Statistical models for the deterioration of kidney function in a primary care population: a retrospective database analysis
<p><b>Background:</b> Evidence for kidney function monitoring intervals in primary care is weak, and based mainly on expert opinion. In the absence of trials of monitoring strategies, an approach combining a model for the natural history of kidney function over time combined with a...
| Main Authors: | , , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
F1000Research
2019
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| _version_ | 1811139245341933568 |
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| author | Oke, JL Feakins, BG Schlackow, I Mihaylova, B Simons, C O'Callaghan, CA Lasserson, DS Hobbs, FDR Stevens, RJ Perera, R |
| author_facet | Oke, JL Feakins, BG Schlackow, I Mihaylova, B Simons, C O'Callaghan, CA Lasserson, DS Hobbs, FDR Stevens, RJ Perera, R |
| author_sort | Oke, JL |
| collection | OXFORD |
| description | <p><b>Background:</b> Evidence for kidney function monitoring intervals in primary care is weak, and based mainly on expert opinion. In the absence of trials of monitoring strategies, an approach combining a model for the natural history of kidney function over time combined with a cost-effectiveness analysis offers the most feasible approach for comparing the effects of monitoring under a variety of policies. This study aimed to create a model for kidney disease progression using routinely collected measures of kidney function.</p>
<p><b>Methods:</b> This is an open cohort study of patients aged ≥18 years, registered at 643 UK general practices contributing to the Clinical Practice Research Datalink between 1 April 2005 and 31 March 2014. At study entry, no patients were kidney transplant donors or recipients, pregnant or on dialysis. Hidden Markov models for estimated glomerular filtration rate (eGFR) stage progression were fitted to four patient cohorts defined by baseline albuminuria stage; adjusted for sex, history of heart failure, cancer, hypertension and diabetes, annually updated for age.</p>
<p><b>Results:</b> Of 1,973,068 patients, 1,921,949 had no recorded urine albumin at baseline, 37,947 had normoalbuminuria (<3mg/mmol), 10,248 had microalbuminuria (3–30mg/mmol), and 2,924 had macroalbuminuria (>30mg/mmol). Estimated annual transition probabilities were 0.75–1.3%, 1.5–2.5%, 3.4–5.4% and 3.1–11.9% for each cohort, respectively. Misclassification of eGFR stage was estimated to occur in 12.1% (95%CI: 11.9–12.2%) to 14.7% (95%CI: 14.1–15.3%) of tests. Male gender, cancer, heart failure and age were independently associated with declining renal function, whereas the impact of raised blood pressure and glucose on renal function was entirely predicted by albuminuria.</p>
<p><b>Conclusions:</b> True kidney function deteriorates slowly over time, declining more sharply with elevated urine albumin, increasing age, heart failure, cancer and male gender. Consecutive eGFR measurements should be interpreted with caution as observed improvement or deterioration may be due to misclassification.</p> |
| first_indexed | 2024-03-07T07:26:50Z |
| format | Journal article |
| id | oxford-uuid:0fa6c9a7-529e-4053-9059-ae1c957031e5 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:03:01Z |
| publishDate | 2019 |
| publisher | F1000Research |
| record_format | dspace |
| spelling | oxford-uuid:0fa6c9a7-529e-4053-9059-ae1c957031e52024-05-16T16:18:14ZStatistical models for the deterioration of kidney function in a primary care population: a retrospective database analysisJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:0fa6c9a7-529e-4053-9059-ae1c957031e5EnglishSymplectic Elements at OxfordF1000Research2019Oke, JLFeakins, BGSchlackow, IMihaylova, BSimons, CO'Callaghan, CALasserson, DSHobbs, FDRStevens, RJPerera, R<p><b>Background:</b> Evidence for kidney function monitoring intervals in primary care is weak, and based mainly on expert opinion. In the absence of trials of monitoring strategies, an approach combining a model for the natural history of kidney function over time combined with a cost-effectiveness analysis offers the most feasible approach for comparing the effects of monitoring under a variety of policies. This study aimed to create a model for kidney disease progression using routinely collected measures of kidney function.</p> <p><b>Methods:</b> This is an open cohort study of patients aged ≥18 years, registered at 643 UK general practices contributing to the Clinical Practice Research Datalink between 1 April 2005 and 31 March 2014. At study entry, no patients were kidney transplant donors or recipients, pregnant or on dialysis. Hidden Markov models for estimated glomerular filtration rate (eGFR) stage progression were fitted to four patient cohorts defined by baseline albuminuria stage; adjusted for sex, history of heart failure, cancer, hypertension and diabetes, annually updated for age.</p> <p><b>Results:</b> Of 1,973,068 patients, 1,921,949 had no recorded urine albumin at baseline, 37,947 had normoalbuminuria (<3mg/mmol), 10,248 had microalbuminuria (3–30mg/mmol), and 2,924 had macroalbuminuria (>30mg/mmol). Estimated annual transition probabilities were 0.75–1.3%, 1.5–2.5%, 3.4–5.4% and 3.1–11.9% for each cohort, respectively. Misclassification of eGFR stage was estimated to occur in 12.1% (95%CI: 11.9–12.2%) to 14.7% (95%CI: 14.1–15.3%) of tests. Male gender, cancer, heart failure and age were independently associated with declining renal function, whereas the impact of raised blood pressure and glucose on renal function was entirely predicted by albuminuria.</p> <p><b>Conclusions:</b> True kidney function deteriorates slowly over time, declining more sharply with elevated urine albumin, increasing age, heart failure, cancer and male gender. Consecutive eGFR measurements should be interpreted with caution as observed improvement or deterioration may be due to misclassification.</p> |
| spellingShingle | Oke, JL Feakins, BG Schlackow, I Mihaylova, B Simons, C O'Callaghan, CA Lasserson, DS Hobbs, FDR Stevens, RJ Perera, R Statistical models for the deterioration of kidney function in a primary care population: a retrospective database analysis |
| title | Statistical models for the deterioration of kidney function in a primary care population: a retrospective database analysis |
| title_full | Statistical models for the deterioration of kidney function in a primary care population: a retrospective database analysis |
| title_fullStr | Statistical models for the deterioration of kidney function in a primary care population: a retrospective database analysis |
| title_full_unstemmed | Statistical models for the deterioration of kidney function in a primary care population: a retrospective database analysis |
| title_short | Statistical models for the deterioration of kidney function in a primary care population: a retrospective database analysis |
| title_sort | statistical models for the deterioration of kidney function in a primary care population a retrospective database analysis |
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