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Sánchez-Calavera MA, Navarro RG, Otal EA, González IB, Pardo DE, Celma LH, Lamarre M, Esteban PL, Lozano Del Hoyo ML, Mahulea L, Gallego IM, Romero-Vigara JC, Allué SS, Hueso ST, Gil FA. Prevalence and characteristics of chronic kidney disease in people with type 2 diabetes mellitus in the Autonomous Community of Aragon. Prim Care Diabetes 2024:S1751-9918(24)00117-7. [PMID: 38991895 DOI: 10.1016/j.pcd.2024.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/05/2024] [Accepted: 06/02/2024] [Indexed: 07/13/2024]
Abstract
AIMS The main objective in this study was to determine the prevalence of Chronic Kidney Disease (CKD) in people with Type 2 Diabetes Mellitus (T2DM) in the Autonomous Community (AC) of Aragon (Spain) and to detect whether or not there is under-registration in the patient's history. As a secundary objetive, it was proposed to study the most relevant demographic and clinical characteristics of people with CKD. METHODS Observational and retrospective real world data study of the population over 18 years of age with a diagnosis of T2DM, between January 2017 and December 2021. A descriptive analysis of qualitative and quantitative variables, and a comparison using the parametric Student's t-test or the non-parametric Mann-Whitney U-test between both groups was performed. RESULTS The prevalence of T2DM was 8.07 % and that of CKD 31.4 %, with an under-reporting of 47 %. The main risk factor associated with CKD was arterial hypertension (p<0.001), followed by dyslipidemia (p<0.001). The main treatment used for diabetes control was metformin, both in patients with and without CKD (p<0.001). A total of 56.81 % of people with T2DM and CKD did not undergo annual monitoring of their renal function (glomerular filtration rate) or determination of albuminuria. CONCLUSIONS The prevalence of CKD increases in patients with T2DM (31.4 %), and in almost half of patients the diagnosis is not registered (47 %). This under-reporting delays the implementation of measures needed to prevent CKD progression.
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Affiliation(s)
- María Antonia Sánchez-Calavera
- Health Service of Aragon, Spain; Instituto de Investigación Sanitaria de Aragón (IISA, Aragon Health Research Institute), Spain; Department of Medicine, Psychiatry and Dermatology, University of Zaragoza, Spain; Red de Grupos de Estudio de la Diabetes en Atención Primaria de Salud (redGDPS, Network of Diabetes Study Groups in Primary Healthcare), Spain
| | - Rafael Gómez Navarro
- Health Service of Aragon, Spain; Department of Medicine, Psychiatry and Dermatology, University of Zaragoza, Spain; Red de Grupos de Estudio de la Diabetes en Atención Primaria de Salud (redGDPS, Network of Diabetes Study Groups in Primary Healthcare), Spain.
| | - Elena Asso Otal
- Health Service of Aragon, Spain; Department of Medicine, Psychiatry and Dermatology, University of Zaragoza, Spain; Red de Grupos de Estudio de la Diabetes en Atención Primaria de Salud (redGDPS, Network of Diabetes Study Groups in Primary Healthcare), Spain
| | - Isabel Blasco González
- Health Service of Aragon, Spain; Instituto de Investigación Sanitaria de Aragón (IISA, Aragon Health Research Institute), Spain; Red de Grupos de Estudio de la Diabetes en Atención Primaria de Salud (redGDPS, Network of Diabetes Study Groups in Primary Healthcare), Spain
| | - Daniel Escribano Pardo
- Health Service of Aragon, Spain; Department of Medicine, Psychiatry and Dermatology, University of Zaragoza, Spain; Red de Grupos de Estudio de la Diabetes en Atención Primaria de Salud (redGDPS, Network of Diabetes Study Groups in Primary Healthcare), Spain
| | - Laia Homedes Celma
- Health Service of Aragon, Spain; Red de Grupos de Estudio de la Diabetes en Atención Primaria de Salud (redGDPS, Network of Diabetes Study Groups in Primary Healthcare), Spain
| | - Michelot Lamarre
- Health Service of Aragon, Spain; Red de Grupos de Estudio de la Diabetes en Atención Primaria de Salud (redGDPS, Network of Diabetes Study Groups in Primary Healthcare), Spain
| | - Pilar López Esteban
- Health Service of Aragon, Spain; Red de Grupos de Estudio de la Diabetes en Atención Primaria de Salud (redGDPS, Network of Diabetes Study Groups in Primary Healthcare), Spain
| | - María Luisa Lozano Del Hoyo
- Health Service of Aragon, Spain; Department of Medicine, Psychiatry and Dermatology, University of Zaragoza, Spain; Red de Grupos de Estudio de la Diabetes en Atención Primaria de Salud (redGDPS, Network of Diabetes Study Groups in Primary Healthcare), Spain
| | - Liliana Mahulea
- Health Service of Aragon, Spain; Red de Grupos de Estudio de la Diabetes en Atención Primaria de Salud (redGDPS, Network of Diabetes Study Groups in Primary Healthcare), Spain
| | - Inés Mera Gallego
- Red de Grupos de Estudio de la Diabetes en Atención Primaria de Salud (redGDPS, Network of Diabetes Study Groups in Primary Healthcare), Spain; Sociedad Española de Farmacia Clínica, Familiar y Comunitaria (Spanish Society of Clinical, Family and Community Pharmacy), Spain
| | - Juan Carlos Romero-Vigara
- Health Service of Aragon, Spain; Instituto de Investigación Sanitaria de Aragón (IISA, Aragon Health Research Institute), Spain; Red de Grupos de Estudio de la Diabetes en Atención Primaria de Salud (redGDPS, Network of Diabetes Study Groups in Primary Healthcare), Spain
| | - Sandra Soler Allué
- Health Service of Aragon, Spain; Red de Grupos de Estudio de la Diabetes en Atención Primaria de Salud (redGDPS, Network of Diabetes Study Groups in Primary Healthcare), Spain
| | - Sira Telmo Hueso
- Health Service of Aragon, Spain; Red de Grupos de Estudio de la Diabetes en Atención Primaria de Salud (redGDPS, Network of Diabetes Study Groups in Primary Healthcare), Spain
| | - Fran Adán Gil
- Health Service of Aragon, Spain; Red de Grupos de Estudio de la Diabetes en Atención Primaria de Salud (redGDPS, Network of Diabetes Study Groups in Primary Healthcare), Spain
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Padiyar A, Sarabu N, Ahlawat S, Thatcher EJ, Roeper BA, Anantharamakrishnan A, Runnels P, Bahner C, Lang SE, Barnett TD, Raghuwanshi Y, Pronovost PJ. Bridging the Evidence and Practice Gap in Chronic Kidney Disease: A System Thinking Approach to Population Health. Popul Health Manag 2024; 27:151-159. [PMID: 38800940 DOI: 10.1089/pop.2023.0275] [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] [Indexed: 05/29/2024] Open
Abstract
Chronic kidney disease (CKD) is common, costly, and life-limiting, requiring dialysis and transplantation in advanced stages. Although effective guideline-based therapy exists, the asymptomatic nature of CKD together with low health literacy, adverse social determinants of health, unmet behavioral health needs, and primary care providers' (PCP) limited understanding of CKD result in defects in screening and diagnosis. Care is fragmented between PCPs and specialty nephrologists, with limited time, expertise, and resources to address systemic gaps. In this article, the authors define how they classified defects in care and report the current numbers of patients exposed to these defects, both nationally and in their health system Accountable Care Organization. They describe use of the health system's three-pillar leadership model (believing, belonging, and building) to empower providers to transform CKD care. Believing entailed engaging individuals to believe defects in CKD care could be eliminated and were a collective responsibility. Belonging fostered the creation of learning communities that broke down silos and encouraged open communication and collaboration between PCPs and nephrologists. Building involved constructing a fractal management infrastructure with transparent reporting and shared accountability, which would enable success in innovation and transformation. The result is proactive and relational CKD care organized around the patient's needs in University Hospitals Systems of Excellence. Systems of excellence combine multiple domains of expertise to promote best practice guidelines and integrate care throughout the system. The authors further describe a preliminary pilot of the CKD System of Excellence in primary care.
