530-P: Capillary Blood Ketone Level and the Prediction of Future Diabetic Ketoacidosis Risk in Type 1 Diabetes
Bibliographic record
Abstract
Identification of those at risk of diabetic ketoacidosis (DKA) in T1D is challenging. Rather than during illness while DKA is developing, we aimed to determine if levels of routine point-of-care capillary blood ketones could predict DKA months prior to the event. In exploratory analysis, we examined 484 participants randomly assigned to placebo in an adjunct-to-insulin medication trial program (NCT02414958 and NCT02580591). Participants provided morning fasted capillary blood ketone levels twice per week via electronic logbook, and we calculated the maximum and mean level for each individual during a 2-month baseline period. Outcome was the 6 to 12-month occurrence of trial-adjudicated DKA. Area under the Curve (AUC) for the Receiver Operator Characteristic curves were generated. In sensitivity analysis we applied supervised machine learning methods (gradient-boosted trees). Participants had median age 43 [IQR 33, 54], mean HbA1c 8.2±0.6 percent, and provided 2 [1, 4] ketone measurements per week. Twelve DKA events occurred at median 105 [43, 199] days. Maximum and mean ketone levels were higher at baseline for the 12 cases compared to controls (for example, maximum ketone level 0.8 [0.6, 1.2] compared to 0.3 [0.2, 0.7] mmol/L, p=0.002). Maximum ketone level had AUC of 0.769 (95% CI 0.655-0.883). Maximum ketone ≥0.8 mmol/L had sensitivity 64%, specificity 78%, and likelihood ratios positive and negative of 2.9 and 0.5. Machine learning methods outperformed the single-metric analyses. Results provide proof-of-concept that routine capillary ketone surveillance can identify individuals at high-risk of future DKA. Simple or complex machine learning algorithms could be implemented in ketone meters or in future continuous ketone measurement technology. Disclosure C.Song: 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. P.Bapat: None. A.M.K.Bakhsh: None. D.R.Budhram: 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 | 0.002 | 0.008 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".