Validation of Various Prediction Scores for Cardiac Surgery-Associated Acute Kidney Injury.

Background And Objectives: Following cardiac surgery, acute kidney injury (AKI) is a well-known complication that increases morbidity and mortality. This study was carried out to determine the factors associated with acute kidney injury and to assess the predictive value of three predictive scores for the development of AKI post-cardiac surgery in the Saudi community.

Methods: In this retrospective study, the medical records of patients aged 18 years and above who underwent cardiac surgery on cardiopulmonary bypass (CPB) at Saud Albabtin Cardiac Center between January 2018 and March 2021 were reviewed.

Predicting Postoperative Length of Stay for Isolated Coronary Artery Bypass Graft Patients Using Machine Learning.

Purpose: Predictive analytics (PA) is a new trending approach in the field of healthcare that uses machine learning to build a prediction model using supervised learning algorithms. Isolated coronary artery bypass grafting (iCABG), an open-heart surgery, is commonly performed in the treatment of coronary heart disease.

Aim: The aim of this study was to develop and evaluate a model to predict postoperative length of stay (PLoS) for iCABG patients using supervised machine learning techniques, and to identify the features with the highest contribution to the model.

Missed appointments at a university hospital in eastern saudi arabia: magnitude and association factors.

Unlabelled: Missed appointment is a common problem in ambulatory settings that has serious clinical and economic impacts. The present study aims at quantifying the burden of missed appointments and revealing the factors associated with it at a teaching hospital in Eastern Saudi Arabia. Two designs were used: a retrospective descriptive design to describe the magnitude of missed appointments during the year 2007, and a case-control design to identify factors associated with missed appointment.