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Affiliation(s)
- Aparna Padiyar
- Division of Nephrology, Department of Medicine, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Nagaraju Sarabu
- Division of Nephrology, Department of Medicine, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Shruti Ahlawat
- Division of Nephrology, Department of Medicine, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Esther J Thatcher
- Population Health, University Hospitals Health System, Shaker Heights, Ohio, USA
| | - Brooke A Roeper
- Population Health, University Hospitals Health System, Shaker Heights, Ohio, USA
| | | | - Patrick Runnels
- Population Health, University Hospitals Health System, Shaker Heights, Ohio, USA
- Department of Psychiatry, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Carol Bahner
- Care Management Population Health, University Hospitals Health System, Shaker Heights, Ohio, USA
| | - Sarah E Lang
- Department of Family Medicine, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Tyler D Barnett
- Data Sciences & Analytics, Population Health, University Hospitals Health System, Shaker Heights, Ohio, USA
| | | | - Peter J Pronovost
- University Hospitals Health System, Shaker Heights, Ohio, USA
- Department of Anesthesia and Critical Care Medicine, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
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3
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Bonora BM, Morieri ML, Marassi M, Cappellari R, Avogaro A, Fadini GP. Improved prediction of long-term kidney outcomes in people with type 2 diabetes by levels of circulating haematopoietic stem/progenitor cells. Diabetologia 2023; 66:2346-2355. [PMID: 37712954 PMCID: PMC10627906 DOI: 10.1007/s00125-023-06002-6] [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: 04/23/2023] [Accepted: 07/25/2023] [Indexed: 09/16/2023]
Abstract
AIM/HYPOTHESIS We examined whether prediction of long-term kidney outcomes in individuals with type 2 diabetes can be improved by measuring circulating levels of haematopoietic stem/progenitor cells (HSPCs), which are reduced in diabetes and are associated with cardiovascular risk. METHODS We included individuals with type 2 diabetes who had a baseline determination of circulating HSPCs in 2004-2019 at the diabetes centre of the University Hospital of Padua and divided them into two groups based on their median value per ml of blood. We collected updated data on eGFR and albuminuria up to December 2022. The primary endpoint was a composite of new-onset macroalbuminuria, sustained ≥40% eGFR decline, end-stage kidney disease or death from any cause. The analyses were adjusted for known predictors of kidney disease in the population with diabetes. RESULTS We analysed 342 participants (67.8% men) with a mean age of 65.6 years. Those with low HSPC counts (n=171) were significantly older and had a greater prevalence of hypertension, heart failure and nephropathy (45.0% vs 33.9%; p=0.036), as evidenced by lower eGFR and higher albuminuria at baseline. During a median follow-up of 6.7 years, participants with high vs low HSPC counts had lower rates of the composite kidney outcome (adjusted HR 0.69 [95% CI 0.49, 0.97]), slower decline in eGFR and a similar increase in albuminuria. Adding the HSPC information to the risk score of the CKD Prognosis Consortium significantly improved discrimination of individuals with future adverse kidney outcomes. CONCLUSIONS/INTERPRETATION HSPC levels predict worsening of kidney function and improve the identification of individuals with type 2 diabetes and adverse kidney outcomes over and beyond a clinical risk score.
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Affiliation(s)
- Benedetta Maria Bonora
- Department of Medicine, University of Padova, Padua, Italy
- Veneto Institute of Molecular Medicine, Padua, Italy
| | | | | | | | - Angelo Avogaro
- Department of Medicine, University of Padova, Padua, Italy
| | - Gian Paolo Fadini
- Department of Medicine, University of Padova, Padua, Italy.
- Veneto Institute of Molecular Medicine, Padua, Italy.
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Diamantidis CJ, Cook DJ, Redelosa CK, Vinculado RB, Cabajar AA, Vassalotti JA. CKD and Rapid Kidney Function Decline During the COVID-19 Pandemic. Kidney Med 2023; 5:100701. [PMID: 37649727 PMCID: PMC10462879 DOI: 10.1016/j.xkme.2023.100701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023] Open
Abstract
Rationale & Objective The direct and indirect effects of the coronavirus disease 2019 (COVID-19) pandemic on kidney function in the chronic kidney disease (CKD) population are not well understood. Study Design Cohort study. Setting & Participants Retrospective study of kidney function trajectories using deidentified administrative claims and laboratory data for Medicare Advantage and commercially insured enrollees with CKD stages G3-4 between 2018 and 2021. Predictors COVID-19 infection. Outcome Rapid kidney function decline defined as annual estimated glomerular filtration rate (eGFR) decline of ≥40%. Analytical Approach Propensity score matching was used to identify individuals without COVID-19 infection matched 1:1 to a COVID-19 infected cohort and indexed on the date of diagnosing COVID-19 infection, age, sex, race or ethnicity, and Charlson comorbidity index score. Outpatient kidney function was compared during the prepandemic period (January 1, 2018, to February 29, 2020) with the pandemic period (March 1, 2020, to August 31, 2021). Two creatinine measurements, after the infection date and ≥60 days apart, were required to reduce correlation with acute infection. Results Of 97,203 enrollees with CKD G3-4, 9% experienced a COVID-19 infection. Characteristics of 8,901 propensity matched enrollees include mean age 74 years, 58% women, 67% White, and 63% CKD G3a, 28% CKD G3b, and 9% CKD G4. Median overall annual eGFR change was -2.65 ml/min/1.73m2, with 76% of the cohort experiencing worsened eGFR in the pandemic period. Rapid kidney function decline was observed in 1.9% and 2.0% of enrollees in the prepandemic and pandemic periods, respectively. Rapid kidney function decline was observed in 2.5% of those with COVID-19 infection and 1.5% of those without COVID-19 infection (P < 0.05). Factors associated with increased odds of rapid kidney function decline during pandemic included Asian race, higher Charlson comorbidity index, advancing CKD stage, prepandemic rapid kidney function decline, and COVID-19 infection. Limitations Retrospective study design with potential bias. Conclusions COVID-19 infection increased odds of rapid kidney function decline during the pandemic. The downstream impact of pandemic-related eGFR decline on health outcomes, such as kidney failure or mortality, requires further study. Plain-Language Summary We used a cohort of insured individuals with moderate-to-severe chronic kidney disease (CKD) to compare the rates of rapid kidney function decline in prepandemic and pandemic periods and to evaluate the impact of the coronavirus disease 19 (COVID-19) on kidney function decline. We found that overall rates of rapid kidney function decline did not change during the prepandemic and pandemic periods but were significantly higher in both periods among individuals with a COVID-19 infection. As CKD severity increased, rates of both rapid kidney function decline and COVID-19 increased. Advancing CKD, higher comorbid condition, Asian race, prepandemic rapid kidney function decline, and COVID-19 were all associated with higher odds of rapid kidney function decline in the pandemic. These findings suggest close monitoring is warranted for individuals with CKD and COVID-19.
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Affiliation(s)
| | - David J. Cook
- OptumLabs, Eden Prairie, MN
- National Kidney Foundation, New York, NY
| | | | | | | | - Joseph A. Vassalotti
- National Kidney Foundation, New York, NY
- Icahn School of Medicine at Mount Sinai, New York, NY
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5
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Williams P. Retaining Race in Chronic Kidney Disease Diagnosis and Treatment. Cureus 2023; 15:e45054. [PMID: 37701164 PMCID: PMC10495104 DOI: 10.7759/cureus.45054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/08/2023] [Indexed: 09/14/2023] Open
Abstract
The best overall measure of kidney function is glomerular filtration rate (GFR) as commonly estimated from serum creatinine concentrations (eGFRcr) using formulas that correct for the higher average creatinine concentrations in Blacks. After two decades of use, these formulas have come under scrutiny for estimating GFR differently in Blacks and non-Blacks. Discussions of whether to include race (Black vs. non-Black) in the calculation of eGFRcr fail to acknowledge that the original race-based eGFRcr provided the same CKD treatment recommendations for Blacks and non-Blacks based on directly (exogenously) measured GFR. Nevertheless, the National Kidney Foundation and the American Society of Nephrology Task Force on Reassessing the Inclusion of Race in Diagnosing Kidney Disease removed race in CKD treatment guidelines and pushed for the immediate adoption of a race-free eGFRcr formula by physicians and clinical laboratories. This formula is projected to negate CKD in 5.51 million White and other non-Black adults and reclassify CKD to less severe stages in another 4.59 million non-Blacks, in order to expand treatment eligibility to 434,000 Blacks not previously diagnosed and to 584,000 Blacks previously diagnosed with less severe CKD. This review examines: 1) the validity of the arguments for removing the original race correction, and 2) the performance of the proposed replacement formula. Excluding race in the derivation of eGFRcr changed the statistical bias from +3.7 to -3.6 ml/min/1.73m2 in Blacks and from +0.5 to +3.9 in non-Blacks, i.e., promoting CKD diagnosis in Blacks at the cost of restricting diagnosis in non-Blacks. By doing so, the revised eGFRcr greatly exaggerates the purported racial disparity in CKD burden. Claims that the revised formulas identify heretofore undiagnosed CKD in Blacks are not supported when studies that used kidney failure replacement therapy and mortality are interpreted as proxies for baseline CKD. Alternatively, a race-stratified eGFRcr (i.e., separate equations for Blacks and non-Blacks) would provide the least biased eGFRcr for both Blacks and non-Blacks and the best medical treatment for all patients.
