4 results match your criteria: "University of Arizona Medical Center Phoenix[Affiliation]"
Article Synopsis
- Early identification of risk factors for prolonged mechanical ventilation (PMV) can lead to timely clinical interventions and reduce complications like infections, especially in the context of COVID-19.
- This study utilized ensemble machine learning (ML) to analyze clinical data at the time of intubation to distinguish between patients at high risk for PMV (more than 14 days) and those not at risk (14 days or less).
- The ML approach demonstrated strong predictive performance, highlighting key clinical markers like glucose levels and platelet counts that can inform patient management and optimize hospital resource allocation.
Pulmonary Fibrosis Diagnosis and Disease Progression Detected Via Hair Metabolome Analysis.
Lung
October 2024
Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40292, USA.
Background: Fibrotic interstitial lung disease is often identified late due to non-specific symptoms, inadequate access to specialist care, and clinical unawareness precluding proper and timely treatment. Biopsy histological analysis is definitive but rarely performed due to its invasiveness. Diagnosis typically relies on high-resolution computed tomography, while disease progression is evaluated via frequent pulmonary function testing.
View Article and Find Full Text PDFIdentification of Idiopathic Pulmonary Fibrosis and Prediction of Disease Severity via Machine Learning Analysis of Comprehensive Metabolic Panel and Complete Blood Count Data.
Lung
April 2024
Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40292, USA.
Background: Diagnosis of idiopathic pulmonary fibrosis (IPF) typically relies on high-resolution computed tomography imaging (HRCT) or histopathology, while monitoring disease severity is done via frequent pulmonary function testing (PFT). More reliable and convenient methods of diagnosing fibrotic interstitial lung disease (ILD) type and monitoring severity would allow for early identification and enhance current therapeutic interventions. This study tested the hypothesis that a machine learning (ML) ensemble analysis of comprehensive metabolic panel (CMP) and complete blood count (CBC) data can accurately distinguish IPF from connective tissue disease ILD (CTD-ILD) and predict disease severity as seen with PFT.
View Article and Find Full Text PDFMechanical circulatory support for cardiac amyloidosis.
Clin Transplant
October 2019
Cedars-Sinai Medical Center, Smidt Heart Institute, Los Angeles, California.
Background: Cardiac amyloidosis, typically from abnormal deposition of AL or ATTR amyloid protein, can result in heart failure requiring transplantation (HTx). The role of mechanical circulatory support (MCS) is not well-established. The purpose of this study was to present our experience with MCS in patients with cardiac amyloidosis.
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