What influences patient choice of treatment modality at the pre-dialysis stage?

Chanouzas, Dimitrios; Ng, Khai Ping; Fallouh, Bassam; Baharani, Jyoti

doi:10.1093/ndt/gfr452

Cited by 110 publications

(117 citation statements)

References 20 publications

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“…The greater the distance of the patient's residence from the main HD hub, greater were the chances of receiving home HD treatment. This is in keeping with the findings of other studies where uptake of home-based therapies was higher when located further from the main unit [27,28] . The significance of home ownership in the uptake of home HD, both in the prevalent HD and predialysis groups requires a relook at the policies towards community re-housing of patients, extensions to patient's houses funded by local councils, stand-alone facilities within easy reach of patients for selfcare HD and the cost implications of these measures compared to hospital-based dialysis.…”

Section: Discussionsupporting

confidence: 92%

The Influence of Renal Centre and Patient Sociodemographic Factors on Home Haemodialysis Prevalence in the UK

Jayanti¹,

Foden²,

Rae

et al. 2017

Nephron

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Background: In the United Kingdom, socioeconomic disadvantage has been associated with lower use of home dialysis, mostly peritoneal dialysis. In this study, we explore the role of a patient's sociodemographic, socioeconomic differences and the centre's influence on home haemodialysis (HD) prevalence. Methods: Data is derived from the cross-sectional arm of the UK multi-centre study investigating barriers and enablers of home HD (BASIC-HHD study). Centres were classified as low- (<3%), medium- (5-8%) and high-prevalence groups (>8%). Sociodemographic and socioeconomic status data were ascertained. Patients were enrolled in hospital HD (n = 213), home HD (n = 93) and predialysis groups (n = 222). Results: The treating renal centre to which the patient belonged was significantly associated with a patient's modality in prevalent HD groups and modality-choice in the “predialysis” group, in confounder-adjusted multivariable analyses. Non-white ethnicity was associated with lower odds of self-care dialysis modality choice (OR 0.21, 95% CI 0.07-0.62) and lower odds of home HD uptake in the prevalent HD group (OR 0.24, 95% CI 0.07-0.80). Other significant associations of home HD uptake in the HD cohort included lower age (OR 0.59, 95% CI 0.39-0.89), higher education (OR 2.99, 95% CI 1.25-7.16), home ownership (OR 0.26, 95% CI 0.09-0.70), childcare responsibility (OR 0.22, 95% CI 0.08-0.66) and unrestricted mobility (OR 0.31, 95% CI 0.11-0.91). Conclusion: “Centre” effect accounts for variation in home HD prevalence between renal units after accounting for sociodemographic parameters and co-morbidities. Unit practices and attitudes to home HD are likely to have a dominating impact on home HD prevalence rates and these aspects need to be explored systematically at the organisational level.

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Section: Discussionsupporting

confidence: 92%

The Influence of Renal Centre and Patient Sociodemographic Factors on Home Haemodialysis Prevalence in the UK

Jayanti¹,

Foden²,

Rae

et al. 2017

Nephron

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“…As we anticipated, MDC was associated with lower risk of starting dialysis including hemodialysis, peritoneal dialysis, and kidney transplant. MDC did not increase the proportion of patients who chose peritoneal dialysis because MDC may not change some of the factors associated with choosing peritoneal dialysis, such as family/home/work circumstances and past medical history [38]. We found no evidence of publication bias based on the analysis of funnel plot (Fig.…”

Section: Discussionmentioning

confidence: 69%

Multidisciplinary care in patients with chronic kidney disease: A systematic review and meta-analysis

Wang

Hsiao

Ting

et al. 2015

European Journal of Internal Medicine

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“…Significantly more patients on PD versus IHD participated in educational incentives, including visual, verbal, and written information, DVDs, and visits to a renal unit (36). Golper et al (37) discuss the importance of evaluating the effectiveness of specific instructional methods in patients with CKD, which is highlighted by the fact that 49% of patients who received such education had not made dialysis modality decisions (10).…”

Section: Discussionmentioning

confidence: 99%

Behavioral Stage of Change and Dialysis Decision-Making

Prakash

McGrail

Lewis

et al. 2015

Clinical Journal of the American Society of Nephrology

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Background and objectives Behavioral stage of change (SoC) algorithms classify patients' readiness for medical treatment decision-making. In the precontemplation stage, patients have no intention to take action within 6 months. In the contemplation stage, action is intended within 6 months. In the preparation stage, patients intend to take action within 30 days. In the action stage, the change has been made. This study examines the influence of SoC on dialysis modality decision-making.Design, setting, participants, & measurements SoC and relevant covariates were measured, and associations with dialysis decision-making were determined. In-depth interviews were conducted with 16 patients on dialysis to elicit experiences. Qualitative interview data informed the survey design. Surveys were administered to adults with CKD (eGFR#25 ml/min/1.73 m 2 ) from August, 2012 to June, 2013. Multivariable logistic regression modeled dialysis decision-making with predictors: SoC, provider connection, and dialysis knowledge score.Results Fifty-five patients completed the survey (71% women, 39% white, and 59% black), and median annual income was $17,500. In total, 65% of patients were in the precontemplation/contemplation (thinking) and 35% of patients were in the preparation/maintenance (acting) SoC; 62% of patients had made dialysis modality decisions. Doctors explaining modality options, higher dialysis knowledge scores, and fewer lifestyle barriers were associated with acting versus thinking SoC (all P,0.02). Patients making modality decisions had doctors who explained dialysis options (76% versus 43%), were in the acting versus the thinking SoC (50% versus 10%), had higher dialysis knowledge scores (1.4 versus 0.5), and had lower eGFR (13.9 versus 16.8 ml/min/1.73 m 2 ; all P,0.05). In adjusted analyses, dialysis knowledge was significantly associated with decision-making (odds ratio, 4.2; 95% confidence interval, 1.4 to 12.9; P=0.01), and SoC was of borderline significance (odds ratio, 5.8; 95% confidence interval, 1.0 to 32.6; P=0.05). The model C statistic was 0.87.Conclusions Dialysis decision-making was associated with SoC, dialysis knowledge, and physicians discussing treatment options. Future studies determining ways to assist patients with CKD in making satisfying modality decisions are warranted.

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What influences patient choice of treatment modality at the pre-dialysis stage?

Cited by 110 publications

References 20 publications

The Influence of Renal Centre and Patient Sociodemographic Factors on Home Haemodialysis Prevalence in the UK

The Influence of Renal Centre and Patient Sociodemographic Factors on Home Haemodialysis Prevalence in the UK

Multidisciplinary care in patients with chronic kidney disease: A systematic review and meta-analysis

Behavioral Stage of Change and Dialysis Decision-Making

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