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602 Childhood-onset Systemic Lupus Erythematosus: Long-term outcomes in a large multi-ethnic Ontario cohort

2022· article· en· W4313532720 on OpenAlexaffabout
Steve Jeoung, Roberta Berard, Janet Pope, Johannes Roth, Carter Thorne, Earl D. Silverman, Deborah M. Levy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsHospital for Sick ChildrenUniversity of OttawaSickKids FoundationUniversity of TorontoChildren's Hospital of Western OntarioSt Joseph's Health CareSouthlake Regional Health Center
Fundersnot available
KeywordsMedicineCohortStroke (engine)DialysisInternal medicineProportional hazards modelDiseaseKidney diseasePediatricsIntensive care medicine

Abstract

fetched live from OpenAlex

Background/Purpose The long-term morbidity and mortality of childhood-onset SLE (cSLE) after transition to adult care is not well documented. The present study aims to fill this knowledge gap by analyzing outcomes in a large province-wide cSLE clinical cohort linked to multiple administrative healthcare databases. Our objectives were to: 1) determine all-cause and cause-specific mortality rates, adverse renal event rates, cardiovascular event rates, and cancer rates in cSLE; and 2) determine baseline characteristics associated with higher rates of transition between 3 different states: event-free (entry), adverse renal event, and death. Methods Clinical data were abstracted for cSLE patients (<18 years at diagnosis) diagnosed between January 1990 and March 2011 and followed for ≥1 year after contacting all pediatric and adult rheumatologists and nephrologists practicing in Ontario. Data and Ontario Health Insurance Plan (OHIP) numbers were securely transferred to the Institute for Clinical and Evaluative Sciences (ICES). OHIP numbers were transformed into an encrypted ICES key number (IKN) used to link the cohort to multiple administrative datasets to determine the outcomes of interest. We examined descriptive summaries of major outcomes including death, adverse renal events (end-stage kidney disease [ESKD] requiring chronic dialysis and renal transplant), cardiovascular events (including angina, transient ischemic attack, endocarditis, myocardial infarction, pericarditis, stroke), and cancer. In addition, we modeled the disease progression with a multi-state Cox model (figure 1) to determine baseline demographic and clinical characteristics that were significantly associated with higher rates of transition from being event-free to experiencing an adverse renal event, from being event-free to experiencing death, and from experiencing an adverse renal event to experiencing death. Results There were 38 deaths in a cohort of 615 patients with the mean follow-up time of 14.4 person years. The all-cause mortality rate was 3.36 per 1000 person-years. The rates for end- stage kidney disease (ESKD) requiring chronic dialysis and renal transplant were 3.87 and 2.43 per 1000 person-years, respectively. The rate for any type of cardiovascular event and cancer were 6.49 and 3.47 per 1000 person-years, respectively. The multi-state Cox model indicated that the Black ethnic group (HR, 3.58; 95% CI, 1.6-8.0) and the presence of renal involvement at baseline (HR, 2.19; 95% CI, 1.2-4.1) were significantly associated with higher rates of transition from event-free to adverse renal event. Additionally, the Black ethnic group (HR, 5.45; 95% CI, 1.6-18.8) was significantly associated with higher rates of transition from event-free to death. None of the variables were significantly associated with higher rates of transition from adverse renal event to death. Conclusion In this large Canadian multi-ethnic long-term cSLE cohort, ethnicity was associated with adverse outcomes including adverse renal events and death. Further analyses will help inform risk for adverse outcomes to improve clinical care for the highest risk patients.

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)0.000
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 score0.072
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.037
GPT teacher head0.323
Teacher spread0.286 · 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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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