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Affiliation(s)
- Paul Williams
- Life Sciences, Lawrence Berkeley National Laboratory, Berkeley, USA
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6
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Chamarthi G, Orozco T, Shell P, Fu D, Hale-Gallardo J, Jia H, Shukla AM. Electronic Phenotype for Advanced Chronic Kidney Disease in a Veteran Health Care System Clinical Database: Systems-Based Strategy for Model Development and Evaluation. Interact J Med Res 2023; 12:e43384. [PMID: 37486757 PMCID: PMC10411421 DOI: 10.2196/43384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 04/14/2023] [Accepted: 04/17/2023] [Indexed: 07/25/2023] Open
Abstract
BACKGROUND Identifying advanced (stages 4 and 5) chronic kidney disease (CKD) cohorts in clinical databases is complicated and often unreliable. Accurately identifying these patients can allow targeting this population for their specialized clinical and research needs. OBJECTIVE This study was conducted as a system-based strategy to identify all prevalent Veterans with advanced CKD for subsequent enrollment in a clinical trial. We aimed to examine the prevalence and accuracy of conventionally used diagnosis codes and estimated glomerular filtration rate (eGFR)-based phenotypes for advanced CKD in an electronic health record (EHR) database. We sought to develop a pragmatic EHR phenotype capable of improving the real-time identification of advanced CKD cohorts in a regional Veterans health care system. METHODS Using the Veterans Affairs Informatics and Computing Infrastructure services, we extracted the source cohort of Veterans with advanced CKD based on a combination of the latest eGFR value ≤30 ml·min-1·1.73 m-2 or existing International Classification of Diseases (ICD)-10 diagnosis codes for advanced CKD (N18.4 and N18.5) in the last 12 months. We estimated the prevalence of advanced CKD using various prior published EHR phenotypes (ie, advanced CKD diagnosis codes, using the latest single eGFR <30 ml·min-1·1.73 m-2, utilizing two eGFR values) and our operational EHR phenotypes of a high-, intermediate-, and low-risk advanced CKD cohort. We evaluated the accuracy of these phenotypes by examining the likelihood of a sustained reduction of eGFR <30 ml·min-1·1.73 m-2 over a 6-month follow-up period. RESULTS Of the 133,756 active Veteran enrollees at North Florida/South Georgia Veterans Health System (NF/SG VHS), we identified a source cohort of 1759 Veterans with advanced nondialysis CKD. Among these, 1102 (62.9%) Veterans had diagnosis codes for advanced CKD; 1391(79.1%) had the index eGFR <30 ml·min-1·1.73 m-2; and 928 (52.7%), 480 (27.2%), and 315 (17.9%) Veterans had high-, intermediate-, and low-risk advanced CKD, respectively. The prevalence of advanced CKD among Veterans at NF/SG VHS varied between 1% and 1.5% depending on the EHR phenotype. At the 6-month follow-up, the probability of Veterans remaining in the advanced CKD stage was 65.3% in the group defined by the ICD-10 codes and 90% in the groups defined by eGFR values. Based on our phenotype, 94.2% of high-risk, 71% of intermediate-risk, and 16.1% of low-risk groups remained in the advanced CKD category. CONCLUSIONS While the prevalence of advanced CKD has limited variation between different EHR phenotypes, the accuracy can be improved by utilizing two eGFR values in a stratified manner. We report the development of a pragmatic EHR-based model to identify advanced CKD within a regional Veterans health care system in real time with a tiered approach that allows targeting the needs of the groups at risk of progression to end-stage kidney disease.
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Affiliation(s)
- Gajapathiraju Chamarthi
- Division of Nephrology, Hypertension and Transplantation, University of Florida, Gainesville, FL, United States
| | - Tatiana Orozco
- Advanced Chronic Kidney Disease and Home Dialysis Program, North Florida/South Georgia Veteran Healthcare System, Gainesville, FL, United States
| | - Popy Shell
- Advanced Chronic Kidney Disease and Home Dialysis Program, North Florida/South Georgia Veteran Healthcare System, Gainesville, FL, United States
| | - Devin Fu
- Advanced Chronic Kidney Disease and Home Dialysis Program, North Florida/South Georgia Veteran Healthcare System, Gainesville, FL, United States
| | - Jennifer Hale-Gallardo
- Advanced Chronic Kidney Disease and Home Dialysis Program, North Florida/South Georgia Veteran Healthcare System, Gainesville, FL, United States
| | - Huanguang Jia
- Advanced Chronic Kidney Disease and Home Dialysis Program, North Florida/South Georgia Veteran Healthcare System, Gainesville, FL, United States
| | - Ashutosh M Shukla
- Division of Nephrology, Hypertension and Transplantation, University of Florida, Gainesville, FL, United States
- Advanced Chronic Kidney Disease and Home Dialysis Program, North Florida/South Georgia Veteran Healthcare System, Gainesville, FL, United States
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Tangri N, Moriyama T, Schneider MP, Virgitti JB, De Nicola L, Arnold M, Barone S, Peach E, Wittbrodt E, Chen H, Järbrink K, Kushner P. Prevalence of undiagnosed stage 3 chronic kidney disease in France, Germany, Italy, Japan and the USA: results from the multinational observational REVEAL-CKD study. BMJ Open 2023; 13:e067386. [PMID: 37217263 DOI: 10.1136/bmjopen-2022-067386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/24/2023] Open
Abstract
OBJECTIVES REVEAL-CKD aims to estimate the prevalence of, and factors associated with, undiagnosed stage 3 chronic kidney disease (CKD). DESIGN Multinational, observational study. SETTING Data from six country-specific electronic medical records and/or insurance claims databases from five countries (France, Germany, Italy, Japan and the USA [two databases]). PARTICIPANTS Eligible participants (≥18 years old) had ≥2 consecutive estimated glomerular filtration rate (eGFR) measurements (calculated from serum creatinine values, sex and age) taken from 2015 onwards that were indicative of stage 3 CKD (≥30 and <60 mL/min/1.73 m2). Undiagnosed cases lacked an International Classification of Diseases 9/10 diagnosis code for CKD (any stage) any time before, and up to 6 months after, the second qualifying eGFR measurement (study index). MAIN OUTCOME MEASURES The primary outcome was point prevalence of undiagnosed stage 3 CKD. Time to diagnosis was assessed using the Kaplan-Meier approach. Factors associated with lacking a CKD diagnosis and risk of diagnostic delay were assessed using logistic regression adjusted for baseline covariates. RESULTS The prevalence of undiagnosed stage 3 CKD was 95.5% (19 120/20 012 patients) in France, 84.3% (22 557/26 767) in Germany, 77.0% (50 547/65 676) in Italy, 92.1% (83 693/90 902) in Japan, 61.6% (13 845/22 470) in the US Explorys Linked Claims and Electronic Medical Records Data database and 64.3% (161 254/250 879) in the US TriNetX database. The prevalence of undiagnosed CKD increased with age. Factors associated with undiagnosed CKD were female sex (vs male, range of odds ratios across countries: 1.29-1.77), stage 3a CKD (vs 3b, 1.81-3.66), no medical history (vs a history) of diabetes (1.26-2.77) or hypertension (1.35-1.78). CONCLUSIONS There are substantial opportunities to improve stage 3 CKD diagnosis, particularly in female patients and older patients. The low diagnosis rates in patients with comorbidities that put them at risk of disease progression and complications require attention. TRIAL REGISTRATION NCT04847531.
