Predictive performance of machine learning models for kidney complications following coronary interventions: a systematic review and meta-analysis

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. https://doi.org/10.1136/bmj.n71

Article  PubMed  PubMed Central  Google Scholar 

Moons KG, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS et al (2019) PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med 170(1):W1–W33

Article  PubMed  Google Scholar 

Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N et al (2010) Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 21(1):128–138

Article  PubMed  PubMed Central  Google Scholar 

Nyaga VN, Arbyn M, Aerts M (2014) Metaprop: a Stata command to perform meta-analysis of binomial data. Arch Public Health 72:1–10

Article  Google Scholar 

Chandler J, Cumpston M, Li T, Page MJ, Welch V (2019) Cochrane handbook for systematic reviews of interventions. Wiley, Hoboken

Google Scholar 

Higgins JP, Thompson SG, Deeks JJ, Altman DG (2003) Measuring inconsistency in meta-analyses. BMJ 327(7414):557–560

Article  PubMed  PubMed Central  Google Scholar 

Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29–36

Article  CAS  PubMed  Google Scholar 

Cai D, Xiao T, Zou A, Mao L, Chi B, Wang Y et al (2022) Predicting acute kidney injury risk in acute myocardial infarction patients: an artificial intelligence model using medical information mart for intensive care databases. Front Cardiovasc Med 9:964894

Article  CAS  PubMed  PubMed Central  Google Scholar 

Che M, Wang X, Liu S, Xie B, Xue S, Yan Y et al (2019) A clinical score to predict severe acute kidney injury in Chinese patients after cardiac surgery. Nephron 142(4):291–300

Article  PubMed  Google Scholar 

Chiofolo C, Chbat N, Ghosh E, Eshelman L, Kashani K (2019) Automated continuous acute kidney injury prediction and surveillance: a random forest model. Mayo Clin Proceed. https://doi.org/10.1016/j.mayocp.2019.02.009

Article  Google Scholar 

Cox M, Panagides J, Di Capua J, Dua A, Kalva S, Kalpathy-Cramer J, Daye D (2023) An interpretable machine learning model for the prevention of contrast-induced nephropathy in patients undergoing lower extremity endovascular interventions for peripheral arterial disease. Clin Imaging 101:1–7

Article  PubMed  Google Scholar 

Du Y, Wang X-Z, Wu W-D, Shi H-P, Yang X-J, Wu W-J, Chen S-X (2021) Predicting the risk of acute kidney injury in patients after percutaneous coronary intervention (PCI) or cardiopulmonary bypass (CPB) surgery: development and assessment of a nomogram prediction model. Med Sci Monit: Int Med J Exp Clin Res 27:e929791–e929801

Article  Google Scholar 

Tao F, Yang H, Wang W, Bi X, Dai Y, Zhu A, Guo P (2024) Acute kidney injury prediction model utility in premature myocardial infarction. Iscience 27(3):109153

Article  PubMed  PubMed Central  Google Scholar 

Thongprayoon C, Hansrivijit P, Bathini T, Vallabhajosyula S, Mekraksakit P, Kaewput W, Cheungpasitporn W (2020) Predicting acute kidney injury after cardiac surgery by machine learning approaches. MDPI 9:1767

Google Scholar 

Wu L, Hu Y, Liu X, Zhang X, Chen W, Yu AS et al (2018) Feature ranking in predictive models for hospital-acquired acute kidney injury. Sci Rep 8(1):17298

Article  PubMed  PubMed Central  Google Scholar 

Yun D, Cho S, Kim YC, Kim DK, Oh K-H, Joo KW et al (2021) Use of deep learning to predict acute kidney injury after intravenous contrast media administration: prediction model development study. JMIR Med Inform 9(10):e27177

Article  PubMed  PubMed Central  Google Scholar 

Zheng S, Li Y, Luo C, Chen F, Ling G, Zheng B (2023) Machine learning for predicting the development of postoperative acute kidney injury after coronary artery bypass grafting without extracorporeal circulation. Cardiovasc Innov Appl. https://doi.org/10.15212/CVIA.2023.0006

Article  Google Scholar 

Al’Aref SJ, Singh G, van Rosendael AR, Kolli KK, Ma X, Maliakal G et al (2019) Determinants of in-hospital mortality after percutaneous coronary intervention: a machine learning approach. J Am Heart Assoc 8(5):e011160

Article  PubMed  PubMed Central  Google Scholar 

Huang Y-C, Chen K-Y, Li S-J, Liu C-K, Lin Y-C, Chen M (2022) Implementing an ensemble learning model with feature selection to predict mortality among patients who underwent three-vessel percutaneous coronary intervention. Appl Sci 12(16):8135

