Literature DB >> 34953934

Longitudinal patient-reported outcomes and survival among early-stage non-small cell lung cancer patients receiving stereotactic body radiotherapy.

Kea Turner1, Naomi C Brownstein2, Zachary Thompson3, Issam El Naqa4, Yi Luo5, Heather S L Jim6, Dana E Rollison7, Rachel Howard8, Desmond Zeng9, Stephen A Rosenberg10, Bradford Perez11, Andreas Saltos12, Laura B Oswald13, Brian D Gonzalez14, Jessica Y Islam15, Amir Alishahi Tabriz16, Wenbin Zhang17, Thomas J Dilling18.   

Abstract

BACKGROUND AND
PURPOSE: The study objective was to determine whether longitudinal changes in patient-reported outcomes (PROs) were associated with survival among early-stage, non-small cell lung cancer (NSCLC) patients undergoing stereotactic body radiation therapy (SBRT).
MATERIALS AND METHODS: Data were obtained from January 2015 through March 2020. We ran a joint probability model to assess the relationship between time-to-death, and longitudinal PRO measurements. PROs were measured through the Edmonton Symptom Assessment Scale (ESAS). We controlled for other covariates likely to affect symptom burden and survival including stage, tumor diameter, comorbidities, gender, race/ethnicity, relationship status, age, and smoking status.
RESULTS: The sample included 510 early-stage NSCLC patients undergoing SBRT. The median age was 73.8 (range: 46.3-94.6). The survival component of the joint model demonstrates that longitudinal changes in ESAS scores are significantly associated with worse survival (HR: 1.04; 95% CI: 1.02-1.05). This finding suggests a one-unit increase in ESAS score increased probability of death by 4%. Other factors significantly associated with worse survival included older age (HR: 1.04; 95% CI: 1.03-1.05), larger tumor diameter (HR: 1.21; 95% CI: 1.01-1.46), male gender (HR: 1.87; 95% CI: 1.36-2.57), and current smoking status (HR: 2.39; 95% CI: 1.25-4.56).
CONCLUSION: PROs are increasingly being collected as a part of routine care delivery to improve symptom management. Healthcare systems can integrate these data with other real-world data to predict patient outcomes, such as survival. Capturing longitudinal PROs-in addition to PROs at diagnosis-may add prognostic value for estimating survival among early-stage NSCLC patients undergoing SBRT.
Copyright © 2021 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Lung cancer; Non small cell; Patient reported outcomes; SBRT

Mesh:

Year:  2021        PMID: 34953934      PMCID: PMC8934278          DOI: 10.1016/j.radonc.2021.12.021

Source DB:  PubMed          Journal:  Radiother Oncol        ISSN: 0167-8140            Impact factor:   6.280


  47 in total

1.  Impact of Accuracy of Survival Predictions on Quality of End-of-Life Care Among Patients With Metastatic Cancer Who Receive Radiation Therapy.

Authors:  Katherine Sborov; Stephanie Giaretta; Amanda Koong; Sonya Aggarwal; Rebecca Aslakson; Michael F Gensheimer; Daniel T Chang; Erqi L Pollom
Journal:  J Oncol Pract       Date:  2019-01-08       Impact factor: 3.840

Review 2.  The Edmonton Symptom Assessment System 25 Years Later: Past, Present, and Future Developments.

Authors:  David Hui; Eduardo Bruera
Journal:  J Pain Symptom Manage       Date:  2016-12-29       Impact factor: 3.612

3.  A global analysis of multitrial data investigating quality of life and symptoms as prognostic factors for survival in different tumor sites.

Authors:  Chantal Quinten; Francesca Martinelli; Corneel Coens; Mirjam A G Sprangers; Jolie Ringash; Carolyn Gotay; Kristin Bjordal; Eva Greimel; Bryce B Reeve; John Maringwa; Divine E Ediebah; Efstathios Zikos; Madeleine T King; David Osoba; Martin J Taphoorn; Henning Flechtner; Joseph Schmucker-Von Koch; Joachim Weis; Andrew Bottomley
Journal:  Cancer       Date:  2013-10-11       Impact factor: 6.860

4.  Evaluation of the Global Leadership Initiative on Malnutrition Criteria Using Different Muscle Mass Indices for Diagnosing Malnutrition and Predicting Survival in Lung Cancer Patients.

Authors:  Liangyu Yin; Xin Lin; Na Li; Mengyuan Zhang; Xiumei He; Jie Liu; Jun Kang; Xiao Chen; Chang Wang; Xu Wang; Tingting Liang; Xiangliang Liu; Li Deng; Wei Li; Chunhua Song; Jiuwei Cui; Hanping Shi; Hongxia Xu
Journal:  JPEN J Parenter Enteral Nutr       Date:  2020-06-01       Impact factor: 4.016

5.  Prognostic factors of survival in patients with advanced cancer admitted to home care.

Authors:  Sebastiano Mercadante; Alessandro Valle; Giampiero Porzio; Federica Aielli; Claudio Adile; Alessandra Casuccio
Journal:  J Pain Symptom Manage       Date:  2012-09-24       Impact factor: 3.612

6.  Acute symptomatic complications among patients with advanced cancer admitted to acute palliative care units: A prospective observational study.

Authors:  David Hui; Renata dos Santos; Suresh Reddy; Maria Salete de Angelis Nascimento; Donna S Zhukovsky; Carlos Eduardo Paiva; Shalini Dalal; Everaldo Donizeti Costa; Paul Walker; Heloisa Helena Scapulatempo; Rony Dev; Camila Souza Crovador; Maxine De La Cruz; Eduardo Bruera
Journal:  Palliat Med       Date:  2015-04-16       Impact factor: 4.762

7.  Predictors of symptom severity and response in patients with metastatic cancer.

Authors:  Camilla Zimmermann; Debika Burman; Matthew Follwell; Kristina Wakimoto; Dori Seccareccia; John Bryson; Lisa W Le; Gary Rodin
Journal:  Am J Hosp Palliat Care       Date:  2009-09-25       Impact factor: 2.500

8.  Physician factors associated with discussions about end-of-life care.

Authors:  Nancy L Keating; Mary Beth Landrum; Selwyn O Rogers; Susan K Baum; Beth A Virnig; Haiden A Huskamp; Craig C Earle; Katherine L Kahn
Journal:  Cancer       Date:  2010-02-15       Impact factor: 6.860

Review 9.  Prognostic value of patient-reported outcomes from international randomised clinical trials on cancer: a systematic review.

Authors:  Justyna Mierzynska; Claire Piccinin; Madeline Pe; Francesca Martinelli; Carolyn Gotay; Corneel Coens; Murielle Mauer; Alexander Eggermont; Mogens Groenvold; Kristin Bjordal; Jaap Reijneveld; Galina Velikova; Andrew Bottomley
Journal:  Lancet Oncol       Date:  2019-12       Impact factor: 41.316

10.  Classifying Lung Cancer Severity with Ensemble Machine Learning in Health Care Claims Data.

Authors:  Savannah L Bergquist; Gabriel A Brooks; Nancy L Keating; Mary Beth Landrum; Sherri Rose
Journal:  Proc Mach Learn Res       Date:  2017-08
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