Accuracy of venous thromboembolism ICD-10 codes: A systematic review and meta-analysis
Bibliographic record
Abstract
The identification of venous thromboembolism (VTE) using administrative databases is frequently required for reporting and research. The accuracy of International Classification of Diseases 10th revision (ICD-10) codes for VTE, including deep vein thrombosis (DVT) and pulmonary embolism (PE), remains unclear. We examined the accuracy of ICD-10 codes for identifying VTE in adult and pediatric inpatients and outpatients. For this systematic review and meta-analysis, we searched MEDLINE, EMBASE, Web of Science, CENTRAL, Epistemonikos and McMaster Superfilters from inception to July 25, 2023 for studies evaluating the sensitivity, specificity, positive predictive value (PPV), and/or negative predictive value (NPV) of ICD-10 codes for VTE in any anatomical location. We assessed risk of bias using QUADAS and certainty of evidence using GRADE. We calculated pooled sensitivity and specificity with 95% confidence intervals (CI) using a random-effects model. We included 24 studies in the qualitative synthesis and 7 in the meta-analysis. Pooled sensitivity for any VTE based on ICD-10 codes was 72% (95% CI 60–85%, low certainty); pooled specificity was 82% (95% CI 76–88%, low certainty). The PPV for ICD-10 VTE codes ranged from 0% to 100% (median: 80%) while the NPV ranged from 95.4% to 100% (median: 100%). ICD-10 codes for PE had a higher pooled sensitivity (91%) than for DVT (58%). ICD-10 codes have moderate-to-high sensitivity and specificity for the identification of VTE in electronic databases. The certainty of evidence is low due to inconsistency and risk of bias. Further robust studies validating ICD-10 VTE codes are needed to improve reporting and better understand coding limitations.
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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 | 0.031 | 0.075 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.052 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".