Quality of Life of Adult Clients on Renal Replacement Therapies in Nepal

Sapkota, Abja; Sedhain, Arun; Kumari, Mana

doi:10.1111/j.1755-6686.2013.12021.x

Cited by 11 publications

(7 citation statements)

References 14 publications

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“…WHOQOL-BREF questionnaire has been validated for assessing QOL in patients with CKD in different settings 14 and has been used to assess QOL by a number of studies elsewhere. 3,4,19 We used sociodemographic data as predictor variables and all four domains, including items 1 and 2, as outcome variables. 20 Results of descriptive analysis are presented as mean±SD.…”

Section: Methodsmentioning

confidence: 99%

“…3 It has been estimated that over 1.1 million people are currently on MHD globally and this figure is increasing by 7% every year. 4 In developing countries, such as Nepal, limited access to RRT, physical inaccessibility to health facilities, economic and cultural barriers with respect to transplantation, high prevalence of communicable diseases, and lack of infrastructure pose challenges in effective management of CKD. 5 Patients with CKD on MHD have to bear significant physical, psychological, and economic challenges.…”

Section: Introductionmentioning

confidence: 99%

“…However, to the best of our knowledge, only a few studies have been conducted to assess the QOL of patients with CKD in Nepal. 4,17 Therefore, we performed a cross-sectional study of patients at two major dialysis centers in Kathmandu. We assessed QOL of patients undergoing hemodialysis with respect to physical, psychological, social, and environmental domains as outlined in the World Health Organization Quality of Life (WHOQOL-BREF) questionnaire.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of quality of life in patients undergoing hemodialysis using WHOQOL-BREF questionnaire: a multicenter study

Joshi

Subedi

Poudel

et al. 2017

IJNRD

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Background Assessment of quality of life (QOL) of patients with end-stage renal disease has become increasingly important, both in order to evaluate the influence of the disease on patients and the type of renal replacement therapy they require. Therefore, in this study, we aimed to assess QOL in patients undergoing hemodialysis and evaluated the effects of various sociodemographic factors affecting QOL of such patients in Nepal. Methods A cross-sectional study was conducted among 150 patients with chronic kidney disease undergoing hemodialysis at two major centers in Nepal. Demographic data including age, sex, ethnicity, educational status, marital status, employment, income, duration of illness, and duration on hemodialysis were collected. QOL was assessed using the World Health Organization Quality of life (WHOQOL-BREF) questionnaire. Four domains (physical, psychological, social, and environmental) and two items (overall perception of QOL and health) of the WHOQOL-BREF were the primary end points of this study. Bivariate relationship between sociodemographic factors and QOL scores were analyzed using independent samples t -test and one-way analysis of variance. Multiple linear regression analysis was performed to determine independent predictors of QOL. Results Following QOL scores were recorded: environmental domain (53.17±15.59), psychological domain (51.23±18.61), social domain (49.86±21.64), and physical domain (45.93±16.90). Older age was associated with a better QOL score in the social domain ( p =0.005), and employed patients scored better in the environmental domain ( p =0.019). Unemployed patients and those of the Terai/Madhesi ethnic group had significantly low scores in overall perception of health ( p <0.05) as compared to other groups. Low income status and increased duration on hemodialysis were found to be the only independent negative predictors of QOL in patients with hemodialysis ( p <0.05). Conclusion Patients with chronic kidney disease on dialysis had overall low QOL scores in all four domains. Age, ethnicity, employment status, income, and duration on hemodialysis affected one or more domains of QOL in such patients. Low income status and increased duration on hemodialysis were the only independent negative predictors of QOL of patients on maintenance hemodialysis.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of quality of life in patients undergoing hemodialysis using WHOQOL-BREF questionnaire: a multicenter study

Joshi

Subedi

Poudel

et al. 2017

IJNRD

View full text Add to dashboard Cite

show abstract

“…Several factors, such as environmental, social, psychological, financial, and physical, play an important role in determining the QOL that an individual enjoys [ 1 - 3 ]. Several studies have been carried out worldwide with the purpose of identifying the most significant correlates with a better QOL [ 4 - 5 ]. Since there has been no study specifically aimed at the most important predictors of QOL in order of their strength of association using modern machine learning techniques, the purpose of this study is to produce an early warning system, dialysis data interpretation for algorithmic-prediction on quality of life (DIAL), using machine learning to predict a change in QOL in a hemodialysis patient over the coming month.…”

Section: Introductionmentioning

confidence: 99%

Predicting Quality of Life Changes in Hemodialysis Patients Using Machine Learning: Generation of an Early Warning System

Saadat¹,

Aziz²,

Ahmad³

et al. 2017

Cureus

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ObjectiveTo predict changes in the quality of life scores of hemodialysis patients for the coming month and the development of an early warning system using machine learningMethodsIt was a prospective cohort study (one-month duration) at the dialysis center of a tertiary care hospital in Pakistan. The study started on 1st October 2016. About 78 patients have been enrolled till now. Bachelor of Medicine and Bachelor of Surgery (MBBS) qualified doctors administered a proforma with demographics and the validated Urdu version of World Health Organization Quality Of Life-BREF (WHOQOL-BREF). It was to be repeated after one month to the same patient by the same investigator. Simple statistics were computed using SPSS version 24 (IBM Corp., Armonk, NY) while machine learning was performed using R (version 3.0) and Orange (version 3.1).ResultsUsing machine learning algorithms, two models (classification tree and Naïve Bayes) were generated to predict an increase or decrease of 5% in a patient’s WHOQOL-BREF score over one month. The classification tree was selected as the most accurate model with an area under curve (AUC) of 83.3% (accuracy: 81.9%) for the prediction of 5% increase in QOL and an AUC of 76.2% (accuracy: 81.8%) for the prediction of 5% decrease in QOL over the coming month. The factors associated with an increase of QOL by 5% or more over the next month included younger age (<19 years) and higher iron sucrose doses (>278mg/month). Drops in psychological, physical, and social domain scores lead to a decrease of 5% or more in QOL scores over the following month.ConclusionAn early warning system, dialysis data interpretation for algorithmic-prediction on quality of life (DIAL) was built for the early detection of deteriorating QOL scores in the hemodialysis population using machine learning algorithms. The model pointed out that working on psychological and environmental domains, in particular, may prevent the drop in QOL scores from occurring. DIAL, if implemented on a larger scale, is expected to help patients in terms of ensuring a better QOL and in reducing the financial burden in the long term.

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“…Prognosis of ESKD, however, has significantly improved with kidney transplantation. Transplanted patients have improved life expectancy and QOL compared with patients on maintenance dialysis (Niu & Li 2005;Sathvik et al 2008;Sapkota et al 2013).…”

Section: Introductionmentioning

confidence: 99%