Cargando…

The NEAT Predictive Model for Survival in Patients with Advanced Cancer

PURPOSE: We previously developed a model to more accurately predict life expectancy for stage IV cancer patients referred to radiation oncology. The goals of this study are to validate this model and to compare competing published models. MATERIALS AND METHODS: From May 2012 to March 2015, 280 conse...

Descripción completa

Detalles Bibliográficos
Autores principales: Zucker, Amanda, Tsai, Chiaojung Jillian, Loscalzo, John, Calves, Pedro, Kao, Johnny
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Cancer Association 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6192914/
https://www.ncbi.nlm.nih.gov/pubmed/29361815
http://dx.doi.org/10.4143/crt.2017.223
_version_ 1783363973964365824
author Zucker, Amanda
Tsai, Chiaojung Jillian
Loscalzo, John
Calves, Pedro
Kao, Johnny
author_facet Zucker, Amanda
Tsai, Chiaojung Jillian
Loscalzo, John
Calves, Pedro
Kao, Johnny
author_sort Zucker, Amanda
collection PubMed
description PURPOSE: We previously developed a model to more accurately predict life expectancy for stage IV cancer patients referred to radiation oncology. The goals of this study are to validate this model and to compare competing published models. MATERIALS AND METHODS: From May 2012 to March 2015, 280 consecutive patientswith stage IV cancerwere prospectively evaluated by a single radiation oncologist. Patients were separated into training, validation and combined sets. TheNEAT model evaluated number of active tumors (“N”), Eastern Cooperative Oncology Group performance status (“E”), albumin (“A”) and primary tumor site (“T”). The Odette Cancer Center model validated performance status, bone only metastases and primary tumor site. The Harvard TEACHH model investigated primary tumor type, performance status, age, prior chemotherapy courses, liver metastases, and hospitalization within 3 months. Cox multivariable analyses and logisticalregressionwere utilized to compare model performance. RESULTS: Number of active tumors, performance status, albumin, primary tumor site, prior hospitalizationwithin the last 3 months, and liver metastases predicted overall survival on uinvariate and multivariable analysis (p < 0.05 for all). The NEAT model separated patients into four prognostic groups with median survivals of 24.9, 14.8, 4.0, and 1.2 months, respectively (p < 0.001). The NEAT model had a C-index of 0.76 with a Nagelkerke’s R2 of 0.54 suggesting good discrimination, calibration and total performance compared to competing prognostic models. CONCLUSION: The NEAT model warrants further investigation as a clinically useful approach to predict survival in patients with stage IV cancer.
format Online
Article
Text
id pubmed-6192914
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Korean Cancer Association
record_format MEDLINE/PubMed
spelling pubmed-61929142018-10-24 The NEAT Predictive Model for Survival in Patients with Advanced Cancer Zucker, Amanda Tsai, Chiaojung Jillian Loscalzo, John Calves, Pedro Kao, Johnny Cancer Res Treat Original Article PURPOSE: We previously developed a model to more accurately predict life expectancy for stage IV cancer patients referred to radiation oncology. The goals of this study are to validate this model and to compare competing published models. MATERIALS AND METHODS: From May 2012 to March 2015, 280 consecutive patientswith stage IV cancerwere prospectively evaluated by a single radiation oncologist. Patients were separated into training, validation and combined sets. TheNEAT model evaluated number of active tumors (“N”), Eastern Cooperative Oncology Group performance status (“E”), albumin (“A”) and primary tumor site (“T”). The Odette Cancer Center model validated performance status, bone only metastases and primary tumor site. The Harvard TEACHH model investigated primary tumor type, performance status, age, prior chemotherapy courses, liver metastases, and hospitalization within 3 months. Cox multivariable analyses and logisticalregressionwere utilized to compare model performance. RESULTS: Number of active tumors, performance status, albumin, primary tumor site, prior hospitalizationwithin the last 3 months, and liver metastases predicted overall survival on uinvariate and multivariable analysis (p < 0.05 for all). The NEAT model separated patients into four prognostic groups with median survivals of 24.9, 14.8, 4.0, and 1.2 months, respectively (p < 0.001). The NEAT model had a C-index of 0.76 with a Nagelkerke’s R2 of 0.54 suggesting good discrimination, calibration and total performance compared to competing prognostic models. CONCLUSION: The NEAT model warrants further investigation as a clinically useful approach to predict survival in patients with stage IV cancer. Korean Cancer Association 2018-10 2018-01-24 /pmc/articles/PMC6192914/ /pubmed/29361815 http://dx.doi.org/10.4143/crt.2017.223 Text en Copyright © 2018 by the Korean Cancer Association This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Zucker, Amanda
Tsai, Chiaojung Jillian
Loscalzo, John
Calves, Pedro
Kao, Johnny
The NEAT Predictive Model for Survival in Patients with Advanced Cancer
title The NEAT Predictive Model for Survival in Patients with Advanced Cancer
title_full The NEAT Predictive Model for Survival in Patients with Advanced Cancer
title_fullStr The NEAT Predictive Model for Survival in Patients with Advanced Cancer
title_full_unstemmed The NEAT Predictive Model for Survival in Patients with Advanced Cancer
title_short The NEAT Predictive Model for Survival in Patients with Advanced Cancer
title_sort neat predictive model for survival in patients with advanced cancer
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6192914/
https://www.ncbi.nlm.nih.gov/pubmed/29361815
http://dx.doi.org/10.4143/crt.2017.223
work_keys_str_mv AT zuckeramanda theneatpredictivemodelforsurvivalinpatientswithadvancedcancer
AT tsaichiaojungjillian theneatpredictivemodelforsurvivalinpatientswithadvancedcancer
AT loscalzojohn theneatpredictivemodelforsurvivalinpatientswithadvancedcancer
AT calvespedro theneatpredictivemodelforsurvivalinpatientswithadvancedcancer
AT kaojohnny theneatpredictivemodelforsurvivalinpatientswithadvancedcancer
AT zuckeramanda neatpredictivemodelforsurvivalinpatientswithadvancedcancer
AT tsaichiaojungjillian neatpredictivemodelforsurvivalinpatientswithadvancedcancer
AT loscalzojohn neatpredictivemodelforsurvivalinpatientswithadvancedcancer
AT calvespedro neatpredictivemodelforsurvivalinpatientswithadvancedcancer
AT kaojohnny neatpredictivemodelforsurvivalinpatientswithadvancedcancer