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Li K, Pirabhahar S, Thomsett M, Turner K, Wainstein M, Ha JT, Katz I. Use of kidney failure risk equation as a tool to evaluate referrals from primary care to specialist nephrology care. Intern Med J 2024; 54:1126-1135. [PMID: 38532529 DOI: 10.1111/imj.16377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 01/04/2024] [Indexed: 03/28/2024]
Abstract
BACKGROUND With rising costs and burden of chronic kidney disease (CKD), timely referral of patients to a kidney specialist is crucial. Currently, Kidney Health Australia (KHA) uses a 'heat map' based on severity and not future risk of kidney failure, whereas the kidney failure risk equation (KFRE) score predicts future risk of progression. AIMS Evaluate whether a KFRE score assists with timing of CKD referrals. METHODS Retrospective cohort of 2137 adult patients, referred to tertiary hospital outpatient nephrologist between 2012 and 2020, were analysed. Referrals were analysed for concordance with the KHA referral guidelines and, with the KFRE score, a recommended practice. RESULTS Of 2137 patients, 626 (29%) did not have urine albumin-to-creatinine ratio (UACR) measurement at referral. For those who had a UACR, the number who met KFRE preferred referral criteria was 36% less than KHA criteria. If the recommended KFRE score was used, then fewer older patients (≥40 years) needed referral. Positively, many diabetes patients were referred, even if their risk of kidney failure was low, and 29% had a KFRE over 3%. For patients evaluated meeting KFRE criteria, a larger proportion (76%) remained in follow-up, with only 8% being discharged. CONCLUSIONS KFRE could reduce referrals and be a useful tool to assist timely referrals. Using KFRE for triage may allow those patients with very low risk of future kidney failure not be referred, remaining longer in primary care, saving health resources and reducing patients' stress and wait times. Using KFRE encourages albuminuria measurement.
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Affiliation(s)
- Katherine Li
- Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Saiyini Pirabhahar
- Department of Renal Medicine, St George Hospital, Sydney, New South Wales, Australia
| | - Max Thomsett
- Department of Renal Medicine, St George Hospital, Sydney, New South Wales, Australia
| | - Kylie Turner
- Department of Renal Medicine, St George Hospital, Sydney, New South Wales, Australia
| | - Marina Wainstein
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Jeffrey T Ha
- Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
- The George Institute for Global Health, Sydney, New South Wales, Australia
| | - Ivor Katz
- Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
- Department of Renal Medicine, St George Hospital, Sydney, New South Wales, Australia
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Ooi YG, Sarvanandan T, Hee NKY, Lim QH, Paramasivam SS, Ratnasingam J, Vethakkan SR, Lim SK, Lim LL. Risk Prediction and Management of Chronic Kidney Disease in People Living with Type 2 Diabetes Mellitus. Diabetes Metab J 2024; 48:196-207. [PMID: 38273788 PMCID: PMC10995482 DOI: 10.4093/dmj.2023.0244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 11/25/2023] [Indexed: 01/27/2024] Open
Abstract
People with type 2 diabetes mellitus have increased risk of chronic kidney disease and atherosclerotic cardiovascular disease. Improved care delivery and implementation of guideline-directed medical therapy have contributed to the declining incidence of atherosclerotic cardiovascular disease in high-income countries. By contrast, the global incidence of chronic kidney disease and associated mortality is either plateaued or increased, leading to escalating direct and indirect medical costs. Given limited resources, better risk stratification approaches to identify people at risk of rapid progression to end-stage kidney disease can reduce therapeutic inertia, facilitate timely interventions and identify the need for early nephrologist referral. Among people with chronic kidney disease G3a and beyond, the kidney failure risk equations (KFRE) have been externally validated and outperformed other risk prediction models. The KFRE can also guide the timing of preparation for kidney replacement therapy with improved healthcare resources planning and may prevent multiple complications and premature mortality among people with chronic kidney disease with and without type 2 diabetes mellitus. The present review summarizes the evidence of KFRE to date and call for future research to validate and evaluate its impact on cardiovascular and mortality outcomes, as well as healthcare resource utilization in multiethnic populations and different healthcare settings.
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Affiliation(s)
- Ying-Guat Ooi
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Tharsini Sarvanandan
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Nicholas Ken Yoong Hee
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Quan-Hziung Lim
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | | | - Jeyakantha Ratnasingam
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Shireene R. Vethakkan
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Soo-Kun Lim
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Lee-Ling Lim
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
- Asia Diabetes Foundation, Hong Kong SAR, China
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Alexiuk M, Elgubtan H, Tangri N. Clinical Decision Support Tools in the Electronic Medical Record. Kidney Int Rep 2024; 9:29-38. [PMID: 38312784 PMCID: PMC10831391 DOI: 10.1016/j.ekir.2023.10.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 10/23/2023] [Indexed: 02/06/2024] Open
Abstract
The integration of clinical decision support (CDS) tools into electronic medical record (EMR) systems has become common. Although there are many benefits for both patients and providers from successful integration, barriers exist that prevent consistent and effective use of these tools. Such barriers include tool alert fatigue, lack of interoperability between tools and medical record systems, and poor acceptance of tools by care providers. However, successful integration of CDS tools into EMR systems have been reported; examples of these include the Statin Choice Decision Aid, and the Kidney Failure Risk Equation (KFRE). This article reviews the history of EMR systems and its integration with CDS tools, the barriers preventing successful integration, and the benefits reported from successful integration. This article also provides suggestions and strategies for improving successful integration, making these tools easier to use and more effective for care providers.
