Outcomes of Direct Oral Anticoagulants Co-Prescribed with Common Interacting Medications.

Direct oral anticoagulants (DOACs) can potentially interact with multiple prescription medications. We examined the prevalence of co-prescription of DOACs with interacting medications and its impact on outcomes in patients with atrial fibrillation (AF). Patients with AF treated with a DOAC from 2010 to 2017 at the Mayo Clinic and co-prescribed medications that are inhibitors or inducers of the P-glycoprotein and/or Cytochrome P450 3A4 pathways were identified. The outcomes of stroke, transient ischemic attack, or systemic embolism, major bleeding, and minor bleeds were compared between patients with and without an enzyme inducer. Cox proportional hazards model was used to assess the association between interacting medications and outcomes. Of 8,576 patients with AF (mean age 70 ± 12 years, 35% female) prescribed a DOAC (38.6% apixaban, 35.8% rivaroxaban, 25.6% dabigatran), 2,610 (30.4%) were on at least 1 interacting agent: the majority were on an enzyme inhibitor (n = 2,592). Prescribed medications included non-dihydropyridine calcium channel blocker (n = 1,412; 16.5%), antiarrhythmic medication (n = 790; 9.2%), antidepressant (n = 659; 7.7%), antibiotic/antifungal (n = 77; 0.90%), antiepileptics (n = 17; 0.2%) and immunosuppressant medications (n = 19; 0.2%). Patients on an interacting medication were more likely to receive a lower dose of DOAC than indicated by the manufacturer's labeling (15.0% vs 11.4%, p <0.0001). In multivariable analysis, co-prescription of an enzyme inhibitor was not associated with risk of any bleeding (hazard ratio 0.87 [0.71 to 1.05], p = 0.15) or stroke, transient ischemic attack, or systemic embolism (hazard ratio 0.82 [0.51 to 1.31], p = 0.39). In conclusion, DOACs are co-prescribed with medications with potential interactions in 30.4% of patients with AF. Co-prescription of DOACs and these drugs are not associated with increased risk of adverse embolic or bleeding outcomes in our cohort.

Inappropriate Dosing of Direct Oral Anticoagulants in Patients with Atrial Fibrillation.

Direct Oral Anticoagulants (DOACs) require dose adjustment based on specific patient characteristics, making them prone to incorrect dosing. The current study aimed to evaluate the prevalence of inappropriate DOAC dosing, its predictors, and corresponding outcomes in a single-center cohort of atrial fibrillation (AF) patients. We reviewed all patients with AF treated at Mayo Clinic with a DOAC (Apixaban, Rivaroxaban, or Dabigatran) between 2010 and 2017. Outcomes examined were ischemic stroke /transient ischemic attack (TIA)/embolism and bleeding. 8,576 patients (mean age 69.5 ± 11.9 years, 35.1 % female, CHA2DS2-VASc 3.0±1.8) received a DOAC (38.6% apixaban, 35.8% rivaroxaban, 25.6% dabigatran). DOAC dosing was inappropriate in 1,273 (14.8%) with 1071 (12.4%) receiving an inappropriately low dose, and 202(2.4%) an inappropriately high dose. Patients prescribed inappropriate doses were older (72.4 ± 11.7 vs 69.0 ± 11.8, p <0.0001), more likely to be female (43.1% vs 33.7%, p <0.0001), had a higher CHA2DS2-VASc score (3.4 ± 1.8 vs 2.9 ± 1.8, p <0.0001) and a greater Charlson co-morbidity index (3.5 ± 3.3 vs 2.9 ± 3.2, p<0.0001). Over 1.2 ±1.6 years (median 0.5 years) follow up; there was no significant difference in the incidence of stroke and/or TIA and/or embolism and bleeding between patients who were inappropriately dosed versus appropriately dosed. In conclusion, DOAC dosing was not in compliance with current recommendations in 15% of AF patients. Patients at higher risk of stroke and/or TIA based on older age, female gender, and higher CHA2DS2-VASc score were more likely to be underdosed, but there was no significant difference in outcomes including stroke/TIA/embolism and bleeding.

Burden of arrhythmia in hospitalized HIV patients.

BACKGROUND: The improved life expectancy observed in patients living with human immunodeficiency virus (HIV) infection has made age-related cardiovascular complications, including arrhythmias, a growing health concern. HYPOTHESIS: We describe the temporal trends in frequency of various arrhythmias and assess impact of arrhythmias on hospitalized HIV patients using the Nationwide Inpatient Sample (NIS). METHODS: Data on HIV-related hospitalizations from 2005 to 2014 were obtained from the NIS database using International Classification of Diseases, 9th Revision (ICD-9) codes. Data was further subclassified into hospitalizations with associated arrhythmias and those without. Baseline demographics and comorbidities were determined. Outcomes including in-hospital mortality, cost of care, and length of stay were extracted. SAS 9.4 (SAS Institute Inc., Cary, NC) was utilized for analysis. A multivariable analysis was performed to identify predictors of arrhythmias among hospitalized HIV patients. RESULTS: Among 2 370 751 HIV-related hospitalizations identified, the overall frequency of any arrhythmia was 3.01%. Atrial fibrillation (AF) was the most frequent arrhythmia (2110 per 100 000). The overall frequency of arrhythmias increased over time by 108%, primarily due to a 132% increase in AF. Arrhythmias are more frequent among older males, lowest income quartile, and nonelective admissions. Patients with arrhythmias had a higher in-hospital mortality rate (9.6%). In-hospital mortality among patients with arrhythmias decreased over time by 43.8%. The cost of care and length of stay associated with arrhythmia-related hospitalizations were mostly unchanged. CONCLUSIONS: Arrhythmias are associated with significant morbidity and mortality in hospitalized HIV patients. AF is the most frequent arrhythmia in hospitalized HIV patients.

The VT Prediction Model: A simplified means to differentiate wide complex tachycardias.

BACKGROUND: The accurate separation of undifferentiated wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) using conventional, manually-applied 12-lead electrocardiogram (ECG) interpretation methods is difficult. PURPOSE: We sought to devise a new WCT differentiation method that operates solely on automated measurements routinely provided by computerized ECG interpretation software. METHODS: In a two-part analysis, we developed and validated a logistic regression model (ie, VT Prediction Model) that utilizes routinely available computerized measurements derived from patients' paired WCT and baseline ECGs. RESULTS: The derivation cohort consisted of 601 paired WCT (273 VT, 328 SWCT) and baseline ECGs from 421 patients. The VT Prediction Model, composed of WCT QRS duration (ms) (P < .0001), QRS duration change (ms) (P < .0001), QRS axis change (°) (P < .0001) and T axis change (°) (P < .0001), yielded effective VT and SWCT differentiation (area under the curve [AUC]: 0.924; confidence interval [CI]: 0.903-0.944) for the derivation cohort. The validation cohort comprised 241 paired WCT (97 VT, 144 SWCT) and baseline ECGs from 177 patients. The VT Prediction Model's implementation on the validation cohort yielded effective WCT differentiation (AUC: 0.900; CI: 0.862-0.939) with overall accuracy, sensitivity, and specificity of 85.0%, 80.4%, and 88.2%, respectively. CONCLUSION: The VT Prediction Model is an example of how readily available ECG measurements may be used to distinguish VT and SWCT effectively. Further study is needed to develop and refine newer WCT differentiation approaches that utilize computerized measurements provided by ECG interpretation software.