Individual patient variability with the application of the kidney failure risk equation in advanced chronic kidney disease.
The Kidney Failure Risk Equation (KFRE) predicts the need for dialysis or transplantation using age, sex, estimated glomerular filtration rate (eGFR), and urine albumin to creatinine ratio (ACR). The eGFR and ACR have known biological and analytical variability. We examined the effect of biological...
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| _version_ | 1850231100842967040 |
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| author | Christopher McCudden Ayub Akbari Christine A White Mohan Biyani Swapnil Hiremath Pierre Antoine Brown Navdeep Tangri Scott Brimble Greg Knoll Peter G Blake Manish M Sood |
| author_facet | Christopher McCudden Ayub Akbari Christine A White Mohan Biyani Swapnil Hiremath Pierre Antoine Brown Navdeep Tangri Scott Brimble Greg Knoll Peter G Blake Manish M Sood |
| author_sort | Christopher McCudden |
| collection | DOAJ |
| description | The Kidney Failure Risk Equation (KFRE) predicts the need for dialysis or transplantation using age, sex, estimated glomerular filtration rate (eGFR), and urine albumin to creatinine ratio (ACR). The eGFR and ACR have known biological and analytical variability. We examined the effect of biological and analytical variability of eGFR and ACR on the 2-year KFRE predicted kidney failure probabilities using single measure and the average of repeat measures of simulated eGFR and ACR. Previously reported values for coefficient of variation (CV) for ACR and eGFR were used to calculate day to day variability. Variation was also examined with outpatient laboratory data from patients with an eGFR between 15 and 50 mL/min/1.72 m2. A web application was developed to calculate and model day to day variation in risk. The biological and analytical variability related to ACR and eGFR lead to variation in the predicted probability of kidney failure. A male patient age 50, ACR 30 mg/mmol and eGFR 25, had a day to day variation in risk of 7% (KFRE point estimate: 17%, variability range 14% to 21%). The addition of inter laboratory variation due to different instrumentation increased the variability to 9% (KFRE point estimate 17%, variability range 13% to 22%). Averaging of repeated measures of eGFR and ACR significantly decreased the variability (KFRE point estimate 17%, variability range 15% to 19%). These findings were consistent when using outpatient laboratory data which showed that most patients had a KFRE 2-year risk variability of ≤ 5% (79% of patients). Approximately 13% of patients had variability from 5-10% and 8% had variability > 10%. The mean age (SD) of this cohort was 64 (15) years, 36% were females, the mean (SD) eGFR was 32 (10) ml/min/1.73m2 and median (IQR) ACR was 22.7 (110). Biological and analytical variation intrinsic to the eGFR and ACR may lead to a substantial degree of variability that decreases with repeat measures. Use of a web application may help physicians and patients understand individual patient's risk variability and communicate risk (https://mccudden.shinyapps.io/kfre_app/). The web application allows the user to alter age, gender, eGFR, ACR, CV (for both eGFR and ACR) as well as units of measurements for ACR (g/mol versus mg/g). |
| format | Article |
| id | doaj-art-aed91b48a52a4d3a8301dd9d88de6267 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-aed91b48a52a4d3a8301dd9d88de62672025-08-20T02:03:39ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-01136e019845610.1371/journal.pone.0198456Individual patient variability with the application of the kidney failure risk equation in advanced chronic kidney disease.Christopher McCuddenAyub AkbariChristine A WhiteMohan BiyaniSwapnil HiremathPierre Antoine BrownNavdeep TangriScott BrimbleGreg KnollPeter G BlakeManish M SoodThe Kidney Failure Risk Equation (KFRE) predicts the need for dialysis or transplantation using age, sex, estimated glomerular filtration rate (eGFR), and urine albumin to creatinine ratio (ACR). The eGFR and ACR have known biological and analytical variability. We examined the effect of biological and analytical variability of eGFR and ACR on the 2-year KFRE predicted kidney failure probabilities using single measure and the average of repeat measures of simulated eGFR and ACR. Previously reported values for coefficient of variation (CV) for ACR and eGFR were used to calculate day to day variability. Variation was also examined with outpatient laboratory data from patients with