2026-06-14T03:44:54Zhttps://repisalud.isciii.es/rest/oai/requestoai:repisalud.isciii.es:20.500.12105/97982025-05-08T07:49:40Zcom_20.500.12105_2052com_20.500.12105_2051col_20.500.12105_19608
Repisalud
author
Aróstegui, Inmaculada
author
González, Nerea
author
Fernandez de Larrea-Baz, Nerea
author
Lázaro-Aramburu, Santiago
author
Baré, Marisa
author
Redondo, Maximino
author
Sarasqueta, Cristina
author
García-Gutierrez, Susana
author
Quintana, Jose Maria
funder
Instituto de Salud Carlos III
funder
RETICS-Servicios de Salud Orientados a Enfermedades Crónicas (REDISSEC-ISCIII) (España)
funder
Unión Europea. Fondo Europeo de Desarrollo Regional (FEDER/ERDF)
funder
Basque Government (España)
funder
Ministerio de Economía y Competitividad (España)
2020-04-29T07:40:25Z
2020-04-29T07:40:25Z
2018
Clin Epidemiol. 2018 Mar 6;10:235-251.
10.2147/CLEP.S146729
1179-1349
Clinical epidemiology
29563837
http://hdl.handle.net/20.500.12105/9798
Introduction: Colorectal cancer is one of the most frequently diagnosed malignancies and a common cause of cancer-related mortality. The aim of this study was to develop and validate a clinical predictive model for 1-year mortality among patients with colon cancer who survive for at least 30 days after surgery. Methods: Patients diagnosed with colon cancer who had surgery for the first time and who survived 30 days after the surgery were selected prospectively. The outcome was mortality within 1 year. Random forest, genetic algorithms and classification and regression trees were combined in order to identify the variables and partition points that optimally classify patients by risk of mortality. The resulting decision tree was categorized into four risk categories. Split-sample and bootstrap validation were performed. ClinicalTrials.gov Identifier: NCT02488161. Results: A total of 1945 patients were enrolled in the study. The variables identified as the main predictors of 1-year mortality were presence of residual tumor, American Society of Anesthesiologists Physical Status Classification System risk score, pathologic tumor staging, Charlson Comorbidity Index, intraoperative complications, adjuvant chemotherapy and recurrence of tumor. The model was internally validated; area under the receiver operating characteristic curve (AUC) was 0.896 in the derivation sample and 0.835 in the validation sample. Risk categorization leads to AUC values of 0.875 and 0.832 in the derivation and validation samples, respectively. Optimal cut-off point of estimated risk had a sensitivity of 0.889 and a specificity of 0.758. Conclusion: The decision tree was a simple, interpretable, valid and accurate prediction rule of 1-year mortality among colon cancer patients who survived for at least 30 days after surgery.
eng
1-year-mortality
Clinical prediction rules
Colonic neoplasms
Colorectal surgery
Prediction model
Tree-based methods
Combining statistical techniques to predict postsurgical risk of 1-year mortality for patients with colon cancer
research article
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