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Record W4391252430 · doi:10.2215/cjn.0000000000000414

Sotagliflozin and Kidney Outcomes, Kidney Function, and Albuminuria in Type 2 Diabetes and CKD

2024· article· en· W4391252430 on OpenAlexaff
Vikas S. Sridhar, Deepak L. Bhatt, Ayodele Odutayo, Michael Szarek, Michael J. Davies, Phillip Banks, Bertram Pitt, Philippe Gabríel Steg, David Z.I. Cherney

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2024
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineAlbuminuriaRenal functionHazard ratioInternal medicineDialysisPlaceboRandomized controlled trialKidney diseaseKidneyConfidence intervalClinical endpointIntensive care medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: In the initial analysis of the Effect of Sotagliflozin on Cardiovascular and Renal Events in Patients with Type 2 Diabetes and Moderate Renal Impairment Who Are at Cardiovascular Risk (SCORED) trial, because of early trial termination and suspension of adjudication, reconciliation of eGFR laboratory data and case report forms had not been completed. This resulted in a small number of kidney composite events and a nominal effect of sotagliflozin versus placebo on this outcome. This exploratory analysis uses laboratory eGFR data, regardless of case report form completion, to assess the effects of sotagliflozin on the predefined kidney composite end point in the SCORED trial and additional cardiorenal composite end points. METHODS: SCORED was a multicenter, randomized trial evaluating cardiorenal outcomes with sotagliflozin versus placebo in 10,584 patients with type 2 diabetes and CKD. This exploratory analysis used laboratory data to derive the eGFR components and case report form data for the non-laboratory-defined components that together made up the kidney and cardiorenal composites. AKI was also assessed in this dataset. RESULTS: Using laboratory data, 223 events were identified, and sotagliflozin reduced the risk of the composite of first event of sustained ≥50% decline in eGFR, eGFR <15 ml/min per 1.73 m 2 , dialysis, or kidney transplant with 87 events (1.6%) in the sotagliflozin group and 136 events (2.6%) in the placebo group (hazard ratio [95% confidence interval], 0.62 [0.48 to 0.82]), P < 0.001). Sotagliflozin reduced the risk of a cardiorenal composite end point defined as the abovementioned composite plus cardiovascular or kidney death with 239 events (4.5%) in the sotagliflozin group and 306 events (5.7%) in the placebo group (hazard ratio [95% confidence interval], 0.77 [0.65 to 0.91], P = 0.0023). The results were consistent when using different eGFR decline thresholds and when only including kidney death in composites (all P < 0.01). The incidence of AKI was similar between treatment groups. CONCLUSIONS: In this exploratory analysis using the complete laboratory dataset, sotagliflozin reduced the risk of kidney and cardiorenal composite end points in patients with type 2 diabetes and CKD. CLINICAL TRIAL REGISTRY NAME AND REGISTRATION NUMBER: ClinicalTrials.gov Identifier: NCT03315143 .

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)0.004
metaresearch head score (Gemma)0.003
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 score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.327
Teacher spread0.304 · 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

Citations28
Published2024
Admission routes1
Has abstractyes

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