Andexanet reversed the anticoagulant activity of apixaban and rivaroxaban in older healthy participants within minutes after administration and for the duration of infusion, without evidence of clinical toxic effects. (Funded by Portola Pharmaceuticals and others; ANNEXA-A and ANNEXA-R ClinicalTrials.gov numbers, NCT02207725 and NCT02220725.).

Among acutely ill medical patients with an elevated d-dimer level, there was no significant difference between extended-duration betrixaban and a standard regimen of enoxaparin in the prespecified primary efficacy outcome. However, prespecified exploratory analyses provided evidence suggesting a benefit for betrixaban in the two larger cohorts. (Funded by Portola Pharmaceuticals; APEX ClinicalTrials.gov number, NCT01583218.).

BACKGROUND Andexanet alfa (andexanet) is a recombinant modified human factor Xa decoy protein that has been shown to reverse the inhibition of factor Xa in healthy volunteers. METHODS In this multicenter, prospective, open-label, single-group study, we evaluated 67 patients who had acute major bleeding within 18 hours after the administration of a factor Xa inhibitor. The patients all received a bolus of andexanet followed by a 2-hour infusion of the drug. Patients were evaluated for changes in measures of anti–factor Xa activity and were assessed for clinical hemostatic efficacy during a 12-hour period. All the patients were subsequently followed for 30 days. The efficacy population of 47 patients had a baseline value for anti–factor Xa activity of at least 75 ng per milliliter (or ≥0.5 IU per milliliter for those receiving enoxaparin) and had confirmed bleeding severity at adjudication. RESULTS The mean age of the patients was 77 years; most of the patients had substantial cardiovascular disease. Bleeding was predominantly gastrointestinal or intracranial. The mean (±SD) time from emergency department presentation to the administration of the andexanet bolus was 4.8±1.8 hours. After the bolus administration, the median anti– factor Xa activity decreased by 89% (95% confidence interval [CI], 58 to 94) from baseline among patients receiving rivaroxaban and by 93% (95% CI, 87 to 94) among patients receiving apixaban. These levels remained similar during the 2-hour infusion. Four hours after the end of the infusion, there was a relative decrease from baseline of 39% in the measure of anti–factor Xa activity among patients receiving rivaroxaban and of 30% among those receiving apixaban. Twelve hours after the andexanet infusion, clinical hemostasis was adjudicated as excellent or good in 37 of 47 patients in the efficacy analysis (79%; 95% CI, 64 to 89). Thrombotic events occurred in 12 of 67 patients (18%) during the 30-day follow-up. CONCLUSIONS On the basis of a descriptive preliminary analysis, an initial bolus and subsequent 2-hour infusion of andexanet substantially reduced anti–factor Xa activity in patients with acute major bleeding associated with factor Xa inhibitors, with effective hemostasis occurring in 79%. (Funded by Portola Pharmaceuticals; ANNEXA-4 ClinicalTrials.gov number, NCT02329327.)

Background The IMPROVE score is a validated venous thromboembolism (VTE) assessment tool to risk stratify hospitalized, medically ill patients based on clinical variables. It was hypothesized that addition of D-dimer measurement to derive a new IMPROVEDD score would improve identification of at risk of VTE. Methods The association of the IMPROVE score and D-dimer ≥ 2 × the upper limit of normal (ULN) with the risk of symptomatic deep vein thrombosis, nonfatal pulmonary embolism, or VTE-related death was evaluated in 7,441 hospitalized, medically ill patients randomized in the APEX trial. Based on the Cox regression analysis, the IMPROVEDD score was derived by adding two points to the IMPROVE score if the D-dimer was ≥ 2 × ULN. Results Baseline D-dimer was independently associated with symptomatic VTE through 77 days (adjusted HR: 2.22 [95% CI: 1.38–1.58], p = 0.001). Incorporation of D-dimer into the IMPROVE score improved VTE risk discrimination (ΔAUC: 0.06 [95% CI: 0.02–0.09], p = 0.0006) and reclassification (continuous NRI: 0.34 [95% CI: 0.17–0.51], p = 0.001; categorical NRI: 0.13 [95% CI: 0.03–0.23], p = 0.0159). Patients with an IMPROVEDD score of ≥2 had a greater VTE risk compared with those with an IMPROVEDD score of 0 to 1 (HR: 2.73 [95% CI: 1.52–4.90], p = 0.0007). Conclusion Incorporation of D-dimer into the IMPROVE VTE risk assessment model further improves risk stratification in hospitalized, medically ill patients who received thromboprophylaxis. An IMPROVEDD score of ≥2 identifies hospitalized, medically ill patients with a heightened risk for VTE through 77 days.

