371-P: Predicting Real-World Nonsevere Hypoglycemia in Americans with Diabetes (iNPHORM)
Bibliographic record
Abstract
Iatrogenic non-severe hypoglycemia (NSH) is a common and known precursor of severe hypoglycemia. Still, virtually no valid risk estimators exist to predict daytime and nocturnal NSH (NSDH, NSNH) in the general US population with diabetes. To redress this gap, we analyzed primary, self-reported data from the 1-year, prospective iNPHORM study. Adults (18-90 years old) with insulin- and/or secretagogue-treated type 1 or 2 diabetes (T1D, T2D) were recruited from a US-wide probability-based internet panel. Twelve monthly emailed questionnaires assessed NSH risk. Prognostic models were built for recurrent 30-day NSDH and NSNH using negative binomial and machine learning penalized regression with lasso. Missing data were multiply imputed with chained equations. N=986 were analyzed (T1D: 17%; age: 51 [SD: 14.3] years; male: 49.6%; T1D/T2D duration: 12 [IQR: 14] years; retention rate: 86.2%) . Among T2D respondents, 38% were on insulin alone, 38% secretagogues alone, and 24% insulin plus secretagogues. Follow-up incidence proportions and 30-day rates of NSDH and NSNH were 79.6% (95% CI: 77.0-82.0%) and 1.70 (95% CI: 1.59-1.82) , and 53.7% (95% CI: 50.5-56.7%) and 0.69 (95% CI: 0.64-0.75) , respectively. Risks of 30-day NSDH and NSNH increased with insulin+secretagogue therapy; A1C≤7%; insulin pump, continuous glucose monitoring, beta blockers, and antibiotics use; decreased number of medications; T1D and diabetes education; increased past severe hypoglycemia requiring healthcare; chronic kidney disease; depression; food insecurity; lack of insurance; younger age; female sex; and White race; risks decreased with A1C≥7.1%, cognitive impairment, hypoglycemia unawareness, and insurance. As well, higher income predicted NSNH risk. The optimism adjusted c-statistics for NSDH and NSNH risks were 0.78 and 0.77, respectively. As the first US study to prospectively estimate real-world NSDH and NSNH risk, iNPHORM provides important insight into individual-level event detection and prevention. Disclosure A.Ratzki-leewing: Consultant; Eli Lilly and Company, Other Relationship; Sanofi. S.B.Harris: Consultant; Abbott, AstraZeneca, Eli Lilly and Company, Novo Nordisk, Sanofi, Other Relationship; Abbott, AstraZeneca, Bayer Inc., Dexcom, Eli Lilly and Company, HLS Therapeutics, Janssen Pharmaceuticals, Inc., Novo Nordisk, Sanofi, Research Support; Applied Therapeutics Inc., AstraZeneca, Canadian Institutes of Health Research, Juvenile Diabetes Research Foundation (JDRF) , Novo Nordisk, Sanofi, The Lawson Foundation. J.E.Black: None. G.Zou: None. S.Webster-bogaert: None. B.L.Ryan: None. Funding Sanofi Global
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".