Positive predictive value of ICD‐9 codes 410 and 411 in the identification of cases of acute coronary syndromes in the Saskatchewan Hospital automated database
Bibliographic record
Abstract
BACKGROUND: Case definitions are essential to epidemiological research. OBJECTIVES: To evaluate ICD-9 codes 410 and 411 to identify cases of acute coronary syndromes (ACS), and the clinical information availability in the administrative and hospital discharge records of Saskatchewan, Canada. METHODS: In the context of a safety cohort study, we identified hospitalisations with primary discharge codes 410 (2260) and 411 (799). We selected all records with code 411, and a random sample (200) with code 410. Based on information obtained by trained abstractors from hospital records, events were classified by two cardiologists as definite or possible according to adapted AHA/ESC criteria. The validity of 410 and 411 codes was assessed by calculating the positive predictive value (PPV). Completeness of the recorded information on risk factors and use of aspirin was explored. RESULTS: The PPVs of the codes 410 and 411 for ACS were 0.96 (95%CI: 0. 92-0.98) and 0.86 (95%CI: 0.83-0.88), respectively. The PPV of 410 for acute myocardial infarction (AMI) was 0.95 (95%CI: 0.91-0.98). The PPV of 411 was 0.73 (95%CI: 0.70-0.77) for primary unstable angina (UA) and 0.09 (95%CI: 0.07-0.11) for AMI. Hospital charts review revealed key information for clinical variables, smoking, obesity and use of aspirin at admission. CONCLUSIONS: ICD-9 410 code has high PPV for AMI cases, likewise 411 for UA cases. Case validation remains important in epidemiological studies with administrative health databases. Given the pathophysiology of ACS, both AMI and UA might be used as study end points. In addition to code 410, we recommend the use of 411 plus validation.
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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 | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".