Defining and validating comorbidities and procedures in ICD-10 health data in ST-elevation myocardial infarction patients
Why this work is in the frame
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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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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 | 0.003 | 0.005 |
| 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 it