Publications by authors named "Ahmed N Balshi"
Article Synopsis
- - The study explored the use of the electronic poor outcome screening (ePOS) score to predict do-not-resuscitate (DNR) orders for critically ill patients in an ICU setting in Saudi Arabia.
- - Conducted with 857 patients, the results showed that an ePOS score above 17 effectively indicated the likelihood of DNR decisions, achieving high sensitivity (87.2%) and a solid area under the curve (81.8%).
- - The findings suggest that the ePOS score can aid healthcare professionals in making informed decisions about resuscitation efforts, potentially improving patient care outcomes.
Background: Rapid Response Teams were developed to provide interventions for deteriorating patients. Their activation depends on timely detection of deterioration. Automated calculation of warning scores may lead to early recognition, and improvement of RRT effectiveness.
View Article and Find Full Text PDFBackground: Practices of Do-Not-Resuscitate (DNR) orders show discrepancies worldwide, but there are only few such studies from Saudi Arabia.
Objective: To describe the practice of DNR orders in a Saudi Arabian tertiary care ICU.
Methods: This retrospective study included all patients who died with a DNR order at the ICU of King Saud Medical City, Riyadh, Saudi Arabia, between January 1 to December 31, 2021.
Objective: To study the impact of delayed admission by more than 4 hours on the outcomes of critically ill patients.
Methods: This was a retrospective observational study in which adult patients admitted directly from the emergency department to the intensive care unit were divided into two groups: Timely Admission if they were admitted within 4 hours and Delayed Admission if admission was delayed for more than 4 hours. Intensive care unit length of stay and hospital/intensive care unit mortality were compared between the groups.
Objective: To evaluate the hypothesis that the Modified Early Warning Score (MEWS) at the time of intensive care unit discharge is associated with readmission and to identify the MEWS that most reliably predicts intensive care unit readmission within 48 hours of discharge.
Methods: This was a retrospective observational study of the MEWSs of discharged patients from the intensive care unit. We compared the demographics, severity scores, critical illness characteristics, and MEWSs of readmitted and non-readmitted patients, identified factors associated with readmission in a logistic regression model, constructed a Receiver Operating Characteristic (ROC) curve of the MEWS in predicting the probability of readmission, and presented the optimum criterion with the highest sensitivity and specificity.