Publications by authors named "A Dubrawski"

Article Synopsis
  • - The study aimed to evaluate a closed-loop resuscitation algorithm called ReFit1 and ReFit2, which uses various hemodynamic parameters to effectively manage severe hemorrhagic shock in a pig model.
  • - The ReFit algorithm determines the need for fluids and medications based on real-time monitoring of vital signs, such as mean arterial pressure and mixed venous oxygen saturation, to drive automated treatments.
  • - Results showed that the time to stabilize the pigs using these algorithms was comparable to traditional methods used by expert clinicians, with similar treatment volumes, and ReFit1 also successfully addressed complications like acute air embolism in some animals.
View Article and Find Full Text PDF

We tested the ability of a physiologically driven minimally invasive closed-loop algorithm, called Resuscitation based on Functional Hemodynamic Monitoring (ReFit), to stabilize for up to 3 h a porcine model of noncompressible hemorrhage induced by severe liver injury and do so during both ground and air transport. Twelve animals were resuscitated using ReFit to drive fluid and vasopressor infusion to a mean arterial pressure (MAP) > 60 mmHg and heart rate < 110 min 30 min after MAP < 40 mmHg following liver injury. ReFit was initially validated in 8 animals in the laboratory, then in 4 animals during air (23nm and 35nm) and ground (9 mi) to air (9.

View Article and Find Full Text PDF

Forecasting healthcare time series data is vital for early detection of adverse outcomes and patient monitoring. However, forecasting is challenging in practice due to variable medication administration and unique pharmacokinetic (PK) properties for each patient. To address these challenges, we propose a novel hybrid global-local architecture and a PK encoder that informs deep learning models of patient-specific treatment effects.

View Article and Find Full Text PDF

Background: Despite the morbidity associated with acute atrial fibrillation (AF), no models currently exist to forecast its imminent onset. We sought to evaluate the ability of deep learning to forecast the imminent onset of AF with sufficient lead time, which has important implications for inpatient care.

Methods: We utilized the Physiobank Long-Term AF Database, which contains 24-h, labeled ECG recordings from patients with a history of AF.

View Article and Find Full Text PDF

Background: Healthcare-associated bacterial pathogens frequently carry plasmids that contribute to antibiotic resistance and virulence. The horizontal transfer of plasmids in healthcare settings has been previously documented, but genomic and epidemiologic methods to study this phenomenon remain underdeveloped. The objectives of this study were to apply whole-genome sequencing to systematically resolve and track plasmids carried by nosocomial pathogens in a single hospital, and to identify epidemiologic links that indicated likely horizontal plasmid transfer.

View Article and Find Full Text PDF