Background: Support for health-related quality of life (HRQOL) is an essential part of cancer care in the final stages of life, yet empirical guidance regarding HRQOL and symptom trajectories is lacking.
Aim: To assess the change in HRQOL and symptom burden in the last year of life in patients with advanced cancer and its association with health care-related factors, cancer-specific treatment, and comorbidity.
Methods: A prospective, multicenter, observational study in patients with advanced cancer (eQuiPe). Three monthly questionnaires included European Organization for Research and Treatment of Cancer Quality of Life-C30 and reported continuity of care. Multivariable mixed-effects analysis was used to assess the association between HRQOL and health care-related factors.
Results: A total of 762 deceased patients were included with a mean age of 66 (SD, 10) years and 52% were male. The most common primary tumors were lung (29%), colorectal (20%), and breast cancer (13%). Mean overall HRQOL decreased in the last 9 months of life, with the greatest decrease in the last 3 months (β -16.2). Fatigue, pain, appetite loss, dyspnea, constipation, and nausea worsened significantly in the last year of life. Multimorbidity (β -7.5) and a better reported continuity of care (β 0.7) were both significantly associated with the trajectory of HRQOL.
Conclusion: Mean overall HRQOL begins to decline 9 months before death, highlighting the need for early identification and (re)assessment of different symptoms as aspects of HRQOL follow different trajectories. Multimorbidity and reported continuity of care may be associated with the trajectory of HRQOL.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1002/cncr.35060 | DOI Listing |
Minerva Obstet Gynecol
January 2025
Obstetrics and Gynecology Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy.
Background: Vaginal delivery in twins is feasible but challenging. Successful vaginal delivery of a non-vertex second twin depends on knowledge of specific obstetrical maneuvers. Skill acquisition at the patient's bedside is difficult, making simulation training an integral part of obstetrics and gynecology residency programs.
View Article and Find Full Text PDFJ Adv Nurs
January 2025
Center for Wise Information Technology of Mental Health Nursing Research, School of Nursing, Wuhan University, Wuhan, China.
Aims: To explore the relationship between neighbourhood environments and mental health by integrating subjective and objective perspectives.
Design: A cross-sectional study.
Methods: From September 2023 to January 2024, adult residents at the physical examination centers of two public hospitals in China completed measurements of subjective neighbourhood environment, depressive and anxiety symptoms, psychological stress, and socio-demographic characteristics.
BI 1703880, a novel STimulator of INterferon Genes (STING) agonist, has demonstrated preclinical antitumor activity. As STING activation can upregulate programmed death ligand 1 and human leukocyte antigen in tumor cells, a combination of BI 1703880 and an anti-programmed cell death protein 1-antibody, such as ezabenlimab, may improve efficacy. This first-in-human phase Ia study (NCT05471856) is evaluating BI 1703880 plus ezabenlimab in patients with advanced solid tumors.
View Article and Find Full Text PDFCancer Prev Res (Phila)
January 2025
Rice University, Houston, Texas, United States.
Oral cancer is a major global health problem. It is commonly diagnosed at an advanced stage although often preceded by clinically visible oral mucosal lesions, termed oral potentially malignant disorders associated with an increased risk for oral cancer development. There is an unmet clinical need for effective screening tools to assist front-line healthcare providers to determine which patients should be referred to an oral cancer specialist for evaluation.
View Article and Find Full Text PDFDiagn Interv Radiol
January 2025
Huadong Hospital, Fudan University, Department of Thoracic Surgery, Shanghai, China.
Purpose: Patients with advanced non-small cell lung cancer (NSCLC) have varying responses to immunotherapy, but there are no reliable, accepted biomarkers to accurately predict its therapeutic efficacy. The present study aimed to construct individualized models through automatic machine learning (autoML) to predict the efficacy of immunotherapy in patients with inoperable advanced NSCLC.
Methods: A total of 63 eligible participants were included and randomized into training and validation groups.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!