Predictive Model of the Risk of In-Hospital Mortality in Colorectal Cancer Surgery, Based on the Minimum Basic Data Set

Abstract

Background: Various models have been proposed to predict mortality rates for hospital patients undergoing colorectal cancer surgery. However, none have been developed in Spain using clinical administrative databases and none are based exclusively on the variables available upon admission. Our study aim is to detect factors associated with in-hospital mortality in patients undergoing surgery for colorectal cancer and, on this basis, to generate a predictive mortality score. Methods: A population cohort for analysis was obtained as all hospital admissions for colorectal cancer during the period 2008–2014, according to the Spanish Minimum Basic Data Set. The main measure was actual and expected mortality after the application of the considered mathematical model. A logistic regression model and a mortality score were created, and internal validation was performed. Results: 115,841 hospitalization episodes were studied. Of these, 80% were included in the training set. The variables associated with in-hospital mortality were age (OR: 1.06, 95%CI: 1.05–1.06), urgent admission (OR: 4.68, 95% CI: 4.36–5.02), pulmonary disease (OR: 1.43, 95%CI: 1.28–1.60), stroke (OR: 1.87, 95%CI: 1.53–2.29) and renal insufficiency (OR: 7.26, 95%CI: 6.65–7.94). The level of discrimination (area under the curve) was 0.83. Conclusions: This mortality model is the first to be based on administrative clinical databases and hospitalization episodes. The model achieves a moderate–high level of discrimination.

Validation of the Surgical Outcome Risk Tool (SORT) and SORT v2 for Predicting Postoperative Mortality in Patients with Pancreatic Cancer Undergoing Surgery

“…Consequently, this clinically relevant question requires further investigation by a future trial mainly focusing on complex cases. Moreover, the findings of the current study regarding the value of clinical variables employed by SORT remain in accordance with the evidence provided by administrative datasets [ 20 ]. Finally, according to our outcomes, SORT and SORT v2 are associated with higher accuracy compared with other pre-operative (BH 2009—Barwon Health 2009) [ 21 ] and intraoperative risk-stratification tools (SAS—Surgical Apgar Score) [ 22 ], while remaining user-friendly as they implement six clinical variables.…”

Validation of the Surgical Outcome Risk Tool (SORT) for Predicting Postoperative Mortality in Colorectal Cancer Patients Undergoing Surgery and Subgroup Analysis

“…In fact, this observation is clinically important, since age and preoperative frailty are associated with postoperative morbidity in patients undergoing surgery for colorectal cancer [14, 15]. Furthermore, the findings of the present study regarding the value of clinical variables employed by SORT, are in accordance with evidence provided by administrative datasets [16]. Besides, according to our outcomes SORT presents higher accuracy compared with other preoperative (Barwon Health 2009–BH 2009) [17], along with intraoperative risk stratification models (Surgical Apgar Score) [18], while remaining friendly‐to‐use, since it implements only six preoperative variables.…”

Understanding Variation in In-hospital Mortality After Major Surgery in the United States

“…In-hospital mortality (IHM) after surgery is multifactorial and influenced by patient factors (such as age, 1 comorbidity, 1 type of insurance, 2 and race 2 ), hospital characteristics (such as the presence of critical care facilities 3 and teaching status 4 ), and hospital case volume. However, over the last 2 decades, much of the policy focus has been on the role of case volume and the volume-outcome association to improve postoperative outcomes.…”