Longitudinal Determination of Diabetes Complications and Other Clinical Variables as Risk Factors for Diabetic Ketoacidosis in Type 1 Diabetes
Bibliographic record
Abstract
OBJECTIVE: We aimed to determine whether diabetes complications, such as kidney disease that may impair acid-base buffering capacity, independently predict the risk of subsequent diabetic ketoacidosis (DKA). RESEARCH DESIGN AND METHODS: We accessed previously collected 34-year data from the Diabetes Control and Complications Trial and Epidemiology of Diabetes Interventions and Complications study through public data access. Multivariable Cox proportional hazards models with time-varying exposures and covariates were used to examine the associations of macrovascular disease and early and late stages of neuropathy, nephropathy, and retinopathy, with subsequent DKA occurrence as the outcome. RESULTS: Of 1,441 participants, 297 experienced 488 DKA events over follow-up. Major adverse cardiovascular events [hazard ratio (HR) 3.16, 95% CI 1.57-6.35, P = 0.001] and late-stage neuropathy, which comprised serious foot ulcer or amputation (HR 1.59, 95% CI 1.04-2.45, P = 0.03) were independently associated with higher DKA risk. Higher risk was also associated with shorter diabetes duration (HR 0.76, 95% CI 0.64-0.91, P = 0.002), female sex (HR 2.04, 95% CI 1.56-2.67, P < 0.001), current insulin pump use (HR 3.04, 95% CI 2.29-4.02, P < 0.001), higher time-updated HbA1c (per additional 1%: HR 1.39, 95% CI 1.29-1.50, P < 0.001), and higher current insulin dose (per 1 additional unit/kg/day: HR 2.32, 95% CI 1.62-3.33, P < 0.001). CONCLUSIONS: A major cardiovascular event, foot ulcer, or amputation confers the greatest risk of future DKA independent of previously recognized risk factors, implying a need to target patients with these events for DKA prevention interventions, such as self-management skills for metabolic control, management of depression, and DKA education.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".