Trial-based cost-effectiveness analysis of ultrathin Descemet stripping automated endothelial keratoplasty (UT-DSAEK) versus DSAEK

Purpose: To evaluate the cost-effectiveness of ultrathin Descemet stripping automated endothelial keratoplasty (UT-DSAEK) versus standard DSAEK. Methods: A cost-effectiveness analysis using data from a multicentre randomized clinical trial was performed. The time horizon was 12 months postoperatively. Sixty-four eyes of 64 patients with Fuchs’ endothelial dystrophy were included and randomized to UT-DSAEK (n = 33) or DSAEK (n = 31). Relevant resources from healthcare and societal perspectives were included in the cost analysis. Quality-adjusted life years (QALYs) were determined using the Health Utilities Index Mark 3 questionnaire. The main outcome was the incremental cost-effectiveness ratio (ICER; incremental societal costs per QALY). Results: Societal costs were €9431 (US$11 586) for UT-DSAEK and €9110 (US$11 192) for DSAEK. Quality-adjusted life years (QALYs) were 0.74 in both groups. The ICER indicated inferiority of UT-DSAEK. The cost-effectiveness probability ranged from 37% to 42%, assuming the maximum acceptable ICER ranged from €2500–€80 000 (US$3071–US$98 280) per QALY. Additional analyses were performed omitting one UT-DSAEK patient who required a regraft [ICER €9057 (US$11 127) per QALY, cost-effectiveness probability: 44–62$29 271) per QALY, cost-effectiveness probability: 36–59%]. Furthermore, the ICER was €2101 (US$2581) per patient with clinical improvement in best spectacle-corrected visual acuity (≥0.2 logMAR) and €3274 (US$4022) per patient with clinical improvement in National Eye Institute Visual Functioning Questionnaire-25 composite score (≥10 points). Conclusion: The base case analysis favoured DSAEK, since costs of UT-DSAEK were higher while QALYs were comparable. However, additional analyses revealed no preference for UT-DSAEK or DSAEK. Further cost-effectiveness studies are required to reduce uncertainty

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

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