Raymond J Strobel1, Qixing Liang2, Min Zhang2, Xiaoting Wu3, Mary A M Rogers4, Patricia F Theurer5, Astrid B Fishstrom5, Steven D Harrington6, Alphonse DeLucia7, Gaetano Paone8, Himanshu J Patel3, Richard L Prager9, Donald S Likosky10. 1. Medical School, University of Michigan, Ann Arbor, Michigan. 2. Department of Biostatistics, University of Michigan, Ann Arbor, Michigan. 3. Department of Cardiac Surgery, University of Michigan, Ann Arbor, Michigan. 4. Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan. 5. Michigan Society of Thoracic and Cardiovascular Surgeons Quality Collaborative, Ann Arbor, Michigan. 6. Heart and Vascular Institute, Henry Ford Macomb Hospitals, Clinton Township, Michigan. 7. Department of Cardiac Surgery, Bronson Methodist Hospital, Kalamazoo, Michigan. 8. Division of Cardiac Surgery, Henry Ford Hospital, Detroit, Michigan. 9. Department of Cardiac Surgery, University of Michigan, Ann Arbor, Michigan; Michigan Society of Thoracic and Cardiovascular Surgeons Quality Collaborative, Ann Arbor, Michigan. 10. Department of Cardiac Surgery, University of Michigan, Ann Arbor, Michigan; Michigan Society of Thoracic and Cardiovascular Surgeons Quality Collaborative, Ann Arbor, Michigan. Electronic address: likosky@umich.edu.
Abstract
BACKGROUND: Postoperative pneumonia is the most prevalent of all hospital-acquired infections after isolated coronary artery bypass graft surgery (CABG). Accurate prediction of a patient's risk of this morbid complication is hindered by its low relative incidence. In an effort to support clinical decision making and quality improvement, we developed a preoperative prediction model for postoperative pneumonia after CABG. METHODS: We undertook an observational study of 16,084 patients undergoing CABG between the third quarter of 2011 and the second quarter of 2014 across 33 institutions participating in the Michigan Society of Thoracic and Cardiovascular Surgeons Quality Collaborative. Variables related to patient demographics, medical history, admission status, comorbid disease, cardiac anatomy, and the institution performing the procedure were investigated. Logistic regression through forward stepwise selection (p < 0.05 threshold) was utilized to develop a risk prediction model for estimating the occurrence of pneumonia. Traditional methods were used to assess the model's performance. RESULTS: Postoperative pneumonia occurred in 3.30% of patients. Multivariable analysis identified 17 preoperative factors, including demographics, laboratory values, comorbid disease, pulmonary and cardiac function, and operative status. The final model significantly predicted the occurrence of pneumonia, and performed well (C-statistic: 0.74). These findings were confirmed through sensitivity analyses by center and clinically important subgroups. CONCLUSIONS: We identified 17 readily obtainable preoperative variables associated with postoperative pneumonia. This model may be used to provide individualized risk estimation and to identify opportunities to reduce a patient's preoperative risk of pneumonia through prehabilitation.
BACKGROUND:Postoperative pneumonia is the most prevalent of all hospital-acquired infections after isolated coronary artery bypass graft surgery (CABG). Accurate prediction of a patient's risk of this morbid complication is hindered by its low relative incidence. In an effort to support clinical decision making and quality improvement, we developed a preoperative prediction model for postoperative pneumonia after CABG. METHODS: We undertook an observational study of 16,084 patients undergoing CABG between the third quarter of 2011 and the second quarter of 2014 across 33 institutions participating in the Michigan Society of Thoracic and Cardiovascular Surgeons Quality Collaborative. Variables related to patient demographics, medical history, admission status, comorbid disease, cardiac anatomy, and the institution performing the procedure were investigated. Logistic regression through forward stepwise selection (p < 0.05 threshold) was utilized to develop a risk prediction model for estimating the occurrence of pneumonia. Traditional methods were used to assess the model's performance. RESULTS:Postoperative pneumonia occurred in 3.30% of patients. Multivariable analysis identified 17 preoperative factors, including demographics, laboratory values, comorbid disease, pulmonary and cardiac function, and operative status. The final model significantly predicted the occurrence of pneumonia, and performed well (C-statistic: 0.74). These findings were confirmed through sensitivity analyses by center and clinically important subgroups. CONCLUSIONS: We identified 17 readily obtainable preoperative variables associated with postoperative pneumonia. This model may be used to provide individualized risk estimation and to identify opportunities to reduce a patient's preoperative risk of pneumonia through prehabilitation.
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