Publications by authors named "A N Tintu"
Objectives: This study aimed to evaluate discrepancies in potassium measurements between point-of-care testing (POCT) and central laboratory (CL) methods, focusing on the impact of hemolysis on these measurements and its impact in the clinical practice in the emergency department (ED).
Methods: A retrospective analysis was conducted using data from three European university hospitals: Technische Universitat München (Germany), Hospital Universitario La Paz (Spain), and Erasmus University Medical Center (The Netherlands). The study compared POCT potassium measurements in EDs with CL measurements.
Article Synopsis
- * A systematic review was undertaken, examining multiple studies to evaluate the diagnostic accuracy of POC devices against conventional laboratory-based bilirubin quantification specifically in infants aged 0 to 28 days.
- * The findings of the review indicate that the POC devices showed varying degrees of agreement with laboratory results, analyzing factors like turnaround time, blood volume needed, and instances of failed quantifications across ten included studies involving over 3,000 infants.
Background: The rising level of laboratory automation provides an increasing number of logged events that can be used for the characterization of laboratory performance and process improvements. This abundance of data is often underutilized for improving laboratory efficiency.
Objectives: The first aim of this descriptive study is to provide a structured approach for transforming raw laboratory data to data that is suitable for process mining.
Objectives: Turnaround time (TAT) is an essential performance indicator of a medical diagnostic laboratory. Accurate TAT prediction is crucial for taking timely action in case of prolonged TAT and is important for efficient organization of healthcare. The objective was to develop a model to accurately predict TAT, focusing on the automated pre-analytical and analytical phase.
View Article and Find Full Text PDFBackground: Timely identification of deteriorating COVID-19 patients is needed to guide changes in clinical management and admission to intensive care units (ICUs). There is significant concern that widely used Early warning scores (EWSs) underestimate illness severity in COVID-19 patients and therefore, we developed an early warning model specifically for COVID-19 patients.
Methods: We retrospectively collected electronic medical record data to extract predictors and used these to fit a random forest model.