Abstract
OBJECTIVES To develop a model for predicting long-term survival following coronary artery bypass graft surgery. METHODS This study included 46 573 patients from the Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZCTS) registry, who underwent isolated coronary artery bypass graft surgery between 2001 and 2014. Data were randomly split into development (23 282) and validation (23 291) samples. Cox regression models were fitted separately, using the important preoperative variables, for 4 'time intervals' (31-90 days, 91-365 days, 1-3 years and >3 years), with optimal predictors selected using the bootstrap bagging technique. Model performance was assessed both in validation data and in combined data (development and validation samples). Coefficients of all 4 final models were estimated on the combined data adjusting for hospital-level clustering. RESULTS The Kaplan-Meier mortality rates estimated in the sample were 1.7% at 90 days, 2.8% at 1 year, 4.4% at 2 years and 6.1% at 3 years. Age, peripheral vascular disease, respiratory disease, reduced ejection fraction, renal dysfunction, arrhythmia, diabetes, hypercholesterolaemia, cerebrovascular disease, hypertension, congestive heart failure, steroid use and smoking were included in all 4 models. However, their magnitude of effect varied across the time intervals. Harrell's C-statistics was 0.83, 0.78, 0.75 and 0.74 for 31-90 days, 91-365 days, 1-3 years and >3 years models, respectively. Models showed excellent discrimination and calibration in validation data. CONCLUSIONS Models were developed for predicting long-term survival at 4 time intervals after isolated coronary artery bypass graft surgery. These models can be used in conjunction with the existing 30-day mortality prediction model.
| Original language | English |
|---|---|
| Pages (from-to) | 257-263 |
| Number of pages | 7 |
| Journal | Interactive Cardiovascular and Thoracic Surgery |
| Volume | 26 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 1 Feb 2018 |
Keywords
- Cardiac surgery
- Coronary artery bypass graft
- Coronary revascularization
- Long-term survival
- Risk prediction model
- Risk stratification
Cite this
}
Predicting long-term survival after coronary artery bypass graft surgery. / Karim, Md N.; Reid, Christopher M.; Huq, Molla; Brilleman, Samuel L.; Cochrane, Andrew; Tran, Lavinia; Billah, Baki.
In: Interactive Cardiovascular and Thoracic Surgery, Vol. 26, No. 2, 01.02.2018, p. 257-263.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Predicting long-term survival after coronary artery bypass graft surgery
AU - Karim, Md N.
AU - Reid, Christopher M.
AU - Huq, Molla
AU - Brilleman, Samuel L.
AU - Cochrane, Andrew
AU - Tran, Lavinia
AU - Billah, Baki
PY - 2018/2/1
Y1 - 2018/2/1
N2 - OBJECTIVES To develop a model for predicting long-term survival following coronary artery bypass graft surgery. METHODS This study included 46 573 patients from the Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZCTS) registry, who underwent isolated coronary artery bypass graft surgery between 2001 and 2014. Data were randomly split into development (23 282) and validation (23 291) samples. Cox regression models were fitted separately, using the important preoperative variables, for 4 'time intervals' (31-90 days, 91-365 days, 1-3 years and >3 years), with optimal predictors selected using the bootstrap bagging technique. Model performance was assessed both in validation data and in combined data (development and validation samples). Coefficients of all 4 final models were estimated on the combined data adjusting for hospital-level clustering. RESULTS The Kaplan-Meier mortality rates estimated in the sample were 1.7% at 90 days, 2.8% at 1 year, 4.4% at 2 years and 6.1% at 3 years. Age, peripheral vascular disease, respiratory disease, reduced ejection fraction, renal dysfunction, arrhythmia, diabetes, hypercholesterolaemia, cerebrovascular disease, hypertension, congestive heart failure, steroid use and smoking were included in all 4 models. However, their magnitude of effect varied across the time intervals. Harrell's C-statistics was 0.83, 0.78, 0.75 and 0.74 for 31-90 days, 91-365 days, 1-3 years and >3 years models, respectively. Models showed excellent discrimination and calibration in validation data. CONCLUSIONS Models were developed for predicting long-term survival at 4 time intervals after isolated coronary artery bypass graft surgery. These models can be used in conjunction with the existing 30-day mortality prediction model.
AB - OBJECTIVES To develop a model for predicting long-term survival following coronary artery bypass graft surgery. METHODS This study included 46 573 patients from the Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZCTS) registry, who underwent isolated coronary artery bypass graft surgery between 2001 and 2014. Data were randomly split into development (23 282) and validation (23 291) samples. Cox regression models were fitted separately, using the important preoperative variables, for 4 'time intervals' (31-90 days, 91-365 days, 1-3 years and >3 years), with optimal predictors selected using the bootstrap bagging technique. Model performance was assessed both in validation data and in combined data (development and validation samples). Coefficients of all 4 final models were estimated on the combined data adjusting for hospital-level clustering. RESULTS The Kaplan-Meier mortality rates estimated in the sample were 1.7% at 90 days, 2.8% at 1 year, 4.4% at 2 years and 6.1% at 3 years. Age, peripheral vascular disease, respiratory disease, reduced ejection fraction, renal dysfunction, arrhythmia, diabetes, hypercholesterolaemia, cerebrovascular disease, hypertension, congestive heart failure, steroid use and smoking were included in all 4 models. However, their magnitude of effect varied across the time intervals. Harrell's C-statistics was 0.83, 0.78, 0.75 and 0.74 for 31-90 days, 91-365 days, 1-3 years and >3 years models, respectively. Models showed excellent discrimination and calibration in validation data. CONCLUSIONS Models were developed for predicting long-term survival at 4 time intervals after isolated coronary artery bypass graft surgery. These models can be used in conjunction with the existing 30-day mortality prediction model.
KW - Cardiac surgery
KW - Coronary artery bypass graft
KW - Coronary revascularization
KW - Long-term survival
KW - Risk prediction model
KW - Risk stratification
UR - http://www.scopus.com/inward/record.url?scp=85041519945&partnerID=8YFLogxK
U2 - 10.1093/icvts/ivx330
DO - 10.1093/icvts/ivx330
M3 - Article
VL - 26
SP - 257
EP - 263
JO - Interactive Cardiovascular and Thoracic Surgery
JF - Interactive Cardiovascular and Thoracic Surgery
SN - 1569-9293
IS - 2
ER -