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Affiliation(s)
- Navdeep Tangri
- Department of Internal Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | | | - Markus P Schneider
- Department of Nephrology and Hypertension, Erlangen University Hospital, Erlangen, Germany
| | | | - Luca De Nicola
- Department of Advanced Medical and Surgical Sciences, Nephrology and Dialysis Unit, Università degli Studi della Campania Luigi Vanvitelli, Napoli, Italy
| | - Matthew Arnold
- Real World Evidence Data & Analytics, BioPharmaceuticals Medical, AstraZeneca, Cambridge, UK
| | - Salvatore Barone
- Global Medical Affairs, BioPharmaceuticals Medical, AstraZeneca, Gaithersburg, Maryland, USA
| | - Emily Peach
- Cardiovascular, Renal and Metabolism Epidemiology, BioPharmaceuticals Medical, AstraZeneca, Cambridge, UK
| | - Eric Wittbrodt
- Cardiovascular, Renal and Metabolism Medical Affairs, BioPharmaceuticals Medical, AstraZeneca, Gaithersburg, Maryland, USA
| | - Hungta Chen
- Medical/Payer Evidence Statistics, BioPharmaceuticals Medical, AstraZeneca, Gaithersburg, Maryland, USA
| | - Krister Järbrink
- Cardiovascular, Renal and Metabolism Evidence, BioPharmaceuticals Medical, AstraZeneca, Gothenburg, Sweden
| | - Pamela Kushner
- Department of Family Medicine, University of California Irvine Medical Center, Orange, California, USA
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8
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Diamantidis CJ, Cook DJ, Dunning S, Redelosa CK, Bartolome MFD, Romero RAA, Vassalotti JA. Missing Care: the Initial Impact of the COVID-19 Pandemic on CKD Care Delivery. J Gen Intern Med 2022; 37:4241-4247. [PMID: 36163529 PMCID: PMC9512959 DOI: 10.1007/s11606-022-07805-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 09/08/2022] [Indexed: 01/04/2023]
Abstract
BACKGROUND Chronic kidney disease (CKD) is a common condition with adverse health outcomes addressable by early disease management. The impact of the COVID-19 pandemic on care utilization for the CKD population is unknown. OBJECTIVE To examine pandemic CKD care and identify factors associated with a high care deficit. DESIGN Retrospective observational study PARTICIPANTS: 248,898 insured individuals (95% Medicare Advantage, 5% commercial) with stage G3-G4 CKD in 2018 MAIN MEASURES: Predicted (based on the pre-pandemic period of January 1, 2019-February 28, 2020) to observed per-member monthly face-to-face and telehealth encounters, laboratory testing, and proportion of days covered (PDC) for medications, evaluated during the early (March 1, 2020-June 30, 2020), pre-vaccine (July 1, 2020-December 31, 2020), and late (January 2021-August 2021) periods and overall. KEY RESULTS In-person encounters fell by 24.1% during the pandemic overall; this was mitigated by a 14.2% increase in telehealth encounters, resulting in a cumulative observed utilization deficit of 10% relative to predicted. These reductions were greatest in the early pandemic period, with a 19.8% cumulative deficit. PDC progressively decreased during the pandemic (range 9-20% overall reduction), with the greatest reductions in hypertension and diabetes medicines. CKD laboratory monitoring was also reduced (range 11.8-43.3%). Individuals of younger age (OR 1.63, 95% CI 1.16, 2.28), with commercial insurance (1.43, 95% CI 1.25, 1.63), residing in the Southern US (OR 1.17, 95% CI 1.14, 1.21), and with stage G4 CKD (OR 1.21, 95% CI 1.17, 1.26) had greater odds of a higher care deficit overall. CONCLUSIONS The early COVID-19 pandemic resulted in a marked decline of healthcare services for individuals with CKD, with an incomplete recovery during the later pandemic. Increased telehealth use partially compensated for this deficit. The downstream impact of CKD care reduction on health outcomes requires further study, as does evaluation of effective care delivery models for this population.
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Affiliation(s)
| | | | | | | | | | | | - Joseph A Vassalotti
- National Kidney Foundation, New York, NY, USA.,Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Lamprea-Montealegre JA, Joshi P, Shapiro AS, Madden E, Navarra K, Potok OA, Gregg LP, Podchiyska T, Robinson A, Goldstein MK, Peralta CA, Jassal SK, Navaneethan SD, Rifkin DE, Wang V, Shlipak MG, Estrella MM. Improving chronic kidney disease detection and treatment in the United States: the chronic kidney disease cascade of care (C 3) study protocol. BMC Nephrol 2022; 23:331. [PMID: 36224528 PMCID: PMC9554861 DOI: 10.1186/s12882-022-02943-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 09/14/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND There are major gaps in the implementation of guideline-concordant care for persons with chronic kidney disease (CKD). The CKD Cascade of Care (C3) initiative seeks to improve CKD care by improving detection and treatment of CKD in primary care. METHODS C3 is a multi-modal initiative deployed in three major academic medical centers within the Department of Veterans Affairs (VA) Health Care System: San Francisco VA, San Diego VA, and Houston VA. The main objective of the first phase of C3 described in this protocol is to establish the infrastructure for universal CKD detection among primary care patients at high-risk for CKD with a triple-marker screen comprising cystatin C, creatinine, and albuminuria. Across the three sites, a comprehensive educational intervention and the integration of primary care-based clinical champions will be employed with the goal of improving CKD detection and treatment. The San Francisco VA will also implement a practice-facilitation intervention leveraging telehealth and health informatics tools and capabilities for enhanced CKD detection. Parallel formative evaluation across the three sites will assess the feasibility and acceptability of integrating cystatin C as part of routine CKD detection in primary care practice. The effectiveness of the interventions will be assessed using a pre-post observational design for change in the proportion of patients tested annually for CKD. Secondary outcomes will assess change in the initiation of cardio-kidney protective therapies and in nephrology referrals of high-risk patients. DISCUSSION The first phase of C3 is a multi-facility multi-modal initiative that aims to improve CKD care by implementing a triple-marker screen for enhanced CKD detection in primary care.
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Affiliation(s)
- Julio A. Lamprea-Montealegre
- grid.266102.10000 0001 2297 6811University of California San Francisco, 4150 Clement St., Building 2, Room 145, San Francisco, CA 94121 USA ,grid.266102.10000 0001 2297 6811Kidney Health Research Collaborative, Department of Medicine, University of California, San Francisco, CA USA ,grid.429734.fSan Francisco VA Health Care System, San Francisco, CA USA
| | - Priya Joshi
- grid.266102.10000 0001 2297 6811University of California San Francisco, 4150 Clement St., Building 2, Room 145, San Francisco, CA 94121 USA ,grid.266102.10000 0001 2297 6811Kidney Health Research Collaborative, Department of Medicine, University of California, San Francisco, CA USA ,grid.429734.fSan Francisco VA Health Care System, San Francisco, CA USA
| | - Abigail S. Shapiro
- grid.26009.3d0000 0004 1936 7961Duke University, Durham, NC USA ,grid.512153.1Durham VA Health Care System, Durham, NC USA
| | - Erin Madden
- grid.266102.10000 0001 2297 6811Kidney Health Research Collaborative, Department of Medicine, University of California, San Francisco, CA USA ,grid.429734.fSan Francisco VA Health Care System, San Francisco, CA USA
| | - Krista Navarra
- grid.266102.10000 0001 2297 6811Kidney Health Research Collaborative, Department of Medicine, University of California, San Francisco, CA USA ,grid.429734.fSan Francisco VA Health Care System, San Francisco, CA USA
| | - O. Alison Potok
- grid.266100.30000 0001 2107 4242University of California San Diego, La Jolla, CA USA ,grid.410371.00000 0004 0419 2708San Diego VA Health Care System, San Diego, CA USA
| | - L. Parker Gregg
- grid.39382.330000 0001 2160 926XBaylor College of Medicine, Michael E. DeBakey VA Medical Center, Houston, TX USA ,grid.413890.70000 0004 0420 5521Veterans Affairs Health Services Research and Development Center for Innovations in Quality, Effectiveness, and Safety and Section of Nephrology, Michael E. DeBakey VA Medical Center, TX Houston, USA
| | - Tanya Podchiyska
- grid.280747.e0000 0004 0419 2556VA Palo Alto Health Care System, Palo Alto, CA USA
| | - Amy Robinson
- grid.484327.e0000 0004 0420 8415VA Sierra Pacific Network (VISN 21), Pleasant Hill, CA USA
| | - Mary K. Goldstein
- grid.280747.e0000 0004 0419 2556VA Palo Alto Health Care System, Palo Alto, CA USA ,grid.168010.e0000000419368956Stanford University, Stanford, CA USA
| | - Carmen A. Peralta
- grid.266102.10000 0001 2297 6811University of California San Francisco, 4150 Clement St., Building 2, Room 145, San Francisco, CA 94121 USA ,grid.266102.10000 0001 2297 6811Kidney Health Research Collaborative, Department of Medicine, University of California, San Francisco, CA USA ,grid.429734.fSan Francisco VA Health Care System, San Francisco, CA USA ,Cricket Health, Inc, San Francisco, CA USA
| | - Simerjot K. Jassal
- grid.266100.30000 0001 2107 4242University of California San Diego, La Jolla, CA USA ,grid.410371.00000 0004 0419 2708San Diego VA Health Care System, San Diego, CA USA
| | - Sankar D. Navaneethan
