July 2019
Volume 60, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2019
Refining Metrics for Assessing the Quality of Care of Patients Undergoing Cataract Surgery for Use in Big Data Analyses
Author Affiliations & Notes
  • Eric Weinlander
    Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, United States
  • Nidhi Talwar
    Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, United States
  • Jennifer Weizer
    Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, United States
  • Sophia Saleem
    Ophthalmology, New York Eye and Ear Infirmary of Mount Sinai, New York, United States
  • Suzann Pershing
    Ophthalmology, Stanford University, California, United States
  • Brian Stagg
    Ophthalmology, Duke University, North Carolina, United States
  • Jean-Claude Mwanza
    Ophthalmology, University of North Carolina at Chapel Hill, North Carolina, United States
  • Anne Lynch
    Ophthalmology, University of Colorado, Colorado, United States
  • Alan Sugar
    Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, United States
  • Paul P Lee
    Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, United States
  • Joshua D Stein
    Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, United States
  • Footnotes
    Commercial Relationships   Eric Weinlander, None; Nidhi Talwar, None; Jennifer Weizer, None; Sophia Saleem, None; Suzann Pershing, None; Brian Stagg, None; Jean-Claude Mwanza, None; Anne Lynch, None; Alan Sugar, None; Paul Lee, None; Joshua Stein, None
  • Footnotes
    Support  Lighthouse Guild; RPB; R01 EY026641
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 2076. doi:
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      Eric Weinlander, Nidhi Talwar, Jennifer Weizer, Sophia Saleem, Suzann Pershing, Brian Stagg, Jean-Claude Mwanza, Anne Lynch, Alan Sugar, Paul P Lee, Joshua D Stein; Refining Metrics for Assessing the Quality of Care of Patients Undergoing Cataract Surgery for Use in Big Data Analyses. Invest. Ophthalmol. Vis. Sci. 2019;60(9):2076.

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

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Abstract

Purpose : The AAO has developed metrics that can be applied to registry or EHR data to assess and compare eye care quality. Two such metrics assess cataract surgery quality: IRIS Measure 191 (≥20/40 BCVA within 90 days) and Measure 192 (complications requiring return to OR within 30 days). In these metrics, patients with various ocular comorbidities are excluded. We determined the success rates using these metrics and examined how success rates change after adjusting the definitions of success and the criteria to be eligible for inclusion in each metric.

Methods : We identified all cataract surgeries in the Sight Outcomes Research (SOURCE) multicenter ophthalmology EHR repository, which captures surgeries performed at academic centers participating in the collaborative. We identified the BCVA for every postoperative visit and used CPT codes to identify return trips to the OR. We calculated the % of cataract surgeries with ≥20/40 BCVA and % with return trips to the OR within 30 or 90 days of surgery. We explored how success rates would change by altering the BCVA cutoff for success, the postoperative period of assessment, and the eligibility criteria for each metric.

Results : Among the 13487 surgeries, 91.6% achieved ≥20/40 BCVA at 90 days. If the definition of success was adjusted to ≥20/30 or ≥20/25 BCVA, the 90 day success rate would be 88.4% and 82.9%, respectively. After excluding 6791 surgeries for patients with ocular comorbidities outlined in Measure 191 (50.4%), the 90 day success rate of achieving BCVA of ≥20/40, ≥20/30, and ≥20/25 was 96.6%, 94.4%, and 90.8%, respectively. Among all surgeries, 99.4% had no return to the OR within 30 days. After excluding surgeries for patients with ocular comorbidities outlined in Measure 192 (40.5%), the success rate was 99.7%.

Conclusions : The success rate for both existing AAO IRIS quality metrics for cataract surgery is very high (>96%) and nearly 50% of all surgeries are excluded due to ocular comorbidities. While applying more stringent definitions of success (e.g. BCVA ≥20/25) would generate a better distribution of outcomes for more meaningful comparisons, current performance falls short of the envisioned 100% goal. Including patients with ocular comorbidities would increase sample size, albeit with the need for added analytical approaches for proper case-mix adjustment.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

 

Table 1. Best Recorded Visual Acuity

Table 1. Best Recorded Visual Acuity

 

Table 2. No Re-operation

Table 2. No Re-operation

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