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Journal article
Use of predictive models to identify patients who are likely to benefit from refraction at a follow-up visit after cataract surgery
Indian journal of ophthalmology, v 69(10), pp 2695-2701
Oct 2021
PMID: 34571618
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
To develop predictive models to identify cataract surgery patients who are more likely to benefit from refraction at a four-week postoperative exam.
In this retrospective study, we used data of all 86,776 cataract surgeries performed in 2015 at a large tertiary-care eye hospital in India. The outcome variable was a binary indicator of whether the difference between corrected distance visual acuity and uncorrected visual acuity at the four-week postoperative exam was at least two lines on the Snellen chart. We examined the following statistical models: logistic regression, decision tree, pruned decision tree, random forest, weighted k-nearest neighbor, and a neural network. Predictor variables included in each model were patient sex and age, source eye (left or right), preoperative visual acuity, first-day postoperative visual acuity, intraoperative and immediate postoperative complications, and combined surgeries. We compared the predictive performance of models and assessed their clinical impact in test samples.
All models demonstrated predictive accuracy better than chance based on area under the receiver operating characteristic curve. In a targeting exercise with a fixed intervention budget, we found that gains from predictive models in identifying patients who would benefit from refraction ranged from 7.8% (increase from 1500 to 1617 patients) to 74% (increase from 250 to 435 patients).
The use of predictive statistical models to identify patients who are likely to benefit from refraction at follow-up can improve the economic efficiency of interventions. Simpler models like logistic regression perform almost as well as more complex machine-learning models, but are easier to implement.
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Details
- Title
- Use of predictive models to identify patients who are likely to benefit from refraction at a follow-up visit after cataract surgery
- Creators
- Sachin Gupta - Cornell UniversityMatthew J Schneider - Drexel UniversityS Ashok Vardhan - Aravind Eye HospitalThulasiraj Ravilla - LAICO, Aravind Eye Care System, Madurai, Tamilnadu, India
- Publication Details
- Indian journal of ophthalmology, v 69(10), pp 2695-2701
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:000754475500030
- Scopus ID
- 2-s2.0-85116435794
- Other Identifier
- 991019169569304721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Ophthalmology