June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
The P-DUKE calculator: a novel tool for predicting outcomes after penetrating keratoplasty
Author Affiliations & Notes
  • Hazem M Mousa
    Duke University Department of Ophthalmology, Durham, North Carolina, United States
  • James Feghali
    Johns Hopkins Medicine, Baltimore, Maryland, United States
  • Matias Soifer
    Duke University Department of Ophthalmology, Durham, North Carolina, United States
  • Ailin Song
    Duke University School of Medicine, Durham, North Carolina, United States
  • Nadim Azar
    Duke University Department of Ophthalmology, Durham, North Carolina, United States
  • Alexander J Snyder
    Duke University Department of Ophthalmology, Durham, North Carolina, United States
  • Victor L Perez
    Duke University Department of Ophthalmology, Durham, North Carolina, United States
  • Footnotes
    Commercial Relationships   Hazem Mousa None; James Feghali None; Matias Soifer None; Ailin Song None; Nadim Azar None; Alexander Snyder None; Victor Perez Asclepix, Brill, Dompe, Kala, Kiora, Novartis, Oyster Point Pharma, Code C (Consultant/Contractor), Alcon, Heat Biologics, NIH, Code F (Financial Support), Trefoil, Code I (Personal Financial Interest)
  • Footnotes
    Support  NIH/NEI R01EY030283 (Dr. Perez), NIH/NEI R01EY024485 (Dr. Perez), Duke NIH Center Core Grant and Duke Research to Prevent Blindness Unrestricted Grant (Dr. Perez).
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 4343 – A0280. doi:
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      Hazem M Mousa, James Feghali, Matias Soifer, Ailin Song, Nadim Azar, Alexander J Snyder, Victor L Perez; The P-DUKE calculator: a novel tool for predicting outcomes after penetrating keratoplasty. Invest. Ophthalmol. Vis. Sci. 2022;63(7):4343 – A0280.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To identify pre-operative characteristics in penetrating keratoplasty (PK) predictive of graft survival in order to develop a standardized web-based outcome risk calculator.

Methods : A retrospective chart review was carried out at the Duke Eye Center using procedure code 65730. Inclusion criteria were adults that underwent full-thickness PK from January 1, 2013, till December 31, 2017, that had at least a 4-year follow-up or failed within. Lack of success was concluded if the patient underwent rejection (acute or chronic) despite anti-inflammatory therapy, graft failure, graft dehiscence, visually significant edema, or complications requiring repeat transplantation. Univariable analysis of pre-operative characteristics was performed followed by a stepwise multivariable logistic regression to obtain a predictive model with a p<0.05 cutoff. Discrimination and calibration were assessed using the area under the curve (AUC) and Hosmer-Lemeshow (HL) test, respectively.

Results : 178 transplants were included of which 62 (34.8%) were unsuccessful. History of anterior segment surgery (same eye) (p<0.001), diabetes (p=0.008), smoking (p<0.001), corneal neovascularization (p<0.001), conjunctival injection (p<0.001), synechiae presence (p=0.002), and indication other than keratoconus (p<0.001) were associated with non-success in the univariable analysis. Multivariable analysis produced a model consisting of: (1) previous ocular surface surgery in the same eye (OR: 2.09, p=0.06), (2) diabetes mellitus (OR: 3.48, p=0.013), (3) use of tobacco (OR: 3.77, p=0.007), (4) KNV (i.e. corneal neovascularization) (OR: 2.42, p= 0.042), and (5) erythematous conjunctiva (i.e. conjunctival injection) (OR: 4.36, p=0.001). The predictive model was summarized by the acronym P-DUKE. The AUC achieved was 0.82 and the HL test indicated adequate calibration (p>0.05). The calculator was then deployed under the URL: https://pduke.shinyapps.io/PDUKE/

Conclusions : The P-DUKE calculator is a readily accessible tool allowing for a standardized assessment of risk in patients planned for PKs and can supplement clinical judgment regarding follow-up and patient expectations irrespective of physician expertise.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

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