271-OR: Capillary Blood Ketone Level and Future Ketoacidosis Risk in Type 1 Diabetes Using Sodium–Glucose Linked Transporter Inhibitors
Bibliographic record
Abstract
Adjunctive-to-insulin Sodium-Glucose Linked Transporter inhibitors (SGLTi) improve metabolic control in T1D but cause diabetic ketoacidosis (DKA). We aimed to determine if time-varying ketone levels could predict future DKA. We explored data on 1194 participants randomly assigned to empagliflozin in a T1D trial program (NCT02414958 and NCT02580591). Protocol called for fasted capillary blood ketone levels 2-3 times/week, categorized into maximum, mean, and last values for sequential 1-month ‘index test’ periods during 6-12-month follow-up. Trial-adjudicated DKA/Ketosis was determined in subsequent 1-month ‘outcome’ periods. Area under the Receiver Operator Characteristic curves (AROC) were calculated. Gradient-boosted trees determined if prediction could be improved. Participants had mean age 44.3 years, HbA1c 8.14±0.60 percent, and contributed 3.70±2.97 ketone measurements/week. 617 outcomes (568 ketosis, 49 DKA events) occurred over follow-up. On-treatment ketone levels were higher for index test periods preceding an outcome period with an event compared to those without (for example, maximum ketone was 1.32±1.03 vs 0.80±0.71 mmol/l, respectively, p<0.001). AROC for maximum, mean and last ketone levels were 0.764, 0.755, and 0.682 respectively. Maximum ketone level ≥ 0.9 mmol/l had sensitivity 59%, specificity 84%, likelihood ratio positive 3.65, likelihood ratio negative 0.49, and diagnostic odds ratio 7.39. Inclusion of the other ketone measures (mean or last) or clinical variables did not improve the predictive validity of maximum ketone levels on their own. Routine surveillance of morning capillary ketone levels in T1D patient using SGLTi can predict 1-month risk of DKA/Ketosis and could allow for interventions to mitigate risk. Disclosure P.Bapat: None. D.Cherney: Other Relationship; Boehringer Ingelheim-Lilly, Merck, AstraZeneca, Sanofi, Mitsubishi-Tanabe, Abbvie, Janssen, Bayer, Prometic, BMS, Maze, Gilead, CSL-Behring, Otsuka, Novartis, Youngene, Lexicon and Novo-Nordisk, Research Support; Boehringer Ingelheim-Lilly, Merck, Janssen, Sanofi, AstraZeneca, CSL-Behring and Novo-Nordisk. D.Mumford: None. G.Tomlinson: None. L.Lovblom: None. B.A.Perkins: Advisory Panel; Dexcom, Inc., Insulet Corporation, Novo Nordisk, Sanofi, Vertex Pharmaceuticals Incorporated, Other Relationship; Abbott, Medtronic, Sanofi, Research Support; Novo Nordisk, Bank of Montreal (BMO). S.Dhaliwal: None. C.Song: None. D.R.Budhram: None. A.M.K.Bakhsh: None. D.Scarr: None. A.Weisman: None. M.Fralick: None. N.Ivers: Consultant; Novo Nordisk Canada Inc., IQVIA Inc. Funding Diabetes Canada (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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".