- grid.39382.330000 0001 2160 926XBaylor College of Medicine, Michael E. DeBakey VA Medical Center, Houston, TX USA ,grid.413890.70000 0004 0420 5521Veterans Affairs Health Services Research and Development Center for Innovations in Quality, Effectiveness, and Safety and Section of Nephrology, Michael E. DeBakey VA Medical Center, TX Houston, USA
| | - Dena. E. Rifkin
- grid.266100.30000 0001 2107 4242University of California San Diego, La Jolla, CA USA ,grid.410371.00000 0004 0419 2708San Diego VA Health Care System, San Diego, CA USA
| | - Virginia Wang
- grid.26009.3d0000 0004 1936 7961Duke University, Durham, NC USA ,grid.512153.1Durham VA Health Care System, Durham, NC USA
| | - Michael G. Shlipak
- grid.266102.10000 0001 2297 6811University of California San Francisco, 4150 Clement St., Building 2, Room 145, San Francisco, CA 94121 USA ,grid.266102.10000 0001 2297 6811Kidney Health Research Collaborative, Department of Medicine, University of California, San Francisco, CA USA ,grid.429734.fSan Francisco VA Health Care System, San Francisco, CA USA
| | - Michelle M. Estrella
- grid.266102.10000 0001 2297 6811University of California San Francisco, 4150 Clement St., Building 2, Room 145, San Francisco, CA 94121 USA ,grid.266102.10000 0001 2297 6811Kidney Health Research Collaborative, Department of Medicine, University of California, San Francisco, CA USA ,grid.429734.fSan Francisco VA Health Care System, San Francisco, CA USA
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10
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Grams ME, Brunskill NJ, Ballew SH, Sang Y, Coresh J, Matsushita K, Surapaneni A, Bell S, Carrero JJ, Chodick G, Evans M, Heerspink HJ, Inker LA, Iseki K, Kalra PA, Kirchner HL, Lee BJ, Levin A, Major RW, Medcalf J, Nadkarni GN, Naimark DM, Ricardo AC, Sawhney S, Sood MM, Staplin N, Stempniewicz N, Stengel B, Sumida K, Traynor JP, van den Brand J, Wen CP, Woodward M, Yang JW, Wang AYM, Tangri N. Development and Validation of Prediction Models of Adverse Kidney Outcomes in the Population With and Without Diabetes. Diabetes Care 2022; 45:2055-2063. [PMID: 35856507 PMCID: PMC9472501 DOI: 10.2337/dc22-0698] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 05/16/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To predict adverse kidney outcomes for use in optimizing medical management and clinical trial design. RESEARCH DESIGN AND METHODS In this meta-analysis of individual participant data, 43 cohorts (N = 1,621,817) from research studies, electronic medical records, and clinical trials with global representation were separated into development and validation cohorts. Models were developed and validated within strata of diabetes mellitus (presence or absence) and estimated glomerular filtration rate (eGFR; ≥60 or <60 mL/min/1.73 m2) to predict a composite of ≥40% decline in eGFR or kidney failure (i.e., receipt of kidney replacement therapy) over 2-3 years. RESULTS There were 17,399 and 24,591 events in development and validation cohorts, respectively. Models predicting ≥40% eGFR decline or kidney failure incorporated age, sex, eGFR, albuminuria, systolic blood pressure, antihypertensive medication use, history of heart failure, coronary heart disease, atrial fibrillation, smoking status, and BMI, and, in those with diabetes, hemoglobin A1c, insulin use, and oral diabetes medication use. The median C-statistic was 0.774 (interquartile range [IQR] = 0.753, 0.782) in the diabetes and higher-eGFR validation cohorts; 0.769 (IQR = 0.758, 0.808) in the diabetes and lower-eGFR validation cohorts; 0.740 (IQR = 0.717, 0.763) in the no diabetes and higher-eGFR validation cohorts; and 0.750 (IQR = 0.731, 0.785) in the no diabetes and lower-eGFR validation cohorts. Incorporating the previous 2-year eGFR slope minimally improved model performance, and then only in the higher-eGFR cohorts. CONCLUSIONS Novel prediction equations for a decline of ≥40% in eGFR can be applied successfully for use in the general population in persons with and without diabetes with higher or lower eGFR.
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Affiliation(s)
- Morgan E. Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Division of Precision of Medicine, Department of Medicine, New York University Grossman School of Medicine, New York, NY
| | - Nigel J. Brunskill
- Department of Cardiovascular Sciences, University of Leicester, Leicester, U.K
- John Walls Renal Unit, Leicester General Hospital, University Hospitals of Leicester NHS Trust, Leicester, U.K
| | - Shoshana H. Ballew
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Yingying Sang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Kunihiro Matsushita
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Aditya Surapaneni
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Samira Bell
- Renal Unit, Ninewells Hospital, Dundee, U.K
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, U.K
| | - Juan J. Carrero
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Huddinge, Sweden
| | - Gabriel Chodick
- Medical Division, Maccabi Healthcare Services, and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Marie Evans
- Department of Clinical Intervention, and Technology, Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden
| | - Hiddo J.L. Heerspink
- Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center, Groningen, Netherlands
| | | | | | - Philip A. Kalra
- Department of Renal Medicine, Salford Royal NHS Foundation Trust, Salford, U.K
| | | | - Brian J. Lee
- Kaiser Permanente, Hawaii Region, and Moanalua Medical Center, Honolulu, HI
| | - Adeera Levin
- Division of Nephrology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Rupert W. Major
- John Walls Renal Unit, Leicester General Hospital, University Hospitals of Leicester NHS Trust, Leicester, U.K
- Department of Health Sciences, University of Leicester, Leicester, U.K
| | - James Medcalf
- John Walls Renal Unit, Leicester General Hospital, University Hospitals of Leicester NHS Trust, Leicester, U.K
- UK Renal Registry, The Renal Association, Bristol, U.K
- Department of Cardiovascular Sciences, University of Leicester, Leicester, U.K
| | - Girish N. Nadkarni
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | | | - Ana C. Ricardo
- Department of Medicine, University of Illinois, Chicago, IL
| | - Simon Sawhney
- Aberdeen Centre for Health Data Science, University of Aberdeen, Aberdeen, U.K
| | - Manish M. Sood
- Division of Nephrology, Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
- Division of Nephrology, Department of Medicine, The Ottawa Hospital, Ottawa, Ontario, Canada
| | - Natalie Staplin
- MRC Population Health Research Unit, Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, Oxford, U.K
| | | | - Benedicte Stengel
- University Paris-Saclay, University of Versailles Saint-Quentin-en-Yvelines, INSERM, Clinical Epidemiology Team, Centre for Epidemiology and Population Health, Villejuif, France
| | - Keiichi Sumida
- Division of Nephrology, Department of Medicine, University of Tennessee Health Science Center, Memphis, TN
| | - Jamie P. Traynor
- Glasgow Renal and Transplant Unit, Queen Elizabeth University Hospital, Glasgow, U.K
| | - Jan van den Brand
- Department of Nephrology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Chi-Pang Wen
- National Health Research Institutes, Miaoli, Taiwan
- China Medical University Hospital, Taichung, Taiwan
| | - Mark Woodward
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
- The George Institute for Global Health, Imperial College, London, U.K
| | - Jae Won Yang
- Department of Internal Medicine, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | - Angela Yee-Moon Wang
- Department of Medicine, Queen Mary Hospital, The University of Hong Kong, Hong Kong
| | - Navdeep Tangri
- Division of Nephrology, Department of Medicine, University of Manitoba, Winnipeg, Manitoba Canada
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11
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De Nicola L, Cozzolino M, Genovesi S, Gesualdo L, Grandaliano G, Pontremoli R. Can SGLT2 inhibitors answer unmet therapeutic needs in chronic kidney disease? J Nephrol 2022; 35:1605-1618. [PMID: 35583597 PMCID: PMC9300572 DOI: 10.1007/s40620-022-01336-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 04/18/2022] [Indexed: 11/25/2022]
Abstract
Chronic kidney disease (CKD) is a global health problem, affecting more than 850 million people worldwide. The number of patients receiving renal replacement therapy (dialysis or renal transplantation) has increased over the years, and it has been estimated that the number of people receiving renal replacement therapy will more than double from 2.618 million in 2010 to 5.439 million in 2030, with wide differences among countries. The main focus of CKD treatment has now become preserving renal function rather than replacing it. This is possible, at least to some extent, through the optimal use of multifactorial therapy aimed at preventing end-stage kidney disease and cardiovascular events. Sodium/glucose cotransporter 2 inhibitors (SGLT2i) reduce glomerular hypertension and albuminuria with beneficial effects on progression of renal damage in both diabetic and non-diabetic CKD. SGLT2 inhibitors also show great benefits in cardiovascular protection, irrespective of diabetes. Therefore, the use of these drugs will likely be extended to the whole CKD population as a new standard of care.
