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Retina  |   August 2022
Prediction of Visual Outcomes After Diabetic Vitrectomy Using Clinical Factors From Common Data Warehouse
Author Affiliations & Notes
  • Seong-Su Lee
    Department of Endocrinology, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
  • Dong Jin Chang
    Department of Ophthalmology, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
  • Jin Woo Kwon
    Department of Ophthalmology, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
  • Ji Won Min
    Department of Nephrology, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
  • Kwanhoon Jo
    Department of Endocrinology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
  • Young-Sik Yoo
    Department of Ophthalmology, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
  • Byul Lyu
    Department of Ophthalmology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
  • Jiwon Baek
    Department of Ophthalmology, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Gyeonggi-do, Republic of Korea
    Department of Ophthalmology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
  • Correspondence: Jiwon Baek, Department of Ophthalmology, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, #327 Sosa-ro, Wonmi-gu, Bucheon, Gyeonggi-do 14647, Republic of Korea. e-mail: md.jiwon@gmail.com 
  • Footnotes
    *  SSL and DJC contributed equally to this work.
Translational Vision Science & Technology August 2022, Vol.11, 25. doi:https://doi.org/10.1167/tvst.11.8.25
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      Seong-Su Lee, Dong Jin Chang, Jin Woo Kwon, Ji Won Min, Kwanhoon Jo, Young-Sik Yoo, Byul Lyu, Jiwon Baek; Prediction of Visual Outcomes After Diabetic Vitrectomy Using Clinical Factors From Common Data Warehouse. Trans. Vis. Sci. Tech. 2022;11(8):25. doi: https://doi.org/10.1167/tvst.11.8.25.

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Abstract

Purpose: We sought to analyze the visual outcome and systemic prognostic factors for diabetic vitrectomy and predicted outcomes using these factors.

Methods: This was a multicenter electronic medical records (EMRs) review study of 1504 eyes with type 2 diabetes that underwent vitrectomy for proliferative diabetic retinopathy at 6 university hospitals. Demographics, laboratory results, intra-operative findings, and visual acuity (VA) values were analyzed and correlated with visual outcomes at 1 year after the vitrectomy. Prediction models for visual outcomes were obtained using machine learning.

Results: At 1 year, VA was 1.0 logarithm of minimal angle resolution (logMAR) or greater (poor visual outcome group) in 456 eyes (30%). Baseline visual acuity, duration of diabetes treatment, tractional membrane, silicone oil tamponade, smoking, and vitreous hemorrhage correlated with logMAR VA at 1 year (r = 0.450, −0.159, 0.221, 0.280, 0.067, and −0.105; all P ≤ 0.036). An ensemble decision tree model trained using all variables generated accuracy, specificity, F1 score (the harmonic means of which precision and sensitivity), and receiver-operating characteristic curve area under curve values of 0.77, 0.66, 0.85, and 0.84 for the prediction of poor visual outcomes at 1 year after vitrectomy.

Conclusions: Visual outcome after diabetic vitrectomy is associated with pre- and intra-operative findings and systemic factors. Poor visual outcome after diabetic vitrectomy was predictable using clinical factors. Intensive care in patients who are predicted to result in poor vision may limit vision loss resulting from type 2 diabetes.

Translational Relevance: This study demonstrates that a real world EMR big data could predict outcome after diabetic vitrectomy using clinical factors.

