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2021 OMIG Abstract

The C-DU(KE) Calculator: A Novel Tool for Risk Stratification in Infectious Keratitis

Hazem M Mousa, MD¹, James Feghali, MD², Ailin Song ³, Matias Soifer, MD¹; Victor L Perez, MD^1
1Foster Center for Ocular Immunology, Department of Ophthalmology, Duke Eye Center, Duke University School of Medicine, Durham, North Carolina; ²Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland; ³Duke University School of Medicine, Durham, North Carolina

Purpose: To identify characteristics of infectious keratitis at time of presentation predictive of poor outcome in order to develop a web-based calculator allowing for risk stratification.

Methods: 215 adult culture-proven infectious keratitis patients presenting to the Duke Eye Center between January 1, 2016 and December 31, 2020 were separated into a derivation set (136 patients: 53 poor outcome; 83 controls) and a temporal validation set (79 patients: 26 poor outcome; 53 controls). Univariable analysis was performed followed by stepwise multivariable logistic regression to obtain a model predictive of poor outcome in the derivation dataset. A culture-naïve (without culture-dependent variables) and post-culture model were constructed. Discrimination and calibration were assessed using the area under the receiver operating characteristic curves (AUC) and calibration plots, respectively. Poor outcome was defined as the requirement of surgery throughout the disease course whether it was to address the acute infectious complications (i.e., endophthalmitis, corneal perforation) or chronic post-infectious consequences (i.e., visually significant scarring).

Results: The culture-naïve model consisted of: corticosteroid drop use post-symptom onset (OR=2.3, p=0.054), decreased vision (OR=2.4, p=0.001), and increased ulcer area (OR=1.017, p=0.017). The post-culture model additionally included fungal keratitis (OR=5.4, p=0.006) and elapsed time from symptoms to organism-sensitive therapy (OR=1.027, p=0.014). The models were summarized by the acronym C-DU(KE). The AUCs on the derivation and validation sets were 0.794 and 0.850, respectively, for the culture-naïve model and 0.898 and 0.946, respectively, for the post-culture model. Calibration plots indicated goodness-of-fit in the datasets for both models. The calculator was deployed under the URL: https://duke-eye-calculator.shinyapps.io/Corneal_Ulcers/.

Conclusions: The C-DU(KE) calculator can be used as a predictive tool that supplements clinical judgment, patient education, and risk stratification.

Disclosure: N (HMM, JF, AS, MS) S (VLP; Alcon, Dompe, EyeGate, Kala, Trefoil, Quidel, OBTears)

Support: National Institutes of Health/National Eye Institute: R01EY030283 (Dr. Perez), R01EY024485 (Dr. Perez), Duke NIH Center Core Grant and Duke Research to Prevent Blindness Unrestricted Grant (Dr. Perez).

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