Stable Ventricular Fibrillation: A Paradigm Rather Than Septal Shift?

Awake patients in ventricular fibrillation is a phenomenon limited to patients who are mechanically supported. We describe a cohort of patients supported by left ventricular assist devices (LVADs) presenting to the emergency department (ED) at a high-volume LVAD center while in awake ventricular fibrillation (VF)/ventricular tachycardia (VT). Among 175 patients reviewed, a total of 19 LVAD patients presented to the ED in awake VF/VT between December 2015 and July 2021. On ED presentation, patients maintained a median mean arterial blood pressure (MAP) of 70 mm Hg with a mean LVAD flow of 3.77 L/minute. ED management included cardioversion in the majority of cases: 58% were defibrillated once, 21% were defibrillated multiple times, 68% received amiodarone, and 21% received lidocaine. Inpatient management included defibrillation, ablation, and antiarrhythmic initiation in 37%, 11%, and 84% of cases, respectively. In total, five patients (26%) died with one death attributed to recurrent VT. Our findings support the short-term tolerability of sustained ventricular arrhythmias in LVAD patients, as evidenced by the maintained MAPs and mental status. Clinical teams, however, should be aware of the potential harbinger for in-hospital mortality heralded by an awake VF/VT presentation.

A systematic review and evaluation of Zika virus forecasting and prediction research during a public health emergency of international concern.

INTRODUCTION: Epidemic forecasting and prediction tools have the potential to provide actionable information in the midst of emerging epidemics. While numerous predictive studies were published during the 2016-2017 Zika Virus (ZIKV) pandemic, it remains unknown how timely, reproducible, and actionable the information produced by these studies was. METHODS: To improve the functional use of mathematical modeling in support of future infectious disease outbreaks, we conducted a systematic review of all ZIKV prediction studies published during the recent ZIKV pandemic using the PRISMA guidelines. Using MEDLINE, EMBASE, and grey literature review, we identified studies that forecasted, predicted, or simulated ecological or epidemiological phenomena related to the Zika pandemic that were published as of March 01, 2017. Eligible studies underwent evaluation of objectives, data sources, methods, timeliness, reproducibility, accessibility, and clarity by independent reviewers. RESULTS: 2034 studies were identified, of which n = 73 met the eligibility criteria. Spatial spread, R0 (basic reproductive number), and epidemic dynamics were most commonly predicted, with few studies predicting Guillain-Barré Syndrome burden (4%), sexual transmission risk (4%), and intervention impact (4%). Most studies specifically examined populations in the Americas (52%), with few African-specific studies (4%). Case count (67%), vector (41%), and demographic data (37%) were the most common data sources. Real-time internet data and pathogen genomic information were used in 7% and 0% of studies, respectively, and social science and behavioral data were typically absent in modeling efforts. Deterministic models were favored over stochastic approaches. Forty percent of studies made model data entirely available, 29% provided all relevant model code, 43% presented uncertainty in all predictions, and 54% provided sufficient methodological detail to allow complete reproducibility. Fifty-one percent of predictions were published after the epidemic peak in the Americas. While the use of preprints improved the accessibility of ZIKV predictions by a median of 119 days sooner than journal publication dates, they were used in only 30% of studies. CONCLUSIONS: Many ZIKV predictions were published during the 2016-2017 pandemic. The accessibility, reproducibility, timeliness, and incorporation of uncertainty in these published predictions varied and indicates there is substantial room for improvement. To enhance the utility of analytical tools for outbreak response it is essential to improve the sharing of model data, code, and preprints for future outbreaks, epidemics, and pandemics.