#1933 Clinico-pathologic classification of focal segmental glomerulosclerosis to inform on treatment and prognosis
Bibliographic record
Abstract
Abstract Background and Aims Focal segmental glomerulosclerosis (FSGS) is classified into primary (idiopathic, immune-mediated), secondary (maladaptive, medication-induced or viral-associated), and genetic forms, each differing in disease progression and treatment responses. Despite advancements, misclassification of subtypes remains common due to limited access to genetic testing and lack of definitive biomarkers for primary FSGS. We aimed to determine whether serum albumin, degree of proteinuria, and podocyte foot process effacement (FPE) could be used to improve subtype classification and predict response to immunosuppressive treatments. Method This retrospective, observational cohort study included adult patients with biopsy-proven FSGS at The Ottawa Hospital between 2010 and 2023. Participants were classified into 3 subtypes of FSGS based on clinical and histologic criteria: presumed primary FSGS (serum albumin <35 g/L and proteinuria >3.5 g/day at time of biopsy, and diffuse FPE [>80%] on electron microscopy (EM)), presumed secondary FSGS (serum albumin ≥35 g/L and no diffuse FPE on EM, regardless of the level of proteinuria), and uncategorized FSGS (cases not meeting either criterion). Outcomes included achievement of complete remission (CR) (proteinuria <0.3 g/day), partial remission (PR) (proteinuria <3.5 g/day and > 50% reduction from baseline), progression to end-stage kidney disease (ESKD) (requiring dialysis for ≥12 weeks or initiation of kidney transplant evaluation), death, and change in estimated glomerular filtration rate (eGFR). Logistic regression was performed to examine the association of FSGS category with outcomes, using the presumed secondary FSGS subtype as the reference group, and adjusting for age, sex, and baseline serum creatinine at the time of biopsy. Linear mixed models were performed to examine eGFR throughout follow-up, by FSGS category. Results 187 patients were included with a mean age of 53.8 ± 15.6 years, mean serum creatinine of 167.9 ± 124 μmol/L, and mean urine albumin to creatinine ratio (ACR) of 370.9 ± 431 mg/g at time of kidney biopsy. 54 patients were categorized as having presumed primary FSGS, 72 as presumed secondary, and 61 patients were uncategorized. In the presumed primary FSGS group, 29.6% (aOR = 2.67, reference group Presumed Secondary FSGS; 95% CI: 1.07, 6.63) of patients achieved CR, compared to 15.3% and 9.8% in the presumed secondary and uncategorized FGSG groups, respectively. PR was achieved by 40.7% in the presumed primary FSGS group (aOR = 0.52, reference group Presumed Secondary FSGS; 95% CI: 0.24, 1.10), 58.3% in the presumed secondary and 60.7% in the uncategorized FSGS groups. Those in the presumed primary group more frequently received immunosuppressive treatment in the first 6 months post-biopsy (42.6%, compared to 1.4% in presumed secondary and 16.4% in the uncategorized groups) and had an early improvement in mean eGFR relative to the other groups based on a linear mixed model adjusted for baseline age and eGFR (Fig. 1). Conclusion In patients with biopsy-proven FSGS, disease subtype classification based on serum albumin, level of proteinuria, and degree of FPE at the time of diagnosis may help identify patients with an underlying immune-mediated disease who could respond to immunosuppressive therapy, in terms of achieving remission and potentially improving kidney function. These simple, readily available biomarkers may be useful when deciding on the optimal therapeutic approach for patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".