Affiliation:
1. Department of Pathology Queen Elizabeth University Hospital Glasgow UK
2. John Lynch Renal Unit University Hospital Crosshouse Crosshouse UK
3. Dumfries and Galloway Royal Infirmary Renal Unit Dumfries UK
4. Glasgow Renal and Transplant Unit Queen Elizabeth University Hospital Glasgow UK
Abstract
AimsTo assess retrospectively the association between histopathological lesions on renal biopsy and subsequent impairment of renal function across the spectrum of kidney diseases and to explore the influence of immunosuppressive therapy within the first 6 months after biopsy on this association.Methods and resultsClinical data from 488 adult patients having a renal biopsy reported at a single centre from 2017 to 2019 were obtained during a median follow‐up period of 786 days. Seventeen semi‐quantitative histology parameters were recorded at the time of biopsy, 14 of which were suitable for assessment of association with loss of eGFR by multivariable Cox regression analysis, measurement of eGFR slope and measurement of eGFR 12 months after biopsy. A widely used histopathological chronicity score was also assessed. Clinical baseline variables including prescription of immunosuppression were recorded. Seven of 14 histology parameters: mesangial matrix expansion, global glomerulosclerosis, tubular atrophy, interstitial fibrosis, arteriolosclerosis, mesangial hypercellularity and acute tubular injury; and the chronicity score, predicted loss of kidney function by all three measures. Prescription of immunosuppression was more likely in patients with active inflammatory pathology and less likely in patients with chronic fibrotic pathology, and was associated with reduced risk of loss of eGFR.ConclusionsThis retrospective study demonstrates the prognostic significance and complex relationship with immunosuppression of routinely reported histopathological variables in patients having native kidney biopsies, across the spectrum of kidney diseases. It provides useful information for renal biopsy prognostication and design of retrospective studies, including machine learning models.
Subject
General Medicine,Histology,Pathology and Forensic Medicine