Use of linked electronic health records to evaluate cardiovascular risk prediction models in Ontario, Canada
Bibliographic record
Abstract
IntroductionElectronic health records (EHR) contain individual-level clinical information not found in traditional administrative databases. As part of the CANHEART-Strategy for Patient Oriented Research (SPOR) initiative, we created a linked EHR-administrative data cohort that enables us to measure the Framingham and ACC/AHA Pooled Cohort cardiovascular risk prediction scores in Ontario, Canada.
 Objectives and ApproachAn EHR primary care cohort was created using the Electronic Medical Record Administrative data Linked Database (EMRALD) database, which contains the blood pressure and lipid values, weight and height measures, prescriptions and smoking status of up to 350,000 patients in Ontario, Canada. We enriched the lipid information through linkage to the Ontario Laboratory Information System, which is a repository of 90%+ of all lipid tests in Ontario. Individual-level information on co-morbidities, hospitalizations and mortality attributed to cardiovascular causes (e.g. myocardial infarction, stroke, cardiovascular mortality) were obtained through linkage to provincial health administrative and vital statistics databases using CANHEART methodology (www.canheart.ca).
 ResultsPatients were entered into the cohort between 2008 and 2014 if they had measurements for blood pressure and lipids (total cholesterol and high-density lipoprotein) taken within a year of each other during this accrual window. The earliest such group of values was chosen and determined the individual’s index date. Age, sex, smoking, diabetes and anti-hypertensive treatment status were extracted from EHR or administrative data to calculate the two scores. Patients were excluded if not aged 40-75 on the index date or if they had a history of cardiovascular disease. A cohort of 84,628 Ontario residents (mean age 55.0 years) had the elements required to calculate both scores. Follow-up for outcome events were done through record linkage to the end of 2014, with a mean follow-up of 3.62 years.
 Conclusion/ImplicationsThe creation of this cohort will allow for the validation of the Framingham and AHA/ACC Pooled Cohort equations in the diverse Ontario population. It would also enable the possible development of a new ‘made-in-Canada’ cardiovascular risk prediction model.
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 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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".