404-P: Cardiovascular Risk Scoring and Stratification in Patients with Type 2 Diabetes Enrolled in a Medicare Advantage Plan
Bibliographic record
Abstract
Current tools to identify patients at risk for cardiovascular (CV) disease or events are variable, limiting their utility and generalizability. The purpose of this study was to develop a predictive model to identify patients with type 2 diabetes (T2D) who are ≥65 years of age, enrolled in a Medicare Advantage Prescription Drug (MAPD) plan and at risk for future CV events. This retrospective cohort study used administrative claims, laboratory and enrollment data from 1/2011-12/2018. Patients with T2D were identified from 1/2012-12/2013 using diagnosis codes and/or claims for antihyperglycemic medications. The dependent variable was the first composite CV event defined as ≥1 inpatient hospitalization for myocardial infarction, ischemic stroke, unstable angina, or heart failure or any evidence of revascularization. Independent variables for the prediction survival model included baseline demographic and clinical characteristics prognostic for the dependent variable. C-statistic, accuracy, sensitivity, and specificity were used to assess model performance. Risk ranking was conducted, whereby patients were classified as low, medium or at high risk of an event based on probability distribution derived cut-points. A total of 362,791 patients with T2D were identified. The proportion of patients with ≥1 CV event was 18.0% up to 5 years after T2D identification. The final model included 42 demographic and clinical variables. The C-statistic was 0.68, and accuracy, sensitivity and specificity were 0.63. Results were consistent across the training, test and holdout datasets suggesting internal validity. Up to 5 years after identification, 11% of patients classified as low, 27% as medium, and 51% as high risk had a future CV event. A predictive model for composite CV events utilizing administrative claims to identify and risk stratify patients may be used to scale focused interventions to high-risk T2D patients in a MAPD population to prevent future CVD events. Disclosure J.D. Hayden: Employee; Self; Humana. P. Pimple: Employee; Self; Boehringer Ingelheim Pharmaceuticals, Inc. Employee; Spouse/Partner; CVS Caremark. R. Luthra: Employee; Self; Boehringer Ingelheim Pharmaceuticals, Inc. T.G. Prewitt: None. V. Chiguluri: None. M. Kattan: Consultant; Self; GlaxoSmithKline plc. Research Support; Self; Boehringer Ingelheim (Canada) Ltd., Novo Nordisk Inc. R. Harvey: None. A.M. Goss: Employee; Self; Boehringer Ingelheim Pharmaceuticals, Inc. E.O. Caplan: Employee; Self; Humana. Funding Boehringer Ingelheim; Humana
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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".