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Affiliation(s)
- Luca De Nicola
- Nephrology and Dialysis Unit, Department of Advanced Medical and Surgical Sciences, University Vanvitelli, Naples, Italy
| | - Mario Cozzolino
- Renal Division, ASST Santi Paolo e Carlo, Department of Health Sciences, University of Milan, Milan, Italy.
| | - Simonetta Genovesi
- School of Medicine and Surgery, Nephrology Clinic, University of Milano-Bicocca, Milan, Italy.,Istituto Auxologico Italiano, IRCCS, Milan, Italy
| | - Loreto Gesualdo
- Department of Emergency and Organ Transplantation (DETO), School of Medicine, University of Bari "Aldo Moro", Bari, Italy
| | - Giuseppe Grandaliano
- Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Rome, Italy.,Dipartimento di Scienze Mediche e Chirurgiche, U.O.C. Nefrologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Roberto Pontremoli
- Department of Internal Medicine, University of Genoa and IRCCS Ospedale Policlinico San Martino, Genoa, Italy
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12
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Tummalapalli SL, Mendu ML. Value-Based Care and Kidney Disease: Emergence and Future Opportunities. Adv Chronic Kidney Dis 2022; 29:30-39. [PMID: 35690401 PMCID: PMC9199582 DOI: 10.1053/j.ackd.2021.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 09/16/2021] [Accepted: 10/05/2021] [Indexed: 01/03/2023]
Abstract
The United States health care system has increasingly embraced value-based programs that reward improved outcomes and lower costs. Health care value, defined as quality per unit cost, was a major goal of the 2010 Patient Protection and Affordable Care Act amid high and rising US health care expenditures. Many early value-based programs were specifically designed for patients with end-stage renal disease (ESRD) and targeted toward dialysis facilities, including the ESRD Prospective Payment System, ESRD Quality Incentive Program, and ESRD Seamless Care Organizations. While a great deal of attention has been paid to these ESRD-focused programs, other value-based programs targeted toward hospitals and health systems may also affect the quality and costs of care for a broader population of patients with kidney disease. Value-based care for kidney disease is increasingly relevant in light of the Advancing American Kidney Health initiative, which introduces new value-based payment models: the mandatory ESRD Treatment Choices Model in 2021 and voluntary Kidney Care Choices Model in 2022. In this review article, we summarize the emergence and impact of value-based programs on the quality and costs of kidney care, with a focus on federal programs. Key opportunities in value-based kidney care include shifting the focus toward chronic kidney disease, enhancing population health management capabilities, improving quality measurement, and leveraging programs to advance health equity.
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Affiliation(s)
- Sri Lekha Tummalapalli
- Division of Healthcare Delivery Science & Innovation, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY,The Rogosin Institute, New York, NY
| | - Mallika L. Mendu
- Division of Renal Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA,Center for Population Health, Mass General Brigham, Boston, MA
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13
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Kushner P, Peach E, Wittbrodt E, Barone S, Chen H, Sanchez JJG, Järbrink K, Arnold M, Tangri N. Investigating the global prevalence and consequences of undiagnosed stage 3 chronic kidney disease: methods and rationale for the REVEAL-CKD study. Clin Kidney J 2021; 15:738-746. [PMID: 35371463 PMCID: PMC8967537 DOI: 10.1093/ckj/sfab235] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Indexed: 12/03/2022] Open
Abstract
Background Timely diagnosis and treatment of stage 3 chronic kidney disease (CKD) can prevent further loss of kidney function and progression to kidney failure. However, contemporary data on the global prevalence of undiagnosed stage 3 CKD are scarce. REVEAL-CKD is a multinational, multifocal and observational study aiming to provide insights into undiagnosed stage 3 CKD in a large population. Methods Patients (aged ≥18 years) with data in selected secondary databases from 11 countries will be included if they have at least two estimated glomerular filtration rate (eGFR) measurements from 2015 onwards that are ≥30 and <60 mL/min/1.73 m2, recorded >90 and ≤730 days apart. Undiagnosed cases are those without an International Classification of Diseases 9/10 diagnosis code for CKD (any stage) any time before and up to 6 months after the second qualifying eGFR measurement. Time to diagnosis will be assessed using a Kaplan–Meier approach; patient characteristics associated with undiagnosed CKD will be assessed using adjusted logistical regression analyses. Results REVEAL-CKD will assess the point prevalence of undiagnosed stage 3 CKD and time to CKD diagnosis in initially undiagnosed cases overall and in individual countries. Trends in undiagnosed CKD prevalence by calendar year will be assessed. Patient characteristics, healthcare resource utilization, adverse clinical outcomes, and CKD management and monitoring practices in patients with versus without a CKD diagnosis will be compared. Conclusions REVEAL-CKD will increase awareness of the global clinical and economic burden of undiagnosed stage 3 CKD and provide valuable insights to inform clinical practice and policy changes.
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Affiliation(s)
| | - Emily Peach
- Cardiovascular, Renal and Metabolism Epidemiology, BioPharmaceuticals Medical, AstraZeneca, Cambridge, UK
| | - Eric Wittbrodt
- Cardiovascular, Renal and Metabolism Medical Affairs, BioPharmaceuticals Medical, AstraZeneca, Gaithersburg, MD, USA
| | - Salvatore Barone
- Global Medical Affairs, BioPharmaceuticals Medical, AstraZeneca, Gaithersburg, MD, USA
| | - Hungta Chen
- Medical/Payer Evidence Statistics, BioPharmaceuticals Medical, AstraZeneca, Gaithersburg, MD, USA
| | | | - Krister Järbrink
- Cardiovascular, Renal and Metabolism Evidence, BioPharmaceuticals Medical, AstraZeneca, Gothenburg, Mölndal, Sweden
| | - Matthew Arnold
- Real World Evidence Data & Analytics, BioPharmaceuticals Medical, AstraZeneca, Cambridge, UK
| | - Navdeep Tangri
- Department of Internal Medicine, University of Manitoba, Winnipeg, MB, Canada
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14
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Diamantidis CJ, Zepel L, Wang V, Smith VA, Hudson Scholle S, Tamayo L, Maciejewski ML. Disparities in Chronic Kidney Disease Progression by Medicare Advantage Enrollees. Am J Nephrol 2021; 52:949-957. [PMID: 34875668 DOI: 10.1159/000519758] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 09/06/2021] [Indexed: 11/19/2022]
Abstract
INTRODUCTION The prevalence of chronic kidney disease (CKD) in Medicare beneficiaries has quadrupled in the past 2 decades, but little is known about risk factors affecting the progression of CKD. This study aims to understand the progression in Medicare Advantage enrollees and whether it differs by provider recognition of CKD, race and ethnicity, or geographic location. In a large cohort of Medicare Advantage (MA) enrollees, we examined whether CKD progression, up to 5 years after study entry, differed by demographic and clinical factors and identified additional risk factors of CKD progression. METHODS In a cohort of 1,002,388 MA enrollees with CKD stages 1-4 based on 2013-2018 labs, progression was estimated using a mixed-effects model that adjusted for demographics, geographic location, comorbidity, urine albumin-to-creatinine ratio, clinical recognition via diagnosed CKD, and time-fixed effects. Race and ethnicity, geographic location, and clinical recognition of CKD were interacted with time in 3 separate regression models. RESULTS Mean (median) follow-up was 3.1 (3.0) years. Black and Hispanic MA enrollees had greater kidney function at study entry than other beneficiaries, but their kidney function declined faster. MA enrollees with clinically recognized CKD had estimated glomerular filtration rate levels that were 18.6 units (95% confidence interval [CI]: 18.5-18.7) lower than levels of unrecognized patients, but kidney function declined more slowly in enrollees with clinical recognition. There were no differences in CKD progression by geography. After removal of the race coefficient from the eGFR equation in a sensitivity analysis, kidney function was much lower in all years among Black MA enrollees, but patterns of progression remained the same. DISCUSSION/CONCLUSIONS These results suggest that patients with clinically recognized CKD and racial and ethnic minorities merit closer surveillance and management to reduce their risk of faster progression.
