Clinical Performance of Bleeding Risk Scores for Predicting Major and Clinically Relevant Non-Major Bleeding Events in Patients Receiving Oral Anticoagulant Therapy
Bibliographic record
Abstract
Abstract Abstract 2311 Oral anticoagulant therapy (OAT) is effective in preventing thrombotic complications in atrial fibrillation (AF) and venous thrombosis but its use is associated with increased bleeding. Risk scores such as CHADS2 are used to predict thrombotic complications in patients with AF, but scores predicting bleeding are less studied. A number of bleeding risk scores (BRS) has been proposed, however they might have different predictive abilities and performance. Moreover, these scores aim to identify major bleeding (MB) but have not evaluated clinically relevant non-major bleeding (CRNMB). Recent guidelines advocate the use of scores to assess bleeding risk in patients with atrial fibrillation being considered for OAT despite studies suggesting their limited utility. The purpose of this study was to evaluate the performance of 4 validated BRS for predicting MB and CRNMB. We conducted a retrospective, cohort study of consecutive patients enrolled in an academic OAT clinic between September 2008 and February 2011. Information regarding bleeding risk factors was collected for 4 BRS: Outpatient Bleeding Risk Index (OBRI; Beyth et al., Am J Med 1998), Contemporary Bleeding Risk Model (CBRM; Shireman et al., Chest 2006), HEMORR2HAGES (Gage et al. Am Heart J 2006), and HAS-BLED (Pisters et al., Chest 2010). Main outcomes were MB (Schulman J Thromb Haemost 2005) and a composite of MB + CRNMB (defined as overt bleeding that does not meet the criteria for MB but is associated with medical intervention, unscheduled contact, cessation of treatment, or associated with other discomfort (e.g. pain, impairment of daily activities). Incidence rates (IR) were calculated for each BRS and risk category. Correlation of bleeding risk categories among different BRS was assessed using the Kendall's tau-b coefficient. Predictive ability of each tool was evaluated using the C-statistic. Groups were compared using Fisher's exact, χ2, Mann-Whitney U, or Student's T tests. Hazard ratios (HR) for each score and risk category were estimated using Cox regression. We included 321 consecutive patients with a total follow-up of 319.2 patient-years. Mean age (SD) was 69.2 (13.6) years, 57% were males and 72.6% had AF. Overall IR for MB and MB + CRNMB were 3.7, and 11.2 events/100 patient-years, respectively. IRs for MB and MB + CRNMB separated by BRS and risk category are shown in Table 1 together with % of patients within each category. Overall, agreement among the 4 BRS was low to moderate with Kendall's tau-b coefficients ranging from 0.295 (OBRI vs CBRM) to 0.537 (HEMORR2HAGES vs HAS-BLED). C-statistics (95%CI) for predicting MB were 0.606 (0.435–0.777), 0.714 (0.548–0.879), 0.735 (0.583–0.886), and 0.672 (0.523–0.820), whereas those for predicting MB + CRNMB were 0.549 (0.452–0.645), 0.591 (0.489–0.692), 0.613 (0.517–0.709), and 0.587 (0.487–0.686) for OBRI, CBRM, HEMORR2HAGES and HAS-BLED, respectively. HRs for MB and MB + CRNMB are shown in Table 2. The best predictive ability for both MB and MB + CRNMB was for CBRM and HEMORR2HAGES. In conclusion, BRS classified bleeding risks differently. Predictive ability was moderate for MB and poor for MB + CRNMB. Overall, BRS are more helpful to identify patients at high bleeding risk, but they did not adequately identify patients at intermediate risk. Further studies assessing both MB and CRNMB are needed.Table 1.IR for bleeding eventsEvents/100 person-years (% patients in category)Score/OutcomeRisk CategoryMBLowIntermediateHigh OBRI6.98 (16.2)2.63 (69.8)6.15 (14.0) CBRM1.76 (70.1)6.62 (29.0)79.00 (0.9) HEMORR2HAGES1.32 (48.9)3.71 (41.1)14.68 (10.0) HAS-BLED0 (10.3)2.60 (60.1)7.38 (29.6)MB + CRNMB OBRI9.3411.9714.68 CBRM9.6216.1279.00 HEMORR2HAGES8.2014.0620.94 HAS-BLED9.879.0718.91Table 2.HR for bleeding eventsMBMB+CRNBBleeding Risk ScoreHR95% CIpHR95% CIpOBRI LowRefRef0.278RefRef0.798 Intermediate0.380.09–1.510.1691.290.45–3.690.636 High0.900.18–4.460.8951.520.44–5.220.503CBRM LowRefRef<0.001RefRef0.007 Intermediate3.671.04–13.010.0441.790.92–3.480.085 High39.016.99–217.70<0.0018.712.02–37.520.004HEMORR2HAGES LowRefRef0.008RefRef0.110 Intermediate2.770.54–14.280.2241.800.88–3.720.110 High10.942.12–56.420.0042.541.00–6.460.050HAS-BLED LowRefRef0.212RefRef0.118 IntermediateNENE0.9490.970.28–3.290.959 HighNENE0.9431.910.56–6.520.302 Disclosures: Lazo-Langner: Pfizer Inc.: Honoraria; Leo Pharma: Honoraria.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".