{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T19:58:11Z","timestamp":1787169491034,"version":"build-2736575974"},"reference-count":23,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"10","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose:<\/jats:title>\n                    <jats:p>To develop predictive models to identify cataract surgery patients who are more likely to benefit from refraction at a four-week postoperative exam.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/ijo.ijo_661_21","type":"journal-article","created":{"date-parts":[[2021,10,5]],"date-time":"2021-10-05T11:42:39Z","timestamp":1633434159000},"page":"2695-2701","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":2,"title":["Use of predictive models to identify patients who are likely to benefit from refraction at a follow-up visit after cataract surgery"],"prefix":"10.4103","volume":"69","author":[{"given":"Sachin","family":"Gupta","sequence":"first","affiliation":[{"name":"SC Johnson College of Business, Cornell University, Ithaca, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew J","family":"Schneider","sequence":"additional","affiliation":[{"name":"LeBow College of Business, Drexel University, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S Ashok","family":"Vardhan","sequence":"additional","affiliation":[{"name":"Aravind Eye Hospital, Tirupati, Andhra Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thulasiraj","family":"Ravilla","sequence":"additional","affiliation":[{"name":"LAICO, Aravind Eye Care System, Madurai, Tamilnadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2021,9,25]]},"reference":[{"key":"B1-20240828","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1016\/j.ophtha.2018.12.044","article-title":"Evidence on importance of follow-up visits after cataract surgery","volume":"126","author":"Gupta","year":"2019","journal-title":"Ophthalmology"},{"key":"B2-20240828","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1016\/j.jcrs.2019.02.024","article-title":"Predictors of patient compliance with follow-up visits after cataract surgery","volume":"45","author":"Gupta","year":"2019","journal-title":"J Cataract Refract Surg"},{"key":"B3-20240828","doi-asserted-by":"crossref","first-page":"63","DOI":"10.2471\/BLT.07.041210","article-title":"Global magnitude of visual impairment caused by uncorrected refractive errors in 2004","volume":"86","author":"Resnikoff","year":"2008","journal-title":"Bull World Health Organ"},{"key":"B4-20240828","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1136\/bjo.2008.152744","article-title":"Outcome of cataract surgery at one year in Kenya, the Philippines and Bangladesh","volume":"93","author":"Lindfield","year":"2009","journal-title":"Br J Ophthalmol"},{"key":"B5-20240828","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1016\/j.jcrs.2019.06.023","article-title":"You don't know what you don't know","volume":"45","author":"Dupps","year":"2019","journal-title":"J Cataract Refract Surg"},{"key":"B6-20240828","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1136\/bjo.2004.045369","article-title":"Routine monitoring of visual outcome of cataract surgery. Part 2:Results from eight study centres","volume":"89","author":"Limburg","year":"2005","journal-title":"Br J Ophthalmol"},{"key":"B7-20240828","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1111\/j.1442-9071.2012.02754.x","article-title":"The challenge of follow-up visits after cataract surgery in the developing world","volume":"40","author":"Briesen","year":"2012","journal-title":"Clin Experiment Ophthalmol"},{"key":"B8-20240828","doi-asserted-by":"crossref","first-page":"e37","DOI":"10.1016\/S2214-109X(13)70003-2","article-title":"Assessment of cataract surgical outcomes in settings where follow-up is poor:PRECOG, a multicentre observational study","volume":"1","author":"Congdon","year":"2013","journal-title":"Lancet Glob Health"},{"key":"B9-20240828","doi-asserted-by":"crossref","first-page":"e0144853","DOI":"10.1371\/journal.pone.0144853","article-title":"Cataract surgery visual outcomes and associated risk factors in secondary level eye care centers of L V Prasad Eye Institute, India","volume":"11","author":"Matta","year":"2016","journal-title":"PloS One"},{"key":"B10-20240828","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1136\/bjo.86.5.543","article-title":"Does