Introduction
Diabetic retinopathy (DR) is the leading cause of blindness among adults aged 20 to 74 years.1,2 As DR progresses, the development of new vessels leads to vitreous hemorrhage and tractional membrane formation, which can result in a devastating deterioration of vision in patients with diabetes.1,3 To minimize vision loss, surgical intervention is often required. To date, vitrectomy has been the mainstay surgical treatment for blinding complications of advanced DR, including vitreous hemorrhage and tractional retinal detachment.4 
Factors associated with visual outcome after diabetic vitrectomy have previously been analyzed.5,6 Systemic conditions, such as the duration of diabetes, comorbid hypertension, and coronary vascular disease, as well as pre-operative ocular findings, including vision in the operated and fellow eyes, macular detachment, and long-acting intraocular tamponade, are known to be prognostic factors.7,8 However, available results are limited by small sample study population numbers and various follow-up durations. 
Recent advances in machine learning and deep learning in the field of medicine have shown promising performance in the prediction of diseases based on a larger-sized database.911 The application of artificial intelligence in DR has mostly focused on the diagnosis and prognosis prediction of DR stage using retinal images.12,13 In this study, we analyzed 1-year visual outcomes in a large number of patients who underwent vitrectomy for proliferative DR (PDR) at 6 university hospitals and assessed correlated systemic prognostic factors. Using clinical factors, models for predicting visual outcome were trained and validated. 
Materials and Methods
This study was approved by the institutional review board of the Catholic University Medical Center as well as each of the following involved hospitals: Bucheon St. Mary's Hospital (in Gyeonggi-do, Korea), Incheon St. Mary's Hospital (in Incheon, Korea), Yeoeuido St. Mary's Hospital (in Seoul, Korea), Euijeongbu St. Mary's Hospital (in Gyeonggi-do, Korea), Eunpyeong St. Mary's Hospital (in Seoul, Korea), and St. Vincent's Hospital (in Gyeonggi-do, Korea). The need for written informed consent was waived due to this study's retrospective design, and the investigation was conducted in accordance with the tenets of the Declaration of Helsinki (institutional review board [IRB] number: XC20WIDI0127). 
Data Preparation
From 6 referral hospitals that share the same electronic medical records system, the medical records of patients diagnosed with type 2 diabetes mellitus (T2DM) by internists who underwent vitrectomy for DR and were followed up with for at least 1 year between January 2009 and July 2020 were obtained. The diagnosis of type T2DM was made based on a fasting plasma glucose level of at least 126 mg/dL or 2-hour post-glucose level of at least 200 mg/dL after a 75-g oral glucose tolerance test.14 Patients who underwent vitrectomy for PDR were identified by operation title and diagnosis for operation. Included diagnoses were vitreous hemorrhage, proliferative membrane, and/or tractional retinal detachment. 
Clinical data—including age at operation; duration of T2DM treatment in the referral hospital; sex, height, and weight; smoking status; systolic and diastolic blood pressure values; and the use of insulin, aspirin, and clopidogrel—were collected. Body mass index (BMI) and mean arterial pressure (MAP) were calculated. Co-existing hypertension, chronic kidney disease (CKD), cardiovascular disease, and cerebrovascular disease were assessed. From laboratory tests, serum levels of glucose at random, glycated hemoglobin (HbA1c), alanine aminotransferase (AST), aspartate aminotransferase (ALT), blood urea nitrogen (BUN), and creatinine within 1 month prior to surgery were collected. From ophthalmologic records, visual acuity (VA) values at baseline and 1, 3, 6, and 12 months after surgery; intra-operative findings (e.g. vitreous hemorrhage, tractional membrane, macular edema, and neovascular glaucoma), use of pre-, intra-, or postoperative bevacizumab; and concomitant procedures (e.g. phacoemulsification, scleral encircling, and silicone oil tamponade) were collected. 
Training and Evaluation of the Prediction Models
All collected variables were included for developing a prediction model for poor visual outcomes (i.e. VA 1.0 logarithm of minimal angle resolution [logMAR] or greater) after diabetic vitrectomy at 1 year. The data were randomly divided into training and validation (80%), and test sets (20%) using “cvpartition” function in MATLAB. Training and validation were performed using 15-fold cross validation. Prediction models were trained using support vector machine (SVM), naïve Bayes, decision tree, ensemble decision tree, and neural network approaches. Fifteen-fold cross-validation was used to validate these models. Naïve Bayes and ensemble decision tree models were obtained using the optimization process. Each trained model was tested on a test set. All experiments were performed using MATLAB 2020a (MathWorks, Inc., Natick, MA, USA). 
Statistics
Statistical analysis was performed using MATLAB 2020a. VA values were converted to logMAR values for statistical purposes. A t-test was used to compare continuous variables between groups, whereas the Mann–Whitney U test was used when normal distribution was not confirmed. The chi-squared test was used for categorical variables. Repeated measures analysis of variance (RM-ANOVA) was used to compare VA values at each time point. Pearson's correlation was used to assess the relationship between final VA and continuous clinical variables. The performance of models was evaluated using accuracy, specificity, F1 score, and area under the receiver operating characteristic (ROC) curve (AUC). The F1 score was calculated as 2 × (precision) × (sensitivity) / [(precision) + (sensitivity)]. Continuous variables are presented as mean ± standard deviation values. 
Results
Baseline Characteristics
A total of 1504 eyes from 1175 patients with a mean age of 54.5 ± 11.4 years (range = 19.2–90.1 years), 54% of whom were male patients, were included. All study participants were Korean. The mean duration of diabetes mellitus (DM) treatment was 3.4 ± 3.9 years (range = 0–18.9 years). Forty-five percent of participants had CKD, 64% had hypertension, 25% had cerebrovascular disease, and 25% had cardiovascular disease. Fifty-three percent of the included eyes were right eyes. 
Among the total study group, 456 eyes (30.3%) had final vision at 1 year of 1.0 logMAR or greater (Snellen equivalent 20/200 or less; poor vision group) and 939 eyes had final vision at 1 year of less than 1.0 logMAR (good vision group). The duration of DM treatment, presence of vitreous hemorrhage, tractional membrane, and concurrent scleral encircling and silicone oil tamponade significantly differed between the 2 groups (P < 0.001, < 0.001, < 0.001, 0.024, and < 0.001). The baseline patient characteristics are summarized in Table 1
Table 1.
 
