A machine learning approach to explore predictors of graft detachment following posterior lamellar keratoplasty: a nationwide registry study

M. B. Muijzer*, C. M.W. Hoven, The Netherlands Corneal Transplant Network (NCTN), L. E. Frank, G. Vink, R. P.L. Wisse, Marjolijn C. Bartels, Yanny Y. Cheng, Mario R.P. Dhooge, Mor Dickman, Bart T.H. van Dooren, Cathrien A. Eggink, Annette J.M. Geerards, Tom A. van Goor, Ruth Lapid-Gortzak, Chantal M. van Luijk, Ivanka J. van der Meulen, Carla P. Nieuwendaal, Rudy M.M.A. Nuijts, Siamak NobachtAbdulkarim Oahalou, Emile C.A.A. van Oosterhout, Lies Remeijer, Jeroen van Rooij, Nathalie T.Y. Santana, Remco Stoutenbeek, Mei L. Tang, Thijs Vaessen, Nienke Visser, Robert H.J. Wijdh, Robert P.L. Wisse

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Scopus)
15 Downloads (Pure)


Machine learning can be used to explore the complex multifactorial patterns underlying postsurgical graft detachment after endothelial corneal transplantation surgery and to evaluate the marginal effect of various practice pattern modulations. We included all posterior lamellar keratoplasty procedures recorded in the Dutch Cornea Transplant Registry from 2015 through 2018 and collected the center-specific practice patterns using a questionnaire. All available data regarding the donor, recipient, surgery, and practice pattern, were coded into 91 factors that might be associated with the occurrence of a graft detachment. In this research, we used three machine learning methods; a regularized logistic regression (lasso), classification tree analysis (CTA), and random forest classification (RFC), to select the most predictive subset of variables for graft detachment. A total of 3647 transplants were included in our analysis and the overall prevalence of graft detachment was 9.9%. In an independent test set the area under the curve for the lasso, CTA, and RFC was 0.70, 0.65, and 0.72, respectively. Identified risk factors included: a Descemet membrane endothelial keratoplasty procedure, prior graft failure, and the use of sulfur hexafluoride gas. Factors with a reduced risk included: performing combined procedures, using pre-cut donor tissue, and a pre-operative laser iridotomy. These results can help surgeons to review their practice patterns and generate hypotheses for empirical research regarding the origins of graft detachments.

Original languageEnglish
Article number17705
JournalScientific Reports
Issue number1
Publication statusPublished - 21 Oct 2022

Bibliographical note

Funding Information:
This research is financially supported by unrestricted grants from the Dr. F.P. Fischer Foundation, Stichting Vrienden van het UMC Utrecht, Carl Zeiss Meditec AG, and applied data science grant of the University Utrecht.

Publisher Copyright: © 2022, The Author(s).


Dive into the research topics of 'A machine learning approach to explore predictors of graft detachment following posterior lamellar keratoplasty: a nationwide registry study'. Together they form a unique fingerprint.

Cite this