Derivation and validation of a clinical prediction rule for nosocomial pneumonia after coronary artery bypass graft surgery.
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.
Kinlin
Laura M
LM
Research Institute of the Hospital for Sick Children, Toronto, Ontario, Canada.
Kirchner
Cheryl
C
Zhang
Huiling
H
Daley
Jennifer
J
Fisman
David N
DN
D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D019540:Area Under Curve; D016009:Chi-Square Distribution; D001026:Coronary Artery Bypass / Q000009:adverse effects* / Q000401:mortality; D003428:Cross Infection / Q000453:epidemiology / Q000209:etiology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011014:Pneumonia / Q000453:epidemiology / Q000209:etiology*; D012372:ROC Curve; D015203:Reproducibility of Results; D012307:Risk Factors