Baseline Characteristics of Enrolled Subjects
Table 1.
 
Baseline Characteristics of Enrolled Subjects
VA Change
At 1 year after vitrectomy, vision was 20/40 or better in 586 eyes (39.0%). During the year after surgery, 1188 eyes (78.9%) experienced improved or consistent vision. The mean VA improved from 1.15 ± 0.82 logMAR to 0.85 ± 0.79 logMAR (P < 0.001, RM-ANOVA) in the operated eye. The mean VA of the fellow eye also improved—albeit to a lesser extent than that in the operated eye—from 0.72 ± 0.77 logMAR to 0.64 ± 0.72 logMAR (P < 0.001, RM-ANOVA). When divided into bilateral and unilateral cases, the VA improvement in the fellow eye was observed in the bilateral cases only (P = 0.054 in the unilateral group and P < 0.001 in the bilateral group, RM-ANOVA; Supplementary Fig. S1). Visual improvement was greatest from postoperative months 1 to 3 in both eyes (both P < 0.001; Fig. 1A). 
Figure 1.
 
VA changes during the 1-year follow-up after diabetic vitrectomy. (A) VA improved in the operated eyes and in the fellow eyes (both P < 0.001, RM-ANOVA). (B) The good visual outcome group showed significant improvement in vision (P < 0.001, RM-ANOVA), whereas the poor visual outcome group experienced deterioration in vision (P < 0.001, RM-ANOVA). P values: paired t-test with the value of the previous follow-up period. *Statistically significant P value.
Figure 1.
 
VA changes during the 1-year follow-up after diabetic vitrectomy. (A) VA improved in the operated eyes and in the fellow eyes (both P < 0.001, RM-ANOVA). (B) The good visual outcome group showed significant improvement in vision (P < 0.001, RM-ANOVA), whereas the poor visual outcome group experienced deterioration in vision (P < 0.001, RM-ANOVA). P values: paired t-test with the value of the previous follow-up period. *Statistically significant P value.
When participants were divided into poor and good vision groups, the good vision group experienced a significant improvement in vision (from 0.95 ± 0.80 logMAR to 0.41 ± 0.37 logMAR; P < 0.001, RM-ANOVA), whereas the poor vision group experienced a deterioration in vision (from 1.57 ± 0.71 logMAR to 1.77 ± 0.59 logMAR; P < 0.001, RM-ANOVA). In the poor vision group, vision did not improve at any time during the follow-up period relative to at baseline (Fig. 1B). 
Risk Factor Analysis
Baseline VA positively correlated with the VA at 1 year after vitrectomy (r = 0.450 and P < 0.001) whereas the duration of T2DM treatment showed a negative correlation (r = −0.159 and P < 0.001; Table 2) on Pearson's correlation analysis. 
Table 2.
 