Article  CAS  Google Scholar 

Kuno T, Numasawa Y, Mikami T, Niimi N, Sawano M, Kodaira M et al (2021) Association of decreasing hemoglobin levels with the incidence of acute kidney injury after percutaneous coronary intervention: a prospective multi-center study. Heart Vessels 36:330–336

Article  PubMed  Google Scholar 

Li Y, Chan T-M, Feng J, Tao L, Jiang J, Zheng B et al (2022) A pattern-discovery-based outcome predictive tool integrated with clinical data repository: design and a case study on contrast related acute kidney injury. BMC Med Inform Decis Mak 22(1):103

Article  PubMed  PubMed Central  Google Scholar 

Matheny ME, Miller RA, Ikizler TA, Waitman LR, Denny JC, Schildcrout JS et al (2010) Development of inpatient risk stratification models of acute kidney injury for use in electronic health records. Med Decis Making 30(6):639–650

Article  PubMed  PubMed Central  Google Scholar 

Wang J, Wang S, Zhu MX, Yang T, Yin Q, Hou Y (2022) Risk prediction of major adverse cardiovascular events occurrence within 6 months after coronary revascularization: machine learning study. JMIR Med Inform 10(4):e33395

Article  PubMed  PubMed Central  Google Scholar 

Weisenthal SJ, Quill C, Farooq S, Kautz H, Zand MS (2018) Predicting acute kidney injury at hospital re-entry using high-dimensional electronic health record data. PLoS ONE 13(11):e0204920

Article  PubMed  PubMed Central  Google Scholar 

Zhang X, Liu T, Tian C (2022) Artificial intelligence algorithm-based computed tomography image in assessment of acute renal insufficiency of patients undergoing percutaneous coronary intervention. Contrast Media Mol Imaging 2022(1):2214583

Article  PubMed  PubMed Central  Google Scholar 

Zhu X, Zhang P, Jiang H, Kuang J, Wu L (2024) Using the Super Learner algorithm to predict risk of major adverse cardiovascular events after percutaneous coronary intervention in patients with myocardial infarction. BMC Med Res Methodol 24(1):59

Article  PubMed  PubMed Central  Google Scholar 

Chen P-Y, Liu Y, Chen S, Xian Y, Chen J-Y, Tan N (2018) A novel tool for pre-procedural risk stratification for contrast-induced nephropathy and associations between hydration volume and clinical outcomes following coronary angiography at different risk levels. J Am Coll Cardiol 2018(71):144

Article  Google Scholar 

Fanous H, Mohammad KO, Patel AP, Liu Y (2023) Simplifying heart-catheterization, contrast-induced acute kidney injury predictive models, using machine learning. J Am Coll Cardiol 81:2402

Article  Google Scholar 

Kuno T, Mikami T, Sahashi Y, Numasawa Y, Suzuki M, Noma S et al (2021) TCT-332 machine learning methods in prediction of acute kidney injury: application of the us national cardiovascular data registry model on Japanese Percutaneous Coronary Intervention Patients. J Am Coll Cardiol 78:B135

Article  Google Scholar 

Lu Y, Zhou F, Xu Y, Zhang S, Luo Q (2022) The correlation between neutrophil-to-lymphocyte ratio and contrast-induced AKI and establishment of new predictive models by machine learning: FR-PO077. J Am Soc Nephrol 33(11S):348

Article  Google Scholar 

Tsutsui RS, Johnston JD, Felix C, Alberts JL, Reed GW, Puri R et al (2019) TCT-615 A supervised machine learning approach for predicting acute kidney injury following percutaneous coronary intervention. J Am Coll Cardiol. https://doi.org/10.1016/j.jacc.2019.08.730

Article  PubMed  Google Scholar 

Yuan N, Ebinger J (2019) A new multivariate model for safe contrast limits to prevent contrast induced nephropathy after percutaneous coronary intervention. Circulation 140(Suppl_1):A15026-A

Google Scholar 

Dodson JA, Hajduk A, Curtis J, Geda M, Krumholz HM, Song X et al (2019) Acute kidney injury among older patients undergoing coronary angiography for acute myocardial infarction: the SILVER-AMI study. Am J Med 132(12):e817–e826

Article  PubMed  PubMed Central  Google Scholar 

Ma B, Allen DW, Graham MM, Har BJ, Tyrrell B, Tan Z et al (2019) Comparative performance of prediction models for contrast-associated acute kidney injury after percutaneous coronary intervention. Circ Cardiovasc Qual Outcomes 12(11):e005854

Article  PubMed 

Comments (0)

No login
gif