Kakkar, Ajay K.

AbstractBackgroundAn unmet need exists to reliably predict the risk of intracranial hemorrhage (ICH) in patients with atrial fibrillation (AF) treated with oral anticoagulants (OACs).HypothesisAn externally validated model improves ICH risk stratification.MethodsIndependent factors associated with ICH were identified by Cox proportional hazard modeling, using pooled data from the GARFIELD‐AF (Global Anticoagulant Registry in the FIELD‐Atrial Fibrillation) and ORBIT‐AF (Outcomes Registry for Better Informed Treatment of Atrial Fibrillation) registries. A predictive model was developed and validated by bootstrap sampling and by independent data from the Danish National Patient Register.ResultsIn the combined training data set, 284 of 53 878 anticoagulated patients had ICH over a 2‐year period (0.31 per 100 person‐years; 95% confidence interval [CI]: 0.28–0.35). Independent predictors of ICH included: older age, prior stroke or transient ischemic attack, concomitant antiplatelet (AP) use, and moderate‐to‐severe chronic kidney disease (CKD). Vitamin K antagonists (VKAs) were associated with a significantly higher risk of ICH compared with non‐VKA oral anticoagulants (NOACs) (adjusted hazard ratio: 1.61; 95% CI: 1.25–2.08; p = .0002). The ability of the model to discriminate individuals in the training set with and without ICH was fair (optimism‐corrected C‐statistic: 0.68; 95% CI: 0.65–0.71) and outperformed three previously published methods. Calibration between predicted and observed ICH probabilities was good in both training and validation data sets.ConclusionsAge, prior ischemic events, concomitant AP therapy, and CKD were important risk factors for ICH in anticoagulated AF patients. Moreover, ICH was more frequent in patients receiving VKA compared to NOAC. The new validated model is a step toward mitigating this potentially lethal complication.

The management of atrial fibrillation (AF), the most common sustained arrhythmia, impacts healthcare resource utilization (HCRU). This study aims to estimate global resource use in AF patients, using the GARFIELD-AF registry. A prospective cohort study was conducted to characterize HCRU in AF patients enrolled in sequential cohorts from 2012 to 2016 in 35 countries. Components of HCRU studied were hospital admissions, outpatient care visits, and diagnostic and interventional procedures occurring during follow-up. AF-related HCRU was reported as the percentage of patients demonstrating at least one event and was quantified as rate-per-patient-per-year (PPPY) over time. A total of 49,574 patients was analyzed, having an overall median follow-up of 719 days. Almost all patients (99.5%) had at least one outpatient care visit, while hospital admissions were the second most frequent medical contact, with similar proportions in North America (37.5%) and Europe (37.2%), and slightly higher in the other GARFIELD-AF countries (42.0%; namely Australia, Egypt, and South Africa). Asia and Latin America showed lower percentages of hospitalizations, outpatient care visits, and diagnostic and interventional procedures. Analyses of GARFIELD-AF highlighted the vast AF-related HCRU, underlying significant geographical differences in the type, quantity, and frequency of AF-related HCRU. These differences were likely attributable to health service availability and differing models of care.

Objective While greater body mass index (BMI) is associated with increased risk of developing atrial fibrillation (AF), the impact of BMI on outcomes in newly diagnosed AF is unclear. We examine the influence of BMI on outcomes and whether this is modified by sex and evaluate the effect of non-vitamin K oral anticoagulants (NOACs) in patients with high BMI. Methods GARFIELD-AF is a prospective registry of 52 057 newly diagnosed AF patients. The study population comprised 40 482 participants: 703 underweight (BMI <18.5 kg/m 2 ), 13 095 normal (BMI=18.5–24.9 kg/m 2 ), 15 043 overweight (BMI=25.0–29.9 kg/m 2 ), 7560 obese (BMI=30.0–34.9 kg/m 2 ) and 4081 extremely obese (BMI ≥35.0 kg/m 2 ). Restricted cubic splines quantified the association of BMI with outcomes. Comparative effectiveness of NOACs and vitamin K antagonists (VKAs) by BMI was performed using propensity score overlap-weighted Cox models. Results The median age of participants was 71.0 years (Q1; Q3 62.0; 78.0), and 55.6% were male. Those with high BMI were younger, more often had vascular disease, hypertension and diabetes. Within 2-year follow-up, a U-shaped relationship between BMI and all-cause mortality was observed, with BMI of ~30 kg/m 2 associated with the lowest risk. The association with new/worsening heart failure was similar. Only low BMI was associated with major bleeding and no association emerged for non-haemorrhagic stroke. BMI was similarly associated with outcomes in men and women. BMI did not impact the lower rate of all-cause mortality of NOACs compared with VKAs. Conclusions In the GARFIELD-AF registry, underweight and extremely obese AF patients have increased risk of mortality and new/worsening heart failure compared with normal or obese patients.