Correlation Between Visual Acuity at 1 Year After Vitrectomy and Clinical Variables
Table 2.
 
Correlation Between Visual Acuity at 1 Year After Vitrectomy and Clinical Variables
Forward conditional binary logistic regression for poor visual outcome revealed sex; diabetes treatment duration, tractional membrane; silicone oil tamponade; and baseline VA values of the operated eye fellow to be significant associated factors (B = 1.479, 1.060, 0.405, 0.403, 0.278, and 0.726; P = 0.018, = 0.008, < 0.001, < 0.001, < 0.001, and = 0.004; Table 3). 
Table 3.
 
Multivariable Binary Logistic Regression for Poor Visual Outcome
Table 3.
 
Multivariable Binary Logistic Regression for Poor Visual Outcome
Prediction Models
Machine learning models for the prediction of a poor visual outcome were trained using all the variables in Tables 1 and 2. Prediction models trained using logistic regression, SVM, naïve Bayes, decision trees, ensemble decision trees, and neural networks yielded AUC values of 0.74, 0.83, 0.74, 0.75, 0.84, and 0.77, respectively, and F1 scores of 0.81, 0.85, 0.81, 0.84, 0.85, and 0.84 points, respectively, for the test set (Table 4). Predictor importance analysis of the ensemble decision tree revealed baseline VA of the study eye, age at vitrectomy, duration of DM, glucose, ALT, HbA1c, BMI, BUN, creatinine, smoking, AST, MAP, VA of the fellow eye, systolic blood pressure, tractional membrane, and silicone oil tamponade as important predictors for poor visual outcome after diabetic vitrectomy (Fig. 2). 
Table 4.
 
Performance of Machine Learning Classifiers in the Prediction of Poor Visual Outcome After Diabetic Vitrectomy
Table 4.
 
Performance of Machine Learning Classifiers in the Prediction of Poor Visual Outcome After Diabetic Vitrectomy
Figure 2.
 
Important predictors for poor visual outcome after diabetic vitrectomy. A histogram of the importance of variables obtained from an ensemble decision tree prediction model for predicting poor visual outcomes after diabetic vitrectomy.
Figure 2.
 
