Development of predictive risk models for major adverse cardiovascular events among patients with type 2 diabetes mellitus using health insurance claims data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: There exist several predictive risk models for cardiovascular disease (CVD), including some developed specifically for patients with type 2 diabetes mellitus (T2DM). However, the models developed for a diabetic population are based on information derived from medical records or laboratory results, which are not typically available to entities like payers or quality of care organizations. The objective of this study is to develop and validate models predicting the risk of cardiovascular events in patients with T2DM based on medical insurance claims data. METHODS: Patients with T2DM aged 50 years or older were identified from the Optum™ Integrated Real World Evidence Electronic Health Records and Claims de-identified database (10/01/2006-09/30/2016). Risk factors were assessed over a 12-month baseline period and cardiovascular events were monitored from the end of the baseline period until end of data availability, continuous enrollment, or death. Risk models were developed using logistic regressions separately for patients with and without prior CVD, and for each outcome: (1) major adverse cardiovascular events (MACE; i.e., non-fatal myocardial infarction, non-fatal stroke, CVD-related death); (2) any MACE, hospitalization for unstable angina, or hospitalization for congestive heart failure; (3) CVD-related death. Models were developed and validated on 70% and 30% of the sample, respectively. Model performance was assessed using C-statistics. RESULTS: A total of 181,619 patients were identified, including 136,544 (75.2%) without prior CVD and 45,075 (24.8%) with a history of CVD. Age, diabetes-related hospitalizations, prior CVD diagnoses and chronic pulmonary disease were the most important predictors across all models. C-statistics ranged from 0.70 to 0.81, indicating that the models performed well. The additional inclusion of risk factors derived from pharmacy claims (e.g., use of antihypertensive, and use of antihyperglycemic) or from medical records and laboratory measures (e.g., hemoglobin A1c, urine albumin to creatinine ratio) only marginally improved the performance of the models. CONCLUSION: The claims-based models developed could reliably predict the risk of cardiovascular events in T2DM patients, without requiring pharmacy claims or laboratory measures. These models could be relevant for providers and payers and help implement approaches to prevent cardiovascular events in high-risk diabetic patients.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it