Objectives This study evaluated the comparative effectiveness of vitamin K antagonists (VKAs), direct thrombin inhibitors (DTIs) and factor Xa inhibitors (FXaI) in patients with atrial fibrillation (AF) at risk of stroke in everyday practice. Methods Data from patients with AF and Congestive heart failure, Hypertension, Age 75 years, Diabetes mellitus, prior Stroke, TIA, or thromboembolism, Vascular disease, Age 65-74 years, Sex category (CHA2DS2-VASc) score ≥2 (excluding gender) in the Global Anticoagulant Registry in the FIELD–Atrial Fibrillation registry were analysed using an improved method of propensity weighting, overlap weights and Cox proportional hazards models. Results All-cause mortality, non-haemorrhagic stroke/systemic embolism (SE) and major bleeding over 2 years were compared in 25 551 patients, 7162 (28.0%) not treated with oral anticoagulant (OAC) and 18 389 (72.0%) treated with OAC (FXaI (41.8%), DTI (11.4%) and VKA (46.8%)). OAC treatment compared with no OAC treatment was associated with decreased risk of all-cause mortality (HR 0.82 (95% CI 0.74 to 0.91)) and non-haemorrhagic stroke/SE (HR 0.71 (95% CI 0.57 to 0.88)) but increased risk of major bleeding (HR 1.46 (95% CI 1.15 to 1.86)). Non-vitamin K antagonist oral anticoagulant (NOAC) use compared with no OAC treatment was associated with lower risks of all-cause mortality and non-haemorrhagic stroke/SE (HR 0.67 (95% CI 0.59 to 0.77)) and 0.65 (95% CI 0.50 to 0.86)) respectively, with no increase in major bleeding (HR 1.10 (95% CI 0.82 to 1.47)). NOAC use compared with VKA use was associated with lower risk of all-cause mortality and major bleeding (rates/100 patient-years 3.6 (95% CI 3.3 to 3.9) vs 4.8 (95% CI 4.5 to 5.2) and 1.0 (95% CI 0.9 to 1.1) vs 1.4 (95% CI 1.2 to 1.6); HR 0.79 (95% CI 0.70 to 0.89) and 0.77 (95% CI 0.61 to 0.98) respectively), with similar risk of non-haemorrhagic stroke/SE (rates/100 patient-years 0.8 (95% CI 0.7 to 0.9) versus 1.0 (95% CI 0.8 to 1.1); HR 0.96 (95% CI 0.73 to 1.25). Conclusion Important benefits in terms of mortality and major bleeding were observed with NOAC versus VKA with no difference among NOAC subtypes. Trial registration number NCT01090362.

