BACKGROUND: Simplified risk scores for coronary artery bypass graft surgery are frequently in lieu of more complicated statistical models and are valuable for informed consent and choice of intervention. Previous risk scores have been based on in-hospital mortality, but a substantial number of patients die within 30 days of the procedure. These deaths should also be accounted for, so we have developed a risk score based on in-hospital and 30-day mortality. METHODS: New York's Cardiac Surgery Reporting System was used to develop an in-hospital and 30-day logistic regression model for patients undergoing coronary artery bypass graft surgery in 2009, and this model was converted into a simple linear risk score that provides estimated in-hospital and 30-day mortality rates for different values of the score. The accuracy of the risk score in predicting mortality was tested. This score was also validated by applying it to 2008 New York coronary artery bypass graft data. Subsequent analyses evaluated the ability of the risk score to predict complications and length of stay. RESULTS: The overall in-hospital and 30-day mortality rate for the 10,148 patients in the study was 1.79%. There are seven risk factors comprising the score, with risk factor scores ranging from 1 to 5, and the highest possible total score is 23. The score accurately predicted mortality in 2009 as well as in 2008, and was strongly correlated with complications and length of stay. CONCLUSIONS: The risk score is a simple way of estimating short-term mortality that accurately predicts mortality in the year the model was developed as well as in the previous year. Perioperative complications and length of stay are also well predicted by the risk score.
BACKGROUND: Simplified risk scores for coronary artery bypass graft surgery are frequently in lieu of more complicated statistical models and are valuable for informed consent and choice of intervention. Previous risk scores have been based on in-hospital mortality, but a substantial number of patients die within 30 days of the procedure. These deaths should also be accounted for, so we have developed a risk score based on in-hospital and 30-day mortality. METHODS: New York's Cardiac Surgery Reporting System was used to develop an in-hospital and 30-day logistic regression model for patients undergoing coronary artery bypass graft surgery in 2009, and this model was converted into a simple linear risk score that provides estimated in-hospital and 30-day mortality rates for different values of the score. The accuracy of the risk score in predicting mortality was tested. This score was also validated by applying it to 2008 New York coronary artery bypass graft data. Subsequent analyses evaluated the ability of the risk score to predict complications and length of stay. RESULTS: The overall in-hospital and 30-day mortality rate for the 10,148 patients in the study was 1.79%. There are seven risk factors comprising the score, with risk factor scores ranging from 1 to 5, and the highest possible total score is 23. The score accurately predicted mortality in 2009 as well as in 2008, and was strongly correlated with complications and length of stay. CONCLUSIONS: The risk score is a simple way of estimating short-term mortality that accurately predicts mortality in the year the model was developed as well as in the previous year. Perioperative complications and length of stay are also well predicted by the risk score.
Authors: Chuntao Wu; Fabian T Camacho; Spencer B King; Gary Walford; David R Holmes; Nicholas J Stamato; Peter B Berger; Samin Sharma; Jeptha P Curtis; Ferdinand J Venditti; Alice K Jacobs; Edward L Hannan Journal: Circ Cardiovasc Interv Date: 2014-01-14 Impact factor: 6.546
Authors: Krzysztof Wrobel; Susanna R Stevens; Robert H Jones; Craig H Selzman; Andre Lamy; Thomas M Beaver; Ljubomir T Djokovic; Nan Wang; Eric J Velazquez; George Sopko; Irving L Kron; J Michael DiMaio; Robert E Michler; Kerry L Lee; Michael Yii; Chua Yeow Leng; Marian Zembala; Jean L Rouleau; Richard C Daly; Hussein R Al-Khalidi Journal: Circulation Date: 2015-08-25 Impact factor: 29.690
Authors: Louise Y Sun; Anna Chu; Derrick Y Tam; Xuesong Wang; Jiming Fang; Peter C Austin; Christopher M Feindel; Garth H Oakes; Vicki Alexopoulos; Natasa Tusevljak; Maral Ouzounian; Douglas S Lee Journal: CMAJ Date: 2021-11-22 Impact factor: 8.262