Publications by authors named "Hani Almahariq"
Purpose: Intraoperative hypotension is linked to increased incidence of perioperative adverse events such as myocardial and cerebrovascular infarction and acute kidney injury. Hypotension prediction index (HPI) is a novel machine learning guided algorithm which can predict hypotensive events using high fidelity analysis of pulse-wave contour. Goal of this trial is to determine whether use of HPI can reduce the number and duration of hypotensive events in patients undergoing major thoracic procedures.
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- * Methods: Researchers conducted a retrospective analysis over 11 months in a Croatian COVID-19 hospital, examining clinical data and using regression analysis to assess the predictive values of inflammatory biomarkers.
- * Results: More than half (55.3%) of the patients developed superinfections, primarily in the lower respiratory tract. Multidrug-resistant pathogens were prevalent and linked to higher mortality and extended ICU stays, with elevated ferritin and neutrophil counts indicating reduced survival chances.
Aim: To describe epidemiological characteristics and baseline clinical features, laboratory findings at intensive care unit (ICU) admission, and survival rates of critically ill coronavirus disease 2019 (COVID-19) patients treated at a tertiary institution specialized for COVID-19 patients.
Methods: This retrospective study recruited 692 patients (67.1% men).
Background: Survival rates of critically ill COVID-19 patients are affected by various clinical features and laboratory parameters at ICU admission. Some of these predictors are universal but others may be population specific.
Objective: To determine utility of baseline clinical and laboratory parameters in a multivariate regression model to predict outcomes in critically ill COVID-19 patients in a tertiary hospital in Croatia.