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

Juan Manuel García-Torrecillas, María Carmen Olvera-Porcel, Manuel Ferrer-Márquez, Carmen Rosa-Garrido, Miguel Rodríguez-Barranco, María Carmen Lea-Pereira, Francisco Rubio-Gil and María-José Sánchez
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Juan Manuel García-Torrecillas: Department of Emergency Medicine, Hospital Universitario Torrecárdenas, 04009 Almería, Spain
María Carmen Olvera-Porcel: Fundación FIBAO, Hospital Universitario Torrecárdenas, 04009 Almería, Spain
Manuel Ferrer-Márquez: Department of General and Digestive Surgery, Hospital Universitario Torrecárdenas, 04009 Almería, Spain
Carmen Rosa-Garrido: Fundación FIBAO, Hospital Universitario de Jaén, 23007 Jaén, Spain
Miguel Rodríguez-Barranco: Instituto de Investigación Biosanitaria ibs.Granada, 18012 Granada, Spain
María Carmen Lea-Pereira: Empresa Pública Hospital de Poniente, El Ejido, 04700 Almería, Spain
Francisco Rubio-Gil: Department of General and Digestive Surgery, Hospital Universitario Torrecárdenas, 04009 Almería, Spain
María-José Sánchez: Instituto de Investigación Biosanitaria ibs.Granada, 18012 Granada, Spain

IJERPH, 2020, vol. 17, issue 12, 1-13

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.

Keywords: predictive model; colorectal cancer; epidemiology; public health; mortality (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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