Treating Early-Stage CKD With New Medication Therapies: Results of a CKD Patient Survey Informing the 2020 NKF-FDA Scientific Workshop on Clinical Trial Considerations for Developing Treatments for Early Stages of Common, Chronic Kidney Diseases

Damron, Kelli Collins; Friedman, Robert; Inker, Lesley A.; Thompson, Aliza; Grams, Morgan E.; Guðmundsdóttir, Hrefna; Willis, Kerry; Manley, Tom; Heerspink, Hiddo Lambers; Weiner, Daniel E.

doi:10.1016/j.xkme.2022.100442

Cited by 6 publications

(7 citation statements)

References 26 publications

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“…An accurate prediction of CKD progression to late stages not only refines risk stratification, but also provides evidences for clinicians to initiate high-risk patients on these medications earlier, given the anticipated greater benefit 42 . Such predictions may motivate patients at a high risk of CKD progression to persist with these new medications, despite potential regimen complexities, side effects, and higher co-pays 43 . This can also encourage patients to make lifestyle changes essential for reducing the risk of CKD progression 44 .…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Prediction of chronic kidney disease progression using recurrent neural network and electronic health records

Zhu,

Bi,

Saunders

et al. 2023

Sci Rep

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Chronic kidney disease (CKD) is a progressive loss in kidney function. Early detection of patients who will progress to late-stage CKD is of paramount importance for patient care. To address this, we develop a pipeline to process longitudinal electronic heath records (EHRs) and construct recurrent neural network (RNN) models to predict CKD progression from stages II/III to stages IV/V. The RNN model generates predictions based on time-series records of patients, including repeated lab tests and other clinical variables. Our investigation reveals that using a single variable, the recorded estimated glomerular filtration rate (eGFR) over time, the RNN model achieves an average area under the receiver operating characteristic curve (AUROC) of 0.957 for predicting future CKD progression. When additional clinical variables, such as demographics, vital information, lab test results, and health behaviors, are incorporated, the average AUROC increases to 0.967. In both scenarios, the standard deviation of the AUROC across cross-validation trials is less than 0.01, indicating a stable and high prediction accuracy. Our analysis results demonstrate the proposed RNN model outperforms existing standard approaches, including static and dynamic Cox proportional hazards models, random forest, and LightGBM. The utilization of the RNN model and the time-series data of previous eGFR measurements underscores its potential as a straightforward and effective tool for assessing the clinical risk of CKD patients concerning their disease progression.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Prediction of chronic kidney disease progression using recurrent neural network and electronic health records

Zhu,

Bi,

Saunders

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…A total of 1,029 participants responded to the survey; full results of the survey are published elsewhere, along with a perspective from the patient member of the planning committee. 47,48 Three people participated in the panel, which is described in Item S2.…”

Section: The Cost Versus Benefit For Treating Early Ckdmentioning

confidence: 99%

“…• Continued evaluation of the role of real-world evidence and registries to augment the understanding of the safety profile of a drug following approval • Continued evaluation of the size and duration of trials needed to obtain adequate information on a drug's risks before approval as a treatment for early-stage CKD we think the perspectives of these engaged individuals can provide guidance as to patients' values. 47,48 The variation in responses among both groups highlights the importance of individualizing benefit-risk discussions and for increased education and activation of patients earlier in the disease course. These discussions should recur over time to keep up with changes in health and life goals.…”

Section: The Cost Versus Benefit For Treating Early Ckdmentioning

confidence: 99%

Clinical Trial Considerations in Developing Treatments for Early Stages of Common, Chronic Kidney Diseases: A Scientific Workshop Cosponsored by the National Kidney Foundation and the US Food and Drug Administration

Inker

Grams

Guðmundsdóttir

et al. 2022

American Journal of Kidney Diseases

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“…In December 2020, the National Kidney Foundation and the US Food and Drug Administration cosponsored a scientific workshop, highlighting that treatment of CKD in its earlier stages could yield long-term benefits. 1 Although workshop participants recognized the need for public health campaigns and patient education, we need more than just marketing to change the current paradigm that focuses on treating later-stage CKD. We need to rewrite “the kidney story” in the public and professional imagination.…”

mentioning

confidence: 99%

Treating Early-Stage CKD With New Medication Therapies: Results of a CKD Patient Survey Informing the 2020 NKF-FDA Scientific Workshop on Clinical Trial Considerations for Developing Treatments for Early Stages of Common, Chronic Kidney Diseases

Cited by 6 publications

References 26 publications

Prediction of chronic kidney disease progression using recurrent neural network and electronic health records

Prediction of chronic kidney disease progression using recurrent neural network and electronic health records

Clinical Trial Considerations in Developing Treatments for Early Stages of Common, Chronic Kidney Diseases: A Scientific Workshop Cosponsored by the National Kidney Foundation and the US Food and Drug Administration

How to Successfully Develop Treatments for Early-Stage CKD: A Patient Perspective

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