Important predictors for poor visual outcome after diabetic vitrectomy. A histogram of the importance of variables obtained from an ensemble decision tree prediction model for predicting poor visual outcomes after diabetic vitrectomy.
Discussion
Vitrectomy for DR significantly improved the visual outcome, thereby enhancing the quality of life of patients with PDR. Since its introduction, several decades have passed and vitrectomy has since achieved remarkable advancements using modern operating systems. Nonetheless, some patients do not experience improvements in vision and may persist at the level of legal blindness even after surgery. Knowing risk factors for poor visual outcome and developing a prediction model for patient stratification may be of great help in reducing blindness caused by complications of T2DM. 
In this study, we analyzed systemic and intra-operative risk factors of poor visual outcome after diabetic vitrectomy, developed prediction models for visual outcomes using these factors, and assessed the performance of these prediction models. The results revealed baseline VA, duration of diabetes treatment at the referral hospital, tractional membrane, silicone oil tamponade, smoking, and vitreous hemorrhage to be relevant factors. Machine learning models trained using these factors could predict poor visual outcomes at 1 year after vitrectomy with an accuracy of up to 0.77. 
Final vision at 1 year was 20/40 or better in about 39% of the treated eyes, which is comparable to findings of other recent studies.7,15,16 The Diabetic Retinopathy in Various Ethnic Groups (DRIVE-UK) study reported that visual outcomes were improved significantly in eyes with complications attributed to DR relative to those previously reported in the Diabetic Retinopathy Vitrectomy Study.7,17 The proportion of eyes achieving vision of 20/40 or better improved from 11% to 20% to 38% in the last 3 decades. A large proportion of patients with end-stage DR can retain their vision with vitrectomy. Furthermore, as the tendency for the VA to stabilize by 1 year after vitrectomy performed for DR had been reported, this outcome may suggest the eventual or long-term visual outcome.18 
Tractional membrane, silicone oil tamponade, smoking, and baseline VA were correlated with the final VA and also associated with poor vision after diabetic vitrectomy. These factors were reported also to be predictors for poor vision in previous studies.7,19,20 Conversely, vitreous hemorrhage was reported to be a protective factor, and this was made evident again in the current study. The duration of T2DM treatment at the referral hospital was also a protective factor in this study. This may imply that the visual outcome can be enhanced with rigorous DM control for longer periods prior to the operation. Additionally, BUN and creatinine were important predictors in the machine learning model in this study. The association between kidney function and DR progression is well studied.2123 Regarding diabetic vitrectomy, kidney function was also reported to be a factor affecting postoperative vision and recurrent hemorrhage.20,24 However, this result requires further validation as estimated glomerular filtration rate, an important parameter for kidney function, did not correlate with visual outcome after diabetic vitrectomy.25 Other factors identified as important predictors in the machine learning model, such as liver function and smoking, should similarly be studied in depth in subsequent studies. 
There is currently no available model for the prediction of the outcome after diabetic vitrectomy. Although factors to help predict the outcome have been studied, participant numbers in previous studies were relatively inadequate for developing a proper prediction model.19,20,26 In contrast, we were able to train and test prediction models for discerning poor visual outcomes after diabetic vitrectomy by including a large number of patients from multiple referral hospitals. The models trained using machine learning demonstrated relatively fair performance in prediction. In particular, the ensemble decision tree and SVM models showed the best performance with AUC, F1 score, and accuracy values of 0.84, 0.85, and 0.77 and 0.83, 0.85, and 0.76, respectively. Additional pre-operative imaging results, such as those from fundus photography, optical coherence tomography, or B-scan ultrasonography, may enhance the performance of prediction models in the future. 
This study has several limitations inherent to its nature of being retrospective and a medical records review study. The duration of diabetes was not included in the study because the exact necessary information could not be acquired. In addition, factors other than those evaluated in this study, such as duration of surgery, cholesterol level, and serum albumin concentration, may have affected the results.7,27 To minimize this disadvantage, we tried to include as many available relevant factors as possible. Additionally, both 23-gauge and 25-gauge systems for vitrectomy were included in the analysis, but the effect of this is likely negligible according to Ding et al.24 Difference in skill level of the surgeon and duration of surgery might have affected the visual outcome. Furthermore, medical and ophthalmologic diagnoses were assessed based on the disease code entered by clinicians and were not evaluated in detail. A more detailed evaluation of patients’ medical conditions would have been ideal. In addition, lack of generalizability needs to be considered because data were from same network of clinics. Nonetheless, the diagnostic codes were entered by experienced clinicians according to their expert judgments. 
