Accuracy of a Bleeding Assessment Tool in Predicting the Diagnosis of an Inherited Bleeding Disorder: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction Assessing bleeding symptoms is essential for diagnosing bleeding disorders. Bleeding assessment tools (BATs) were developed to standardize the collection of bleeding symptoms and their severity, thereby enhancing the diagnostic accuracy. However, their diagnostic precision across various inherited bleeding disorders remains unclear. Therefore, we conducted a systematic literature and meta-analysis to evaluate the diagnostic accuracy of BATs in pediatric and adult populations. This systematic review and meta-analysis aimed to evaluate the diagnostic accuracy of BATs in pediatric and adult populations. Objective: To evaluate the diagnostic effectiveness of BATs in identifying laboratory-confirmed inherited bleeding disorders. Methods Literature Search Strategy: A comprehensive systematic literature search was conducted across Scopus, Embase, Web of Science, MEDLINE, ClinicalTrials.gov, Cochrane Central Register of Controlled Trials, and the World Health Organization databases from inception until January 27, 2024 Study Selection: Two independent reviewers screened titles and abstracts using Covidence (Covidence systematic review software, Veritas Health Innovation). Data extraction was performed independently by two authors reviewing and collecting data in duplicate from all selected articles. Eligibility Criteria: Included studies reported on the diagnostic accuracy of any BAT in diagnosing inherited bleeding disorders. Exclusion criteria included abstracts, review articles, case reports/series, studies focusing solely on distinguishing specific bleeding disorders, and those lacking sufficient data to assess test accuracy. Quality Assessment: Risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Two authors assessed each article independently and each study was categorized as having a low, moderate, or high risk of bias. Results Of 7,009 studies identified, 31 met the inclusion criteria, comprising 6,962 patients (2,152 with bleeding disorders, 4,810 controls). Most studies (74%) were prospective. Studies assessed various bleeding disorders: 39% all types, 32% von Willebrand Disease (VWD), 10% Inherited Platelet Function Disorder (IPFD), 10% both VWD and IPFD, and 10% other rare disorders. The ISTH-BAT was the most frequently used tool (47%), followed by MCMDM-1 VWD (21%) and PBQ (18%). The pooled prevalence of confirmed bleeding disorder in all prospective studies was 32% (range, 3%-71%). The pooled sensitivity for BAT in all included studies (31 studies) was 82% (95% CI, 76.5%-86.7%) and the specificity was 66% (95% CI, 54%-76%). Subgroup analysis revealed that the ISTH-BAT (studies: BD 6, VWD 5, IPFD 3, GT/BSS 2), had a sensitivity of 77% (95% CI, 69%-84%) and a specificity of 70% (95% CI, 58%-80%), the MCMDM-1 VWD (studies: VWD 4, BD 2, GT/BSS 1) had sensitivity of 78% (95% CI, 61%-89%) and a specificity of 79% (95% CI, 54%-92%) , and the Pediatric Bleeding Questionnaire (studies: BD 3, VWD 2, VWD/IPFD 1) had a sensitivity of 93% (95% CI, 85%-97%) and a specificity of 73% (95% CI, 41%-92%) . The sensitivity and specificity of BATs for studies investigating all types of bleeding disorders (12 studies) soley VWD (10 studies), and soley IPFD (3 studies) were 80% (95% CI, 67%-88%) and 50% (95% CI, 34%-67%) , 79% (95% CI, 71%-86%) and 83% (95% CI, 59%-95%), and 85% (95% CI, 79%-89%) and 74% (95% CI, 56%-86%), respectively. Significant heterogeneity was observed across studies in terms of populations, BATs used, and diagnostic methods. This heterogeneity limits the generalizability of the results and suggests that BAT performance may vary depending on the specific clinical context and patient population. Additionally, 26% of studies were classified as having a high risk of bias, potentially affecting the reliability of some findings. Conclusion Despite heterogeneity, this meta-analysis supports the use of BATs as valuable screening tools for inherited bleeding disorders, demonstrating generally high sensitivity across various patient populations. However, the variability in specificity across different disorders and tools underscores the need for careful selection and interpretation of BATs in clinical practice. Further standardization of BATs and diagnostic criteria could improve their utility in diverse clinical settings.
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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 | 0.030 | 0.092 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".