Advanced search
Publication Cover
Acta Cardiologica Volume 78, 2023 - Issue 8
Submit an article Journal homepage
142
Views
2
CrossRef citations to date
0
Altmetric
Original Scientific Papers

Prediction of the development of contrast‑induced nephropathy following percutaneous coronary artery intervention by machine learning

Xiao Maa Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, P. R. China;b Guangxi Key Laboratory Base of Precision Medicine in Cardiocerebrovascular Diseases Control and Prevention, Nanning, P. R. China;c Guangxi Clinical Research Center for Cardiocerebrovascular Diseases, Nanning, P. R. ChinaView further author information
,
Changhua Moa Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, P. R. China;b Guangxi Key Laboratory Base of Precision Medicine in Cardiocerebrovascular Diseases Control and Prevention, Nanning, P. R. China;c Guangxi Clinical Research Center for Cardiocerebrovascular Diseases, Nanning, P. R. ChinaView further author information
,
Yujuan Lia Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, P. R. China;b Guangxi Key Laboratory Base of Precision Medicine in Cardiocerebrovascular Diseases Control and Prevention, Nanning, P. R. China;c Guangxi Clinical Research Center for Cardiocerebrovascular Diseases, Nanning, P. R. ChinaView further author information
,
Xinyuan Chena Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, P. R. ChinaView further author information
&
Chun Guia Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, P. R. China;b Guangxi Key Laboratory Base of Precision Medicine in Cardiocerebrovascular Diseases Control and Prevention, Nanning, P. R. China;c Guangxi Clinical Research Center for Cardiocerebrovascular Diseases, Nanning, P. R. ChinaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-4484-3339View further author information
ORCID Icon
Pages 912-921 | Received 03 Aug 2022, Accepted 30 Mar 2023, Published online: 13 Apr 2023