To summarize, the visual outcome after diabetic vitrectomy was associated with pre- and intra-operative findings and systemic factors, which included baseline VA, tractional membrane, and silicone oil tamponade. Prediction models trained using these factors via machine learning could identify eyes that may demonstrate poor vision after diabetic vitrectomy. Intensive care in these patients may reduce vision loss caused by diabetes. 
Acknowledgments
The authors thank the Catholic Medical Center Research Foundation for financial support made in the program year 2022. 
Disclosure: S.-S. Lee, None; D.J. Chang, None; J.W. Kwon, None; J.W. Min, None; K. Jo, None; Y.-S. Yoo, None; B. Lyu, None; J. Baek, None 
References
Cheung N, Mitchell P, Wong TY. Diabetic retinopathy. Lancet. 2010; 376: 124–136. [CrossRef] [PubMed]
Yau JW, Rogers SL, Kawasaki R, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care. 2012; 35: 556–564. [CrossRef] [PubMed]
Ting DS, Cheung GC, Wong TY. Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review. Clin Exp Ophthalmol. 2016; 44: 260–277. [CrossRef] [PubMed]
Aaberg TM, Abrams GW. Changing indications and techniques for vitrectomy in management of complications of diabetic retinopathy. Ophthalmology. 1987; 94: 775–779. [CrossRef] [PubMed]
Ratnarajan G, Mellington F, Saldanha M, de Silva SR, Benjamin L. Long-term visual and retinopathy outcomes in a predominately type 2 diabetic patient population undergoing early vitrectomy and endolaser for severe vitreous haemorrhage. Eye (Lond). 2011; 25: 704–708; quiz 709. [CrossRef] [PubMed]
Kim BZ, Lee KL, Guest SJ, Worsley D. Long-term survival following diabetic vitrectomy. N Z Med J. 2017; 130: 69–77. [PubMed]
Gupta B, Sivaprasad S, Wong R, et al. Visual and anatomical outcomes following vitrectomy for complications of diabetic retinopathy: the DRIVE UK study. Eye (Lond). 2012; 26: 510–516. [CrossRef] [PubMed]
Yorston D, Wickham L, Benson S, Bunce C, Sheard R, Charteris D. Predictive clinical features and outcomes of vitrectomy for proliferative diabetic retinopathy. Br J Ophthalmol. 2008; 92: 365–368. [CrossRef] [PubMed]
Arcadu F, Benmansour F, Maunz A, Willis J, Haskova Z, Prunotto M. Deep learning algorithm predicts diabetic retinopathy progression in individual patients. NPJ Digit Med. 2019; 2: 92. [CrossRef] [PubMed]
Makino M, Yoshimoto R, Ono M, et al. Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning. Sci Rep. 2019; 9: 11862. [CrossRef] [PubMed]
Heo J, Yoon JG, Park H, Kim YD, Nam HS, Heo JH. Machine Learning-Based Model for Prediction of Outcomes in Acute Stroke. Stroke. 2019; 50: 1263–1265. [CrossRef] [PubMed]
Ting DSW, Cheung CY-L, Lim G, et al. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. JAMA. 2017; 318: 2211–2223. [CrossRef] [PubMed]
Ting DSW, Cheung CY, Nguyen Q, et al. Deep learning in estimating prevalence and systemic risk factors for diabetic retinopathy: a multi-ethnic study. NPJ Digit Med. 2019; 2: 24. [CrossRef] [PubMed]
Lipsky BA, Senneville É, Abbas ZG, et al. Guidelines on the diagnosis and treatment of foot infection in persons with diabetes (IWGDF 2019 update). Diabetes Metab Res Rev. 2020; 36(Suppl 1): e3280. [PubMed]
Rice JC, Steffen J. Outcomes of vitrectomy for advanced diabetic retinopathy at Groote Schuur Hospital, Cape Town, South Africa. S Afr Med J. 2015; 105: 496–499. [CrossRef] [PubMed]
Kaidonis G, Hassall MM, Phillips R, et al. Visual outcomes following vitrectomy for diabetic retinopathy amongst Indigenous and non-Indigenous Australians in South Australia and the Northern Territory. Clin Exp Ophthalmol. 2018; 46: 417–423. [CrossRef] [PubMed]
The Diabetic Retinopathy Vitrectomy Study Research Group. Early vitrectomy for severe vitreous hemorrhage in diabetic retinopathy. Two-year results of a randomized trial. Diabetic Retinopathy Vitrectomy Study report 2. Arch Ophthalmol. 1985; 103: 1644–1652. [CrossRef] [PubMed]
Nakazawa M, Kimizuka Y, Watabe T, et al. Visual outcome after vitrectomy for diabetic retinopathy. A five-year follow-up. Acta Ophthalmol (Copenh). 1993; 71: 219–223. [CrossRef] [PubMed]
Nishi K, Nishitsuka K, Yamamoto T, Yamashita H. Factors correlated with visual outcomes at two and four years after vitreous surgery for proliferative diabetic retinopathy. PLoS One. 2021; 16: e0244281. [CrossRef] [PubMed]
Someya H, Takayama K, Takeuchi M, et al. Outcomes of 25-Gauge Vitrectomy for Tractional and Nontractional Diabetic Macular Edema with Proliferative Diabetic Retinopathy. J Ophthalmol. 2019; 2019: 5304524. [CrossRef] [PubMed]
Min JW, Kim HD, Park SY, et al. Relationship Between Retinal Capillary Nonperfusion Area and Renal Function in Patients With Type 2 Diabetes. Invest Ophthalmol Vis Sci. 2020; 61: 14. [CrossRef] [PubMed]
Grunwald JE, Pistilli M, Ying GS, et al. Association Between Progression of Retinopathy and Concurrent Progression of Kidney Disease: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study. JAMA Ophthalmol. 2019; 137: 767–774. [CrossRef] [PubMed]
Lee WJ, Sobrin L, Kang MH, et al. Ischemic diabetic retinopathy as a possible prognostic factor for chronic kidney disease progression. Eye (Lond). 2014; 28: 1119–1125. [CrossRef] [PubMed]
Ding Y, Yao B, Hang H, Ye H. Multiple factors in the prediction of risk of recurrent vitreous haemorrhage after sutureless vitrectomy for non-clearing vitreous haemorrhage in patients with diabetic retinopathy. BMC Ophthalmol. 2020; 20: 292. [CrossRef] [PubMed]
Larrañaga-Fragoso P, Laviers H, McKechnie C, Zambarakji H. Surgical outcomes of vitrectomy surgery for proliferative diabetic retinopathy in patients with abnormal renal function. Graefes Arch Clin Exp Ophthalmol. 2020; 258: 63–70. [CrossRef] [PubMed]
Sulak M, Urbancic M, Petrovic MG. Predicting visual outcomes of second eye vitrectomy for proliferative diabetic retinopathy. Retina. 2018; 38: 698–707. [CrossRef] [PubMed]
Motoda S, Shiraki N, Ishihara T, et al. Predictors of postoperative bleeding after vitrectomy for vitreous hemorrhage in patients with diabetic retinopathy. J Diabetes Investig. 2018; 9: 940–945. [CrossRef] [PubMed]
Figure 1.
 
