A step towards the application of an artificial intelligence model in the prediction of intradialytic complications. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Elbasha, Ahmed Mustafa; Naga, Yasmine Salah; Othman, Mai; Moussa, Nancy Diaa; Elwakil, Hala Sadik
• Is Part Of: Alexandria journal of medicine. Volume 58:Issue 1(2022)
• Page Start: 18
• Page End: 30
• Publication Date: 2022-12-31
• Subjects: Hemodialysis -- intradialytic complications -- artificial intelligence -- COVID 19; Medicine -- Periodicals; 610.5
• DOI: 10.1080/20905068.2021.2024349
• Languages: English
• ISSNs: 2090-5068

Abstract

ABSTRACT: Introduction: Acute intradialytic complications remain a major burden in end stage renal disease (ESRD) patients on hemodialysis (HD). They often lead to early termination of the HD session affecting dialysis adequacy and patient overall health. The aim of the study was to create an artificial intelligence model and to assess its performance in the prediction of the occurrence of intradialytic clinical events. Methods: We studied 6000 HD sessions performed for 215 ESRD patients, recording many predictors that included: patient, machine, and environmental factors. These data were collected within 24 weeks, including 12 weeks in the COVID 19 era and were used to develop and train an artificial neural network model (ANN) to predict the occurrence of intradialytic clinical events such as: hypotension, headache, hypertension, cramps, chest pain, nausea, vomiting, and dyspnea. Findings: Our ANN model showed mean precision and recall of 96% and AUC of 99.3% in binary ANN to predict occurrence of an intradialytic complication (event or no event), while the accuracy of the categorical ANN in predicting the type of event was 82%. We found that heart rate changes, mean systolic pressure, ultrafiltration rate, dialyzate sodium, meal, urea reduction ratio, room humidity and dialysis session duration most strongly influence occurrence of an intradialytic complication. Discussion: Our ANN model can be used to predict the risk of intradialytic clinical events among HD patients and can support decision-making for healthcare in the frequently under-staffed dialysis units, especially in COVID 19 era.