Using health administrative data to identify patients with pulmonary hypertension: A single center, proof of concept validation study in Ontario, Canada
Bibliographic record
Abstract
Abstract Real‐world identification of pulmonary hypertension (PH) is largely based on the use of administrative databases identified by ICD codes. This approach has not been validated. The aim of this study was to validate a diagnosis of PH and its comorbidities using ICD 9/10 codes. Health records from Kingston Health Sciences Centre (2010 to 2012) were abstracted to identify a diagnosis of PH. Cohort 1 patients (n = 300) were selected because they had attended a cardiology or respirology clinic without knowledge of PH status. Cohort 2 patients (n = 200) were patients with a diagnosis of PH, identified using International Classification of Diseases (ICD) codes at the time of hospitalizations (CIHI‐DAD) or emergency department (ED) visits (CIHI‐NACRS). These cohorts were combined and reviewed to validate the diagnosis of PH. These data were securely transferred to the Institute of Clinical Evaluative Sciences (ICES). The diagnosis of PH from chart abstraction was used as the gold standard. The classification of PH into WHO groups, based on chart abstraction, was also compared to classification based on ICD code‐defined comorbidities. Cohort 1 and Cohort 2 were merged to yield 449 unique patients in the combined cohort. In the combined cohort, 248 of 449 (55.2%) had a diagnosis of PH by ICD code criteria. The mean age of this PH group was 70 years, and the majority were females (65.5%). One hospitalization or ED visit resulting in a diagnostic code for PH had a sensitivity of 73% and a specificity of 99% for a confirmed PH diagnosis on chart abstraction. When WHO classification by chart abstraction and ICD codes for comorbidities were compared, there was 87% agreement. Identification of PH and its comorbidities using ICD codes is a valid approach, and this single‐center study supports its application to identify PH.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".