Abstract
Background
The clinician-administered ISTH bleeding assessment tool (ISTH-BAT) and its self-report counterpart (self-BAT) were developed to standardize and quantify bleeding history. How BATs relate to overall clinical assessment and quality of life (QoL) is uncertain.
Methods
A cross-sectional analysis of the ISTH-BAT, self-BAT, and PROMIS-29 QoL instruments was performed in 101 adult patients referred for bleeding disorder evaluation. We assessed reliability/agreement (Krippendorf’s α) and diagnostic test characteristics of the ISTH-BAT and self-BAT. Linear regression models were used to evaluate the association between BATs and QoL domains, measured as standardized Z-scores.
Results
Surveyed patients received the following diagnoses: Bleeding disorder of unknown cause (n = 46, 45.5%), qualitative platelet defect (n = 11, 10.9%), von Willebrand disease (n = 9, 8.9%), hypodysfibrinogenemia (n = 1, 1.0%), other/unclassified (n = 13, 12.9%), and no bleeding disorder (n = 21, 20.8%). The mean ISTH-BAT score was 8.1 (SD = 4.8), compared with a mean self-BAT score of 9.1 (SD = 5.8). Reliability between the ISTH-BAT and self-BAT was moderate/high for postpartum hemorrhage (α = 0.79) and menstrual bleeding (α = 0.69) and lowest for hemarthrosis (α = 0.26). The self-BAT had a sensitivity of 79.7% and specificity of 53.1% for an abnormal ISTH-BAT. Adjusting for age and sex, a higher ISTH-BAT (and similarly self-BAT) was associated with impairments in fatigue (β = −0.07, [95% CI, −0.12 to −0.02]), social roles (β = −0.07, [95% CI, −0.11 to −0.03]), sleep (β = −0.04, [95% CI, −0.08 to −0.009]), pain interference (β = −0.06, [95% CI, −0.11 to −0.02]), and physical function (β = −0.07, (95% CI, −0.11 to −0.04]).
Conclusion
Regardless of diagnosis, higher ISTH-BAT and self-BAT scores are associated with QoL impairments. Self-BAT items may be meaningful surrogates for clinician assessment of heavy menstrual bleeding and postpartum hemorrhage, although domain-level reliability is variable.
Keywords: bleeding assessment tool, ISTH-BAT, self-BAT, quality of life, inherited bleeding disorder, bleeding disorder of unknown cause
Essentials
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Bleeding assessment tools (BATs; e.g., ISTH-BAT and self-BAT) are increasingly used in clinical evaluation.
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In 101 referral patients, we analyzed the ISTH-BAT, self-BAT, diagnosis, and quality of life.
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ISTH-BAT and self-BAT agreement was strongest for menstrual and postpartum bleeding assessment.
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Regardless of diagnosis, elevated BAT scores were associated with impairments in quality of life.
1. Background
A patient’s history has long been considered the single best diagnostic test in the evaluation of a bleeding disorder. How to best ascertain a bleeding history and distinguish normal from abnormal bleeding, remains an ongoing diagnostic challenge. In an effort to both standardize and quantify bleeding history, the Vicenza group introduced the first clinician-administered bleeding assessment tool (BAT) in 2005 [1]. To calculate a BAT score, a numeric severity rating is assigned based on a patient’s history for each bleeding domain. The individual domain scores are then summed to provide a single aggregate score. The ISTH endorsed a version of this tool in 2010 (ISTH-BAT) [1]. A self-report version of the ISTH-BAT (self-BAT, letstalkperiod.ca) was subsequently developed and tested in a cohort of von Willebrand disease patients [2]. BATs were developed as screening tools, with a focus on understanding their sensitivity and specificity for the diagnosis of von Willebrand disease (VWD) and other congenital bleeding disorders. Thresholds for an abnormal BAT score were established based on 95th percentile population norms [3].
There are several valid critiques of BAT scores, including how to interpret these scores in patients with limited hemostatic challenges—which includes males (who lack a history of reproductive tract bleeding) and younger individuals. BAT scores, as a whole, tend to increase with age as patients experience procedures, surgeries, and pregnancies [4]. For individuals who have multiple bleeding events within the same domain, the score may become saturated (i.e., not increase further as a result of the additional events). The scoring symptom gives points to patients who seek and receive care for bleeding events, presenting a potential bias to those who are underserved or lack access to medical care.
Despite these critiques, the use of BATs has expanded over time. In the evaluation of a patient with mild to moderate bleeding tendency, administration of a BAT is advised, in conjunction with clinical gestalt, to help distinguish bleeders from nonbleeders. This combined assessment approach informs the extent of additional hemostatic testing and subsequent diagnostic labeling. In the diagnostic guidelines for VWD, a BAT score may help clinicians classify patients with moderately reduced von Willebrand factor levels [5]. For patients who have a negative minimum hemostatic work-up, a diagnosis of bleeding disorder of unknown cause (BDUC) may be considered if clinical suspicion (aided by the ISTH-BAT) is high, as detailed in the advised diagnostic algorithm [6]. Under this paradigm, characterization of bleeding phenotype (rather than a laboratory abnormality) is central to the diagnosis itself, and thus a BAT score may influence other aspects of clinical decision-making, including the decision to follow patients longitudinally, provide hemostatic therapies, and register a patient for future follow-up in a hemophilia treatment center.