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Affiliation(s)
- Mackenzie Alexiuk
- Chronic Disease Innovation Centre, Winnipeg, Manitoba, Canada
- Community Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Heba Elgubtan
- Chronic Disease Innovation Centre, Winnipeg, Manitoba, Canada
- Community Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Navdeep Tangri
- Chronic Disease Innovation Centre, Winnipeg, Manitoba, Canada
- Department of Internal Medicine, Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
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Jahan S, Hale J, Malacova E, Hurst C, Kark A, Mallett A. Real world evaluation of kidney failure risk equations in predicting progression from chronic kidney disease to kidney failure in an Australian cohort. J Nephrol 2024; 37:231-237. [PMID: 37285006 PMCID: PMC10920458 DOI: 10.1007/s40620-023-01680-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 05/08/2023] [Indexed: 06/08/2023]
Abstract
BACKGROUND Chronic kidney disease progression to kidney failure is diverse, and progression may be different according to genetic aspects and settings of care. We aimed to describe kidney failure risk equation prognostic accuracy in an Australian population. METHODS A retrospective cohort study was undertaken in a public hospital community-based chronic kidney disease service in Brisbane, Australia, which included a cohort of 406 adult patients with chronic kidney disease Stages 3-4 followed up over 5 years (1/1/13-1/1/18). Risk of progression to kidney failure at baseline using Kidney Failure Risk Equation models with three (eGFR/age/sex), four (add urinary-ACR) and eight variables (add serum-albumin/phosphate/bicarbonate/calcium) at 5 and 2 years were compared to actual patient outcomes. RESULTS Of 406 patients followed up over 5 years, 71 (17.5%) developed kidney failure, while 112 died before reaching kidney failure. The overall mean difference between observed and predicted risk was 0.51% (p = 0.659), 0.93% (p = 0.602), and - 0.03% (p = 0.967) for the three-, four- and eight-variable models, respectively. There was small improvement in the receiver operating characteristic-area under the curve from three-variable to four-variable models: 0.888 (95%CI = 0.819-0.957) versus 0.916 (95%CI = 0.847-0.985). The eight-variable model showed marginal receiver operating characteristic-area under the curve improvement: 0.916 (95%CI = 0.847-0.985) versus 0.922 (95%CI = 0.853-0.991). The results were similar in predicting 2 year risk of kidney failure. CONCLUSIONS The kidney failure risk equation accurately predicted progression to kidney failure in an Australian chronic kidney disease population. Younger age, male sex, lower estimated glomerular filtration rate, higher albuminuria, diabetes mellitus, tobacco smoking and non-Caucasian ethnicity were associated with increased risk of kidney failure. Cause-specific cumulative incidence function for progression to kidney failure or death, stratified by chronic kidney disease stage, demonstrated differences within different chronic kidney disease stages, highlighting the interaction between comorbidity and outcome.
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Affiliation(s)
- Sadia Jahan
- Kidney Health Service, Royal Brisbane and Women's Hospital, Herston, Brisbane, QLD, 4029, Australia
- Central Northern Adelaide Renal and Transplantation Service, Royal Adelaide Hospital, Adelaide, SA, 5000, Australia
| | - Janine Hale
- Kidney Health Service, Royal Brisbane and Women's Hospital, Herston, Brisbane, QLD, 4029, Australia
- Renal Unit, Gold Coast University Hospital, Southport, QLD, 4215, Australia
| | - Eva Malacova
- QIMR Berghofer Medical Research Institute, Herston Road, Herston, Brisbane, QLD, 4029, Australia
| | - Cameron Hurst
- QIMR Berghofer Medical Research Institute, Herston Road, Herston, Brisbane, QLD, 4029, Australia
| | - Adrian Kark
- Kidney Health Service, Royal Brisbane and Women's Hospital, Herston, Brisbane, QLD, 4029, Australia
- Renal Unit, Mount Isa Base Hospital, Mount Isa, QLD, 4825, Australia
| | - Andrew Mallett
- Kidney Health Service, Royal Brisbane and Women's Hospital, Herston, Brisbane, QLD, 4029, Australia.
- Department of Renal Medicine, Townsville University Hospital, Douglas, QLD, 4814, Australia.
- Institute for Molecular Bioscience and Faculty of Medicine, The University of Queensland, Brisbane, QLD, 4072, Australia.
- College of Medicine and Dentistry, James Cook University, Townsville, QLD, 4814, Australia.
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