#117 Patients’ perspectives on the use of the kidney failure risk equation and shared decision making in multimorbidity and frailty: a qualitative interview study
Bibliographic record
Abstract
Abstract Background and Aims Clinical guidance recommends the use of the Kidney Failure Risk Equation (KFRE) to guide referral and management of individuals with chronic kidney disease (CKD) to secondary kidney care services. People living with CKD frequently experience multiple long-term conditions (multimorbidity) and/or frailty. This may impact individuals’ perceptions of kidney failure in the context of other health problems and associated risks and emphasises the need for shared decision-making. This study aims to investigate patients’ perspectives on the use of KFRE in individuals with CKD and multimorbidity and/or frailty, communication of kidney failure risk from health professionals, and experiences of shared decision-making. Method In person semi-structured interviews with patients with a diagnosis of CKD and multimorbidity and/or frailty from primary and secondary care settings in the United Kingdom were conducted between March–December 2024. Interviews used open-ended questioning, supported by a topic guide that was developed based on the research questions, the Ottawa Decision Support Framework and the Ottawa Personal Decision Guide. Long-term conditions were self-reported and clinical frailty status assessed with support from the interviewer. Interviews were recorded and transcribed verbatim. Data were analysed by framework analysis, underpinned by Normalisation Process Theory, to explore the patient experience of the processes involved in the communication of kidney failure risk and shared decision making about CKD management. A second researcher check coded a subset of interviews to enhance rigour. We explored data that fell outside of the coding frame to determine any findings undetected using the predefined theoretical framework. Nvivo 14 was used for data analysis. Results Nineteen participants from primary care settings and twelve from a secondary care setting were interviewed. Participants were aged between 46–94 years old, reported living with 3–10 long-term health conditions and had a clinical frailty scale of 1–7. Five factors were found that influence the experience of communication of risk and shared decision making: (i) Individuals knowledge and understanding CKD and kidney failure as part of multimorbidity and/or frailty; (ii) The work required to understand and be involved in the shared decision making processes for CKD together with other health conditions and risk of kidney failure; (iii) Relationships and interactions with healthcare professionals, the healthcare system and their support networks; (iv) An individual's context and priorities and (v) Uncertainty of health conditions/functional status and consideration of death. Conclusion We found many individuals were unaware of having a diagnosis of CKD and described a desire to understand this and their risk of kidney failure alongside other chronic health problems. The relationships and interactions between individuals, healthcare professionals, the healthcare system and their support networks are complex and vary by individual context and priorities. This study aids our understanding of how individuals understand CKD and kidney failure risk alongside multimorbidity and/or frailty. Additionally, it describes how kidney failure risk prediction and the use of KFRE can be embedded in the care of and impact individuals with multimorbidity or frailty and shared decision making in this context. The findings will help promote a more holistic and comprehensive approach to shared decision making around kidney failure risk for this sub-group of patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".