Abstract Aims Most clinical risk stratification models are based on measurement at a single time-point rather than serial measurements. Artificial intelligence (AI) is able to predict one-dimensional outcomes from multi-dimensional datasets. Using data from Global Anticoagulant Registry in the Field (GARFIELD)-AF registry, a new AI model was developed for predicting clinical outcomes in atrial fibrillation (AF) patients up to 1 year based on sequential measures of prothrombin time international normalized ratio (PT-INR) within 30 days of enrolment. Methods and results Patients with newly diagnosed AF who were treated with vitamin K antagonists (VKAs) and had at least three measurements of PT-INR taken over the first 30 days after prescription were analysed. The AI model was constructed with multilayer neural network including long short-term memory and one-dimensional convolution layers. The neural network was trained using PT-INR measurements within days 0–30 after starting treatment and clinical outcomes over days 31–365 in a derivation cohort (cohorts 1–3; n = 3185). Accuracy of the AI model at predicting major bleed, stroke/systemic embolism (SE), and death was assessed in a validation cohort (cohorts 4–5; n = 1523). The model’s c-statistic for predicting major bleed, stroke/SE, and all-cause death was 0.75, 0.70, and 0.61, respectively. Conclusions Using serial PT-INR values collected within 1 month after starting VKA, the new AI model performed better than time in therapeutic range at predicting clinical outcomes occurring up to 12 months thereafter. Serial PT-INR values contain important information that can be analysed by computer to help predict adverse clinical outcomes.

Abstract Background: Patients on systemic therapy for cancer are at varying risk for venous thromboembolism (VTE) and its consequences. Thromboprophylaxis is recommended in hospitalized medical and surgical cancer patients, but most VTE occurs in ambulatory cancer patients where risk can be estimated with a validated score. However, the benefit of extended outpatient thromboprophylaxis is uncertain with heparins and has not been tested with direct oral anticoagulants. Methods: We conducted a double-blind, randomized, placebo-controlled, parallel-group, multicenter study in adult ambulatory patients with various cancers initiating a new systemic regimen and at increased risk for VTE (defined as Khorana score ≥ 2). Enrolled subjects were screened for deep-vein thrombosis (DVT) and if none found randomized 1:1 to rivaroxaban 10 mg once daily or placebo up to day 180. Subjects were screened with lower extremity ultrasounds every 8 weeks on study. Primary efficacy endpoint was a composite of objectively confirmed symptomatic or asymptomatic lower-extremity proximal DVT, symptomatic upper- or lower-extremity distal DVT, symptomatic or incidental pulmonary embolism and VTE-related death. ISTH-defined major bleeding was the primary safety endpoint. All endpoints were adjudicated by blinded independent committees. Analyses for efficacy endpoints were conducted for intent-to-treat (ITT) population (all randomized patients) for the up-to-day 180 observation period (primary) and the on-treatment period (supportive). Safety analyses were conducted for on-treatment period only for patients who received at least one dose of study drug. Results: Of 1,080 patients who provided consent, 49 (4.53%) had DVT on baseline screening and another 190 screen-failed due to other reasons. Of 841 randomized patients, 274 (32.6%) had pancreatic cancer; 698 (83%) were white and 428 (50.9%) were male. The primary efficacy endpoint occurred in 25 of 420 patients (5.95%) and 37 of 421 patients (8.79%) (HR, 0.66; 95% CI, 0.40 to 1.09; p=0.101) in the rivaroxaban and placebo groups respectively (number needed to treat, NNT=35) in the up-to-day 180 observation period (Figure 1A). Of all patients with VTE, 38.70% experienced events after discontinuing study drug. In a pre-specified analysis of all randomized patients during the on-treatment period, the primary endpoint occurred in 11 of 420 patients (2.62%) and 27 of 421 (6.41%) in rivaroxaban and placebo groups respectively (HR 0.40, 95% CI 0.20 to 