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A Cost-Minimization Analysis of Nurse-Led Virtual Case Management in Late-Stage CKD

INTRODUCTION: Interventions are needed to improve early detection of indications for dialysis before development of severe symptoms or complications. This may reduce suboptimal dialysis starts, prevent hospitalizations, and decrease costs. Our objectives were to explore assumptions around a nurse-le...

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Autores principales: Ferguson, Thomas W., Hager, Drew, Whitlock, Reid H., Di Nella, Michelle, Tangri, Navdeep, Komenda, Paul, Rigatto, Claudio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7271003/
https://www.ncbi.nlm.nih.gov/pubmed/32518867
http://dx.doi.org/10.1016/j.ekir.2020.03.016
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author Ferguson, Thomas W.
Hager, Drew
Whitlock, Reid H.
Di Nella, Michelle
Tangri, Navdeep
Komenda, Paul
Rigatto, Claudio
author_facet Ferguson, Thomas W.
Hager, Drew
Whitlock, Reid H.
Di Nella, Michelle
Tangri, Navdeep
Komenda, Paul
Rigatto, Claudio
author_sort Ferguson, Thomas W.
collection PubMed
description INTRODUCTION: Interventions are needed to improve early detection of indications for dialysis before development of severe symptoms or complications. This may reduce suboptimal dialysis starts, prevent hospitalizations, and decrease costs. Our objectives were to explore assumptions around a nurse-led virtual case management intervention for patients with late-stage chronic kidney disease (CKD) with a 2-year Kidney Failure Risk Equation (KFRE) estimated risk of kidney failure ≥80% and to estimate how these assumptions affect potential cost savings. METHODS: We performed a cost-minimization analysis by developing a decision analytic microsimulation model constructed from the perspective of the health payer. Our primary outcome was the break-even point, defined as the maximum amount a health payer could spend on the intervention without incurring any net financial loss or gain. The intervention group received remote telemonitoring, including daily measurement of several health metrics (blood pressure, oxygen saturation, and weight), and a validated symptom questionnaire accompanied by nurse-led case management, whereas the comparator group received usual care. We assumed patients received the intervention for a maximum of 2 years. RESULTS: The break-even point was $7339 per late-stage CKD patient enrolled in the intervention. Based on the distribution of time receiving the intervention, we determined a maximum monthly intervention cost of $703.37. In probabilistic sensitivity analyses, we found that 75% of simulations produced break-even points between $3929 and $9460. CONCLUSION: Nurse-led virtual home monitoring interventions in patients with CKD at high risk of kidney failure have the potential for significant cost savings from the perspective of the health payer.
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spelling pubmed-72710032020-06-08 A Cost-Minimization Analysis of Nurse-Led Virtual Case Management in Late-Stage CKD Ferguson, Thomas W. Hager, Drew Whitlock, Reid H. Di Nella, Michelle Tangri, Navdeep Komenda, Paul Rigatto, Claudio Kidney Int Rep Clinical Research INTRODUCTION: Interventions are needed to improve early detection of indications for dialysis before development of severe symptoms or complications. This may reduce suboptimal dialysis starts, prevent hospitalizations, and decrease costs. Our objectives were to explore assumptions around a nurse-led virtual case management intervention for patients with late-stage chronic kidney disease (CKD) with a 2-year Kidney Failure Risk Equation (KFRE) estimated risk of kidney failure ≥80% and to estimate how these assumptions affect potential cost savings. METHODS: We performed a cost-minimization analysis by developing a decision analytic microsimulation model constructed from the perspective of the health payer. Our primary outcome was the break-even point, defined as the maximum amount a health payer could spend on the intervention without incurring any net financial loss or gain. The intervention group received remote telemonitoring, including daily measurement of several health metrics (blood pressure, oxygen saturation, and weight), and a validated symptom questionnaire accompanied by nurse-led case management, whereas the comparator group received usual care. We assumed patients received the intervention for a maximum of 2 years. RESULTS: The break-even point was $7339 per late-stage CKD patient enrolled in the intervention. Based on the distribution of time receiving the intervention, we determined a maximum monthly intervention cost of $703.37. In probabilistic sensitivity analyses, we found that 75% of simulations produced break-even points between $3929 and $9460. CONCLUSION: Nurse-led virtual home monitoring interventions in patients with CKD at high risk of kidney failure have the potential for significant cost savings from the perspective of the health payer. Elsevier 2020-03-18 /pmc/articles/PMC7271003/ /pubmed/32518867 http://dx.doi.org/10.1016/j.ekir.2020.03.016 Text en © 2020 International Society of Nephrology. Published by Elsevier Inc. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Clinical Research
Ferguson, Thomas W.
Hager, Drew
Whitlock, Reid H.
Di Nella, Michelle
Tangri, Navdeep
Komenda, Paul
Rigatto, Claudio
A Cost-Minimization Analysis of Nurse-Led Virtual Case Management in Late-Stage CKD
title A Cost-Minimization Analysis of Nurse-Led Virtual Case Management in Late-Stage CKD
title_full A Cost-Minimization Analysis of Nurse-Led Virtual Case Management in Late-Stage CKD
title_fullStr A Cost-Minimization Analysis of Nurse-Led Virtual Case Management in Late-Stage CKD
title_full_unstemmed A Cost-Minimization Analysis of Nurse-Led Virtual Case Management in Late-Stage CKD
title_short A Cost-Minimization Analysis of Nurse-Led Virtual Case Management in Late-Stage CKD
title_sort cost-minimization analysis of nurse-led virtual case management in late-stage ckd
topic Clinical Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7271003/
https://www.ncbi.nlm.nih.gov/pubmed/32518867
http://dx.doi.org/10.1016/j.ekir.2020.03.016
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