Clinical Performance of Bleeding Risk Scores for Predicting Major and Clinically Relevant Non-Major Bleeding Events in Patients Receiving Oral Anticoagulant Therapy
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».