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Affiliation(s)
- Clarissa Jonas Diamantidis
- Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
- Division of Nephrology, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Lindsay Zepel
- Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA
- OptumLabs Visiting Fellow, Cambridge, Massachusetts, USA
| | - Virginia Wang
- Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA
- Center for Health Services Research in Primary Care, Durham Veterans Affairs Medical Center, Durham, North Carolina, USA
| | - Valerie A Smith
- Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA
- Center for Health Services Research in Primary Care, Durham Veterans Affairs Medical Center, Durham, North Carolina, USA
| | | | - Loida Tamayo
- Centers for Medicare & Medicaid Services, Baltimore, Maryland, USA
| | - Matthew L Maciejewski
- Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA
- Center for Health Services Research in Primary Care, Durham Veterans Affairs Medical Center, Durham, North Carolina, USA
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15
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Chu CD, Powe NR, McCulloch CE, Crews DC, Han Y, Bragg-Gresham JL, Saran R, Koyama A, Burrows NR, Tuot DS. Trends in Chronic Kidney Disease Care in the US by Race and Ethnicity, 2012-2019. JAMA Netw Open 2021; 4:e2127014. [PMID: 34570204 PMCID: PMC8477264 DOI: 10.1001/jamanetworkopen.2021.27014] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 07/25/2021] [Indexed: 12/13/2022] Open
Abstract
Importance Significant racial and ethnic disparities in chronic kidney disease (CKD) progression and outcomes are well documented, as is low use of guideline-recommended CKD care. Objective To examine guideline-recommended CKD care delivery by race and ethnicity in a large, diverse population. Design, Setting, and Participants In this serial cross-sectional study, adult patients with CKD that did not require dialysis, defined as a persistent estimated glomerular filtration rate less than 60 mL/min/1.73 m2 or a urine albumin-creatinine ratio of 30 mg/g or higher for at least 90 days, were identified in 2-year cross-sections from January 1, 2012, to December 31, 2019. Data from the OptumLabs Data Warehouse, a national data set of administrative and electronic health record data for commercially insured and Medicare Advantage patients, were used. Exposures The independent variables were race and ethnicity, as reported in linked electronic health records. Main Outcomes and Measures On the basis of guideline-recommended CKD care, the study examined care delivery process measures (angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker prescription for albuminuria, statin prescription, albuminuria testing, nephrology care for CKD stage 4 or higher, and avoidance of chronic nonsteroidal anti-inflammatory drug prescription) and care delivery outcome measures (blood pressure and diabetes control). Results A total of 452 238 patients met the inclusion criteria (mean [SD] age, 74.0 [10.2] years; 262 089 [58.0%] female; a total of 7573 [1.7%] Asian, 49 970 [11.0%] Black, 15 540 [3.4%] Hispanic, and 379 155 [83.8%] White). Performance on process measures was higher among Asian, Black, and Hispanic patients compared with White patients for angiotensin-converting enzyme inhibitor and angiotensin II receptor blocker use (79.8% for Asian patients, 76.7% for Black patients, and 79.9% for Hispanic patients compared with 72.3% for White patients in 2018-2019), statin use (72.6% for Asian patients, 69.1% for Black patients, and 74.1% for Hispanic patients compared with 61.5% for White patients), nephrology care (64.8% for Asian patients, 72.9% for Black patients, and 69.4% for Hispanic patients compared with 58.3% for White patients), and albuminuria testing (53.9% for Asian patients, 41.0% for Black patients, and 52.6% for Hispanic patients compared with 30.7% for White patients). Achievement of blood pressure control to less than 140/90 mm Hg was similar or lower among Asian (71.8%), Black (63.3%), and Hispanic (69.8%) patients compared with White patients (72.9%). Achievement of diabetes control with hemoglobin A1c less than 7.0% was 50.1% in Asian patients, 49.3% in Black patients, and 46.0% in Hispanic patients compared with 50.3% for White patients. Conclusions and Relevance Higher performance on CKD care process measures among Asian, Black, and Hispanic patients suggests that differences in medication prescription and diagnostic testing are unlikely to fully explain known disparities in CKD progression and kidney failure. Improving care delivery processes alone may be inadequate for reducing these disparities.
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Affiliation(s)
- Chi D. Chu
- Division of Nephrology, University of California, San Francisco
- OptumLabs Visiting Fellow, OptumLabs, Eden Prairie, Minnesota
| | - Neil R. Powe
- Department of Medicine, University of California, San Francisco
- Department of Medicine, Mark Zuckerberg and Priscilla Chan San Francisco General Hospital, San Francisco, California
- Center for Vulnerable Populations, University of California, San Francisco
| | - Charles E. McCulloch
- Department of Epidemiology and Biostatistics, University of California, San Francisco
| | - Deidra C. Crews
- Division of Nephrology, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Johns Hopkins Center for Health Equity, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Yun Han
- Division of Nephrology, Department of Medicine, University of Michigan, Ann Arbor
- Kidney Epidemiology and Cost Center, University of Michigan, Ann Arbor
| | | | - Rajiv Saran
- Division of Nephrology, Department of Medicine, University of Michigan, Ann Arbor
- Kidney Epidemiology and Cost Center, University of Michigan, Ann Arbor
| | - Alain Koyama
- Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Nilka R. Burrows
- Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Delphine S. Tuot
- Division of Nephrology, University of California, San Francisco
- Department of Medicine, University of California, San Francisco
- Department of Medicine, Mark Zuckerberg and Priscilla Chan San Francisco General Hospital, San Francisco, California
- Center for Vulnerable Populations, University of California, San Francisco
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16
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Norton JM, Grunwald L, Banaag A, Olsen C, Narva AS, Marks E, Koehlmoos TP. CKD Prevalence in the Military Health System: Coded Versus Uncoded CKD. Kidney Med 2021; 3:586-595.e1. [PMID: 34401726 PMCID: PMC8350811 DOI: 10.1016/j.xkme.2021.03.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Rationale & Objective Chronic kidney disease (CKD) is common but often goes unrecorded. Study Design Cross-sectional. Setting & Participants Military Health System (MHS) beneficiaries aged 18 to 64 years who received care during fiscal years 2016 to 2018. Predictors Age, sex, active duty status, race, diabetes, hypertension, and numbers of kidney test results. Outcomes We defined CKD by International Classification of Diseases, Tenth Revision (ICD-10) code and/or a positive result on a validated electronic phenotype that uses estimated glomerular filtration rate and measures of proteinuria with evidence of chronicity. We defined coded CKD by the presence of an ICD-10 code. We defined uncoded CKD by a positive e-phenotype result without an ICD-10 code. Analytical Approach We compared coded and uncoded populations using 2-tailed t tests (continuous variables) and Pearson χ2 test for independence (categorical variables). Results The MHS population included 3,330,893 beneficiaries. Prevalence of CKD was 3.2%, based on ICD code and/or positive e-phenotype result. Of those identified with CKD, 63% were uncoded. Compared with beneficiaries with coded CKD, those with uncoded CKD were younger (aged 45 ± 13 vs 52 ± 11 years), more often women (54.4% vs 37.6%) and active duty (20.2% vs 12.5%), and less often of Black race (18.5% vs 31.5%) or with diabetes (23.5% vs 43.5%) or hypertension (46.6% vs 77.1%; P < 0.001). Beneficiaries with coded (vs uncoded) CKD had greater numbers of kidney test results (P < 0.001). Limitations Use of cross-sectional administrative data prevents inferences about causality. The CKD e-phenotype may fail to capture CKD in individuals without laboratory data and may underestimate CKD. Conclusions The prevalence of CKD in the MHS is ~3.2%. Beneficiaries with well-known CKD risk factors, such as older age, male sex, Black race, diabetes, and hypertension, were more likely to be coded, suggesting that clinicians may be missing CKD in groups traditionally considered lower risk, potentially resulting in suboptimal care.