Given the evolving use of BATs in undiagnosed bleeding patients, we need a deeper understanding of how our measurement of bleeding history relates to overall clinical assessment, and a patient’s quality of life (QoL), in clinical evaluation. In this study, we assessed agreement between 2 different BATs: as interpreted by a treating clinician in the context of a clinical encounter (via ISTH-BAT) or as directly reported by a patient (using self-BAT) in patients referred for evaluation of a bleeding disorder. As part of this analysis, we examined how well BAT scores correlate with laboratory investigation, final diagnostic assessments, and QoL.
2. Methods
2.1. Setting & Patient Recruitment
This study was conducted from July 2023 to January 2025 at a hematology clinic in a tertiary academic medical center in the United States with a large Hemophilia Treatment Center. The clinic is staffed by attending hematologists who specialize in bleeding disorder care and supervise hematology trainees. Referral sources include primary care, obstetrics and gynecology, and surgery, as well as community hematology/oncology practices. Same-day platelet aggregation testing is available on site.
Adult subjects (greater than 18 years of age) who presented for new patient evaluation and lacked a definite laboratory diagnosis of a bleeding disorder prior to day of evaluation were eligible for inclusion. Patients who were unable to read and answer questionnaires in English or complete the consent process were excluded.
From July 2023 to January 2025, 193 patients were screened for eligibility: 30 were ineligible, 21 missed scheduled appointment, 9 were not approached due to staffing/availability, and 28 declined to participate. One hundred and five patients were consented, 101 of whom had a complete ISTH-BAT, self-BAT, and clinical evaluation (Figure 1).
Figure 1.
Participant flow diagram (CONSORT).
2.2. Survey Measures
The ISTH-BAT is a clinician-administered instrument consisting of 14 bleeding domains, with a single item per domain. Severity of bleeding is rated on a scale of 0 (no or trivial symptoms) to 4 (most severe symptoms; e.g., requiring blood transfusion or replacement therapy) [7]. The overall score represents a sum of the domain scores. An electronic health record-embedded template was created using terminology identical to ISTH-BAT scoring rubric, facilitating real-time completion of the ISTH-BAT within a clinical encounter.
The self-BAT is a self-report questionnaire designed to mimic, one-to-one, the ISTH-BAT [8], with an online-adapted version publicly available [9]. For example, the domain hemarthrosis on the ISTH-BAT is translated to the question ‘Have you ever had bleeding into a joint? (a collection of blood in a joint that causes extreme pain and swelling).’ Scoring for each domain is preserved between the ISTH-BAT and Self-BAT.
The Patient-Reported Outcomes Measurement Information System (PROMIS) is a state-of-the-science collection of self-report tools for evaluating general populations and patients with chronic conditions. The PROMIS-29 profile measures 7 core health domains: anxiety, depression, fatigue, pain interference, sleep, physical function, and social roles [10].
2.3. Procedure
On the day of their appointment, and prior to consultation, each eligible patient was invited to complete a series of questionnaires using an iPad. Assessments included the Self-BAT and the PROMIS-29. Questions on menstrual and postpartum history were presented solely to female patients. Patients were not shown their self-BAT score or provided any additional summative information after survey completion. During the subsequent clinical encounter, an attending hematologist or supervised hematology trainee completed an ISTH-BAT as part of the routine clinical encounter, aided by an electronic health record-embedded template. Physicians administering the ISTH-BAT were blinded to self-BAT scores. Results of diagnostic evaluation, pursued at the discretion of the treating physician, were recorded. Study data were collected and managed using REDCap electronic data capture tools hosted at UNC [11,12]. This project was approved by the UNC institutional review board (IRB# 23-0409). Work was carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki).
2.4. Diagnostic testing & classification
Final diagnoses were derived from clinical evaluation and assessment of treating clinicians in accordance with established guidelines, where available. A clinical evaluation included a complete history, ISTH-BAT score, and bleeding-focused physical examination (including assessment of bruising, oral health, presence of telangiectasias, and joint hypermobility). The most frequently encountered diagnoses were defined as follows:
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von Willebrand disease (VWD) - von Willebrand antigen (VWF:Ag) or activity level ≤ 50 (in the setting of compatible bleeding symptoms) as measured by either the Ristocetin cofactor (VWF:RCo), collagen binding (VWF:CB), or or VWF:GP1bM assays [5].
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Qualitative platelet defect (QPD) - Any reduced aggregation/agglutination or secretion defect on impedance aggregometry. Patients with a prolonged PFA-100, in absence of other abnormality, were classified as BDUC. Repeat testing, confirming abnormal results, was not required.
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Bleeding disorder of unknown cause (BDUC) - criteria for BDUC were adapted from ISTH guidance [6] and modified in accordance with local clinical practice patterns. At a minimum, a normal complete blood count, prothrombin time (PT), activated partial thromboplastin time (aPTT), von Willebrand factor antigen, von Willebrand factor function, factor VIII, and platelet aggregometry were required for diagnosis. Factors IX and XI were not always measured unless a prolongation of the aPTT was observed. Thrombin time was not routinely performed, but the Claus fibrinogen assay was used to test for potential fibrinogen functional disorders.