References

  • Mamoulakis C, Tsarouhas K, Fragkiadoulaki I, et al. Contrast-induced nephropathy: basic concepts, pathophysiological implications and prevention strategies. Pharmacol Ther. 2017;180:99–112.
  • Mehran R, Aymong E, Nikolsky E, et al. A simple risk score for prediction of contrast-induced nephropathy after percutaneous coronary intervention: development and initial validation. J Am Coll Cardiol. 2004;44(7):1393–1399.
  • Subramanian S, Tumlin J, Bapat B, et al. Economic burden of contrast-induced nephropathy: implications for prevention strategies. J Med Econ. 2007;10(2):119–134.
  • Rihal CS, Textor SC, Grill DE, et al. Incidence and prognostic importance of acute renal failure after percutaneous coronary intervention. Circulation. 2002;105(19):2259–2264.
  • Akin F, Celik O, Altun I, et al. Relation of red cell distribution width to contrast-induced acute kidney injury in patients undergoing a primary percutaneous coronary intervention. Coron Artery Dis. 2015;26(4):289–295.
  • Seibert Felix S, Heringhaus A, Pagonas N, et al. Biomarkers in the prediction of contrast media induced nephropathy - the BITCOIN study. PLoS One. 2020;15(7):e0234921.
  • Gurm HS, Seth M, Kooiman J, et al. A novel tool for reliable and accurate prediction of renal complications in patients undergoing percutaneous coronary intervention. J Am Coll Cardiol. 2013;61(22):2242–2248.
  • Qu C, Gao L, Yu X-Q, et al. Machine learning models of acute kidney injury prediction in acute pancreatitis patients. Gastroenterol Res Pract. 2020;2020:3431290.
  • Le S, Hoffman J, Barton C, et al. Pediatric severe sepsis prediction using machine learning. Front Pediatr. 2019;7:413.
  • D'Ascenzo F, De Filippo O, Gallone G, et al. Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets. Lancet. 2021;397(10270):199–207.
  • Wu TT, Lin XQ, Mu Y, et al. Machine learning for early prediction of in-hospital cardiac arrest in patients with acute coronary syndromes. Clin Cardiol. 2021;44(3):349–356.
  • Silvain J, Collet JP, Montalescot G. Contrast-induced nephropathy: the sin of primary percutaneous coronary intervention. Eur Heart J. 2014;35(23):1504–1506.
  • Inker LA, Schmid CH, Tighiouart H, et al. Estimating glomerular fltration rate from serum creatinine and cystatin C. N Engl J Med. 2012;367(1):20–29.
  • Chen Q, Zhang B, Yang J, et al. Predicting intensive care unit length of stay after acute type a aortic dissection surgery using machine learning. Front Cardiovasc Med. 2021;8:675431.
  • Hyung-Chul L, Hyun-Kyu Y, Karam N, et al. Derivation and validation of machine learning approaches to predict acute kidney injury after cardiac surgery. J Clin Med. 2018;7(10):322.
  • Parastoo G, Majid G-M, Azadeh S, et al. Comparison of support vector machine, naïve bayes and logistic regression for assessing the necessity for coronary angiography. Int J Environ Res Public Health. 2020;17(18):6449.
  • Lanza B, Parashar D. Do support vector machines play a role in stratifying patient population based on cancer biomarkers? Arch Proteom Bioinform. 2021;2:20–38.
  • Abellás-Sequeiros RA, Raposeiras-Roubín S, Abu-Assi E, et al. Mehran contrast nephropathy risk score: is it still useful 10 years later? J Cardiol. 2016;67(3):262–267.
  • Ji L, Su X, Qin W, et al. Novel risk score of contrast-induced nephropathy after percutaneous coronary intervention. Nephrology. 2015;20(8):544–551.
  • Khera R, Haimovich J, Hurley NC, et al. Use of machine learning models to predict death after acute myocardial infarction. JAMA Cardiol. 2021;6(6):633–641.
  • Hassannataj Joloudari J, Mojrian S, Nodehi I, et al. Application of artificial intelligence techniques for automated detection of myocardial infarction: a review. Physiol Meas. 2022;43(8):08TR01.
  • Liu R, Wang M, Zheng T, et al. An artificial intelligence-based risk prediction model of myocardial infarction. BMC Bioinf. 2022;23(1):217.
  • Heo J, Yoo J, Lee H, et al. Prediction of hidden coronary artery disease using machine learning in patients with acute ischemic stroke. Neurology. 2022;99(1):e55–e65.
  • Sun L, Zhu W, Chen X, et al. Machine learning to predict Contrast-Induced acute kidney injury in patients with acute myocardial infarction. Front Med. 2020;7:592007.
  • Wiemken TL, Kelley RR. Machine learning in epidemiology and health outcomes research. Annu Rev Public Health. 2020;41:21–36.
  • Watanabe M, Saito Y, Aonuma K, et al. Prediction of contrast-induced nephropathy by the serum creatinine level on the day following cardiac catheterization. J Cardiol. 2016;68(5):412–418.
  • Mo H, Ye F, Chen D, et al. A predictive model based on a new CI-AKI definition to predict contrast induced nephropathy in patients with coronary artery disease with relatively normal renal function. Front Cardiovasc Med. 2021;8:762576.
  • Liu Y-h, Liu Y, Chen J-y, et al. LDL cholesterol as a novel risk factor for contrast-induced acute kidney injury in patients undergoing percutaneous coronary intervention. Atherosclerosis. 2014;237(2):453–459.
  • Xu J, Zhang M, Ni Y, et al. Impact of low hemoglobin on the development of contrast-induced nephropathy: a retrospective cohort study. Exp Ther Med. 2016;12(2):603–610.
  • Fu X, Dong J, Wang H, et al. Association between plasma endothelial microparticles and contrast-induced nephropathy in patients underwent coronary angiography. Medicine. 2021;100(28):e24004.
  • Kelesoglu S, Yilmaz Y, Elcık D, et al. Systemic immune inflammation index: a novel predictor of contrast-induced nephropathy in patients with Non-ST segment elevation myocardial infarction. Angiology. 2021;72(9):889–895.
  • Nakahashi T, Tada H, Sakata K, et al. Impact of concomitant peripheral artery disease on contrast-induced acute kidney injury and mortality in patients with acute coronary syndrome after percutaneous coronary intervention. Heart Vessels. 2020;35(10):1360–1367.
  • Heyman SN, Rosenberger C, Rosen S, et al. Why is diabetes mellitus a risk factor for contrast-induced nephropathy? Biomed Res Int. 2013;2013:123589.
  • Ali ZA, Escaned J, Dudek D, et al. Strategies for renal protection in cardiovascular interventions. Korean Circ J. 2022;52(7):485–495.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

A worldwide pioneer in biotechnology.
A bold vision to stop cardiovascular disease from being a leading cause of death.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.