Predicting progression is one of the most important and challenging jobs for a nephrologist. Current estimation in clinical practice is based on demographics, clinical parameters, and laboratory findings. About 15% of the adult population in the United States have chronic kidney disease (CKD) and are at increased risk for cardiovascular events and progression to kidney failure. With an accurate predictive model, adverse kidney and cardiovascular outcomes can be prevented or delayed by early detection and treatment.
Why is histopathology important in predicting kidney disease progression risk?
Due to increased accessibility, utilization of kidney biopsies is increasing worldwide. Higher use of kidney biopsies has facilitated the combination of kidney biopsy findings with clinical and laboratory parameters to prognosticate various kidney diseases. Kidney biopsy findings can be acute or chronic. While acute changes are relevant primarily for diagnostic purposes, the chronic changes may appear within weeks or months after the initiating event and hold strong relevance for prognosis and guiding treatment, and assessing treatment response (For more, see this 2016 RPS Consensus Report on standardized reporting and nomenclature). To aid in prognostication, it is important to understand the independent contribution of individual chronic histologic features to kidney outcomes.
Is this new?
The incorporation of histopathological findings with epidemiological factors to differentiate high-risk from low-risk patients has been the basis for classification of lupus nephritis, the Nephrotic Syndrome Study Network digital pathology scoring system, and Oxford/MEST-C classification of IgA nephropathy. Similarly, in the Banff schema of renal allograft biopsy reporting, chronic changes are key and well-recognized markers for prognosis. Success of these scoring systems for prognosis highlights the importance and clinical utility of standardized global reporting systems that can be utilized for the prognostication of kidney diseases.
What have we learnt thus far?
Chronic changes in biopsies may be useful for prediction formulas because of their high degree of reliability. Pathologists generally grade chronic changes with a higher degree of interobserver agreement than for acute lesions such as mesangial and endocapillary hypercellularity. The higher interobserver agreement of chronic biopsy changes heightens the reliability of scoring and grading classification systems when chronic changes are incorporated. In a prospective observational cohort study in 2018, biopsies from 676 individuals that underwent native kidney biopsies were evaluated over a median follow-up of 34.3 months. Across a diverse group of kidney diseases, chronic histopathologic lesions on biopsy were independently associated with progression of kidney disease after adjustment for demographics and clinical variables (eGFR and proteinuria).
In a recent study published in AJKD, Eadon et al performed a retrospective study in which all biopsies from 5 major hospital systems in Indianapolis over a 13-year period with a median follow-up of 3.1 years were included. A total of 2,720 biopsies were reviewed with their clinical variables, and interpreted by a single renal pathologist. The primary outcome of kidney failure (KF) in this study was defined as a sustained (6 months) drop in estimated glomerular filtration rate (eGFR) to 10 mL/min/1.73 m2 or need for dialysis or kidney transplantation. The authors collected demographic data, comorbid conditions, and laboratory data, including eGFR and proteinuria. The histologic variables that were evaluated in this study included glomerular obsolescence, nodular mesangial sclerosis, crescents, arteriolar hyalinosis, and percentage of interstitial fibrosis and tubular atrophy (IFTA)
Crescents (cellular or fibrocellular) and nodular mesangial sclerosis (at least 1 glomerulus): presence or absence (binary system).
The primary diagnoses are outlined in Table 2 below:
The primary outcome of kidney failure was met by 15.1% of all patients. Unsurprisingly, lower baseline eGFR and higher baseline proteinuria were associated with this endpoint, along with comorbid conditions of CHF, DM, hypertension, CAD, and smoking history.
Histologic features improved prediction of kidney failure after accounting for adjusted clinical models. Arteriolar hyalinosis, IFTA, and glomerular obsolescence were the 3 exposure variables that were strongly correlated with kidney failure progression.
These data suggest that a hybrid model that combines clinical and histologic variables can improve prediction of kidney failure. This expands on currently known prediction models (eg Tangri et al) that use only clinical variables.
- Without an adequate cortical sample (minimum of 10 glomeruli and 2 arteries), histopathological report might be suboptimal. The fact that subcapsular cortex may disproportionately show a higher extent of chronic changes and is not often a representative of the total renal cortex should be considered.
- None of the current scoring and grading systems incorporated electron microscopy or immunofluorescence results
- Selection bias must be considered, since all biopsies were clinically indicated, and may have excluded individuals with better presumed prognoses, or patients with limited follow-up.
- A careful review and semiquantitative scoring of the kidney biopsy chronicity provides additional prognostic value above routine clinical variables.
- Consistent grading scale offers uniformity in assessing treatment responses and outcomes not only in the patients with CKD, but also for patients enrolled in clinical trials or observational studies.
- More severe chronic lesions on kidney biopsies (independent of clinical factors such as eGFR, and proteinuria) are associated with a greater risk of kidney disease progression across a variety of kidney disease etiologies.
– Post prepared by Sai Sudha Mannemuddhu @drM_sudha, AJKDBlog Guest Contributor
Title: Kidney Histopathology and Prediction of Kidney Failure: A Retrospective Cohort Study
Author: M.T. Eadon, T.-H. Schwantes-An, C.L. Phillips, A.R. Roberts, C.V. Greene, A. Hallab, K.J. Hart, S.N. Lipp, C. Perez-Ledezma, K.O. Omar, K.J. Kelly, S.M. Moe, P.C. Dagher, T.M. El-Achkar, and R.N. Moorthi