For purposes of analysis, patients were considered to have a diagnosis of an inherited bleeding disorder if they had a laboratory hemostatic abnormality that was presumed to be inherited (e.g., VWD or platelet defect without any apparent acquired cause). Additional testing (e.g., vitamin C levels, platelet electron microscopy, and genetic testing) was performed at the discretion of the treating clinician in response to information collected regarding patient medical history, physical examination, and laboratory results. Patients with an incomplete laboratory workup, medication use impacting laboratory results (e.g., antiplatelet medication), or patients who were pregnant during evaluation (without repeat testing of nonpregnant levels) were considered unclassified. Patients who had a negative hemostatic evaluation and for whom clinical suspicion for an underlying bleeding diathesis remained low were given the diagnosis of no bleeding disorder.
2.5. Analysis
Descriptive statistics were used to characterize the analytic sample (N = 101), and by extension, the bleeding disorder referral population. The ISTH-BAT and self-BAT were considered normal or abnormal using the same established cutoff values (>5 for women or >3 for men) [3]. We additionally examined ISTH-BAT score by population tertiles (low, mid-range, and high). Correlation and reliability for the ISTH-BAT and self-BAT were assessed using Pearson’s r and Krippendorff’s α, respectively. Pearson’s r correlation coefficient ranges from −1 to +1, with values closer to 1 indicating strong positive correlations. Krippendorff’s α reliability coefficient ranges from 0 to 1.0, with values closer to 1 indicating high reliability. Diagnostic test characteristics (sensitivity, specificity, positive predictive value, and negative predictive value) of the ISTH-BAT and self-BAT were assessed against multiple referent standards.
Standardized PROMIS Z-scores were calculated for each PROMIS domain, representing standard deviations from a US normative population mean. Z-scores are described as representing worse health or better health, as compared to normative population. Linear regression models, adjusted for sex and age, were used to assess the association between PROMIS domain Z-scores and BAT scores. Assumptions of linearity and homoscedasticity were assessed through examination of residual plots for each PROMIS domain. In this analysis, sex is treated as a binary variable (sex assigned at birth), an approach required due to key genetic and reproductive aspects of bleeding disorders.
Patients who lacked a complete ISTH-BAT, self-BAT, or PROMIS-29 were excluded from analysis. Analysis was completed using STATA 18 (StataCorp, 2023) and Microsoft Excel (Version 16.98).
3. Results
3.1. Study population
Baseline characteristics of the sample are presented in Table 1. Patient ages ranged from 18 to 86, with a mean age of 40 (SD = 16.6). The majority of patients were female (86%); 73% were White and 16% were Black; 11% identified as being of Hispanic or Latino ethnicity. Fifty-seven percent of the females reporting having a menstrual period within the last 3 months. Iron deficiency was prevalent, with 42.9% of female patients exhibiting a ferritin level less than 30 ng/mL. Following clinical evaluation, the following diagnostic labels were assigned: BDUC (n = 46, 45.5%), QPD (n = 11, 10.9%), VWD (n = 9, 8.9%), congenital hypodysfibrinogenemia (n = 1, 0.99%), no bleeding disorder (n = 21, 20.8%), other/unclassified (n = 13, 12.9%). Unclassified diagnoses include patients with incomplete laboratory workup, medication use impacting laboratory results, or patients who were pregnant during evaluation. The distribution of ISTH-BAT and self-BAT scores is visualized in Figure 2.
Table 1.
Demographic and clinical characteristics of bleeding disorder referral population
| Characteristic | All (N = 101) | BDUC (n = 46) | Inherited BD (n = 21) | No BD (n = 21) | Other/ unclassified (n = 13) |
|---|---|---|---|---|---|
| Age | 40.0 (16.6) | 36.9 (11.7) | 37.6 (17.8) | 41.9 (17.0) | 52.2 (23.5) |
