Multidimensional fatigue in pulmonary hypertension: prevalence, severity and predictors

Tartavoulle, Todd; Karpinski, Aryn C.; Aubin, A.; Kluger, Benzi M.; Distler, Oliver; Saketkoo, Lesley Ann

doi:10.1183/23120541.00079-2017

Cited by 15 publications

(14 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…With the development of the disease, it can cause emotional and psychological fluctuations and obstacles in patients and clinically manifests as anxiety and depression. Studies suggested that targeted psychological intervention and psychological counseling for PH patients during treatment can remarkably improve the psychological state of patients and can increase the 5-year survival rate of patients [ 19 ]. In this research, no substantial difference was found in the SAR and SDS scores between the two groups of patients before treatment ( P > 0.05).…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Effect of Curcumin on Psychological State of Patients with Pulmonary Hypertension by Magnetic Resonance Image under Deep Learning

Zhang

et al. 2021

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

This research aimed to evaluate the right ventricular segmentation ability of magnetic resonance imaging (MRI) images based on deep learning and evaluate the influence of curcumin (Cur) on the psychological state of patients with pulmonary hypertension (PH). The heart MRI images were detected based on the You Only Look Once (YOLO) algorithm, and then the MRI image right ventricle segmentation algorithm was established based on the convolutional neural network (CNN) algorithm. The segmentation effect of the right ventricle in cardiac MRI images was evaluated regarding intersection-over-union (IOU), Dice coefficient, accuracy, and Jaccard coefficient. 30 cases of PH patients were taken as the research object. According to different treatments, they were rolled into control group (conventional treatment) and Cur group (conventional treatment + Cur), with 15 cases in each group. Changes in the scores of the self-rating anxiety scale (SAS) and self-rating depression scale (SDS) of the two groups of patients before and after treatment were analyzed. It was found that the average IOU of the heart target detection frame of the MRI image and the true bounding box before correction was 0.7023, and the IOU after correction was 0.9016. The Loss of the MRI image processed by the CNN algorithm was 0.05, which was greatly smaller than those processed by other algorithms. The Dice coefficient, Jaccard coefficient, and accuracy of the MRI image processed by CNN were 0.89, 0.881, and 0.994, respectively. The MRI images of PH patients showed that the anterior wall of the right ventricle was notably thickened, and the main pulmonary artery was greatly widened. After treatment, the SAR and SDS scores of the two groups were lower than those before treatment ( P < 0.05 ), and the SAR and SDS scores of the curcumin group were lower than those of the control group ( P < 0.05 ). To sum up, the right ventricular segmentation ability of MRI images based on deep learning was improved, and Cur can remarkably alleviate the psychological state of PH patients, which provided a reference for the diagnosis and treatment for PH patients.

show abstract

Section: Discussionmentioning

confidence: 99%

Evaluation of Effect of Curcumin on Psychological State of Patients with Pulmonary Hypertension by Magnetic Resonance Image under Deep Learning

Zhang

et al. 2021

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

show abstract

“…It comprises five subscales (General Fatigue, Mental Fatigue, Physical Fatigue, Reduced Activity, and Reduced Motivation), each with four items (two positively and two negatively worded items). The sum of the respective items generates scores for different dimensions, with scores categorized as indicating mild (5)(6)(7)(8), moderate (9-12), severe (13)(14)(15)(16), and very severe (17)(18)(19)(20) fatigue [15]. The total score is the sum of the five-dimensional fatigue scores, and ranges from 20 to 80, where > 60 indicates severe fatigue [14].…”

Section: Multidimensional Fatigue Inventory-20mentioning

confidence: 99%

Multidimensional fatigue in patients with nasopharyngeal carcinoma receiving concurrent chemoradiotherapy: incidence, severity, and risk factors

