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Dericioğlu V, Sevik MO, Bağatur Vurgun E, Çerman E. Predictive Factors of Complications and Visual Outcomes after Pediatric Cataract Surgery: A Single Referral Center Study from Türkiye. Turk J Ophthalmol 2023; 53:267-274. [PMID: 37867431 PMCID: PMC10599340 DOI: 10.4274/tjo.galenos.2023.50951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 02/26/2023] [Indexed: 10/24/2023] Open
Abstract
Objectives To evaluate the predictive factors of complications and visual acuity outcomes in pediatric cataract patients. Materials and Methods This retrospective, observational clinical study included 80 eyes of 50 patients treated for pediatric cataracts between 2010 and 2020. The eyes were divided into Group I (congenital cataracts, n=38) and Group II (developmental cataracts, n=42). Group II was also divided into Group IIA (aphakic, n=21) and Group IIB (pseudophakic, n=21). The effects of the age, laterality, cataract morphology, intraocular lens implantation, preoperative nystagmus/strabismus, and intraoperative anterior hyaloid rupture on complications and final best-corrected visual acuity (BCVA; logMAR) were evaluated. Results The median (interquartile range) age and follow-up time were 28 (5-79) months and 60 (29-84) months, respectively. There was a significant difference in mean final BCVA between Group I (0.79±0.46) and Group II (0.57±0.51) (p=0.047); however, no difference was observed between Group IIA and Group IIB (p=0.541). Having congenital cataract (p=0.045), preoperative nystagmus/strabismus (p=0.042), total/mature cataract (p<0.001), and postoperative complications (p=0.07) were significantly associated with final BCVA. However, in multivariate analysis, only total/mature cataract (β: 0.52, p<0.001) and having any complication (β: 0.24, p=0.018) were associated with final BCVA. Congenital cataract and intraoperative anterior hyaloid rupture were the only significant risk factors of postoperative complications on univariate (p=0.027 and p=0.003, respectively) and binary logistic regression analysis (odds ratio [OR]: 2.95 [95% confidence interval: 1.07-8.15], p=0.036 and OR: 4.28 [95% confidence interval: 1.55-11.77], p=0.005, respectively). Conclusion Total/mature cataract and the presence of any postoperative complication adversely affected the final BCVA. Having a congenital cataract and intraoperative anterior hyaloid membrane rupture increased the risk of complications.
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Affiliation(s)
- Volkan Dericioğlu
- Marmara University Faculty of Medicine, Department of Ophthalmology, İstanbul, Türkiye
| | - Mehmet Orkun Sevik
- Marmara University Faculty of Medicine, Department of Ophthalmology, İstanbul, Türkiye
| | - Elif Bağatur Vurgun
- Marmara University Faculty of Medicine, Department of Ophthalmology, İstanbul, Türkiye
| | - Eren Çerman
- Marmara University Faculty of Medicine, Department of Ophthalmology, İstanbul, Türkiye
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Long E, Chen J, Wu X, Liu Z, Wang L, Jiang J, Li W, Zhu Y, Chen C, Lin Z, Li J, Li X, Chen H, Guo C, Zhao L, Nie D, Liu X, Liu X, Dong Z, Yun B, Wei W, Xu F, Lv J, Li M, Ling S, Zhong L, Chen J, Zheng Q, Zhang L, Xiang Y, Tan G, Huang K, Xiang Y, Lin D, Zhang X, Dongye M, Wang D, Chen W, Liu X, Lin H, Liu Y. Artificial intelligence manages congenital cataract with individualized prediction and telehealth computing. NPJ Digit Med 2020; 3:112. [PMID: 32904507 PMCID: PMC7455726 DOI: 10.1038/s41746-020-00319-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 08/12/2020] [Indexed: 12/20/2022] Open
Abstract
A challenge of chronic diseases that remains to be solved is how to liberate patients and medical resources from the burdens of long-term monitoring and periodic visits. Precise management based on artificial intelligence (AI) holds great promise; however, a clinical application that fully integrates prediction and telehealth computing has not been achieved, and further efforts are required to validate its real-world benefits. Taking congenital cataract as a representative, we used Bayesian and deep-learning algorithms to create CC-Guardian, an AI agent that incorporates individualized prediction and scheduling, and intelligent telehealth follow-up computing. Our agent exhibits high sensitivity and specificity in both internal and multi-resource validation. We integrate our agent with a web-based smartphone app and prototype a prediction-telehealth cloud platform to support our intelligent follow-up system. We then conduct a retrospective self-controlled test validating that our system not only accurately detects and addresses complications at earlier stages, but also reduces the socioeconomic burdens compared to conventional methods. This study represents a pioneering step in applying AI to achieve real medical benefits and demonstrates a novel strategy for the effective management of chronic diseases.