| Sex | |||||
| Female | 87 (86.1%) | 39 (84.8%) | 18 (85.7%) | 17 (81.0%) | 13 (100%) |
| Male | 14 (13.9%) | 7 (15.2%) | 3 (14.3%) | 4 (19.0%) | 0 (0.0%) |
| Race | |||||
| White | 74 (73.3%) | 33 (71.7%) | 16 (76.2%) | 14 (66.7%) | 11 (84.6%) |
| Black | 16 (15.8%) | 8 (17.4%) | 2 (9.5%) | 4 (19.0%) | 2 (15.4%) |
| Asian | 4% (4.0%) | 2 (4.4%) | 2 (9.5%) | 0 (0.0%) | 0 (0.0%) |
| American Indian | 1% (1.0%) | 0 (0.0%) | 1 (4.8%) | 0 (0.0%) | 0 (0.0%) |
| Unknown | 6% (5.9%) | 3 (6.5%) | 0 (0.0%) | 14.3% | 0 (0.0%) |
| Ethnicity | |||||
| Hispanic or Latino | 11 (10.9%) | 7 (15.2%) | 1 (4.8%) | 3 (14.3%) | 0 (0.0%) |
| Not Hispanic or Latino | 89 (88.1%) | 39 (84.8%) | 19 (90.4%) | 18 (85.7%) | 13 (100%) |
| Unknown | 1 (1.0%) | 0 (0.0%) | 1 (4.8%) | 0 (0%) | 0 (0.0%) |
| BAT total scores | |||||
| ISTH-BAT | 8.1 (4.8) | 9.6 (3.6) | 8.5 (4.7) | 4.2 (1.9) | 8.7 (7.8) |
| Self-BAT | 9.1 (5.8) | 10.6 (5.6) | 9.0 (5.4) | 4.7 (3.2) | 10.3 (7.6) |
| ABO group | |||||
| O group | 40 (39.6%) | 16 (34.8%) | 16 (76.2%) | 4 (30.8%) | 4 (39.6%) |
| Other | 39 (38.6%) | 24 (52.2%) | 1 (33.3%) | 7 (53.8%) | 7 (38.6%) |
| Unknown | 22 (21.8%) | 6 (13.0%) | 4 (47.6%) | 10 (15.4%) | 2 (21.8%) |
| Anemia | |||||
| Hemoglobin ≤ 12 g/dL | 12 (11.9%) | 3 (6.5%) | 2 (9.5%) | 4 (19%) | 3 (11.9%) |
| Hemoglobin > 12 g/dL | 89 (88.1%) | 43 (93.5%) | 19 (90.5%) | 17 (81%) | 10 (88.1%) |
| Menstrual and Iron Parameters (Female Participants Only) | |||||
| Menstruation (last 3 months) | (n = 87) | (n = 37) | (n = 17) | (n = 17) | (n = 13) |
| Unknown | 3 (3.4%) | 2 (5.1%) | 1 (5.6%) | 0 (0.0%) | 0 (0.0%) |
| No menstruation | 36 - (41.4%) | 12 (30.8%) | 6 (33.3%) | 10 (58.8%) | 8 (61.5%) |
| Light | 4 - (4.6%) | 1 (2.5%) | 1 (5.6%) | 2 (11.8%) | 0 (0.0%) |
| Moderate | 10 (11.5%) | 4 (10.3%) | 3 (16.7%) | 3 (17.6%) | 0 (0.0%) |
| Heavy or very heavy | 34 (39.1%) | 20 (51.3%) | 7 (38.8%) | 2 (11.8%) | 5 (38.5%) |
| Iron parameters | |||||
| Ferritin > 30 ng/mL) | 48 (57.1%) | 23 (62.2%) | 9 (52.9%) | 10 (58.8%) | 6 (57.8%) |
| Ferritin ≤ 30 ng/mL) | 36 (42.9%) | 14 (37.8%) | 8 (47.1%) | 7 (41.2%) | 7 (42.2%) |
Continuous variables are presented as mean ± SD; categorical variables are presented as n (%). BDUC, bleeding disorder of unknown cause; Inherited BD, inherited bleeding disorder or any laboratory diagnosis of a bleeding disorder presumed to be congenital in nature; No BD, no bleeding disorder.
Figure 2.
Clinician ISTH-BAT and self-BAT scores in male and female participants. ISTH-BAT scores are plotted against self-BAT scores for male (n = 14, left panel) and female (n = 87, right panel) participants. Score cut-off (males: >3, females: >5) are represented via dotted line. Inherited bleeding disorder diagnoses are color-coded: QPD (qualitative platelet disorder) is shown in red, VWD (von Willebrand disease) in blue, and HDF (hypodysfibrinogenemia) in green. One participant is noted as an outlier at (30, 32).
3.2. ISTH-BAT and self-BAT composite scores
The mean ISTH-BAT score was 8.1 (SD = 4.8), compared with a mean self-BAT score of 9.1 (SD = 5.8). Sixty-nine patients (68.3%) had positive ISTH-BAT scores and 70 patients (69.3%) had positive self-BAT scores using established sex-specific cut-offs. There was a moderate-strong positive correlation between the ISTH-BAT and self-BAT total scores (r = 0.71) with moderate reliability/agreement (α = 0.65).
Diagnostic test characteristics compared with various reference standards are shown in Table 2. The self-BAT sensitivity was 79.7%, specificity 53.1%, positive predictive value 78.6%, with a negative predictive value of 54.8% in detection of an abnormal ISTH-BAT score. The overall agreement was 71.3%. Evaluated as a screening test for VWD, the sensitivity of the Self-BAT was 66.7% and the sensitivity of the ISTH-BAT was 77.8%. The specificity of both instruments was similar (30.4% and 32.6%, respectively). Both instruments had a very high negative predictive value for diagnosis of VWD (ISTH-BAT: 93.8% and self-BAT: 90.3%).
Table 2.
