Key result
AI models predict cardiovascular complications in CKD with moderate-to-high accuracy, but most carry high bias risk.
Why the study?
Cardiovascular complications are the leading cause of death in CKD, but standard cardiovascular disease risk prediction models used in the general population are not validated in CKD patients.
Do computational models such as AI or regression-based models accurately predict cardiovascular complications in patients with chronic kidney disease?
Systematic Review
Yes
Do computational models such as AI or regression-based models accurately predict cardiovascular complications in patients with chronic kidney disease?
Computational models show encouraging performance for predicting cardiovascular complications in CKD patients, but require more rigorous methodology and external validation before clinical implementation.
Answers are grounded in this paper and cite their sources.
Bias in most studies warrants caution before clinical use; leaves open need for validated, low-bias models in CKD.
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.
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Burlacu et al. (2021) conducted a systematic review in Chronic kidney disease (CKD). Computational prediction models (artificial intelligence/machine learning and regression-based methods) was evaluated on AI/ML and regression-based predictions of cardiovascular complications in CKD through single or composite endpoints (MACE, sudden cardiac death, ischemic heart disease, heart failure, and arrhythmias). Computational AI/ML and regression models predicted cardiovascular complications in CKD with AUCs >0.71 and accuracies of 64%-99.88%, though 68% of studies had high or unclear risk of bias.
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