VA changes during the 1-year follow-up after diabetic vitrectomy. (A) VA improved in the operated eyes and in the fellow eyes (both P < 0.001, RM-ANOVA). (B) The good visual outcome group showed significant improvement in vision (P < 0.001, RM-ANOVA), whereas the poor visual outcome group experienced deterioration in vision (P < 0.001, RM-ANOVA). P values: paired t-test with the value of the previous follow-up period. *Statistically significant P value.
Figure 1.
 
VA changes during the 1-year follow-up after diabetic vitrectomy. (A) VA improved in the operated eyes and in the fellow eyes (both P < 0.001, RM-ANOVA). (B) The good visual outcome group showed significant improvement in vision (P < 0.001, RM-ANOVA), whereas the poor visual outcome group experienced deterioration in vision (P < 0.001, RM-ANOVA). P values: paired t-test with the value of the previous follow-up period. *Statistically significant P value.
Figure 2.
 
Important predictors for poor visual outcome after diabetic vitrectomy. A histogram of the importance of variables obtained from an ensemble decision tree prediction model for predicting poor visual outcomes after diabetic vitrectomy.
Figure 2.
 
Important predictors for poor visual outcome after diabetic vitrectomy. A histogram of the importance of variables obtained from an ensemble decision tree prediction model for predicting poor visual outcomes after diabetic vitrectomy.
Table 1.
 
Baseline Characteristics of Enrolled Subjects
Table 1.
 
Baseline Characteristics of Enrolled Subjects
Table 2.
 
Correlation Between Visual Acuity at 1 Year After Vitrectomy and Clinical Variables
Table 2.
 
Correlation Between Visual Acuity at 1 Year After Vitrectomy and Clinical Variables
Table 3.
 
Multivariable Binary Logistic Regression for Poor Visual Outcome
Table 3.
 
Multivariable Binary Logistic Regression for Poor Visual Outcome
Table 4.
 
Performance of Machine Learning Classifiers in the Prediction of Poor Visual Outcome After Diabetic Vitrectomy
Table 4.
 
Performance of Machine Learning Classifiers in the Prediction of Poor Visual Outcome After Diabetic Vitrectomy
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