Sensitivity, specificity, and predictive value of clinician ISTH-BAT and self-BAT scores across diagnostic categories
| Referent standard | ISTH-BAT |
Self-BAT |
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|---|---|---|---|---|---|---|---|---|
| Sensitivity % (95% CI) | Specificity % (95% CI) | PPV (95% CI) | NPV (95% CI) | Sensitivity % (95% CI) | Specificity % (95% CI) | PPV (95% CI) | NPV (95% CI) | |
| ISTH-BAT (n = 101) | N/A | N/A | N/A | N/A | 79.7 (68.3-88.4) | 53.1 (34.7-70.9) | 78.6 (67.1-87.5) | 54.8 (36.0-72.7) |
| Laboratory diagnosis: VWD (n = 9) | 77.8 (40.0-97.2) | 32.6 (23.2-43.2) | 10.1 (4.2-19.8) | 93.8 (79.2-99.2) | 66.7 (29.9-92.5) | 30.4 (21.3-40.9) | 8.6 (3.2-17.7) | 90.3 (74.2-98.0) |
| Laboratory diagnosis: inherited BD (n = 21) | 71.4 (47.8-88.7) | 32.5 (22.4-43.9) | 21.7 (12.7-33.3) | 81.2 (63.6-92.8) | 66.7 (43.0-85.4) | 30.0 (20.3-41.3) | 20.0 (11.4-31.3) | 77.4 (58.9-90.4) |
| Clinical diagnosis: BDUC or inherited BD (n = 67) | 81.7 (69.6-90.5) | 51.2 (35.1-67.1) | 71.0 (58.8-81.3) | 65.6 (46.8-81.4) | 73.3 (60.3-83.9) | 36.6 (22.1-53.1) | 62.9 (50.5-74.1) | 48.4 (30.2-66.9) |
ISTH-BAT and self-BAT scores cut offs are >3 for males and >5 for females. VWD, von Willebrand disease; Inherited BD, inherited bleeding disorder or any laboratory diagnosis of a bleeding disorder presumed to be congenital in nature; BDUC, bleeding disorder of unknown cause; PPV, positive predictive value; NPV, negative predictive value.
3.3. ISTH-BAT and self-BAT bleeding domain scores
Descriptive and psychometric properties of each BAT domain assessed by the Clinician (ISTH)-BAT and self-BAT are shown in Table 3. On average, self-BAT scores for hematuria, GI bleeding, muscle hematoma, and hemarthrosis were higher than ISTH-BAT scores. ISTH-BAT scores for tooth extraction were on average higher than self-BAT scores. Domains with strong positive correlation included postpartum hemorrhage (r = 0.82) and GI bleeding (r = 0.72). Menorrhagia (r = 0.68) and epistaxis (r = 0.63) had moderate to strong correlations. Reliability between the 2 measures was moderate to high for postpartum hemorrhage (α = 0.79), menstrual bleeding (α =0.69), and epistaxis (α=0.66). Reliability was lowest for hematuria (α = 0.42), muscle hematoma (α = 0.41), and hemarthrosis (α = 0.26).
Table 3.
ISTH-BAT and self-BAT domain agreement and reliability (n = 101)
| Bleeding domain |
Score per domain, median (range) |
Score per domain, mean ± SD |
Symptom prevalencea (%) |
Exact agreement (%) |
Correlation co-efficientb (Pearson’s r) |
Reliability co-efficientc (Krippendorf’s α) |
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|---|---|---|---|---|---|---|---|---|---|
| ISTH-BAT | Self-BAT | ISTH-BAT | Self-BAT | ISTH-BAT | Self-BAT | ||||
| Total | 7 (1-32) | 8 (0-30) | 8.13 ± 4.8 | 9.05 ± 5.8 | 11.9% | 0.71 | 0.65 | ||
| Epistaxis | 0 (0-4) | 0 (0-3) | 0.55 ± 0.9 | 0.54 ± 0.9 | 34.6% | 29.7% | 75.2% | 0.63 | 0.66 |
| Cutaneous | 1 (0-3) | 1 (0-4) | 1.21 ± 0.9 | 1.28 ± 1.2 | 76.2% | 66.3% | 37.6% | 0.44 | 0.50 |
| Bleeding from minor wounds | 0 (0-4) | 0 (0-2) | 0.53 ± 0.7 | 0.52 ± 0.7 | 46.5% | 43.5% | 59.4% | 0.39 | 0.45 |
| Hematuria | 0 (0-4) | 0 (0-4) | 0.18 ± 0.6 | 0.31 ± 0.7 | 14.8% | 20.8% | 81.2% | 0.48 | 0.42 |
| GI bleeding | 0 (0-4) | 0 (0-4) | 0.35 ± 0.8 | 0.51 ± 1.0 | 21.7% | 27.7% | 74.3% | 0.72 | 0.53 |
| Oral cavity | 1 (0-2) | 0 (0-3) | 0.58 ± 0.6 | 0.55 ± 0.7 | 53.4% | 43.5% | 62.4% | 0.46 | 0.52 |
| Tooth extraction | 0 (0-4) | 0 (0-4) | 0.76 ± 1.2 | 0.63 ± 1.0 | 34.6% | 42.5% | 57.4% | 0.42 | 0.56 |
| Surgery | 0 (0-4) | 0 (0-4) | 1.21 ± 1.6 | 1.25 ± 1.6 | 45.5% | 45.5% | 61.4% | 0.58 | 0.58 |
| Menorrhagia | 2 (0-4) | 3 (0-4) | 2.11 ± 1.4 | 2.28 ± 1.4 | 72.2% | 72.2% | 53.5% | 0.68 | 0.69 |
| Postpartum hemorrhage | 0 (0-4) | 0 (0-4) | 0.83 ± 1.2 | 0.80 ± 1.3 | 36.6% | 32.6% | 77.2% | 0.82 | 0.79 |
| Muscle hematoma | 0 (0-2) | 0 (0-3) | 0.16 ± 0.5 | 0.28 ± 0.7 | 12.8% | 12.8% | 81.2% | 0.49 | 0.41 |
| Hemarthrosis | 0 (0-2) | 0 (0-2) | 0.06 ± 0.3 | 0.17 ± 0.5 | 4.9% | 12.8% | 87.1% | 0.23 | 0.26 |
| CNS bleeding | 0 (0-0) | 0 (0-3) | 0.00 | 0.06 ± 0.4 | 3.9% | 1.99% | 95.0% | 0.00 | 0 .00 |
Symptom prevalence reflects the proportion of participants reporting a domain score ≥1.