Chen

Yang

Duan

et al. 2021

Support Care Cancer

View full text Add to dashboard Cite

Purpose To explore the incidence, severity, and risk factors of multidimensional fatigue in patients with nasopharyngeal carcinoma (NPC) receiving concurrent chemoradiotherapy (CCRT). Methods This prospective study included 79 patients with NPC in Guangzhou (China) from June 2015 to July 2018. Data were collected before and after CCRT, including demographic and clinical characteristics, nutritional parameters, and fatigue scores, based on completion of the Multiple Dimensional Inventory-20 Questionnaire, with five subscales: General Fatigue, Mental Fatigue, Physical Fatigue, Reduced Activity, and Reduced Motivation. Results Increased general fatigue was found to be associated with lower lymphocyte count and body mass index <23 kg/m 2 . Increased physical fatigue was related to age > 42 years. Higher scores for reduced activity were associated with age > 42 years, female sex, and lower serum sodium. Increased mental fatigue was related with lower lymphocyte count and unemployment; and increased total fatigue was associated with lower lymphocyte count, age > 42 years, and 3-6 courses of treatment. Furthermore, 3-6 courses of treatment was an independent predictor of severe general fatigue, while age >42 years was an independent predictor of severe physical fatigue. Importantly, cancer stage IVB and 3-6 courses of treatment could predict severe total fatigue. Conclusions Our data demonstrate that fatigue is increased in all dimensions in NPC patients following CCRT, and that the predictors differ for each fatigue dimension. These results could guide the development of targeted interventions that may reduce the impact of cancer-related fatigue in patients with NPC.

show abstract

“…A higher score indicates more fatigue. Severe fatigue is considered present for a dimension if the dimension score is >12 [30]. The total score is the sum of the ve-dimensional fatigue scores, and ranges from 20 to 100, where > 60 indicates severe total fatigue [31].…”

Section: Assessment Of Patient-reported Outcomesmentioning

confidence: 99%

Multidimensional Fatigue in Chinese Meningioma Patients Newly Diagnosed: Prevalence, Severity and Risk Factors

Zhang

et al. 2021

Preprint

View full text Add to dashboard Cite

Purpose Few studies have assessed fatigue of meningioma patients. The purpose of this study was to explore the prevalence, severity, and risk factors of multidimensional fatigue, as well as the impact on health-related quality of life (HRQoL) in Chinese meningioma patients newly diagnosed. Methods This cross-sectional study included 120 Chinese meningioma patients recruited from Affiliated Hospital of Nantong University from January 2020 to February 2021. Data were collected before surgery, including demographic, clinical and psychological characteristics, as well as fatigue scores, based on completion of the Multidimensional Fatigue Inventory (MFI-20). Spearman correlation and multiple linear regression were used to analyze the data. Results The results demonstrated that a high prevalence of severe fatigue for each dimension: general fatigue (33.3%), physical fatigue (27.5%), reduced activity (28.3%), reduced motivation (12.5%), mental fatigue (11.7%), and total fatigue (23.3%). Headache and anxiety were predictors of general fatigue. Depression was an independent predictor of physical fatigue. Karnofsky Performance Status (KPS) score and depression independently predicted reduced activity. Depression and Epworth Sleepiness Scale (ESS) score were risk factors predicting reduced motivation, while KPS score and anxiety predicted mental fatigue. Importantly, comorbidity, KPS score, headache, depression, sleep disturbances, and ESS score could predict total fatigue. Furthermore, MFI-20 scores in all dimensions were negatively correlated with Short Form 36 Health Survey (SF-36) scores in all dimensions. Conclusion Our findings indicate that meningioma patients newly diagnosed are frequently affected by fatigue, potentially contributing to impair HRQoL. For patients with risk factors of fatigue, targeted interventions are advised in order to decrease fatigue and improve HRQoL.

show abstract

Multidimensional fatigue in pulmonary hypertension: prevalence, severity and predictors

Cited by 15 publications

References 30 publications

Evaluation of Effect of Curcumin on Psychological State of Patients with Pulmonary Hypertension by Magnetic Resonance Image under Deep Learning

Evaluation of Effect of Curcumin on Psychological State of Patients with Pulmonary Hypertension by Magnetic Resonance Image under Deep Learning

Multidimensional fatigue in patients with nasopharyngeal carcinoma receiving concurrent chemoradiotherapy: incidence, severity, and risk factors

Multidimensional Fatigue in Chinese Meningioma Patients Newly Diagnosed: Prevalence, Severity and Risk Factors

Contact Info

Product

Resources

About