0.80, p=0.007) (NNT=26) (Figure 1B). Major bleeding occurred in 8 of 405 (1.98%) in the rivaroxaban group and in 4 of 404 (0.99%) patients in the placebo group (hazard ratio, 1.96; 95% CI, 0.59 to 6.49; p=0.265) (number needed to harm, NNH=101). Clinically relevant non-major bleeding occurred in 2.72% and 1.98% of rivaroxaban and placebo groups, respectively (HR, 1.34; 95% CI, 0.54 to 3.32; p=0.53) (NNH=135). Adverse events were comparable between groups. For secondary efficacy endpoints, a pre-specified analysis of the composite of primary endpoint with addition of arterial and visceral thromboembolic events in the up-to-day 180 observation period showed significantly fewer events in rivaroxaban versus placebo group (6.90% versus 10.70% respectively; HR, 0.62; 95% CI: 0.39, 0.99; p=0.04). All-cause mortality occurred in 20.0% of patients in rivaroxaban group and 23.8% in placebo group (HR=0.83, 95% CI 0.62, 1.11; p=0.213). A pre-specified composite of the primary endpoint with all-cause mortality occurred in 23.1% of patients in rivaroxaban group and 29.5% in placebo group (HR 0.75; 95% CI 0.57 to 0.97; p=0.03) (Figure 1C). Conclusions: Rivaroxaban significantly reduced VTE and VTE-related death during the on-treatment period but not during the full study period; over one-third of events occurred post discontinuation of study drug. The incidence of major bleeding was low. The Khorana risk score cut-off of ≥2 identified cancer patients at high risk of thrombotic events both at baseline (4.53%) and during study (8.79% with additional 1.66% arterial events in placebo group). These results should inform future recommendations regarding thromboprophylaxis in at-risk ambulatory cancer patients. (Funded by Janssen; ClinicalTrials.gov number, NCT02555878) Figure 1 Figure 1. Disclosures Khorana: Parexel: Other: Personal fees and non-financial support for travel; Sanofi: Consultancy, Other: Personal fees and non-financial support for travel; Pfizer: Consultancy, Other: Personal fees and non-financial support for travel; Janssen: Consultancy, Other: Personal fees, Research Funding; TriSalus: Other: Personal fees; Halozyme: Other: Personal fees and non-financial support for travel; Seattle Genetics: Other: Personal fees and non-financial support for travel; AngioDynamics: Other: Personal fees and non-financial support for travel; LEO Pharma: Other: Personal fees and non-financial support for travel; Medscape/WebMD: Other: Personal fees and non-financial support for travel; Pharmacyclics: Other: Personal fees; PharmaCyte: Other: Personal fees; Bayer: Consultancy, Other: Personal fees and non-financial support for travel. Soff:Janssen: Research Funding; Amgen: Research Funding. Kakkar:Bayer AG: Consultancy, Research Funding; Boehringer Ingelheim: Consultancy; Daiichi Sankyo Europe: Consultancy; Janssen Pharmaceuticals: Consultancy; Pfizer: Consultancy; Sanofi S.A.: Consultancy; Verseon: Consultancy. Vadhan-Raj:AMAG Pharmaceuticals, Inc: Other: received funding from Amag to support clinical trial; Janssen: Consultancy, Research Funding. Riess:Janssen Scientific Affairs: Other: Travel reimbursement; Bayer: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Boehringer Ingelheim: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Daiichi Sankyo: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bristol-Myers Squibb: Speakers Bureau; Pfizer: Membership on an entity's Board of Directors or advisory committees. Wun:Eli Lilly, Inc.: Research Funding; Emmaus, Inc.: Membership on an entity's Board of Directors or advisory committees; Glycomimetics, Inc.: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Pfizer, Inc.: Membership on an entity's Board of Directors or advisory committees. Streiff:Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bayer: Consultancy, Membership on an entity's Board of Directors or advisory committees; CSL Behring: Other: outcome adjudication committee; Daiichi Sankyo: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Boehringer Ingelheim: Research Funding; Roche: Research Funding; Portola: Consultancy, Research Funding. Garcia:Shingoi: Consultancy; Portola: Research Funding; Pfizer: Consultancy; Janssen: Consultancy, Research Funding; Incyte: Research Funding; Daiichi Sankyo: Research Funding; Bristol Meyers Squibb: Consultancy; Boehringer Ingelheim: Consultancy; Retham Technologies LLC: Consultancy; Genzyme Corporation: Consultancy; Alexion: Consultancy. Liebman:Janssen (Johnson & Johnson): Other: steering committee of the CASSINI trial during the conduct of the study. Belani:Janssen: Consultancy. O'Reilly:3DMed, Agios, AlignMed, Amgen, Antengene, Aptus, ASLAN, Astellas, AstraZeneca, Bayer, BeiGene, Bioline, BMS, Boston Scientific, Bridgebio, CARsgen, Celgene, CASI, Cipla, CytomX, Daiichi, Debio, Delcath, Eisai, Exelixis, Genoscience, Gilead, Halozyme: Consultancy; Hengrui, Incyte, Inovio, Ipsen, Jazz, Janssen, Kyowa Kirin, LAM, Lilly, Loxo, Merck, Mina, NewLink Genetics, Novella, Onxeo, PCI Biotech, Pfizer, PharmaCyte, Pharmacyclics, Pieris, QED, RedHill, Sanofi, Servier, Silenseed, Sillajen, Sobi, Targovax: Consultancy; Janssen: Research Funding; Acta Biologica, Agios, Array, AstraZeneca, Bayer, BeiGene, BMS, CASI, Celgene, Exelixis, Genentech, Halozyme, Incyte, Lilly, MabVax, Novartis, OncoQuest, Polaris Puma, QED, and Roche: Research Funding; Tekmira, twoXAR, Vicus, Yakult, and Yiviva: Consultancy. Patel:Janssen Pharmaceuticals: Research Funding. Yimer:Seattle Genetics: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees; Janssen: Speakers Bureau; AstraZeneca: Speakers Bureau; Epizyme: Equity Ownership; Clovis Oncology: Equity Ownership; Puma Biotechnology: Equity Ownership. Wildgoose:Janssen Scientific Affairs: Employment; Johnson & Johnson: Equity Ownership. Burton:Janssen Pharmaceuticals: Employment; Johnson & Johnson: Equity Ownership. Vijapurkar:Janssen Pharmaceuticals, Inc.: Employment; Johnson & Johnson: Equity Ownership. Kaul:Janssen Pharmaceuticals, Inc.: Employment; Johnson & Johnson: Equity Ownership. Eikelboom:AstraZeneca, Bayer, Boehringer Ingelheim, Bristol-Myers Squibb/Pfizer, Daiichi Sankyo, GlaxoSmithKline, Janssen, sanofi-aventis, and Eli Lilly: Honoraria, Research Funding; Heart and Stroke Foundation: Other: Personnel award. Bauer:Janssen: Consultancy. Kuderer:Celldex: Consultancy; Mylan: Consultancy, Other: Travel, Accommodations, Expenses; Myriad Genetics: Consultancy; Halozyme: Consultancy; Coherus Biosciences: Consultancy, Other: Travel, Accommodations, Expenses; Janssen Scientific Affairs, LLC: Consultancy, Other: Travel, Accommodations, Expenses; Pfizer: Consultancy; Bayer: Consultancy. Lyman:Halozyme; G1 Therapeutics; Coherus Biosciences: Consultancy; Generex Biotechnology: Membership on an entity's Board of Directors or advisory committees; Amgen: Other: Research support.

AbstractVenous thromboembolism (VTE) is a frequent complication of cancer associated with morbidity, mortality, increased hospitalizations and higher health care costs. Cancer patients at increased risk for VTE can be identified using a validated risk assessment score, and the incidence of VTE can be reduced in high-risk settings using anticoagulation. Rivaroxaban is a potent, oral, direct, factor Xa inhibitor approved for the prevention and treatment of thromboembolic events, including VTE. CASSINI is a double-blind, randomized, parallel-group, multicentre study comparing rivaroxaban with placebo in adult ambulatory patients with various cancers who are initiating systemic cancer therapy and are at high risk of VTE (Khorana score ≥ 2). Patients with primary brain tumours or those at risk for bleeding are excluded. Approximately 700 patients will be randomized 1:1 to rivaroxaban 10 mg daily or placebo for up to 6 months if there is no evidence of VTE from compression ultrasonography (CU) during screening or from routine care imaging within 30 days prior to randomization. Mandatory CU will also be performed at weeks 8 and 16 (±7 days), and at study end (±3 days). The primary efficacy hypothesis is that anticoagulation with rivaroxaban reduces the composite of objectively confirmed symptomatic or asymptomatic, lower-extremity, proximal deep-vein thrombosis (DVT); symptomatic, upper-extremity DVT; symptomatic or incidental pulmonary embolism; and VTE-related death compared with placebo. The primary safety objective is to assess major bleeding events (Clinical trial information: NCT02555878).