an eGFR between 15 and 50 mL/min/1.72 m2. A web application was developed to calculate and model day to day variation in risk. The biological and analytical variability related to ACR and eGFR lead to variation in the predicted probability of kidney failure. A male patient age 50, ACR 30 mg/mmol and eGFR 25, had a day to day variation in risk of 7% (KFRE point estimate: 17%, variability range 14% to 21%). The addition of inter laboratory variation due to different instrumentation increased the variability to 9% (KFRE point estimate 17%, variability range 13% to 22%). Averaging of repeated measures of eGFR and ACR significantly decreased the variability (KFRE point estimate 17%, variability range 15% to 19%). These findings were consistent when using outpatient laboratory data which showed that most patients had a KFRE 2-year risk variability of ≤ 5% (79% of patients). Approximately 13% of patients had variability from 5-10% and 8% had variability > 10%. The mean age (SD) of this cohort was 64 (15) years, 36% were females, the mean (SD) eGFR was 32 (10) ml/min/1.73m2 and median (IQR) ACR was 22.7 (110). Biological and analytical variation intrinsic to the eGFR and ACR may lead to a substantial degree of variability that decreases with repeat measures. Use of a web application may help physicians and patients understand individual patient's risk variability and communicate risk (https://mccudden.shinyapps.io/kfre_app/). The web application allows the user to alter age, gender, eGFR, ACR, CV (for both eGFR and ACR) as well as units of measurements for ACR (g/mol versus mg/g).https://storage.googleapis.com/plos-corpus-prod/10.1371/journal.pone.0198456/1/pone.0198456.pdf?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=wombat-sa%40plos-prod.iam.gserviceaccount.com%2F20210218%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20210218T103224Z&X-Goog-Expires=3600&X-Goog-SignedHeaders=host&X-Goog-Signature=b4532ae59ea94b471aa1206a36c3384c573b87773c76d99feb5a29c4de946d0ce8a5fdcba2a8fc1a032ec6af2f6f93718f03372442ed6a7404e3f4e57151be7ed43a17d6833a382b7a2a2f014aa0e595cfff37d4ed2fd443ed288fd1f29f951594a3dcafc89abf8eb33d1d804b50812bc3ec6ccb879def35f69f1ed53927492ddca910b1cea4843c651110f5a9fbcf85d6cab58267ee32da1f558791be9fb31320c678f710eddb70a5f2a9c408cc7adf52be7b1d22f2ef513fa8cb936461d0a4bbd96122e1fe2427ddb28071ce32fa305061fc149b57e72004c56d15e45305e3111e5c21d712e9d414a1be20e7cae575278499cf56854e89f5abcdc9fb94e80e |
| spellingShingle | Christopher McCudden Ayub Akbari Christine A White Mohan Biyani Swapnil Hiremath Pierre Antoine Brown Navdeep Tangri Scott Brimble Greg Knoll Peter G Blake Manish M Sood Individual patient variability with the application of the kidney failure risk equation in advanced chronic kidney disease. PLoS ONE |
| title | Individual patient variability with the application of the kidney failure risk equation in advanced chronic kidney disease. |
| title_full | Individual patient variability with the application of the kidney failure risk equation in advanced chronic kidney disease. |
| title_fullStr | Individual patient variability with the application of the kidney failure risk equation in advanced chronic kidney disease. |
| title_full_unstemmed | Individual patient variability with the application of the kidney failure risk equation in advanced chronic kidney disease. |
| title_short | Individual patient variability with the application of the kidney failure risk equation in advanced chronic kidney disease. |
| title_sort | individual patient variability with the application of the kidney failure risk equation in advanced chronic kidney disease |
| url | https://storage.googleapis.com/plos-corpus-prod/10.1371/journal.pone.0198456/1/pone.0198456.pdf?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=wombat-sa%40plos-prod.iam.gserviceaccount.com%2F20210218%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20210218T103224Z&X-Goog-Expires=3600&X-Goog-SignedHeaders=host&X-Goog-Signature=b4532ae59ea94b471aa1206a36c3384c573b87773c76d99feb5a29c4de946d0ce8a5fdcba2a8fc1a032ec6af2f6f93718f03372442ed6a7404e3f4e57151be7ed43a17d6833a382b7a2a2f014aa0e595cfff37d4ed2fd443ed288fd1f29f951594a3dcafc89abf8eb33d1d804b50812bc3ec6ccb879def35f69f1ed53927492ddca910b1cea4843c651110f5a9fbcf85d6cab58267ee32da1f558791be9fb31320c678f710eddb70a5f2a9c408cc7adf52be7b1d22f2ef513fa8cb936461d0a4bbd96122e1fe2427ddb28071ce32fa305061fc149b57e72004c56d15e45305e3111e5c21d712e9d414a1be20e7cae575278499cf56854e89f5abcdc9fb94e80e |
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