Systematic review of validated case definitions to identify hypertensive disorders of pregnancy in administrative healthcare databases
Bibliographic record
Abstract
BACKGROUND: Administrative data are frequently used to study cardiovascular disease (CVD) risk in women with hypertensive disorders of pregnancy (HDP). Little is known about the validity of case-finding definitions (CFDs, eg, disease classification codes/algorithms) designed to identify HDP in administrative databases. METHODS: A systematic review of the literature. We searched MEDLINE, Embase, CINAHL, Web of Science and grey literature sources for eligible studies. Two independent reviewers screened articles for eligibility and extracted data. Quality of reporting was assessed using checklists; risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool, adapted for administrative studies. Findings were summarised descriptively. RESULTS: Twenty-six studies were included; most (62%) validated CFDs for a variety of maternal and/or neonatal outcomes. Six studies (24%) reported reference standard definitions for all HDP definitions validated; seven reported all 2×2 table values for ≥1 CFD or they were calculable. Most CFDs (n=83; 58%) identified HDP with high specificity (ie, ≥98%); however, sensitivity varied widely (3%-100%). CFDs validated for any maternal hypertensive disorder had the highest median sensitivity (91%, range: 15%-97%). Quality of reporting was generally poor, and all studies were at unclear or high risk of bias on ≥1 QUADAS-2 domain. CONCLUSIONS: Even validated CFDs are subject to bias. Researchers should choose the CFD(s) that best align with their research objective, while considering the relative importance of high sensitivity, specificity, negative predictive value and/or positive predictive value, and important characteristics of the validation studies from which they were derived (eg, study prevalence of HDP, spectrum of disease studied, methodological rigour, quality of reporting and risk of bias). Higher quality validation studies on this topic are urgently needed. PROSPERO REGISTRATION NUMBER: CRD42021239113.
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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 | 0.062 | 0.289 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.029 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".