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Integrating the patient voice: patient-centred and equitable clinical risk prediction for kidney health and disease.
Current Opinion in Nephrology and Hypertension 2024 April 26
PURPOSE OF REVIEW: Personalized approaches to care are increasingly common in clinical nephrology. Although risk prediction models are developed to estimate the risk of kidney-disease related outcomes, they infrequently consider the priorities of patients they are designed to help.
RECENT FINDINGS: This review discusses certain steps in risk prediction tool development where patients and their priorities can be incorporated. Considering principles of equity throughout the process has been the focus of recent literature.
SUMMARY: Applying a person-centred lens has implications for several aspects of risk prediction research. Incorporating the patient voice may involve partnering with patients as researchers to identify the target outcome for the tool and/or determine priorities for outcomes related to the kidney disease domain of interest. Assessing the list of candidate predictors for associations with inequity is important to ensure the tool will not widen disparity for marginalized groups. Estimating model performance using person-centred measures such as model calibration may be used to compare models and select a tool more useful to inform individual treatment decisions. Finally, there is potential to include patients and families in determining other elements of the prediction framework and implementing the tool once development is complete.
RECENT FINDINGS: This review discusses certain steps in risk prediction tool development where patients and their priorities can be incorporated. Considering principles of equity throughout the process has been the focus of recent literature.
SUMMARY: Applying a person-centred lens has implications for several aspects of risk prediction research. Incorporating the patient voice may involve partnering with patients as researchers to identify the target outcome for the tool and/or determine priorities for outcomes related to the kidney disease domain of interest. Assessing the list of candidate predictors for associations with inequity is important to ensure the tool will not widen disparity for marginalized groups. Estimating model performance using person-centred measures such as model calibration may be used to compare models and select a tool more useful to inform individual treatment decisions. Finally, there is potential to include patients and families in determining other elements of the prediction framework and implementing the tool once development is complete.
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