Predicting the risk of death following coronary artery bypass graft made simple: a retrospective study using the American College of Surgeons National Surgical Quality Improvement Program database.
BACKGROUND
Risk models to predict 30-day mortality following isolated coronary artery bypass graft is an active area of research. Simple risk predictors are particularly important for cardiothoracic surgeons who are coming under increased scrutiny since these physicians typically care for higher risk patients and thus expect worse outcomes. The objective of this study was to develop a 30-day postoperative mortality risk model for patients undergoing CABG using the American College of Surgeons National Surgical Quality Improvement Program database.


METHODS
Data was extracted and analyzed from the American College of Surgeons National Surgical Quality Improvement Program Participant Use Files (2005-2010). Patients that had ischemic heart disease (ICD9 410-414) undergoing one to four vessel CABG (CPT 33533-33536) were selected. To select for acquired heart disease, only patients age 40 and older were included. Multivariate logistic regression analysis was used to create a risk model. The C-statistic and the Hosmer-Lemeshow goodness-of-fit test were used to evaluate the model. Bootstrap-validated C-statistic was calculated.


RESULTS
A total of 2254 cases met selection criteria. Forty-nine patients (2.2%) died within 30 days. Six independent risk factors predictive of short-term mortality were identified including age, preoperative sodium, preoperative blood urea nitrogen, previous percutaneous coronary intervention, dyspnea at rest, and history of prior myocardial infarction. The C-statistic for this model was 0.773 while the bootstrap-validated C-statistic was 0.750. The Hosmer-Lemeshow test had a p-value of 0.675, suggesting the model does not overfit the data.


CONCLUSIONS
The American College of Surgeons National Surgical Quality Improvement Program risk model has good discrimination for 30-day mortality following coronary artery bypass graft surgery. The model employs six independent variables, making it easy to use in the clinical setting.
Department of Surgery, State University of New York Downstate Medical Center, 450 Clarkson Ave, Brooklyn, NY, 11203, USA. paul.chung@downstate.edu.
Paul J
PJ
Chung
Department of Surgery, State University of New York Downstate Medical Center, 450 Clarkson Ave, Brooklyn, NY, 11203, USA. timothy.carter@downstate.edu.
Timothy I
TI
Carter
Department of Cardiothoracic Surgery, State University of New York Downstate Medical Center, Brooklyn, 11203, USA. joshua.burack@downstate.edu.
Joshua H
JH
Burack
College of Medicine, State University of New York Downstate Medical Center, Brooklyn, 11203, USA. sophiaftam@gmail.com.
Sophia
S
Tam
Department of Surgery, State University of New York Downstate Medical Center, 450 Clarkson Ave, Brooklyn, NY, 11203, USA. antonio.alfonso@downstate.edu.
Antonio
A
Alfonso
Department of Surgery, State University of New York Downstate Medical Center, 450 Clarkson Ave, Brooklyn, NY, 11203, USA. gainosuke.sugiyama@downstate.edu.
Gainosuke
G
Sugiyama
D000368:Aged; D001806:Blood Urea Nitrogen; D001026:Coronary Artery Bypass / Q000401:mortality*; D003324:Coronary Artery Disease / Q000601:surgery*; D004417:Dyspnea / Q000453:epidemiology; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009203:Myocardial Infarction / Q000453:epidemiology; D062645:Percutaneous Coronary Intervention / Q000706:statistics & numerical data; D012189:Retrospective Studies; D012306:Risk; D018570:Risk Assessment; D012307:Risk Factors; D012964:Sodium / Q000097:blood; D014481:United States / Q000453:epidemiology
D012964:Sodium