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PREVALENCE, RISK FACTORS, AND OUTCOMES OF CHRONIC KIDNEY DISEASE IN SLE PATIENTS WITH AND WITHOUT LUPUS NEPHRITIS

2025· article· en· W4410513225 on OpenAlexvenueno aff
Keren Cohen‐Hagai, Sydney Benchetrit, Naomi Nacasch, Yael Pri-Paz Basson, Shaye Kivity, Oshrat Tayer Shifman

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLupus nephritisKidney diseaseNephritisInternal medicineSystemic lupus erythematosusRisk factorImmunologyDiseaseDermatology

Abstract

fetched live from OpenAlex

PV055 / #458 Poster Topic: AS06 - Comorbidities Background/Purpose Chronic kidney disease (CKD) is defined as abnormalities of kidney structure or function persisting for >3 months, detected by decreased estimated glomerular filtration rate (eGFR) or albuminuria. While lupus nephritis (LN) is a well-known cause of CKD in patients with systemic lupus erythematosus (SLE), other risk factors also contribute to the development of CKD in these individuals. Currently, guidelines recommend monitoring urinary protein rather than albumin, which may lead to delayed diagnosis of CKD in SLE patients despite potential benefits of earlier detection. Aims: To assess the prevalence, associated factors, and long-term clinical outcomes of CKD among SLE patients, both with and without a history of LN. Methods A retrospective single-center study, conducted between 2014–2023 and included adult patients diagnosed with SLE for at least 12 months. Patients were categorized into CKD or non-CKD groups. CKD was defined as having a decreased eGFR <60 ml/min/1.73m2 and/or albuminuria ≥30 mg/24h both in 2 or more consecutive tests spaced at least 3 months apart. eGFR was calculated using MDRD formula. Patients who developed end-stage kidney disease (ESKD) during follow-up were excluded. Study flowchart is shown in Figure 1. Data on sociodemographic and clinical characteristics were collected. Figure 1. Results A total of 162 SLE patients were included, of them 77 (47.5%) were diagnosed as having CKD. Among these, 57 (35.2%) had albuminuria, 43 (26.5%) had decreased eGFR, and 22 (13.6%) patients had both albuminuria and decreased eGFR. Notably, 47 (61.1%) of the CKD patients, had never been diagnosed with LN. The odds ratio (OR) for having CKD was 2.55 (95% CI 1.3-5.2, p=0.008) in patients with LN as compared to patients without LN. Strikingly, all male patients were categorized as CKD. CKD was associated with higher rates of antiphospholipid syndrome, diabetes, hypertension, and heart disease (Table 1). Additionally, CKD patients experienced higher rates of severe SLE exacerbations and severe infections requiring hospitalizations and more damage accumulation (Table 1). Despite comparable follow-up time, CKD patients had significantly higher mortality rates compared to non-CKD on univariate analysis (23.4% vs 0, p<0.001). Multivariate COX, age- and sex-adjusted model is shown in Figure 2, p<0.01). Table 1. Figure 2. Conclusions CKD is prevalent among patients with SLE, including those without a diagnosis of LN. CKD in SLE patients is associated with higher rates of comorbidities, severe disease exacerbations, and markedly increased mortality. Given the chronic and progressive nature of CKD, these findings suggest that proactive monitoring in SLE patients should include the measurement of albuminuria in addition to proteinuria. This approach could facilitate the introduction of new treatment options, thereby reducing the substantial morbidity and mortality associated with CKD in SLE.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.008
GPT teacher head0.270
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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