Skip to main content

Advertisement

SpringerLink
Log in

Random forest for prediction of contrast-induced nephropathy following coronary angiography

  • Original Article
  • Published:
The International Journal of Cardiovascular Imaging Aims and scope Submit manuscript

Abstract

The majority of prediction models for contrast-induced nephropathy (CIN) have moderate performance. Therefore, we aimed to develop a better pre-procedural prediction tool for CIN following contemporary percutaneous coronary intervention (PCI) or coronary angiography (CAG). A total of 3469 patients undergoing PCI/CAG between January 2010 and December 2013 were randomly divided into a training (n = 2428, 70%) and validation data-sets (n = 1041, 30%). Random forest full models were developed using 40 pre-procedural variables, of which 13 variables were selected for a reduced CIN model. CIN developed in 78 (3.21%) and 37 of patients (3.54%) in the training and validation datasets, respectively. In the validation dataset, the full and reduced models demonstrated improved discrimination over classic Mehran, ACEF CIN risk scores (AUC 0.842 and 0.825 over 0.762 and 0.701, respectively, all P < 0.05) and common estimated glomerular filtration rate. Compared to that for the Mehran risk score model, the full and reduced models had significantly improved fit based on the net reclassification improvement (all P < 0.001) and integrated discrimination improvement (P = 0.001, 0.028, respectively). Using the above models, 2462 (66.7%), 661, and 346 patients were categorized into low (< 1%), moderate (1% to 7%), and high (> 7%) risk groups, respectively. Our pre-procedural CIN risk prediction algorithm (http://cincalc.com) demonstrated good discriminative ability and was well calibrated when validated. Two-thirds of the patients were at low CIN risk, probably needing less peri-procedural preventive strategy; however, the discriminative ability of CIN risk requires further external validation.

Trial registration

ClinicalTrials.gov NCT01400295.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from Jiyan Chen and Yong Liu, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Jiyan Chen and Yong Liu.

References

  1. Christiansen C (2005) X-ray contrast media—an overview. Toxicology 209:185–187

    Article  CAS  Google Scholar 

  2. Hsu RK, McCulloch CE, Dudley RA, Lo LJ, Hsu CY (2013) Temporal changes in incidence of dialysis-requiring AKI. J Am Soc Nephrol 24:37–42

    Article  Google Scholar 

  3. James MT, Samuel SM, Manning MA, Tonelli M, Ghali WA, Faris P, Knudtson ML, Pannu N, Hemmelgarn BR (2013) Contrast-induced acute kidney injury and risk of adverse clinical outcomes after coronary angiography: a systematic review and meta-analysis. Circ Cardiovasc Interv 6:37–43

    Article  Google Scholar 

  4. Levine GN, Bates ER, Blankenship JC, Bailey SR, Bittl JA, Cercek B, Chambers CE, Ellis SG, Guyton RA, Hollenberg SM, Khot UN, Lange RA, Mauri L, Mehran R, Moussa ID, Mukherjee D, Nallamothu BK, Ting HH (2012) 2011 ACCF/AHA/SCAI Guideline for Percutaneous Coronary Intervention: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines and the Society for Cardiovascular Angiography and Interventions. Catheter Cardiovasc Interv Off J Soc Card Angiogr Interv 79:453–495

    Article  Google Scholar 

  5. Silver SA, Shah PM, Chertow GM, Harel S, Wald R, Harel Z (2015) Risk prediction models for contrast induced nephropathy: systematic review. BMJ 351:h4395

    Article  Google Scholar 

  6. Gurm HS, Seth M, Kooiman J, Share D (2013) A novel tool for reliable and accurate prediction of renal complications in patients undergoing percutaneous coronary intervention. J Am Coll Cardiol 61:2242–2248

    Article  Google Scholar 

  7. Maioli M, Toso A, Leoncini M, Micheletti C, Bellandi F (2011) Effects of hydration in contrast-induced acute kidney injury after primary angioplasty: a randomized, controlled trial. Circ Cardiovasc Interv 4:456–462

    Article  Google Scholar 

  8. Zoungas S, Ninomiya T, Huxley R, Cass A, Jardine M, Gallagher M, Patel A, Vasheghani-Farahani A, Sadigh G, Perkovic V (2009) Effects of hydration in contrast-induced acute kidney injury after primary angioplasty: a randomized, controlled trial. Ann Intern Med 151:631–638

    Article  Google Scholar 

  9. Thayssen P, Lassen JF, Jensen SE, Hansen KN, Hansen HS, Christiansen EH, Junker A, Ravkilde J, Thuesen L, Veien KT, Jensen LO (2014) Prevention of contrast-induced nephropathy with N-acetylcysteine or sodium bicarbonate in patients with ST-segment-myocardial infarction: a prospective, randomized, open-labeled trial. Circ Cardiovasc Interv 7:216–224