Background: The high cardiovascular morbidity and mortality in patients with end-stage kidney disease could be partially caused by extensive cardiovascular calcification. SNF472, intravenous myo-inositol hexaphosphate, selectively inhibits the formation and growth of hydroxyapatite. Methods: This double-blind, placebo-controlled phase 2b trial compared progression of coronary artery calcium volume score and other measurements of cardiovascular calcification by computed tomography scan during 52 weeks of treatment with SNF472 or placebo, in addition to standard therapy, in adult patients with end-stage kidney disease receiving hemodialysis. Patients were randomized 1:1:1 to SNF472 300 mg (n=92), SNF472 600 mg (n=91), or placebo (n=91) by infusion in the hemodialysis lines thrice weekly during hemodialysis sessions. The primary end point was change in log coronary artery calcium volume score from baseline to week 52. The primary efficacy analysis combined the SNF472 treatment groups and included all patients who received at least 1 dose of SNF472 or placebo and had an evaluable computed tomography scan after randomization. Results: The mean change in coronary artery calcium volume score was 11% (95% CI, 7–15) for the combined SNF472 dose group and 20% (95% CI, 14–26) for the placebo group ( P =0.016). SNF472 compared with placebo attenuated progression of calcium volume score in the aortic valve (14% [95% CI, 5–24] versus 98% [95% CI, 77–123]; P <0.001) but not in the thoracic aorta (23% [95% CI, 16–30] versus 28% [95% CI, 19–38]; P =0.40). Death occurred in 7 patients (4%) who received SNF472 and 5 patients (6%) who received placebo. At least 1 treatment-emergent adverse event occurred in 86%, 92%, and 87% of patients treated with SNF472 300 mg, SNF472 600 mg, and placebo, respectively. Most adverse events were mild. Adverse events resulted in discontinuation of SNF472 300 mg, SNF472 600 mg, and placebo for 14%, 29%, and 20% of patients, respectively. Conclusions: Compared with placebo, SNF472 significantly attenuated the progression of coronary artery calcium and aortic valve calcification in patients with end-stage kidney disease receiving hemodialysis in addition to standard care. Future studies are needed to determine the effects of SNF472 on cardiovascular events. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02966028.

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Importance Accurate, real-time case identification is needed to target interventions to improve quality and outcomes for hospitalized patients with heart failure. Problem lists may be useful for case identification, but are often inaccurate or incomplete. Machine learning approaches may improve accuracy of identification but can be limited by complexity of implementation. Objective To develop algorithms that use readily available clinical data to identify heart failure patients while in the hospital. Design, Setting, and Participants We performed a retrospective study of hospitalizations at an academic medical center. Hospitalizations for patients≥18 years who were admitted after January 1, 2013 and discharged prior to February 28, 2015 were included. From a random 75% sample of hospitalizations, we developed five algorithms for heart failure identification using electronic health record (EHR) data: 1) heart failure on problem list; 2) presence of at least one of three characteristics: heart failure on problem list, inpatient loop diuretic, or brain natriuretic peptide≥500 pg/ml; 3) logistic regression of 30 clinically relevant structured data elements; 4) machine learning approach using unstructured notes; 5) machine learning approach using both structured and unstructured data. Main Outcome and Measure Heart failure diagnosis, based on discharge diagnosis and physician review of sampled charts. Results Of 47,119 included hospitalizations, 6,549 (13.9%) had a discharge diagnosis of heart failure. Inclusion of heart failure on the problem list (algorithm 1) had a sensitivity of 0.40 and positive predictive value (PPV) of 0.96 for heart failure identification. Algorithm 2 improved sensitivity to 0.77 at the expense of PPV of 0.64. Algorithms 3, 4, and 5 had areas under the receiver operating curves (AUCs) of 0.953, 0.969, and 0.974, respectively. With PPV of 0.9, these algorithms had associated sensitivities of 0.68, 0.77, and 0.83, respectively. Conclusion and Relevance The problem list is insufficient for real-time identification of hospitalized patients with heart failure. The high predictive accuracy of machine learning using free text demonstrates that support of such analytics in future EHR systems can improve cohort identification.

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