Chiara Marzorati1, Ketti Mazzocco1,2, Dario Monzani1,2, Francesca Pavan3, Monica Casiraghi4, Lorenzo Spaggiari2,4, Massimo Monturano3, Gabriella Pravettoni1,2. 1. Applied Research Division for Cognitive and Psychological Science, European Institute of Oncology IRCCS, Milan, Italy. 2. Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy. 3. Patient Safety & Risk Management Service, European Institute of Oncology IRCCS, Milan, Italy. 4. Department of Thoracic Surgery, European Institute of Oncology IRCCS, Milan, Italy.
Abstract
Objective: Quality of Life (QoL) is an important predictor of patient's recovery and survival in lung cancer patients. The aim of the present study is to identify 1-year trends of lung cancer patients' QoL after robot-assisted or traditional lobectomy and investigate whether clinical (e.g., pre-surgery QoL, type of surgery, and perioperative complications) and sociodemographic variables (e.g., age) may predict these trends. Methods: An Italian sample of 176 lung cancer patients undergoing lobectomy completed the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire-Core 30 (QLQ-C30) at the pre-hospitalization (t0), 30 days (t1), 4 months (t2), 8 months (t3), and 12 months (t4) after surgery. Sociodemographic and clinical characteristics (age, gender, perioperative complications, and type of surgery) were also collected. The individual change over time of the 15 dimensions of the EORTC QLQ-C30 and the effects of pre-surgery scores of QoL dimensions, type of surgery, perioperative complications, and age on patients' QoL after surgery were studied with the individual growth curve (IGC) models. Results: Patients had a good recovery after lobectomy: functioning subscales improved over time, while most of the symptoms became less severe over the care process. Perioperative complications, type of surgery, pre-surgery status, and age significantly affected these trends, thus becoming predictors of patients' QoL. Conclusion: This study highlights different 1-year trends of lung cancer patients' QoL. The measurement of pre- and post-surgery QoL and its clinical and sociodemographic covariables would be necessary to better investigate patients' care process and implement personalized medicine in lung cancer hospital divisions.
Objective: Quality of Life (QoL) is an important predictor of patient's recovery and survival in lung cancerpatients. The aim of the present study is to identify 1-year trends of lung cancerpatients' QoL after robot-assisted or traditional lobectomy and investigate whether clinical (e.g., pre-surgery QoL, type of surgery, and perioperative complications) and sociodemographic variables (e.g., age) may predict these trends. Methods: An Italian sample of 176 lung cancerpatients undergoing lobectomy completed the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire-Core 30 (QLQ-C30) at the pre-hospitalization (t0), 30 days (t1), 4 months (t2), 8 months (t3), and 12 months (t4) after surgery. Sociodemographic and clinical characteristics (age, gender, perioperative complications, and type of surgery) were also collected. The individual change over time of the 15 dimensions of the EORTC QLQ-C30 and the effects of pre-surgery scores of QoL dimensions, type of surgery, perioperative complications, and age on patients' QoL after surgery were studied with the individual growth curve (IGC) models. Results:Patients had a good recovery after lobectomy: functioning subscales improved over time, while most of the symptoms became less severe over the care process. Perioperative complications, type of surgery, pre-surgery status, and age significantly affected these trends, thus becoming predictors of patients' QoL. Conclusion: This study highlights different 1-year trends of lung cancerpatients' QoL. The measurement of pre- and post-surgery QoL and its clinical and sociodemographic covariables would be necessary to better investigate patients' care process and implement personalized medicine in lung cancer hospital divisions.
Authors: Teresa A Rummans; Matthew M Clark; Jeff A Sloan; Marlene H Frost; John Michael Bostwick; Pamela J Atherton; Mary E Johnson; Gail Gamble; Jarrett Richardson; Paul Brown; James Martensen; Janis Miller; Katherine Piderman; Mashele Huschka; Jean Girardi; Jean Hanson Journal: J Clin Oncol Date: 2006-02-01 Impact factor: 44.544
Authors: Hao-Xian Yang; Kaitlin M Woo; Camelia S Sima; Manjit S Bains; Prasad S Adusumilli; James Huang; David J Finley; Nabil P Rizk; Valerie W Rusch; David R Jones; Bernard J Park Journal: Ann Surg Date: 2017-02 Impact factor: 12.969
Authors: Patricia M Kenny; Madeleine T King; Rosalie C Viney; Michael J Boyer; Christine A Pollicino; Jocelyn M McLean; Michael J Fulham; Brian C McCaughan Journal: J Clin Oncol Date: 2007-12-17 Impact factor: 44.544