Pearson’s r correlation coefficient ranges from −1 to +1, with values closer to 1 indicating strong positive correlations.
Krippendorff’s α reliability coefficient ranges from 0 to 1.0, with values closer to 1 indicating high reliability.
3.4. QoL & BAT score
Standardized Z-scores on 7 PROMIS QoL are presented based on ISTH-BAT score tertile and diagnostic category (Figure 3). Patients in the highest tertile oh ISTH-BAT scores (12 and greater) demonstrated, on average, worse health in all domains compared with patients with moderate (range 6-11) and low (range 0-5) bleeding scores (Figure 3, panel 1). Patients with ISTH-BAT scores greater than 12 reported fatigue 1.4 standard deviations (SD) worse than a US general population sample, along with 1.0 SD worse pain, 1.0 SD worse sleep, 0.7 SD worse physical function, 0.5 SD worse anxiety, and 0.5 SD worse social roles. The overall group reported worse quality of life than a US normative sample for the domains of anxiety, fatigue, pain interference, sleep, and physical function. Patients with BDUC and inherited bleeding disorders had similar QoL impairments as measured by Z-score in anxiety (−0.4 vs –0.6), fatigue (−0.9 vs –0.5), pain interference (−0.4 vs –0.2), and sleep (−0.6 vs –0.6).
Figure 3.
Grouped box-whisker plots of PROMIS quality-of-life domain Z-scores by ISTH-BAT tertile (panel 1) and bleeding disorder diagnosis (panel 2). The y-axis depicts standardized PROMIS Z-scores, representing standard deviations from a US normative population mean. Negative Z-scores represent worse health as compared with normative population mean, while positive Z-scores indicate better health. Each box plot displays median (horizontal line within box) and mean (x symbol within box), as well as minimum/maximum values and interquartile range (box). Outliers are shown as separate points. No BD, no bleeding disorder diagnosis; Inherited BD, inherited bleeding disorder; BDUC, bleeding disorder of unknown cause.
Using linear regression models, we evaluated the association of the PROMIS domains with (1) the ISTH-BAT and (2) the self-BAT, adjusting for age and sex (Table 4). Both models performed similarly. Higher scores on each assessment were associated with worse health for the domains of fatigue (ISTH-BAT: β = -0.07, [95% CI, −0.12 to −0.02]; self-BAT: β = −0.07, [95% CI, −0.11 to −0.03]), social roles (ISTH-BAT: β = −0.07, [95% CI, −0.11 to −0.03]; self-BAT: β = −0.06, [95% CI, −0.10 to −0.03]), sleep (ISTH-BAT: β = −0.04, [95% CI, −0.08 to −0.009]; self-BAT: β = −0.03, [95% CI, −0.06 to −0.004]), pain interference (ISTH-BAT: β = −0.06, [95% CI, −0.11 to −0.02]; self-BAT: β = −0.06, [95% CI, −0.09 to −0.02]), and physical function (ISTH-BAT: β = −0.07, [95% CI, −0.11 to −0.04]; self-BAT: β = −0.07, [95% CI, −0.10 to −0.04]). Examination of residual plots showed unbiased and homoscedastic results for domains of fatigue, sleep, and social roles; some heteroscedasticity was noted for the domains of pain interference and physical function, although associations still appeared linear. Adjusted R2 values were slightly higher in self-BAT versus ISTH-BAT for fatigue (0.14 vs 0.09), social roles (0.14 vs 0.12), and physical function (0.21 vs 0.17), indicating Self-BAT may explain slightly more of the variance in PROMIS domain ratings.
Table 4.
Age-adjusted and sex-adjusted linear regression models of PROMIS Z-score using the ISTH-BAT (model 1) and self-BAT (model 2)
| PROMIS-29 Domain | Coefficient (95% CI) | Adjusted R2 | P | |
|---|---|---|---|---|
| Fatigue | Model 1: ISTH-BAT | −0.07 (−0.12 to −0.02) | 0.09 | .004 |
| Model 2: self-BAT | −0.07 (−0.11 to −0.03) | 0.14 | < .001 | |
| Anxiety | Model 1: ISTH-BAT | 0.01 (−0.03 to 0.05) | 0.04 | .58 |
| Model 2: self-BAT | 0.0005 (−0.03 to 0.03) | 0.04 | .98 | |
| Social roles | Model 1: ISTH-BAT | −0.07 (−0.11 to −0.03) | 0.12 | .001 |
| Model 2: self-BAT | −0.06 (−0.10 to −0.03) | 0.14 | .001 | |
| Depression | Model 1: ISTH-BAT | −0.004 (−0.04 to 0.04) | 0.005 | .83 |
| Model 2: self-BAT | 0.004 (−0.03 to 0.04) | 0.005 | .81 | |
| Sleep | Model 1: ISTH-BAT | −0.04 (−0.08 to −0.009) | 0.04 | .01 |
| Model 2: self-BAT | −0.03 (−0.06 to −0.004) | 0.03 | .03 | |
| Pain interference | Model 1: ISTH-BAT | −0.06 (−0.11 to −0.02) | 0.12 | .004 |
| Model 2: self-BAT | −0.06 (−0.09 to −0.02) | 0.12 | .004 | |
| Physical function | Model 1: ISTH-BAT | −0.07 (−0.11 to −0.04) | 0.17 | < .001 |
| Model 2: self-BAT | −0.07 (−0.10 to −0.04) | 0.21 | < .001 | |
In Model 1, PROMIS-Z score (continuous) is modeled using the following inputs: age (continuous), sex (binary), and ISTH-BAT (continuous), with the parameter outputs for ISTH-BAT displayed. In Model 2, PROMIS-Z score (continuous) is modeled using the following inputs: age (continuous), sex (binary), and self-BAT (continuous), with the parameter outputs for self-BAT displayed.
