Development of a Nonparametric Predictive Model for Readmission Risk in Elderly Adults After Colon and Rectal Cancer Surgery
- Autores: Heather Yeo-, Jialin Mao-, Jonathan S. Abelson, Mark S. Lachs, Emily Finlayson, Jeffrey W. Milsom, Art- Sedrakyan
- Localización: Journal of the American Geriatrics Society, ISSN 0002-8614, Vol. 64, Nº. 11, 2, 2016, págs. 125-130
- Idioma: inglés
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Resumen
Objectives Primary objective: to use advanced nonparametric techniques to determine risk factors for readmission after colorectal cancer surgery in elderly adults. Secondary objective: to compare this methodology with traditional parametric methods.
Design Using data from the American College of Surgeons National Surgical Quality Improvement Program (NSQIP), nonparametric techniques were used to evaluate the risk of readmission in elderly adults undergoing surgery for colorectal cancer in 2011 and 2012.
Setting More than 200 hospitals participating in the NSQIP database.
Participants Individuals aged 65 and older who underwent surgery for colorectal cancer in 2011 and 2012 (N = 2,117).
Measurements Age-stratified robust nonparametric predictive model (classification and regression tree (CART) analysis) of 30-day readmission for elderly adults undergoing surgery for colorectal cancer.
Results Recent chemotherapy was the most important predictor of readmission in participants aged 65 to 74, with 20% of those with recent chemotherapy and 11% of with no recent chemotherapy being readmitted. Participants aged 75 to 84 who had recently undergone chemotherapy had a readmission rate of 23%, whereas those with no chemotherapy had a readmission rate of 9%. Being underweight was the greatest predictor of readmission (30%) in participants aged 85 and older. These methods were found to be more robust than traditional logistic regression.
Conclusion Specific age-related preoperative factors help predict readmission in elderly adults undergoing colorectal cancer surgery. Results of the nonparametric CART analysis are better than traditional regression analysis and help physicians to clinically stratify based on age. This model may help identify individuals in whom intervention may be helpful in reducing readmission after surgery.
