Computational Models Used to Predict Cardiovascular Complications in Chronic Kidney Disease Patients: A Systematic Review
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Sources
2.2. Study Selection
2.3. Data Extraction
2.4. Outcomes
2.5. Quality Assessment
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Author, Year | Population | Outcomes | Sample Size/Predictors No. | Algorithm | Performance |
---|---|---|---|---|---|
Composite CV outcomes | |||||
de Gonzalo-Calvo et al. 2020 [34] | Hemodialysis | Time to CV death, nonfatal MI, or nonfatal stroke (24 months follow-up) | 778/8 | DT using the CART algorithm | AUC: 0.71 |
Matsushita et al. 2020 [35] | Moderate CKD (GCKD cohort): 5-year follow-up | MI or fatal CHD or stroke | 5217 (validation set) | CKD Patch (Linear regression + Statistical methods) | AUC: 0.698 |
Stage 3–5 CKD (Hong Kong CKD): 10-year follow-up | 300 (validation set) | AUC: 0.73 | |||
Titapiccolo et al. 2013 [38] | Incident hemodialysis | CV events (CV mortality, insurgence of new CV co-morbidity, or CV hospitalization) in the next six months | 4246/39 | RF | AUC: 0.737 ± 1.2; ACC: 67.3 ± 2.8%; SE: 69.2 ± 3.3%; SP: 67.3 ± 2.8% |
Jeong et al. 2021 [30] | Postoperative ESRD patients | MACE (1 month postoperatively) | 3220/40 | RF | F1 score: 0.797 |
Fernandez-Lozano et al. 2018 [27] | Peritoneal dialysis | CVC prediction | 114 | Generalized Linear Model | AUC: 0.96 |
Sudden cardiac death (SCD) | |||||
Goldstein et al. 2014 [39] | Hemodialysis | Sudden cardiac death the day of or day after a dialysis session | 1796/72 | RF | AUC: 0.799 |
Mezzatesta et al., 2019 [40] | Hemodialysis | CV death (2.5-year follow-up) | 861/23 | SVM + RBF kernel | ACC: 80% |
Ischemic heart disease (IHD) | |||||
Mezzatesta et al. 2019 [40] | Hemodialysis | IHD (2.5-year follow-up) | 522/29 | SVM + RBF kernel | ACC: 95.25% |
2677/23 | ACC: 92.15% | ||||
Heart failure (HF) | |||||
Dubin et al. 2018 [21] | CKD | Prognostic proteins associated with HF in CKD | 364 | RSF regression + Cox survival analysis | Angiopoietin-2: HR 1.45 [1.33, 1.59] Spondin-1: HR 1.13 [1.06, 1.20] |
Mezzatesta et al. 2019 [40] | Hemodialysis | HF (2.5-year follow-up) | 522/29 | SVM + RBF kernel | ACC: 93% |
2677/23 | ACC: 64% | ||||
Akbilgic et al. 2019 [22] | ESRD patients with congestive HF | 30-, 90-, 180-, and 365-day all-cause mortality | 14800/49 | RF | AUC: 0.683, 0.716, 0.725, and 0.725 (risk of death within the 4 different time windows) |
Gowda et al. 2020 [23] | CKD | HF admissions in patients with CKD (1-year follow-up) | 117 | Remote IoT sensors | Significant decrease in HF admissions after implantation |
Ahmed et al. [24] | CKD patients with HF and reduced ejection fraction | Safety and efficiency prediction of low-dose ACEIs and ARBs | Not available | ML algorithm (unspecified) | Not available (study ongoing) |
Arrhythmias | |||||
Zelnick et al. 2020 [25] | CKD patients without prior AF | Incident AF | 2690/32 | Lasso regression | AUC: 0.76 |
Mezzatesta et al. 2019 [40] | Hemodialysis | Arrhythmia (2.5-year follow-up) | 522/29 | SVM + RBF kernel | ACC: 95% |
2677/23 | ACC: 67% | ||||
Other CV-related predictions | |||||
Forné et al. 2020 [28] | Stage 3–5 CKD | Atheromatous CVC (4-year follow-up) | 1366/38 | RSF | AUC: 0.744 |
Bermudez-Lopez et al. 2019 [29] | Stage 3–5 CKD + Controls | Discriminate between proatherogenic lipid profile in CKD vs. controls | 395/10 | RF | AUC: 0.789 |
Rodrigues et al. 2017 [31] | CAPD | Stroke risk | 850/7 | K-nearest neighbor | ACC: 99.65%; SE: 95.35%; SP: 99.88% |
Galloway et al. 2019 [26] | Stage 3–5 CKD | Hyperkalemia detection from the ECG | 61,965 ECG-potassium pairs (validation set) | DCNN | AUC: 0.853–0.883 |
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Burlacu, A.; Iftene, A.; Popa, I.V.; Crisan-Dabija, R.; Brinza, C.; Covic, A. Computational Models Used to Predict Cardiovascular Complications in Chronic Kidney Disease Patients: A Systematic Review. Medicina 2021, 57, 538. https://doi.org/10.3390/medicina57060538
Burlacu A, Iftene A, Popa IV, Crisan-Dabija R, Brinza C, Covic A. Computational Models Used to Predict Cardiovascular Complications in Chronic Kidney Disease Patients: A Systematic Review. Medicina. 2021; 57(6):538. https://doi.org/10.3390/medicina57060538
Chicago/Turabian StyleBurlacu, Alexandru, Adrian Iftene, Iolanda Valentina Popa, Radu Crisan-Dabija, Crischentian Brinza, and Adrian Covic. 2021. "Computational Models Used to Predict Cardiovascular Complications in Chronic Kidney Disease Patients: A Systematic Review" Medicina 57, no. 6: 538. https://doi.org/10.3390/medicina57060538