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Affiliation(s)
- Erping Long
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Jingjing Chen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiaohang Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Zhenzhen Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Liming Wang
- School of Computer Science and Technology, Xidian University, Xi’an, China
- School of Software, Xidian University, Xi’an, China
| | - Jiewei Jiang
- School of Electronics Engineering, Xi’an University of Posts and Telecommunications, Xi’an, China
| | - Wangting Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yi Zhu
- Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, Florida USA
| | - Chuan Chen
- Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, Florida USA
| | - Zhuoling Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Jing Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiaoyan Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Hui Chen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Chong Guo
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Lanqin Zhao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Daoyao Nie
- Shenzhen Eye Hospital, Shenzhen Key Laboratory of Ophthalmology, Shenzhen University School of Medicine, Shenzhen, China
| | - Xinhua Liu
- Shenzhen Eye Hospital, Shenzhen Key Laboratory of Ophthalmology, Shenzhen University School of Medicine, Shenzhen, China
| | - Xin Liu
- Shenzhen Eye Hospital, Shenzhen Key Laboratory of Ophthalmology, Shenzhen University School of Medicine, Shenzhen, China
| | - Zhe Dong
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Bo Yun
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Wenbin Wei
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Fan Xu
- Department of Ophthalmology, People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi China
| | - Jian Lv
- Department of Ophthalmology, People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi China
| | - Min Li
- Department of Ophthalmology, People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi China
| | - Shiqi Ling
- Department of Ophthalmology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Lei Zhong
- Department of Ophthalmology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Junhong Chen
- Puning People’s Hospital, Southern Medical University, Jieyang, China
| | - Qishan Zheng
- Puning People’s Hospital, Southern Medical University, Jieyang, China
| | - Li Zhang
- Department of Ophthalmology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Xiang
- Department of Ophthalmology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Gang Tan
- The First Affiliated Hospital of University of South China, Hengyang, China
| | - Kai Huang
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510060 China
| | - Yifan Xiang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Duoru Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xulin Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Meimei Dongye
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Dongni Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Weirong Chen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiyang Liu
- School of Computer Science and Technology, Xidian University, Xi’an, China
- School of Software, Xidian University, Xi’an, China
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yizhi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
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Cao K, Wang J, Zhang J, Yusufu M, Jin S, Hou S, Zhu G, Wang B, Xiong Y, Li J, Li X, Chai L, He H, Wan XH. Efficacy and safety of vitrectomy for congenital cataract surgery: a systematic review and meta-analysis based on randomized and controlled trials. Acta Ophthalmol 2019; 97:233-239. [PMID: 30565873 PMCID: PMC6587933 DOI: 10.1111/aos.13974] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Accepted: 10/18/2018] [Indexed: 02/06/2023]
Abstract
PURPOSE To explore the effectiveness and safety of vitrectomy for congenital cataract surgery. METHODS We searched PubMed, Science Direct, The Cochrane Library, China National Knowledge Infrastructure and the Wanfang Database. Two researchers extracted data and assessed paper quality independently. Posterior capsule opacification (PCO) or visual axis opacification (VAO), reoperation rate, visual acuity, intraocular lenses (IOL) deposit, synechias, uveitis, secondary glaucoma, low-contrast sensitivity and IOL decentration were compared. RESULTS We included 11 randomized controlled trials (RCTs) with 634 congenital cataract eyes. Cases of posterior capsule opacification in vitrectomy group were significantly less than that of control group, with risk ratio (RR) of 0.15 [95% confidence interval (CI): 0.09, 0.26], and there was no heterogeneity (I2 = 0%, p = 0.94). Reoperation rate in vitrectomy group was lower than that of control group either (RR = 0.40, 95%CI: 0.17, 0.94), and there was no heterogeneity (I2 = 0%, p = 0.85). Best-corrected visual acuity (BCVA) measured in LogMAR unit of vitrectomy group was smaller, with a mean difference (MD) of -0.17 (95%CI: -0.28, -0.05), and I2 was only 22%, indicating of a small heterogeneity. No statistical difference was found between two groups on IOL deposit (RR = 1.23, 95%CI: 0.70, 2.17), and the heterogeneity was small (I2 = 16%, p = 0.31). No statistical difference was found between two groups on synechias (RR = 1.08, 95%CI: 0.60, 1.94), with a quite small heterogeneity (I2 = 3%, p = 0.38). No statistical difference was found between two groups on uveitis (RR = 0.55, 95%CI: 0.15, 2.01), and there was no heterogeneity (I2 = 0%, p = 0.94). There was no statistical difference on IOP either, with a MD of 0.25 (95%CI: -1.56, 2.07), and there was no heterogeneity (I2 = 0%). Egger's test showed that there was no publication bias for all assessed outcomes. Low-contrast sensitivity was better in the vitrectomy group. And no evidence indicated vitrectomy could lead to a higher risk on secondary glaucoma or IOL decentration. CONCLUSION Vitrectomy helps lower the PCO risk and reoperation risk after congenital cataract surgery, and also, vitrectomy helps patients gain a better BCVA and achieve a better low-contrast sensitivity, with no trade-off on IOP control, IOL deposit, synechias, uveitis and secondary glaucoma. We recommend performing vitrectomy during congenital cataract surgery.
