Chronic Kidney Disease Patients\u27 Experiences in the Decision Making Process: The Relationship Between Personal Characteristics and Decisional Self-Efficacy on Decisional Conflict and the Dialysis Decision in Patients who Participated in Shared Decision Making

Patients with End Stage Renal Disease (ESRD) experience a complex and stressful decision making when comes to dialysis modality selection. There are pros and cons to both hemodialysis (HD) and peritoneal dialysis (PD). Patients are often uncertain as to which one is the best modality for them. This decisional conflict increases the likelihood of making a decision that is not based on the patient’s values or preferences and may result in undesirable post-decisional consequences. Purpose The purpose of this study was to explore Chronic Kidney Diseases (CKD) patients\u27 experiences regarding their decision making process in selecting a dialysis modality. The study examined individual characteristics and explores the relationship between characteristics such as demographics, decisional self-efficacy, and decisional conflict on perceived participation in shared decision making in a sample of individuals with CKD. Research Objective The objective of this mixed-method study was twofold: (a) to provide a quantitative description of the sample of individuals with CKD who are currently considering any one of the treatment strategies or currently receiving treatment for CKD within the last three months of diagnosis; and (b) to explore relationships between individual characteristics and participation in shared decision making (SDM) as they predict their decisional conflict and satisfaction with the decision made. Method This study employed a mixed-methods approach with an exploratory and a descriptive correlation designed to evaluate constructs of individual characteristics that may impact the decisions of individuals with CKD participating in shared decision making as described by the Ottawa Decisional Support Framework. Sample Study participants were recruited directly from the Kidney Solutions website (kidneysolutions.org). A recruitment flyer was sent out with the researcher’s contact information to Kidney Solutions along with the survey questionnaire using Google Forms. Kidney Solutions then sent out the link directly to all the patients who qualify for the study based on the inclusion criteria via email. No participants were contacted directly by the researcher. Participants were informed that their participation was voluntary, confidential, and that they could opt-out at any time. Procedure Data were collected after obtaining approval from Molloy College IRB. The survey was integrated into a single web-based set of questions using Google Forms. Completing the questionnaire indicated the subject\u27s consent to participate. The online survey included four inclusion criteria questions: (a) 18 years of age and older; (b) individuals who self-identify as having CKD diagnosed by a healthcare provider; (c) individuals currently considering or within the last three months has decided on a treatment option for CKD; (d) individuals who can participate in English and have modest internet skills. Measures Suitable tools were selected after an intense literature review that would be useful in measuring the research concepts in shared decision making, and permission was obtained from the developers of the tool for the study. These instruments capture participants’ self-reported Decisional Self-Efficacy Scale (DSES), decisional conflict (SURE test), shared decision making (9-item Shared Decision Making Questionnaire), and Satisfaction with Decision Instrument. Results Statistical analysis was performed to determine relationships between individual characteristics and concepts in shared decision making using t test, Chi-square, ANOVA and Pearson product-moment correlation coefficient and multiple linear regression. Participants were predominantly White (63.6%), male (54.5%), between 45-64 years old (45.5%), married or partnered (54.5%), college educated (60.0%), with most reporting CKD stage 5 (63.6%). There was a statistically significant difference at the p \u3c .05 level in SDMQ9 scores for Decision making support (t = -2.582, p \u3c .05) and use of a decisional aid in decision making (t = -2.357, p \u3c .05). The study also found a statistically significant association between current treatment option (p = .021) and decisional conflict as measured by the SURE test. There was a statistically significant positive correlation between decisional self-efficacy (as measured by the DSES) and shared decision making (as measured by the 9-item Shared Decision Making Questionnaire) observed (r = .390, n = 55, p \u3c .01). There was a significant relationship between participation in shared decision making and decisional conflict (r = -0.362, p \u3c .01). The results indicated a negative correlation, which means as SDM increases, less decisional conflict is reported by the participants. There was a significant relationship between decisional self-efficacy and decisional conflict (r = -0.489, p \u3c .001). The results indicated a negative correlation, which means as Decisional Self-Efficacy increases, less decisional conflict is reported by the participants. There was also a positive correlation between shared decision making and satisfaction with the decision, r = .701, n = 55, p \u3c .01. There was also a positive correlation between decisional self-efficacy and satisfaction with decision, r = .624, n = 55, p \u3c .01. Prediction for satisfaction with decision (SWD) is significant using a multiple linear regression model by combining decisional self-efficacy (DSES) and SDM, with n = 55, R = 0.797, R square 0.636, Adjusted R square = 0.622, F = 45.408. Prediction for decisional conflict (SURE) was significant using a multiple linear regression model by combining DSES and SDM, with n = 55, R = 0.523, R square 0.273, Adjusted R square = 0.245, F = 9.781. In this model, decisional self-efficacy predicts decisional conflict more than shared decision making. Shared decision making had a non-significant impact on the dependent variable decisional conflict alone, but it adds to the prediction when combined with decisional self-efficacy. Qualitative findings supported several quantitative findings, adding depth to understanding participants’ views. Conclusions The findings contribute to understanding the importance of increasing patient involvement in determining treatment when more than one treatment option exists based on the goals of care. Providers must approach their patients’ critical decision points in their illness trajectory with an openness of sharing in the plan of care. There is a significant need to move away from a “one-size-fits-all” approach to dialysis and provide more individualized care that incorporates patient goals and preferences while still maintaining best practices for quality and safety