Computational Models Used to Predict Cardiovascular Complications in Chronic Kidney Disease Patients: A Systematic Review

• Published in: Medicina (Kaunas, Lithuania) Vol. 57; no. 6; p. 538
• Main Authors: Burlacu, Alexandru, Iftene, Adrian, Popa, Iolanda Valentina, Crisan-Dabija, Radu, Brinza, Crischentian, Covic, Adrian
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 27.05.2021; MDPI
• Subjects: Algorithms; Angina pectoris; Artificial Intelligence; Bias; Business metrics; Cardiac arrhythmia; cardiovascular complications; Cardiovascular disease; chronic kidney disease; Clinical trials; Computer Simulation; Datasets; Disease prevention; Heart attacks; Heart failure; Hemodialysis; Humans; Kidney diseases; Mortality; Population; predictive models; prevention; Prognosis; Renal Insufficiency, Chronic - complications; Risk factors; Systematic Review; Brazil; United States > US; Portugal; Spain; chronic kidney disease; predictive models; cardiovascular complications; artificial intelligence; prevention
• ISSN: 1648-9144; 1010-660X; 1648-9144
• DOI: 10.3390/medicina57060538

cardiovascular complications (CVC) are the leading cause of death in patients with chronic kidney disease (CKD). Standard cardiovascular disease risk prediction models used in the general population are not validated in patients with CKD. We aim to systematically review the up-to-date literature on reported outcomes of computational methods such as artificial intelligence (AI) or regression-based models to predict CVC in CKD patients. the electronic databases of MEDLINE/PubMed, EMBASE, and ScienceDirect were systematically searched. The risk of bias and reporting quality for each study were assessed against transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) and the prediction model risk of bias assessment tool (PROBAST). sixteen papers were included in the present systematic review: 15 non-randomized studies and 1 ongoing clinical trial. Twelve studies were found to perform AI or regression-based predictions of CVC in CKD, either through single or composite endpoints. Four studies have come up with computational solutions for other CV-related predictions in the CKD population. the identified studies represent palpable trends in areas of clinical promise with an encouraging present-day performance. However, there is a clear need for more extensive application of rigorous methodologies. Following the future prospective, randomized clinical trials, and thorough external validations, computational solutions will fill the gap in cardiovascular predictive tools for chronic kidney disease.