    Article  CAS  Google Scholar 

  10. Wijns W, Kolh P, Danchin N, Di Mario C, Falk V, Folliguet T, Garg S, Huber K, James S, Knuuti J, Lopez-Sendon J, Marco J, Menicanti L, Ostojic M, Piepoli MF, Pirlet C, Pomar JL, Reifart N, Ribichini FL, Schalij MJ, Sergeant P, Serruys PW, Silber S, Sousa Uva M, Taggart D (2010) Guidelines on myocardial revascularization. Eur Heart J 31:2501–2555

    Article  Google Scholar 

  11. Nijssen EC, Rennenberg RJ, Nelemans PJ, Essers BA, Janssen MM, Vermeeren MA, Ommen VV, Wildberger JE (2017) Prophylactic hydration to protect renal function from intravascular iodinated contrast material in patients at high risk of contrast-induced nephropathy (AMACING): a prospective, randomised, phase 3, controlled, open-label, non-inferiority trial. Lancet 389:1312–1322

    Article  Google Scholar 

  12. Briguori C, Signoriello G (2017) Acute kidney injury: intravenous hydration for the prevention of CIAKI. Nat Rev Nephrol 13:264–266

    Article  Google Scholar 

  13. Liu Y, Chen JY, Tan N, Zhou YL, Yu DQ, Chen ZJ, He YT, Liu YH, Luo JF, Huang WH, Li G, He PC, Yang JQ, Xie NJ, Liu XQ, Yang DH, Huang SJ, Piao Y, Li HL, Ran P, Duan CY, Chen PY (2015) Safe limits of contrast vary with hydration volume for prevention of contrast-induced nephropathy after coronary angiography among patients with a relatively low risk of contrast-induced nephropathy. Circ Cardiovasc Interv 8:e001859

    Article  Google Scholar 

  14. Liu Y, Lin L, Li Y, Li H, Wu DX, Zhao JB, Lian D, Zhou Y, Liu Y, Ye P, Ran P, Duan C, Chen S, Chen P, Xian Y, Chen J, Tan N (2015) Relationship between the urine flow rate and risk of contrast-induced nephropathy after emergent percutaneous coronary intervention. Medicine (Baltim) 94:e2258

    Article  CAS  Google Scholar 

  15. Slocum NK, Grossman PM, Moscucci M, Smith DE, Aronow HD, Dixon SR, Share D, Gurm HS (2012) The changing definition of contrast-induced nephropathy and its clinical implications: insights from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2). Am Heart J 163:829–834

    Article  Google Scholar 

  16. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Muller M (2011) pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform 12:77

    Article  Google Scholar 

  17. Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2:18–22

    Google Scholar 

  18. DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845

    Article  CAS  Google Scholar 

  19. Pencina MJ, D’Agostino RB Sr, D’Agostino RB Jr, Vasan RS (2008) Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 27:157–172

    Article  Google Scholar 

  20. Cook NR (2007) Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 115:923–935

    Google Scholar 

  21. Mehran R, Aymong ED, Nikolsky E, Lasic Z, Iakovou I, Fahy M, Mintz GS, Lansky AJ, Moses JW, Stone GW, Leon MB, Dangas G (2004) A simple risk score for prediction of contrast-induced nephropathy after percutaneous coronary intervention: development and initial validation. J Am Coll Cardiol 44:1393–1399

    PubMed  Google Scholar 

  22. Allen DW, Ma B, Leung KC, Graham MM, Pannu N, Traboulsi M, Goodhart D, Knudtson ML, James MT (2017) Risk prediction models for contrast-induced acute kidney injury accompanying cardiac catheterization: systematic review and meta-analysis. Can J Cardiol 33:724–736

    Article  Google Scholar 

  23. Agarwal SK, Mohareb S, Patel A, Yacoub R, DiNicolantonio JJ, Konstantinidis I, Pathak A, Fnu S, Annapureddy N, Simoes PK, Kamat S, El-Hayek G, Prasad R, Kumbala D, Nascimento RM, Reilly JP, Nadkarni GN, Benjo AM (2015) Systematic oral hydration with water is similar to parenteral hydration for prevention of contrast-induced nephropathy: an updated meta-analysis of randomised clinical data. Open Heart 2:e000317

    Article  Google Scholar 

  24. Brar SS, Shen AY, Jorgensen MB, Kotlewski A, Aharonian VJ, Desai N, Ree M, Shah AI, Burchette RJ (2008) Sodium bicarbonate vs sodium chloride for the prevention of contrast medium-induced nephropathy in patients undergoing coronary angiography: a randomized trial. JAMA 300:1038–1046

    Article  CAS  Google Scholar 

  25. Luo Y, Wang X, Ye Z, Lai Y, Yao Y, Li J, Liu X (2014) Remedial hydration reduces the incidence of contrast-induced nephropathy and short-term adverse events in patients with ST-segment elevation myocardial infarction: a single-center, randomized trial. Intern Med 53:2265–2272