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Affiliation(s)
- Kai Cao
- Beijing Institute of Ophthalmology Beijing Tongren Eye Center Beijing Key Laboratory of Ophthalmology and Visual Sciences Beijing Tongren Hospital of Capital Medical University Beijing China
| | - Jinda Wang
- Beijing Institute of Ophthalmology Beijing Tongren Eye Center Beijing Key Laboratory of Ophthalmology and Visual Sciences Beijing Tongren Hospital of Capital Medical University Beijing China
| | - Jingshang Zhang
- Beijing Institute of Ophthalmology Beijing Tongren Eye Center Beijing Key Laboratory of Ophthalmology and Visual Sciences Beijing Tongren Hospital of Capital Medical University Beijing China
| | - Mayinuer Yusufu
- Beijing Institute of Ophthalmology Beijing Tongren Eye Center Beijing Key Laboratory of Ophthalmology and Visual Sciences Beijing Tongren Hospital of Capital Medical University Beijing China
| | - Shanshan Jin
- Beijing Institute of Ophthalmology Beijing Tongren Eye Center Beijing Key Laboratory of Ophthalmology and Visual Sciences Beijing Tongren Hospital of Capital Medical University Beijing China
| | - Simeng Hou
- Beijing Institute of Ophthalmology Beijing Tongren Eye Center Beijing Key Laboratory of Ophthalmology and Visual Sciences Beijing Tongren Hospital of Capital Medical University Beijing China
| | - Guyu Zhu
- Beijing Institute of Ophthalmology Beijing Tongren Eye Center Beijing Key Laboratory of Ophthalmology and Visual Sciences Beijing Tongren Hospital of Capital Medical University Beijing China
| | - Bingsong Wang
- Beijing Institute of Ophthalmology Beijing Tongren Eye Center Beijing Key Laboratory of Ophthalmology and Visual Sciences Beijing Tongren Hospital of Capital Medical University Beijing China
| | - Ying Xiong
- Beijing Tongren Eye Center Beijing Key Laboratory of Ophthalmology and Visual Sciences Beijing Tongren Hospital of Capital Medical University Beijing China
| | - Jing Li
- Beijing Tongren Eye Center Beijing Key Laboratory of Ophthalmology and Visual Sciences Beijing Tongren Hospital of Capital Medical University Beijing China
| | - Xiaoxia Li
- Beijing Institute of Ophthalmology Beijing Tongren Eye Center Beijing Key Laboratory of Ophthalmology and Visual Sciences Beijing Tongren Hospital of Capital Medical University Beijing China
| | - Lijing Chai
- Beijing Tongren Eye Center Beijing Key Laboratory of Ophthalmology and Visual Sciences Beijing Tongren Hospital of Capital Medical University Beijing China
| | - Hailong He
- Beijing Institute of Ophthalmology Beijing Tongren Eye Center Beijing Key Laboratory of Ophthalmology and Visual Sciences Beijing Tongren Hospital of Capital Medical University Beijing China
| | - Xiu H. Wan
- Beijing Institute of Ophthalmology Beijing Tongren Eye Center Beijing Key Laboratory of Ophthalmology and Visual Sciences Beijing Tongren Hospital of Capital Medical University Beijing China
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