Accuracy of postpartum hemorrhage coding in the Swedish Pregnancy Register
Bibliographic record
Abstract
INTRODUCTION: Postpartum hemorrhage (PPH) is recognized as a leading cause of obstetric morbidity and mortality. Population-wide studies have used International Classification of Diseases (ICD) diagnostic codes to track and report the prevalence of PPH. Although the 10th revision (ICD-10) was introduced in Sweden in 1997, the accuracy of ICD-10 codes for PPH is not known. Thus, the aim was to determine the accuracy of diagnostic coding for PPH in the Swedish Pregnancy Register. MATERIAL AND METHODS: We performed a retrospective cohort study of 609 807 deliveries in Sweden between 2014 and 2019. Information on ICD-10 codes for PPH and estimated blood loss were extracted from the Swedish Pregnancy Register. Using an estimated blood loss >1000 mL as the reference standard, we evaluated the diagnostic accuracy of ICD-10 codes for PPH by estimating sensitivity, specificity, positive predictive value and negative predictive value with exact binomial 95% confidence intervals (CIs). In our secondary analysis, we assessed the ICD-10 coding accuracy for severe PPH, defined as an estimated blood loss >1000 mL and transfusion of at least 1 unit of red blood cells registered in the Scandinavian Donations and Transfusion database. RESULTS: Of the 609 807 deliveries, 43 312 (7.1%) had an ICD-10 code for PPH and 45 071 (7.4%) had an estimated blood loss >1000 mL. The ICD codes had a sensitivity of 88.5% (95% CI 88.2-88.7), specificity of 99.4% (95% CI 99.4-99.4), positive predictive value of 92.0% (95% CI 91.8-92.3) and negative predictive value of 99.1% (95% CI 99.1-99.1). In our secondary analysis, on deliveries with severe PPH, the sensitivity for an ICD code was 91.3% (95% CI 90.7-91.9), whereas specificity was 83.5% (95% CI 82.3-84.6). CONCLUSIONS: Our findings indicate that ICD-10 codes for PPH in Sweden have moderately high sensitivity and excellent specificity. These results suggest that PPH diagnostic codes in medical records and linked pregnancy and birth registers can be used for research, quality improvement and reporting PPH prevalence in Sweden.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".