1334-P: Prognostic Implications of Diabetic Ketoacidosis on Long-Term Mortality and Diabetes-Related Complications
Bibliographic record
Abstract
Introduction & Objective: Diabetic ketoacidosis (DKA) occurring after diabetes diagnosis is often associated with risk factors for other diabetes-related complications. We aimed to determine the prognostic implications of DKA on mortality and complications in type 1 diabetes (T1D). Methods: Previously collected data from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Study was obtained through the NIDDK central repository. Using Cox proportional-hazards models with time-dependent covariates, we examined age- and sex-, HbA1c-, and fully-adjusted associations of DKA with mortality, cardiovascular disease, microvascular, and acute complications over 34 years. Results: Of 1441 participants, 297 (20.6%) had 488 DKA events. DKA after diabetes diagnosis was associated with a higher risk of age- and sex-adjusted mortality [hazard ratio (HR) 8.28, 95% confidence interval (CI) 3.74-18.32, p<0.001], major adverse cardiovascular events (HR 2.05, 95% CI 1.34-3.13, p<0.001), and all advanced microvascular and acute complications compared to no DKA after diabetes diagnosis. Most associations were significant even after adjustment for covariates. The cumulative incidence and hazard estimates for someone exposed to DKA compared to unexposed were greater for mortality (6.0% vs. 1.3%), major adverse cardiovascular events (12.3% vs. 6.5%), advanced neuropathy (23.1% vs. 14.4%), advanced nephropathy (6.8% vs. 2.2%), advanced retinopathy (51.9% vs. 43.7%), severe hypoglycemia (6 events vs. 5 events), and recurrent DKA (1 event vs. 0 events). Conclusions: DKA is a prognostic marker for diabetes complications, including excess mortality. Intensified clinical interventions, such as enhanced self-management education, glycemic control, and cardiovascular prevention strategies may be warranted following the diagnosis of DKA. Disclosure D.R. Budhram: None. P. Bapat: None. A.M.K. Bakhsh: None. M.I. Abuabat: None. N. Verhoeff: None. D. Mumford: None. A. Orszag: None. A.B. Jain: Advisory Panel; Abbott. Speaker's Bureau; Abbott. Advisory Panel; Boehringer-Ingelheim. Speaker's Bureau; Boehringer-Ingelheim. Advisory Panel; Amgen Inc. Speaker's Bureau; Amgen Inc., AstraZeneca. Advisory Panel; AstraZeneca. Speaker's Bureau; Care to Know, CCRN, Connected in Motion, CPD Network, Dexcom, Diabetes Canada, Eli Lilly, GSK, HLS Therapeutics, Janssen, Master Clinician Alliance, MDBriefcase, Merck, Medtronic, Moderna, Novartis, N. Advisory Panel; Bausch Healthcare, Bayer,, Dexcom, Eli Lilly, Gilead Sciences, GSK, HLS Therapeutics, Insulet, Janssen, Medtronic, Novo Nordisk, Partners in Progressive Medical Education, Pfizer, PocketPills, Roche,. M. Fralick: Consultant; singal1, proofdx. A. Weisman: None. L. Lovblom: None. B.A. Perkins: Advisory Panel; Abbott. Other Relationship; Novo Nordisk. Advisory Panel; Insulet Corporation, Nephris. Other Relationship; Medtronic. Advisory Panel; Sanofi, Vertex Pharmaceuticals Incorporated, Dexcom, Inc. Funding Diabetes Canada (Operating Grant OG-3-21-5572-BP)
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".