Abstract
Background and objectives: 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. Materials and methods: 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). Results: 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. Conclusions: 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.
Highlights
Cardiovascular complications (CVC) are the leading cause of death in patients with chronic kidney disease (CKD) [1]
Original research articles were included if they evaluated computational methods such as artificial intelligence (AI)/ML (random forests (RF), decision trees (DT), support vector machines (SVM), neural networks, K-nearest neighbor, and any other ML) or regression-based models to predict cardiovas
Our electronic search, which was last updated on 12 February 12, 2021, retrieved 524 records (MEDLINE/Pubmed (265), ScienceDirect (196), Embase (62), and other sources (1 ongoing clinical trial))
Summary
Cardiovascular complications (CVC) are the leading cause of death in patients with chronic kidney disease (CKD) [1]. Individuals with CKD are most likely to die from CVC, regardless of the degree of renal failure [2]. CKD appears to be a risk factor for CVC since kidney disease accelerates CVC development through uremic and non-uremic mechanisms [3]. CVC can contribute to renal failure progression, constituting an authentic vicious cycle [4,5]. Preventing CVC in CKD patients could significantly reduce mortality and delay disease progression by breaking this vicious circle [6]. Developing more robust strategies for preventing CVC development in CKD is much needed and would constitute a significant breakthrough. When “traditional” prevention measures reach their climax, the door to computational solutions such as artificial intelligence (AI) and regression-based methods seems to open [9]
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