A systematic review of validated case-finding definitions to identify hypertensive disorders of pregnancy in administrative healthcare databases
Bibliographic record
Abstract
Abstract Background Administrative healthcare data are frequently used to study cardiovascular disease (CVD) risk in women with hypertensive disorders of pregnancy (HDP); however, little is known about the validity of case-finding definitions (CFDs, e.g., International Classification of Disease codes/algorithms) designed to identify these conditions in administrative databases. Purpose To systematically identify and summarize available evidence on the validity of administrative CFDs for HDP. Methods Four bibliographic databases and grey literature sources were searched for eligible studies. The titles/abstracts of all records were independently screened for eligibility by two reviewers, then assessed at full text. Study data (design and participant characteristics, validation statistics) were extracted by two independent reviewers and discrepancies resolved through consensus. Quality of reporting was assessed using checklists; risk of bias was assessed using a modified version of the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool, adapted for administrative validation studies. Results Twenty-five studies, published between 1998 and 2021, met the eligibility criteria. Nearly half (48%) were conducted in the United States. Ten studies validated CFDs for ≥1 HDP as a primary aim; most (60%) validated several maternal and infant morbidities, including ≥1 HDP. Preeclampsia (any, mild, moderate, serious, severe, superimposed) was the most validated HDP subtype. Only six studies reported gold standard definitions for all HDPs validated; definitions were heterogeneous with respect to blood pressure thresholds and timing of diagnosis. Seven studies (∼25%) reported all 2x2 table values (true positives/negatives, false positives/negatives) for ≥1 CFD, or they were calculable. The majority of CFDs reported in primary analyses (n=23) were highly specific (≥98%); however, sensitivity varied widely (3.2% to 100%; Figure 1). Nearly all (n=20, 87%) had a positive predictive value (PPV) of ≥70%, 13 of which had a PPV of >80% in combination with high specificity. Across studies, HDP prevalence ranged from 0.1% (eclampsia) to 37% (any maternal hypertensive disorder). Quality of reporting was generally poor to moderate, and all studies were judged to be at unclear or high risk of bias on ≥1 QUADAS-2 domain. Five studies were judged to be of “low concern” regarding study applicability (Figure 2). Conclusions Clinical understanding of CVD risk in women with HDP could be drastically impacted if there is low confidence that these conditions have been correctly identified from administrative data. Researchers should quantitatively explore the extent to which CVD risk estimates may be impacted by CFDs with low sensitivity and artificially inflated PPVs, influenced by greater study prevalence of HDP than would be expected in the general population. Higher quality validation studies that employ more rigorous methodology and improved reporting are needed. Funding Acknowledgement Type of funding sources: None.
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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 | 0.070 | 0.316 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.037 | 0.032 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".