prospective monitoring improve cataract surgery outcomes in Africa?","volume":"86","author":"Yorston","year":"2002","journal-title":"Br J Ophthalmol"},{"key":"B11-20240828","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s10792-007-9182-2","article-title":"Monitoring cataract surgical outcome in a Nigerian mission hospital","volume":"29","author":"Ezegwui","year":"2009","journal-title":"Int Ophthalmol"},{"key":"B12-20240828","doi-asserted-by":"crossref","first-page":"586","DOI":"10.4103\/ijo.IJO_986_20","article-title":"Correlation between visual acuity at discharge and on final follow-up in patients undergoing manual small incision cataract surgery","volume":"69","author":"Kurian","year":"2021","journal-title":"Indian J Ophthalmol"},{"key":"B13-20240828","doi-asserted-by":"crossref","first-page":"e132","DOI":"10.1016\/S2214-109X(13)70061-5","article-title":"Surgical follow-up in low-income and middle-income countries","volume":"1","author":"Ologunde","year":"2013","journal-title":"Lancet Glob Health"},{"key":"B14-20240828","doi-asserted-by":"crossref","first-page":"67","DOI":"10.3109\/09286586.2011.628777","article-title":"Compliance with follow-up after cataract surgery in rural China","volume":"19","author":"Huang","year":"2012","journal-title":"Ophthalmic Epidemiol"},{"key":"B15-20240828","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1001\/jamaophthalmol.2016.4735","article-title":"Cost and expected visual effect of interventions to improve follow-up after cataract surgery:Prospective review of early cataract outcomes and grading (PRECOG) study","volume":"135","author":"Meltzer","year":"2017","journal-title":"JAMA Ophthalmol"},{"key":"B16-20240828","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1136\/bjo.70.6.411","article-title":"Use of a grading system in the evaluation of complications in a randomised controlled trial on cataract surgery. Oxford Cataract Treatment and Evaluation Team (OCTET)","volume":"70","year":"1986","journal-title":"Br J Ophthalmol"},{"key":"B18-20240828","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"B19-20240828","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.ophtha.2016.12.018","article-title":"Predictive medicine in ophthalmology","volume":"124","author":"Yan","year":"2017","journal-title":"Ophthalmology"},{"key":"B20-20240828","doi-asserted-by":"crossref","first-page":"66","DOI":"10.6000\/1929-6029.2018.07.03.1","article-title":"A note on the area under the gains chart","volume":"7","author":"Lloyd","year":"2018","journal-title":"Int J Stat Med Res"},{"key":"B21-20240828","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1016\/j.ophtha.2006.08.057","article-title":"Why do phacoemulsification? Manual small-incision cataract surgery is almost as effective, but less expensive","volume":"114","author":"Gogate","year":"2007","journal-title":"Ophthalmology"},{"key":"B22-20240828","doi-asserted-by":"crossref","first-page":"150","DOI":"10.4103\/kjo.kjo_54_19","article-title":"Role of artificial intelligence and machine learning in ophthalmology","volume":"31","author":"Akkara","year":"2019","journal-title":"Kerala J Ophthalmol"},{"key":"B23-20240828","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.ajo.2018.10.007","article-title":"Using deep learning and transfer learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images","volume":"198","author":"Asaoka","year":"2019","journal-title":"Am J Ophthalmol"},{"key":"B24-20240828","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"}],"container-title":["Indian Journal of Ophthalmology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.4103\/ijo.IJO_661_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T19:06:39Z","timestamp":1787166399000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.4103\/ijo.IJO_661_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,25]]},"references-count":23,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2021]]}},"URL":"https:\/\/doi.org\/10.4103\/ijo.ijo_661_21","relation":{},"ISSN":["0301-4738","1998-3689"],"issn-type":[{"value":"0301-4738","type":"print"},{"value":"1998-3689","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,25]]},"assertion":[{"value":"2021-03-23","name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-06-13","name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}}]}}