A C Murray1, C Mauro2, J Rein1, R P Kiran3,4. 1. Division of Colorectal Surgery, New York Presbyterian Hospital/Columbia University Medical Center, Herbert Irving Pavilion, 161 Fort Washington Avenue, Floor: 8, New York, NY, 10032, USA. 2. Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA. 3. Division of Colorectal Surgery, New York Presbyterian Hospital/Columbia University Medical Center, Herbert Irving Pavilion, 161 Fort Washington Avenue, Floor: 8, New York, NY, 10032, USA. rpk2118@cumc.columbia.edu. 4. Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA. rpk2118@cumc.columbia.edu.
Abstract
BACKGROUND: The aim of the present study was to develop a clinically relevant, accurate and usable risk assessment scoring system solely for colorectal cancer patients undergoing elective resection. METHODS: All colorectal resections for colorectal cancer 2006-2012 were identified from the American College of Surgeons Quality Improvement Program. Independent risk factors for 30-day mortality after elective surgery were identified using univariable and multivariable logistic regression. A points-calculator based on factors most strongly associated with mortality and accurately predicting risk of mortality was developed. RESULTS: Fifty-nine thousand nine hundred eighty-six patients underwent elective colorectal cancer surgery, and 1096 (1.8 %) died within 30 days. On multivariable analysis, the strongest risk factors for mortality were age ≥65 years [odds ratio (OR) 2.17, 95 % confidence interval (CI) 1.61-2.92], American Society of Anesthesiologists score ≥3 (OR 1.77, 95 % CI 1.29-2.42), renal failure (OR 3.15, 95 % CI 1.01-9.77), disseminated cancer (OR 2.56, 95 % CI 1.96-3.35), hypoalbuminemia (OR 2.84, 95 % CI 2.21-3.65), preoperative ascites (OR 3.17, 95 % CI 2.07-4.87), heart failure (OR 2.08, 95 % CI 1.35-3.20) and functional status (OR 2.05, 95 % CI 1.56-2.70). A model that accurately predicted risk of mortality was created using forward stepwise logistic regression and externally validated (area under the curve 0.826). This allowed for development of an eight-factor predictive score; maximum points conferred mortality of 96.1 % (p < 0.0001). CONCLUSIONS: A simple preoperative scoring system predicting 30-day mortality with good capability may allow better preoperative risk assessment, optimization and decision-making.
BACKGROUND: The aim of the present study was to develop a clinically relevant, accurate and usable risk assessment scoring system solely for colorectal cancerpatients undergoing elective resection. METHODS: All colorectal resections for colorectal cancer 2006-2012 were identified from the American College of Surgeons Quality Improvement Program. Independent risk factors for 30-day mortality after elective surgery were identified using univariable and multivariable logistic regression. A points-calculator based on factors most strongly associated with mortality and accurately predicting risk of mortality was developed. RESULTS: Fifty-nine thousand nine hundred eighty-six patients underwent elective colorectal cancer surgery, and 1096 (1.8 %) died within 30 days. On multivariable analysis, the strongest risk factors for mortality were age ≥65 years [odds ratio (OR) 2.17, 95 % confidence interval (CI) 1.61-2.92], American Society of Anesthesiologists score ≥3 (OR 1.77, 95 % CI 1.29-2.42), renal failure (OR 3.15, 95 % CI 1.01-9.77), disseminated cancer (OR 2.56, 95 % CI 1.96-3.35), hypoalbuminemia (OR 2.84, 95 % CI 2.21-3.65), preoperative ascites (OR 3.17, 95 % CI 2.07-4.87), heart failure (OR 2.08, 95 % CI 1.35-3.20) and functional status (OR 2.05, 95 % CI 1.56-2.70). A model that accurately predicted risk of mortality was created using forward stepwise logistic regression and externally validated (area under the curve 0.826). This allowed for development of an eight-factor predictive score; maximum points conferred mortality of 96.1 % (p < 0.0001). CONCLUSIONS: A simple preoperative scoring system predicting 30-day mortality with good capability may allow better preoperative risk assessment, optimization and decision-making.
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