4. Discussion
Laboratory hemostatic testing in patients evaluated for a potential bleeding disorder is frequently either normal or only partially explains clinical bleeding symptoms. Bleeding history and standardized BATs are now integrated into clinical diagnostic algorithms for VWD and BDUC, highlighting the need for improved understanding of how measurement of bleeding history informs overall clinical assessment and patient-important outcomes.
Overall clinician-assessed ISTH-BAT and patient-reported Self-BAT scores were well-correlated, with patient reports mirroring physician assessments for the evaluation of heavy menses, epistaxis, and history of postpartum hemorrhage. However, we found that the mode of bleeding assessment in the context of a clinical encounter may result in slightly different assessments of bleeding tendency, depending on the bleeding domain. The reasons for this may include:
-
(1)
Differing interpretations of symptom severity between patients and clinicians. Extensive bruising and bleeding from minor wounds, for example, may be quite different when assessed by a patient or a clinician.
-
(2)
Misinterpretation of questions or medical concepts. Joint bleeding and muscle bleeding are rare conditions that may be confused with other more common musculoskeletal complaints (arthritis, muscle sprain, and cutaneous bleeding), leading to potential false positives on the self-BAT.
-
(3)
Changes in event recall and characterization over the course of a clinical encounter. Patients may be prompted to recall additional bleeding events as a result of completing a self-report questionnaire, or conversely, additional bleeding events may be elicited during the interview. Patients may present with a bias towards minimizing symptoms (e.g., in hopes of obtaining clearance for a desired surgery/procedure) or augmenting symptoms (e.g., in hopes that previously dismissed symptoms are taken seriously). Respondent fatigue after answering self-report questions could also play a role, although we note that symptom questionnaires are routine at medical appointments and are often followed by similar in person questions. Clinicians may also integrate information from the medical record into their scoring (e.g., reviewing operative notes and other contemporaneous clinical documentation).
Although domain-level differences were observed between the ISTH and self-BAT, we note that in practical terms, scores are often considered in aggregate. The self-BAT was on average one point higher than the ISTH-BAT in our study, though a similar proportion of patients had a positive score on each measure. In a Danish general population study of the self-BAT including over 1000 healthy adults, 32% of women and 24% of men had abnormal scores using established cut-offs [13], again indicating some modest score inflation with a self-report measure.
Due to ease of administration, it may be especially useful to use the Self-BAT as a screening test in settings where a full BAT score may be less feasible due to time constraints (e.g., in a gynecology or primary care clinic) or limited care access. For example, the online Self-BAT assessment on Letstalkperiod.org led to more timely hematology referrals and bleeding disorder diagnoses in one study [14]. In our study, the Self-BAT demonstrated a similar sensitivity for diagnosis of VWD or any bleeding disorder. Both measures had high negative predictive value for diagnosis of VWD even in a pre-selected referral population. If we consider the ISTH-BAT to be the diagnostic gold standard (and self-BAT as screening tool), the sensitivity and accuracy of the self-BAT were somewhat lower than previously reported in patients with platelet disorders (sensitivity 79.7% vs 96.9%, accuracy 71.3% vs 88.5%) [8]. Notably, our study includes undifferentiated patients and a broader distribution of BAT scores compared with prior studies.
As BATs are integrated into diagnostic classification schemas and more routinely used in clinical care, it is essential to understand whether these measures correlate with patient-important endpoints, including QoL. Patients with the worst QoL impairments due to bleeding may benefit from receiving a clinical diagnosis and accessing comprehensive care services. By contrast, a patient with mild bleeding that does not materially impact QoL may be unduly burdened by a formal bleeding disorder diagnosis. We found patients in the highest tertile of ISTH-BAT scores (>12) to have worse quality of life impairments across multiple QoL domains. Adjusting for age and sex, higher scores for both the ISTH and self-BAT were associated with poorer outcomes for fatigue, social roles, pain interference, sleep and physical function. In our adjusted model, a 7 or 8-point increase in BAT score was associated with ∼0.5 standard deviation worse fatigue, social roles, pain interference, and physical function, a moderate and clinically meaningful effect [15]. For sleep, the effect size is smaller for a similar increase in BAT score (∼0.3). The association between BAT and QoL remained stable regardless of whether a laboratory-diagnosed inherited bleeding disorder was identified. The Self-BAT explained slightly more variability in QoL domains than the ISTH-BAT, although the difference was modest. We note inclusion of certain additional variables (unmeasured in this study) could potentially improve both model fit and generalizability (e.g. comorbid conditions).
