Perspectives of patients with multiple myeloma on accepting their prognosis-A qualitative interview study.

Psychooncology

Charité Comprehensive Cancer Center, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.

Published: January 2021

Objective: Prognostic awareness is essential for making treatment decisions in malignant diseases. Being confronted with a poor prognosis, however, can affect patients' mental health. Therefore, it is important to study coping in the context of malignant diseases. Acceptance is an adaptive coping strategy associated with less psychological distress. This study sought to explore the facilitators and barriers for prognostic acceptance in a sample in which both hope and uncertainty regarding prognosis are pronounced: multiple myeloma patients.

Methods: In a German university hospital, 20 multiple myeloma patients participated in semistructured interviews. Following thematic content analysis by Kuckartz, the interview transcripts were coded for facilitators and barriers for prognostic acceptance. Additionally, patients completed questionnaires on prognostic awareness and sociodemographic characteristics.

Results: Patients described the following facilitators for prognostic acceptance: social support, positive thinking, focusing on the Here and Now, proactive confrontation, having little to no symptoms, and being there for others. The indicated barriers were distressing physical symptoms and restricted functioning, social distress, and additional distress from other areas of life.

Conclusions: Patients reported a variety of factors-related to the social realm, symptom burden, and specific attitudes-that help or hinder them in accepting their prognosis. Oncologists and psycho-oncologists may support prognostic acceptance by encouraging patients to both actively deal with realistic information as well as enjoy pleasant and meaningful moments in the present during which the disease and its prognosis recedes into the background.

Download full-text PDF

Source
http://dx.doi.org/10.1002/pon.5535DOI Listing

Publication Analysis

Top Keywords

prognostic acceptance
16
multiple myeloma
12
prognostic awareness
8
malignant diseases
8
facilitators barriers
8
barriers prognostic
8
prognostic
6
acceptance
5
patients
5
perspectives patients
4

Similar Publications

Predicting Time to First Rejection Episode in Lung Transplant Patients Using a Comprehensive Multi-Indicator Model.

J Inflamm Res

January 2025

Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, 510140, People's Republic of China.

Background: Rejection hinders long-term survival in lung transplantation, and no widely accepted biomarkers exist to predict rejection risk. This study aimed to develop and validate a prognostic model using laboratory data to predict the time to first rejection episode in lung transplant recipients.

Methods: Data from 160 lung transplant recipients were retrospectively collected.

View Article and Find Full Text PDF

Objectives: Nonsmall cell lung cancer (NSCLC) accounts for about 85% of all lung cancers. Asymmetric dimethylarginine (ADMA) is an emerging molecule that is highlighted in carcinogenesis and tumor progression in lung cancer. Since elevated concentrations of ADMA are observed in lung cancer patients, we aimed to explore its associations with inflammation markers and established prognostic indices.

View Article and Find Full Text PDF

A Serial MRI-based Deep Learning Model to Predict Survival in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma.

Radiol Artif Intell

January 2025

From the Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou 510060, P. R. China (J.K., C.F.W., Z.H.C., G.Q.Z., Y.Q.W., L.L., Y.S.); Department of Radiation Therapy, Nanhai People's Hospital, The Sixth Affiliated Hospital, South China University of Technology, Foshan, China (J.Y.P., L.J.L.); and Department of Electronic Engineering, Information School, Yunnan University, Kunming, China (W.B.L.).

Purpose To develop and evaluate a deep learning-based prognostic model for predicting survival in locoregionally- advanced nasopharyngeal carcinoma (LA-NPC) using serial MRI before and after induction chemotherapy (IC). Materials and Methods This multicenter retrospective study included 1039 LA-NPC patients (779 male, 260 female, mean age 44 [standard deviation: 11]) diagnosed between April 2009 and December 2015. A radiomics- clinical prognostic model (Model RC) was developed using pre-and post-IC MRI and other clinical factors using graph convolutional neural networks (GCN).

View Article and Find Full Text PDF

Introduction: Stroke-associated pneumonia (SAP) is a major cause of mortality during the acute phase of stroke. The ADS score is widely used to predict SAP risk but does not include 24-h non-contrast computed tomography-Alberta Stroke Program Early CT Score (NCCT-ASPECTS) or red cell distribution width (RDW). We aim to evaluate the added prognostic value of incorporating 24-h NCCT-ASPECTS and RDW into the ADS score and to develop a novel prediction model for SAP following thrombolysis.

View Article and Find Full Text PDF

Introduction: The population is heterogeneous with varying levels of healthcare needs. Clustering individuals into health segments with more homogeneous healthcare needs allows for better understanding and monitoring of health profiles in the population, which can support data-driven resource allocation.

Methods: Using the developed criteria, data from several of Singapore's national administrative datasets were used to classify individuals into the various health segments.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!