June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
Prediction of Visual Acuity in Patients with Microbial Keratitis
Author Affiliations & Notes
  • Ming-Chen Lu
    Department of Ophthalmology & Visual Sciences, University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
  • Dena Ballouz
    W K Kellogg Eye Center, Ann Arbor, Michigan, United States
  • Leslie Niziol
    Department of Ophthalmology & Visual Sciences, University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
  • Karandeep Singh
    Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, United States
  • Linda Kang
    Department of Ophthalmology & Visual Sciences, University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
  • Alexa Thibodeau
    W K Kellogg Eye Center, Ann Arbor, Michigan, United States
  • Maria A Woodward
    Department of Ophthalmology & Visual Sciences, University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
    W K Kellogg Eye Center, Ann Arbor, Michigan, United States
  • Footnotes
    Commercial Relationships   Ming-Chen Lu None; Dena Ballouz None; Leslie Niziol None; Karandeep Singh Flatiron Health, Code C (Consultant/Contractor), Blue Cross Blue Shield of Michigan; Teva Pharmaceuticals, Code F (Financial Support); Linda Kang None; Alexa Thibodeau None; Maria Woodward None
  • Footnotes
    Support  NIH R01EY031033; RBG (Research to Prevent Blindness Career Advancement Award)
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 731 – F0459. doi:
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    • Get Citation

      Ming-Chen Lu, Dena Ballouz, Leslie Niziol, Karandeep Singh, Linda Kang, Alexa Thibodeau, Maria A Woodward; Prediction of Visual Acuity in Patients with Microbial Keratitis. Invest. Ophthalmol. Vis. Sci. 2022;63(7):731 – F0459.

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

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Abstract

Purpose : To predict visual acuity (VA) in patients with microbial keratitis (MK), at 90-day after diagnosis and at presentation, from data at the initial clinical ophthalmic encounter.

Methods : Patients with MK were identified in the University of Michigan electronic health record between August 2012 and February 2021. VA was extracted for the affected eye in MK patients with a unilateral infection, or for the better seeing eye in patients with bilateral infections, at the time of diagnosis and at day 90 (final). Random forest (RF) models were used to predict initial and final VA (iVA and fVA) that were <20/40. Predictors included age, gender, iVA (for the 90-day prediction model), and information documented in the clinical notes at presentation but excluding the assessment and plan. Model diagnostics are reported with 95% confidence intervals (CI) for area under the curve (AUC), misclassification, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Results : 1791 MK patients were identified. Patients averaged 48.0 years old (standard deviation, SD=20.3) with 1734 (96.8%) unilateral infections and 57 (3.2%) bilateral infections. LogMAR iVA was on average 0.85 (Snellen equivalent=20/142; SD=1.25) in the affected or better eye and was <20/40 in 43.0% of patients. fVA was <20/40 in 26.6% of patients. The RF model for predicting fVA of <20/40 had an AUC of 0.95 (CI, 0.94- 0.97) and a misclassification rate of 11% (8-13%). The sensitivity, specificity, PPV, and NPV were 91% (86-95%), 89% (86- 91%), 73% (65-79%), and 97% (95-98%), respectively. Older age, worse presenting VA, and more mentions of “hypopyon,” “PKP,” and “OS” in the clinical note were found to be associated with 90-day VA <20/40. The RF model for predicting iVA of <20/40 had an AUC of 0.88 (CI, 0.85-0.91) and a misclassification rate of 17% (14- 20%). The sensitivity, specificity, PPV, and NPV were 77% (71- 82%), 88% (84-91%), 83% (78- 88%), and 83% (79-87%), respectively. Older age, less mentions of the term “quiet” in the clinical note, and more mentions of “hypopyon,” “OD,” and “BID” were associated with iVA of <20/40.

Conclusions : RF models showed strong performance in predicting fVA and iVA. The model identified morphologic features, past ocular surgery, and indirect measure of medication use as key risk factors associated with poor VA outcomes. Predicting fVA can inform clinicians when risk stratifying patients with MK.

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

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