1336-P: Predicting the Progression of Stages of Neuropathy Using Routine Eye and Kidney Tests—An Application of Novel Statistical Methods in the Diabetes Control and Complications Trial (DCCT)
Bibliographic record
Abstract
Despite recent advances, neuropathy screening is not consistently performed in clinical practice. We aimed to determine if neuropathy risk could be predicted using the trajectories of routinely-performed eye and kidney screening tests in T1D. We used 28-year data from the DCCT/EDIC study, available via the NIH public repository. We used the Early Treatment Diabetic Retinopathy Study Research (ETDRS) Group 23-point scale, albumin excretion rate (AER), and eGFR treated as longitudinal outcomes. Simultaneously, we represented neuropathy as a 3-state progressive multistate outcome using nerve conduction studies and the Michigan Neuropathy Screening Instrument (MNSI) for early-stage neuropathy and the occurrence of serious foot ulcer or amputation for late-stage neuropathy. To model these interrelated disease processes, we used and developed joint models with multistate submodels. Our “joint multistate model” accounted for interval censoring: due to the visit schedule, the exact time of transition to early-stage neuropathy was known only to occur in between consecutive visits. A Bayesian estimation procedure was used. We found that the risk of transition from no to early-stage neuropathy was affected by the current value of ETDRS, the current slope of ETDRS, and the current value of AER. For example, controlling for all covariates including eGFR, the there was a 23% higher rate of occurrence of this early-stage for a 3-step higher ETDRS scale. In the same model controlling for all covariates, progression from early-stage to late-stage neuropathy was affected mainly by AER (11% higher rate for a doubling of AER). Routinely-performed clinical eye and kidney test results can be leveraged to help identify the risk of neuropathy. Future work will develop dynamic predictions integrated into an electronic medical record. Disclosure L.Lovblom: None. L.Briollais: None. G.Tomlinson: 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).
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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 | 0.024 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".