desirable. We sought to develop a prediction rule for pneumonia after coronary artery bypass grafting (CABG), a common surgical procedure. METHODS: Data on individuals undergoing CABG at 32 hospitals in 6 states were extracted from Tenet Healthcare's Quality and Resource Management System. A logistic regression-based prediction rule was developed in half of the study sample and validated in the remaining patients. RESULTS: Of 17,143 individuals undergoing CABG from January 1999 through February 2004, 361 (2%) developed pneumonia without a known aspiration etiology. Thirteen independent predictors of pneumonia were identified in the derivation subset of the sample: body mass index <18.5 (defined as the weight in kilograms divided by the square of the height in meters), smoking history, admission from a nonresidential setting, cancer history, chronic obstructive pulmonary disease, Canadian Cardiovascular Society score 3, prior internal mammary artery CABG, emergency status, serum creatinine level >1.2 mg/dL, percutaneous transluminal coronary angioplasty, blood transfusion, preoperative vancomycin administration, and receipt of mechanical ventilation for >1 day. The model-based rule was well calibrated (Hosmer-Lemeshow X(2)=5.51; P=.70) and demonstrated good discrimination (area under the receiver-operating characteristic curve [ROC AUC], 0.78) in the derivation group. Discriminatory ability was also reasonable in the validation cohort (ROC AUC, 0.75; P=.18, for difference in ROC AUC between groups). CONCLUSIONS: Using a large cohort of patients treated at community and teaching hospitals, we derived and validated a prediction rule for pneumonia after CABG. This index may prove to be useful in prioritizing receipt of preventive interventions.
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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 | 0.013 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".