    Article  Google Scholar 

  26. Brar SS, Aharonian V, Mansukhani P, Moore N, Shen AY, Jorgensen M, Dua A, Short L, Kane K (2014) Haemodynamic-guided fluid administration for the prevention of contrast-induced acute kidney injury: the POSEIDON randomised controlled trial. Lancet 383:1814–1823

    Article  Google Scholar 

  27. Liu Y, Li H, Chen S, Chen J, Tan N, Zhou Y, Liu Y, Ye P, Ran P, Duan C, Chen P (2016) Excessively high hydration volume may not be associated with decreased risk of contrast-induced acute kidney injury after percutaneous coronary intervention in patients with renal insufficiency. J Am Heart Assoc 5:e003171

    PubMed  PubMed Central  Google Scholar 

  28. Tsai TT, Patel UD, Chang TI, Kennedy KF, Masoudi FA, Matheny ME, Kosiborod M, Amin AP, Weintraub WS, Curtis JP, Messenger JC, Rumsfeld JS, Spertus JA (2014) Validated contemporary risk model of acute kidney injury in patients undergoing percutaneous coronary interventions: insights from the National Cardiovascular Data Registry Cath-PCI Registry. J Am Heart Assoc 3:e001380

    Article  Google Scholar 

  29. Çınar T, Karabağ Y, Ozan Tanık V, Çağdaş M, Rencüzoğulları İ, Öz A (2019) The investigation of TIMI risk index for prediction of contrast-induced acute kidney injury in patients with ST elevation myocardial infarction. Acta Cardiol 25:1–8

    Google Scholar 

  30. Çınar T, Tanık VO, Aruğaslan E, Karabağ Y, Çağdaş M, Rencüzoğulları İ, Keskin M (2019) The association of PRECISE-DAPT score with development of contrast-induced nephropathy in patients with ST-elevation myocardial infarction undergoing primary percutaneous coronary intervention. Cardiovasc Interv Ther 34:207–215

    Article  Google Scholar 

  31. Velibey Y, Tanik O, Oz A, Guvenc TS, Kalenderoglu K, Gumusdag A, Guzelburc O, Tekkesin AI, Uzun AO, Alper AT, Eren M (2017) Off-hour primary percutaneous coronary angioplasty does not affect contrast-induced nephropathy in patients with ST-segment elevation myocardial infarction. Angiology 68:807–815

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank An Fan, MD in Guangdong, China for statistical support.

Funding

This study was funded by The Guangdong Provincial Cardiovascular Clinical Medicine Research Fund (Grant Number, 2009X41), Science and Technology Planning Project of Guangdong Province (Grant Number, 2014B070706010), and Cardiovascular Research Foundation Project of the Chinese Medical Doctor Association (SCRFCMDA201216). The funders had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Author information

Author notes

  1. Yong Liu, Shiqun Chen and Jianfeng Ye have contributed equally to this work.

Authors and Affiliations

  1. Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, South China University of Technology, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China

    Yong Liu, Shiqun Chen, Ning Tan, Jiyan Chen & Zhonghan Ni

  2. The George Institute for Global Health, The University of New South Wales, Sydney, Australia

    Yong Liu, Shiqun Chen, Xia Wang & Qiang Li

  3. Department of Cardiology&Dongguan Division of Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Dongguan TCM Hospital, Dongguan, China

    Shiqun Chen & Jianfeng Ye

  4. Duke Clinical Research Institute, Duke University, Durham, NC, USA

    Ying Xian

  5. University of Florida, Gainesville, FL, USA

    Jianwei Xuan

  6. Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, South China University of Technology, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, 510100, China

    Jiyan Chen & Zhonghan Ni

Authors
  1. Yong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shiqun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianfeng Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ying Xian
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xia Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jianwei Xuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ning Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Qiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jiyan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Zhonghan Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YL, SC, JC, NT: conception and design of the work; JC, SC, YL, QL, and PC: acquisition, analysis, and interpretation of data; YL, SC, JC, PC, YX, XW, JX, NT and QL: drafting the manuscript and revising it critically for important intellectual content; JC, YL, SC, PC, YX, XW, JX, NT and QL: final approval of the version to be published. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jiyan Chen or Zhonghan Ni.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the Ethical Standards of the Institutional and/or National Research Committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. All participants gave their written consent to participate in the study. The study was approved by Guangdong General Hospital Institutional Ethics Research Committee.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Y., Chen, S., Ye, J. et al. Random forest for prediction of contrast-induced nephropathy following coronary angiography. Int J Cardiovasc Imaging 36, 983–991 (2020). https://doi.org/10.1007/s10554-019-01730-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10554-019-01730-6

Keywords

Search

Navigation