Defining and validating comorbidities and procedures in ICD-10 health data in ST-elevation myocardial infarction patients
Bibliographic record
Abstract
Administrative health databases are used in research to define comorbid conditions, diagnosis, and procedures. Our objectives were to validate a diagnosis of ST-elevation myocardial infarction (STEMI) and invasive cardiac procedure coding against a comprehensive registry of STEMI patients and determine an optimal algorithm for defining comorbidities using administrative hospitalization and ambulatory databases, but without using a physician claims database, which is unavailable for use in many jurisdictions.A registry of consecutive STEMI patients was used to define a reference cohort and linked to the hospitalization and ambulatory databases. Four administrative case definitions for defining comorbidities, as well as STEMI diagnosis and in-hospital procedures using the International Classification of Diseases, 10th Revision (ICD-10) and the Canadian Classification of Health Interventions (CCI) were evaluated. Metrics were used to evaluate algorithm performance and compare discriminative ability using the C statistic.The 3236 patients had median age of 60 years (interquartile range 52-71) and 75.7% were male. A diagnosis of STEMI was correctly identified in the administrative records for 3043 (94.0%) patients. In-hospital procedures (coronary artery bypass grafting, percutaneous coronary intervention, and angiogram) were well identified using administrative definitions (Kappa statistic 0.83-1.00). Validation of comorbidities varied by condition but an algorithm using 2 inpatient/ambulatory visits in the previous 2 years maximized PPV, ranging from 28.6% for previous heart failure to 95.7% for previous MI. The c statistic was similar for each of the methods, ranging from 0.76 to 0.80.ICD-10 and CCI codes can identify hospitalized STEMI patients with high sensitivity and accurately define in-hospital cardiac procedures. Comorbidities can be defined with high PPV using a definition of 2 inpatient/ambulatory visits in the previous 2 years.
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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 | 0.044 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".