Sridhar, Haarini

AbstractBackgroundThe 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.HypothesisWe describe the temporal trends in frequency of various arrhythmias and assess impact of arrhythmias on hospitalized HIV patients using the Nationwide Inpatient Sample (NIS).MethodsData 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.ResultsAmong 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.ConclusionsArrhythmias are associated with significant morbidity and mortality in hospitalized HIV patients. AF is the most frequent 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. 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 and Results: Data on HIV-related hospitalizations from 2005 to 2014 were obtained from the NIS using International Classification of Diseases, 9th Revision (ICD-9) codes. Data was further subclassified into hospitalizations with associated arrhythmias and those without arrhythmia. 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, North Carolina) was utilized for analysis. A multivariable analysis was performed to identify predictors of arrhythmias among hospitalized HIV patients. 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 has increased over time by 108%, primarily due to a 132% increase in AF. Arrhythmias are more frequent among older males, lowest income quartile and non-elective admissions. Patients with arrhythmias had a higher in-hospital mortality rate (9.6%). In-hospital mortality among patients with arrhythmias has 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.

AbstractBackgroundThe 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.PurposeWe sought to devise a new WCT differentiation method that operates solely on automated measurements routinely provided by computerized ECG interpretation software.MethodsIn 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.ResultsThe 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.ConclusionThe 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.