The independent effect of cancer on outcomes: a potential limitation of surgical risk prediction

Abstract

Background: Cancer patients are often thought to have worse surgical outcomes. There is a growing view that risk models do not adequately predict these outcomes. This study aims to compare the use of common risk models for benign versus malignant gastrointestinal disease.

Materials and methods: The National Surgical Quality Improvement Program (NSQIP) 2005-2015 participant use files were queried for patients undergoing elective surgery for benign and malignant diseases with a primary procedure code for major colon, pancreas, or stomach resection. Multivariable logistic regression was performed to identify independent predictors of mortality and morbidity.

Results: We identified 264,401 cases (111,563 malignant). The gastrointestinal cancer population was disproportionately male, older than 65, nonwhite, and less functionally independent. Comorbidities more common in the cancer population included diabetes, hypertension, dyspnea, and chronic obstructive pulmonary disease. Cancer patients had a longer length of stay (+0.9 days), higher mortality rate (1.7% versus 1.1%), and higher complication rate (27.4% versus 23.2%). NSQIP prediction models for complications in cancer versus noncancer patients underperformed for predicting mortality (P < 0.001). Multivariable regression demonstrated that a diagnosis of cancer requiring surgery independently conferred an 18% increased odds of death, a 9% increased odds of a complication, and an 8% increased odds of multiple complications compared to patients with benign disease.

Conclusions: NSQIP prediction models less effectively evaluate the risk of death in cancer patients as compared to patients with benign disease. A diagnosis of cancer is independently associated with an increased risk of surgical complications. Incorporating cancer diagnosis into surgical risk models may better inform patient and surgeon expectations.

Keywords: Logistic regression; Morbidity; Neoplasms; Outcomes assessment; Risk; Surgery.

Figure A1. Distribution of aggregate covariate similarity pre- and post-propensity score matching when modeling the risk of death at 30 days

*By Stata nomenclature, the “treated” group represents those with surgery for a cancer indication.

Figure A2. Distribution of aggregate covariate similarity pre- and post-propensity score matching when modeling the risk of any complication at 30 days

*By Stata nomenclature, the “treated” group represents those with surgery for a cancer indication.