Our findings differ from those derived from a prior analysis of patients with mild bleeding disorders in the Vienna Bleeding Biobank (VBB), in which neither the Vicenza bleeding score nor the number of bleeding manifestations was associated with physical or mental health impairments [16]. In the VBB study, QoL was assessed many years after the initial evaluative encounter (allowing time for bleeding symptoms to be addressed), as compared with our study where QoL was assessed prior to diagnosis or treatment of a potential bleeding disorder, when the BAT may be especially relevant. Both our study and the VBB began prior to published guidelines for BDUC diagnosis, but overall testing schemas were comparable and a similar proportion of patients were diagnosed with VWD and QPDs. A smaller proportion of patients were diagnosed with BDUC in our study (45.5% vs 72.5%), which may be explained, in part, by the role of clinical gestalt in diagnostic determination and differing referral bases. Our study additionally includes patients referred for bleeding evaluation who have mild symptoms not judged to rise to the threshold of a disorder (captured as no bleeding disorder).
Similar to the VBB study [16], we found that patients with BDUC had QoL impairments when compared to a general population sample. QoL impairments in VWD are well described [17], and we did not identify a significant difference in QoL between BDUC and inherited bleeding disorders, including VWD, in our study. Future research may help us better understand the underlying etiology that connects bleeding symptoms to QoL impairments. For example, although bleeding, and resultant iron deficiency, may contribute to a person’s fatigue, it is important to consider multifactorial causes. Joint and muscle bleeding is rare in this population, but pain and physical impairment may be related to menstrual bleeding or to generalized joint hypermobility, which is highly prevalent in patients with mucocutaneous bleeding symptoms. Similarly, heavy nocturnal menstrual bleeding and the need to frequently change menstrual products may contribute to deficits in sleep and overall stress.
Limitations of our study include the fact that patients were recruited from a single clinical site, albeit one with a large catchment area. The sample of patients for each diagnosis group was modest, and thus all inherited bleeding disorders were analyzed as a composite group. ISTH-BAT scores were calculated as part of routine clinical practice, rather than by a researcher following a careful script, thus providing real-world relevance. Although clinicians generally adhere to established best practices for diagnosis and evaluation of mild bleeding disorders, it is possible that some known inherited or acquired causes of bleeding were missed. Due to the logistical and financial implications of higher order hemostatic testing, platelet aggregation and secretion were not always repeated to confirm abnormal findings; retesting results could lead to reclassification of some patients labeled as QPDs.
In summary, in a real-world evaluative context, the self-BAT and ISTH-BAT have fairly strong overall agreement. Self-BAT items are viable surrogates for clinician assessment of heavy menstrual bleeding, epistaxis, and history of postpartum hemorrhage but may deviate more significantly from clinician assessment of symptoms that are either more subjective (e.g., bruising and minor wounds) or rare in nonbleeding disorder populations (hemarthrosis and muscle hematoma). As screening tests for inherited bleeding disorders, both measures perform similarly. Further assessment of content validity in undifferentiated patients may help resolve some domain-level discrepancy, and new methods for incorporating both domain and overall scores into diagnostic algorithms should be explored in future work. Importantly, higher ISTH-BAT and self-BAT scores are equally associated with worse self-reported QoL. These findings hold true irrespective of whether a laboratory diagnosis of a bleeding disorder is identified. BAT scores may help inform which patients most stand to benefit from comprehensive bleeding disorder care services, regardless of laboratory diagnosis, as in BDUC.
Acknowledgments
Funding
The authors received no funding for this study.
Authorship contributions
C.B. conceived the research study and oversaw all data collection and analysis, as well as authored manuscript. S.G. participated in data collection and data analysis. A.S. assisted in project planning, including outcome selection and data analysis/interpretation. M.H.B. assisted in project planning and consulted on data and biostatistical analysis/interpretation. A.M. assisted in project planning, facilitation of data collection, and interpretation of results. NS.K contributed to the conception, planning, and analysis/interpretation of results. All authors reviewed and edited the manuscript.
Relationship disclosure
CB work is supported by the Foundation for Women and Girls with Blood Disorders Special Research Award and National Bleeding Disorders Foundation—Takeda Clinical Research Fellowship. CB additionally received support from National Research Service Award predoctoral/postdoctoral traineeship from the Agency for Healthcare Research and Quality sponsored by the Cecil G. Sheps Center for Health Services Research, the University of North Carolina at Chapel Hill (grant number T32-HS000032). AS has research awards to institution from for-profit organizations (UroGen Pharma Ltd, Pfizer Global) and honoraria for serving on advisory boards (Pfizer and UroGen Pharma Ltd). SG, MHB, AM, and NSK have no relevant disclosures. Access to REDCap data capture tool provided through the NC Translational and Clinical Sciences (NC TraCS) Institute, which is supported by the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, through Grant Award Number UM1TR004406
Footnotes
Handling Editor: Dr Suely Meireles Rezende
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