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dc.contributor.authorAróstegui, Inmaculada
dc.contributor.authorGonzalez, Nerea
dc.contributor.authorFernandez de Larrea-Baz, Nerea 
dc.contributor.authorLázaro-Aramburu, Santiago
dc.contributor.authorBaré, Marisa
dc.contributor.authorRedondo, Maximino
dc.contributor.authorSarasqueta, Cristina
dc.contributor.authorGarcía-Gutierrez, Susana
dc.contributor.authorQuintana, José M
dc.date.accessioned2020-04-29T07:40:25Z
dc.date.available2020-04-29T07:40:25Z
dc.date.issued2018
dc.identifier.citationClin Epidemiol. 2018 Mar 6;10:235-251.es_ES
dc.identifier.issn1179-1349es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/9798
dc.description.abstractIntroduction: 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.es_ES
dc.description.sponsorshipFinancial support for this study was provided, in part, by grants from the Instituto de Salud Carlos III (PS09/00314, PS09/00910, PS09/00746, PS09/00805, PI09/90460, PI09/90490, PI09/90453, PI09/90441, PI09/90397 and the thematic network REDISSEC – Red de Investigación en Servicios de Salud en Enfermedades Crónicas), co-funded by European Regional Development Fund/European Social Fund (ERDF/ESF “Investing in your future”); the Research Committee of the Hospital Galdakao; the Department of Health and the Department of Education, Language Policy and Culture from the Basque Government (2010111098, IT620-13 and BERC 2014-2017 program); the Spanish Ministry of Economy and Competitiveness MINECO and FEDER (MTM2013-40941-P, MTM2016-74931-P and BCAM Severo Ochoa excellence accreditation SEV-2013-0323). The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing and publishing the report.es_ES
dc.language.isoenges_ES
dc.publisherDove Medical Press es_ES
dc.type.hasVersionVoRes_ES
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subject1-year-mortalityes_ES
dc.subjectclinical prediction ruleses_ES
dc.subjectcolonic neoplasmses_ES
dc.subjectcolorectal surgeryes_ES
dc.subjectprediction modeles_ES
dc.subjecttree-based methodses_ES
dc.titleCombining statistical techniques to predict postsurgical risk of 1-year mortality for patients with colon canceres_ES
dc.typejournal articlees_ES
dc.rights.licenseAttribution-NonCommercial 4.0 International*
dc.identifier.pubmedID29563837es_ES
dc.format.volume10es_ES
dc.format.page235-251es_ES
dc.identifier.doi10.2147/CLEP.S146729es_ES
dc.contributor.funderInstituto de Salud Carlos III 
dc.contributor.funderRETICS-Servicios de Salud Orientados a Enfermedades Crónicas (REDISSEC-ISCIII) (España) 
dc.contributor.funderUnión Europea. Fondo Europeo de Desarrollo Regional (FEDER/ERDF) 
dc.contributor.funderBasque Government (España) 
dc.contributor.funderMinisterio de Economía y Competitividad (España) 
dc.description.peerreviewedes_ES
dc.relation.publisherversionhttps://doi.org/10.2147/CLEP.S146729es_ES
dc.identifier.journalClinical epidemiologyes_ES
dc.repisalud.centroISCIII::Centro Nacional de Epidemiologíaes_ES
dc.repisalud.institucionISCIIIes_ES
dc.rights.accessRightsopen accesses_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/MICINN//PS09%2F00314/ES/FACTORES DE RIESGO A CORTO Y MEDIO PLAZO DE RESULTADOS ADVERSOS, Y CREACION DE MODELOS PREDICTIVOS, EN PACIENTES CON CANCER DE COLON O RECTO.  ESTUDIO COORDINADO-MULTICENTRICO IRYSS-CCR. PROYECTO COORDINADOR/ 
dc.relation.projectFECYTinfo:eu_repo/grantAgreement/ES/PS09/00910
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/MICINN//PS09%2F00746/ES/FACTORES DE RIESGO A CORTO Y MEDIO PLAZO DE RESULTADOS ADVERSOS, Y CREACION DE MODELOS PREDICTIVOS, EN PACIENTES CON CANCER DE COLON O RECTO.  ESTUDIO COORDINADO-MULTICENTRICO IRYSS-CCR/ 
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/MICINN//PS09%2F00805/ES/FACTORES DE RIESGO A CORTO Y MEDIO PLAZO DE RESULTADOS ADVERSOS, Y CREACION DE MODELOS PREDICTIVOS, EN PACIENTES CON CANCER DE COLON O RECTO.ESTUDIO COORDINADO-MULTICENTRICO IRYSS-CCR/ 
dc.relation.projectFECYTinfo:eu_repo/grantAgreement/ES/PI09/90460
dc.relation.projectFECYTinfo:eu_repo/grantAgreement/ES/PI09/90490
dc.relation.projectFECYTinfo:eu_repo/grantAgreement/ES/PI09/90453
dc.relation.projectFECYTinfo:eu_repo/grantAgreement/ES/PI09/90441
dc.relation.projectFECYTinfo:eu_repo/grantAgreement/ES/PI09/90397
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/MINECO//MTM2013-40941-P/ES/METODOS AVANZADOS EN MODELOS ESPACIALES DE SOBREDISPERSION, MODELIZACION DE LA CVRS Y DATOS LONGITUDINALES: DESARROLLO, IMPLEMENTACION Y VALIDACION/ 
dc.relation.projectFECYTinfo:eu_repo/grantAgreement/ES/PI12/00488
dc.relation.projectFECYTinfo:eu_repo/grantAgreement/ES/MTM2016-74931-P
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/MINECO//SEV-2013-0323/ES/-/ 


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Attribution-NonCommercial 4.0 International
Este Item está sujeto a una licencia Creative Commons: Attribution-NonCommercial 4.0 International