Predictive Model of the Risk of In-Hospital Mortality in Colorectal Cancer Surgery, Based on the Minimum Basic Data Set
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MDPI
Materia
Predictive models Colorectal cancer Epidemiology Public health Mortality
Date
2020Referencia bibliográfica
García-Torrecillas, J.M.; Olvera-Porcel, M.C.; Ferrer-Márquez, M.; Rosa-Garrido, C.; Rodríguez-Barranco, M.; Lea-Pereira, M.C.; Rubio-Gil, F.; Sánchez, M.-J. Predictive Model of the Risk of In-Hospital Mortality in Colorectal Cancer Surgery, Based on the Minimum Basic Data Set. Int. J. Environ. Res. Public Health 2020, 17, 4216. https://doi.org/10.3390/ijerph17124216
Sponsorship
Carlos III Institute of Health, Madrid (Spain) under the 2013-2016 National Plan for RDI PI16/01931; ISCIII-General Subdirectorate for Evaluation and Promotion of Research, within the European Regional Development Fund (FEDER)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.