Validation of Algorithms to Identify Invasive Electrophysiology Procedures Using Administrative Data in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Administrative database research can provide insight into the real-world effectiveness of invasive electrophysiology procedures. However, no validated algorithm to identify these procedures within administrative data currently exists. OBJECTIVE: To develop and validate algorithms to identify atrial fibrillation (AF), atrial flutter (AFL), supraventricular tachycardia (SVT) catheter ablation procedures, and diagnostic electrophysiology studies (EPS) within administrative data. METHODS: Algorithms consisting of physician procedural billing codes and their associated most responsible hospital diagnosis codes were used to identify potential AF, AFL, SVT catheter ablation procedures and diagnostic EPS within large administrative databases in Ontario, Canada. The potential procedures were then limited to those performed between October 1, 2011 and March 31, 2013 at a single large regional cardiac center (Sunnybrook Health Sciences Center) in Ontario, Canada. These procedures were compared with a gold-standard cohort of patients known to have undergone invasive electrophysiology procedures during the same time period at the same institution. The sensitivity, specificity, positive and negative predictive values of each algorithm was determined. RESULTS: Algorithms specific to each of AF, AFL, and SVT ablation were associated with a moderate sensitivity (75%-86%), high specificity (95%-98%), positive (95%-98%), and negative (99%) predictive values. The best algorithm to identify diagnostic EPS was less optimal with a sensitivity of 61% and positive predictive value of 88%. CONCLUSIONS: Algorithms using a combination of physician procedural billing codes and accompanying most responsible hospital diagnosis may identify catheter ablation procedures within administrative data with a high degree of accuracy. Diagnostic EPS may be identified with reduced accuracy.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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 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".