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Affiliation(s)
- Jenna M Norton
- Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD
| | - Lindsay Grunwald
- Henry M Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD
| | - Amanda Banaag
- Henry M Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD
| | - Cara Olsen
- Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD
| | - Andrew S Narva
- College of Agriculture, Urban Sustainability & Environmental Sciences, University of the District of Columbia, Washington, DC
| | - Eric Marks
- Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD.,Division of Nephrology, Department of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD
| | - Tracey P Koehlmoos
- Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD
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17
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Ye JJ, Wang SS, Fang Y, Zhang XJ, Hu CY. Ambient air pollution exposure and risk of chronic kidney disease: A systematic review of the literature and meta-analysis. ENVIRONMENTAL RESEARCH 2021; 195:110867. [PMID: 33582130 DOI: 10.1016/j.envres.2021.110867] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Revised: 01/07/2021] [Accepted: 02/06/2021] [Indexed: 06/12/2023]
Abstract
Ambient air pollution has been identified as one of the leading causes of global burden of disease. The relationship between ambient air pollution exposure and risk of chronic kidney disease (CKD) has stimulated increasing scientific interest in the past few years. However, evidence from human epidemiological studies is still limited and inconsistent. We performed an updated systematic review and meta-analysis to clarify the potential association comprehensively. Selected electronic databases were searched for related English language studies until March 1, 2020 with a final follow-up in December 31, 2020. Risk of bias assessment for individual studies were assessed using the OHAT (Office of Health Assessment and Translation) risk-of-bias rating tool. Confidence rating and level-of-evidence conclusions were developed for bodies of evidence for a given ambient air pollutant. Summary effect estimates were calculated using random-effects meta-analyses when three or more studies are identified for the same air pollutant-CKD combination. A total of 13 studies were finally identified in our study. The meta-analytic estimates (ORs) for risk of CKD were 1.15 (95% CI: 1.07, 1.24) for each 10 μg/m3 increase in PM2.5, 1.25 (95% CI: 1.11, 1.40) for each 10 μg/m3 increase in PM10, 1.10 (95% CI: 1.03, 1.17) for each 10 ppb increase in NO2, 1.06 (95% CI: 0.98, 1.15) for each 1 ppb increase in SO2 and 1.04 (95% CI: 1.00, 1.08) for each 0.1 ppm increase in CO, respectively. The level of evidence was appraised as moderate for four of the five tested air pollutant-CKD combinations using an adaptation of the GRADE (Grading of Recommendations Assessment, Development and Evaluation) tool. In conclusion, this study suggests that certain ambient air pollutant exposure was significantly associated with an increased risk of CKD. Given the limitations, the results of this study should be interpreted with caution, and further well-designed epidemiological studies are needed to draw a definite evidence of a causal relationship.
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Affiliation(s)
- Jia-Jia Ye
- Department of Clinical Medicine, The First School of Clinical Medicine, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China
| | - Shu-Si Wang
- Department of Healthcare-associated Infection Management, Hefei Stomatological Hospital, Anhui Medical University Hefei Oral Clinic College, 265 Changjiang Middle Road, Hefei, 230001, China
| | - Yuan Fang
- Department of Public Health, Erasmus University Medical Center, P.O. Box 2040, 3000, CA Rotterdam, the Netherlands
| | - Xiu-Jun Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China.
| | - Cheng-Yang Hu
- Department of Humanistic Medicine, School of Humanistic Medicine, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China; Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China.
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18
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Folkerts K, Petruski-Ivleva N, Comerford E, Blankenburg M, Evers T, Gay A, Fried L, Kovesdy CP. Adherence to Chronic Kidney Disease Screening Guidelines Among Patients With Type 2 Diabetes in a US Administrative Claims Database. Mayo Clin Proc 2021; 96:975-986. [PMID: 33722396 DOI: 10.1016/j.mayocp.2020.07.037] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 06/24/2020] [Accepted: 07/13/2020] [Indexed: 01/17/2023]
Abstract
OBJECTIVE To examine the screening rates for kidney damage and function among patients with type 2 diabetes (T2D) and chronic kidney disease stage at diabetes diagnosis using a US administrative claims database. PATIENTS AND METHODS This cohort study used a claims database enriched with laboratory results data. Patients with T2D (defined as 1 inpatient or 2 outpatient claims for diabetes), aged 18 years or older, and with at least 1 year of follow-up enrollment were identified. Patients with type 1 diabetes, kidney disease, or other related conditions at baseline were excluded. We estimated screening rates using laboratory orders for serum creatinine and estimated glomerular filtration rate (eGFR) measurement and urine albumin to creatinine ratio (UACR). Chronic kidney disease severity was reported using the Kidney Disease: Improving Global Outcomes classification based on laboratory results. RESULTS A total of 1,881,447 patients with T2D were eligible for analysis. Mean ± SD age was 63.1±13.1 years; 947,150 patients (50.3%) were male. Serum creatinine tests were ordered within 14 days of the index date among 290,722 patients of 622,915 (46.7%) patients with newly-recognized T2D. Overall, 1,595,964 patients (84.8%) had at least one serum creatinine test ordered during the 1-year follow-up period. Fewer patients received a UACR test during follow-up (814,897 [43.3%]). Less than half of all patients with T2D received a laboratory test order for both serum creatinine and urine albumin measurements during the follow-up period. CONCLUSION Physicians treating patients with diabetes are selectively adhering to chronic kidney disease screening guidelines, as indicated by high rates of eGFR testing, but less frequent UACR testing. Despite recommendations to monitor both eGFR and UACR, less than half of patients were screened for albuminuria during the 1-year follow-up.
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Affiliation(s)
| | | | | | | | | | | | - Linda Fried
- Department of Medicine, Renal-Electrolyte Division, University of Pittsburgh School of Medicine and Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, PA
| | - Csaba P Kovesdy
- Division of Nephrology, Department of Medicine, University of Tennessee Health Science Center, Memphis, TN.
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19
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Weber C, Röschke L, Modersohn L, Lohr C, Kolditz T, Hahn U, Ammon D, Betz B, Kiehntopf M. Optimized Identification of Advanced Chronic Kidney Disease and Absence of Kidney Disease by Combining Different Electronic Health Data Resources and by Applying Machine Learning Strategies. J Clin Med 2020; 9:jcm9092955. [PMID: 32932685 PMCID: PMC7563476 DOI: 10.3390/jcm9092955] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 08/26/2020] [Accepted: 08/28/2020] [Indexed: 12/31/2022] Open
Abstract
Automated identification of advanced chronic kidney disease (CKD ≥ III) and of no known kidney disease (NKD) can support both clinicians and researchers. We hypothesized that identification of CKD and NKD can be improved, by combining information from different electronic health record (EHR) resources, comprising laboratory values, discharge summaries and ICD-10 billing codes, compared to using each component alone. We included EHRs from 785 elderly multimorbid patients, hospitalized between 2010 and 2015, that were divided into a training and a test (n = 156) dataset. We used both the area under the receiver operating characteristic (AUROC) and under the precision-recall curve (AUCPR) with a 95% confidence interval for evaluation of different classification models. In the test dataset, the combination of EHR components as a simple classifier identified CKD ≥ III (AUROC 0.96[0.93-0.98]) and NKD (AUROC 0.94[0.91-0.97]) better than laboratory values (AUROC CKD 0.85[0.79-0.90], NKD 0.91[0.87-0.94]), discharge summaries (AUROC CKD 0.87[0.82-0.92], NKD 0.84[0.79-0.89]) or ICD-10 billing codes (AUROC CKD 0.85[0.80-0.91], NKD 0.77[0.72-0.83]) alone. Logistic regression and machine learning models improved recognition of CKD ≥ III compared to the simple classifier if only laboratory values were used (AUROC 0.96[0.92-0.99] vs. 0.86[0.81-0.91], p < 0.05) and improved recognition of NKD if information from previous hospital stays was used (AUROC 0.99[0.98-1.00] vs. 0.95[0.92-0.97]], p < 0.05). Depending on the availability of data, correct automated identification of CKD ≥ III and NKD from EHRs can be improved by generating classification models based on the combination of different EHR components.
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Affiliation(s)
- Christoph Weber
- Department of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), Jena University Hospital, 07747 Jena, Germany; (C.W.); (L.R.)
| | - Lena Röschke
- Department of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), Jena University Hospital, 07747 Jena, Germany; (C.W.); (L.R.)
| | - Luise Modersohn
- Jena University Language & Information Engineering (JULIE) Lab, Friedrich Schiller University Jena, 07743 Jena, Germany; (L.M.); (C.L.); (T.K.); (U.H.)
| | - Christina Lohr
- Jena University Language & Information Engineering (JULIE) Lab, Friedrich Schiller University Jena, 07743 Jena, Germany; (L.M.); (C.L.); (T.K.); (U.H.)
| | - Tobias Kolditz
- Jena University Language & Information Engineering (JULIE) Lab, Friedrich Schiller University Jena, 07743 Jena, Germany; (L.M.); (C.L.); (T.K.); (U.H.)
| | - Udo Hahn
- Jena University Language & Information Engineering (JULIE) Lab, Friedrich Schiller University Jena, 07743 Jena, Germany; (L.M.); (C.L.); (T.K.); (U.H.)
| | - Danny Ammon
- Data Integration Center, Jena University Hospital, 07743 Jena, Germany;
| | - Boris Betz
- Department of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), Jena University Hospital, 07747 Jena, Germany; (C.W.); (L.R.)
- Correspondence: (B.B.); (M.K.); Tel.: +49-3641-9-325074 (B.B.); +49-3641-9-325001 (M.K.)
| | - Michael Kiehntopf
- Department of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), Jena University Hospital, 07747 Jena, Germany; (C.W.); (L.R.)
- Correspondence: (B.B.); (M.K.); Tel.: +49-3641-9-325074 (B.B.); +49-3641-9-325001 (M.K.)
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