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Chen JS, Copado IA, Vallejos C, Kalaw FGP, Soe P, Cai CX, Toy BC, Borkar D, Sun CQ, Shantha JG, Baxter SL. Variations in Electronic Health Record-Based Definitions of Diabetic Retinopathy Cohorts: A Literature Review and Quantitative Analysis. OPHTHALMOLOGY SCIENCE 2024; 4:100468. [PMID: 38560278 PMCID: PMC10973665 DOI: 10.1016/j.xops.2024.100468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 01/04/2024] [Accepted: 01/11/2024] [Indexed: 04/04/2024]
Abstract
Purpose Use of the electronic health record (EHR) has motivated the need for data standardization. A gap in knowledge exists regarding variations in existing terminologies for defining diabetic retinopathy (DR) cohorts. This study aimed to review the literature and analyze variations regarding codified definitions of DR. Design Literature review and quantitative analysis. Subjects Published manuscripts. Methods Four graders reviewed PubMed and Google Scholar for peer-reviewed studies. Studies were included if they used codified definitions of DR (e.g., billing codes). Data elements such as author names, publication year, purpose, data set type, and DR definitions were manually extracted. Each study was reviewed by ≥ 2 authors to validate inclusion eligibility. Quantitative analyses of the codified definitions were then performed to characterize the variation between DR cohort definitions. Main Outcome Measures Number of studies included and numeric counts of billing codes used to define codified cohorts. Results In total, 43 studies met the inclusion criteria. Half of the included studies used datasets based on structured EHR data (i.e., data registries, institutional EHR review), and half used claims data. All but 1 of the studies used billing codes such as the International Classification of Diseases 9th or 10th edition (ICD-9 or ICD-10), either alone or in addition to another terminology for defining disease. Of the 27 included studies that used ICD-9 and the 20 studies that used ICD-10 codes, the most common codes used pertained to the full spectrum of DR severity. Diabetic retinopathy complications (e.g., vitreous hemorrhage) were also used to define some DR cohorts. Conclusions Substantial variations exist among codified definitions for DR cohorts within retrospective studies. Variable definitions may limit generalizability and reproducibility of retrospective studies. More work is needed to standardize disease cohorts. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Jimmy S Chen
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Ivan A Copado
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Cecilia Vallejos
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Fritz Gerald P Kalaw
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Priyanka Soe
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Cindy X Cai
- Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Brian C Toy
- Department of Ophthalmology, Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Durga Borkar
- Department of Ophthalmology, Duke Eye Center, Duke University, Durham, North Carolina
| | - Catherine Q Sun
- F.I. Proctor Foundation, University of California San Francisco, San Francisco, California
- Department of Ophthalmology, University of California San Francisco, San Francisco, California
| | - Jessica G Shantha
- F.I. Proctor Foundation, University of California San Francisco, San Francisco, California
- Department of Ophthalmology, University of California San Francisco, San Francisco, California
| | - Sally L Baxter
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
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Lu Y, Duong T, Miao Z, Thieu T, Lamichhane J, Ahmed A, Delen D. A novel hyperparameter search approach for accuracy and simplicity in disease prediction risk scoring. J Am Med Inform Assoc 2024:ocae140. [PMID: 38899502 DOI: 10.1093/jamia/ocae140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 05/07/2024] [Accepted: 05/28/2024] [Indexed: 06/21/2024] Open
Abstract
OBJECTIVE Develop a novel technique to identify an optimal number of regression units corresponding to a single risk point, while creating risk scoring systems from logistic regression-based disease predictive models. The optimal value of this hyperparameter balances simplicity and accuracy, yielding risk scores of small scale and high accuracy for patient risk stratification. MATERIALS AND METHODS The proposed technique applies an adapted line search across all potential hyperparameter values. Additionally, DeLong test is integrated to ensure the selected value produces an accuracy insignificantly different from the best achievable risk score accuracy. We assessed the approach through two case studies predicting diabetic retinopathy (DR) within six months and hip fracture readmissions (HFR) within 30 days, involving cohorts of 90 400 diabetic patients and 18 065 hip fracture patients. RESULTS Our scores achieve accuracies insignificantly different from those obtained by existing approaches, reaching AUROCs of 0.803 and 0.645 for DR and HFR predictions, respectively. Regarding the scale, our scores ranged 0-53 for DR and 0-15 for HFR, while scores produced by existing methods frequently spanned hundreds or thousands. DISCUSSION According to the assessment, our risk scores offer simple and accurate predictions for diseases. Furthermore, our new DR score provides a competitive alternative to state-of-the-art risk scores for DR, while our HFR case study presents the first risk score for this condition. CONCLUSION Our technique offers a generalizable framework for crafting precise risk scores of compact scales, addressing the demand for user-friendly and effective risk stratification tool in healthcare.
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Affiliation(s)
- Yajun Lu
- Department of Management and Marketing, Jacksonville State University, Jacksonville, AL 36265, United States
| | - Thanh Duong
- Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, United States
- Department of Machine Learning, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, United States
| | - Zhuqi Miao
- School of Business, The State University of New York at New Paltz, New Paltz, NY 12561, United States
| | - Thanh Thieu
- Department of Machine Learning, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, United States
- Department of Oncological Sciences, University of South Florida Morsani College of Medicine, Tampa, FL 33612, United States
| | - Jivan Lamichhane
- The State University of New York Upstate Medical University, Syracuse, NY 13210, United States
| | - Abdulaziz Ahmed
- Department of Health Services Administration, School of Health Professions, The University of Alabama at Birmingham, Birmingham, AL 35233, United States
| | - Dursun Delen
- Center for Health Systems Innovation, Department of Management Science and Information Systems, Oklahoma State University, Stillwater, OK 74078, United States
- Department of Industrial Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Sariyer/Istanbul 34396, Turkey
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Tao R, Yu X, Lu J, Wang Y, Lu W, Zhang Z, Li H, Zhou J. A deep learning nomogram of continuous glucose monitoring data for the risk prediction of diabetic retinopathy in type 2 diabetes. Phys Eng Sci Med 2023; 46:813-825. [PMID: 37041318 DOI: 10.1007/s13246-023-01254-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 03/27/2023] [Indexed: 04/13/2023]
Abstract
Continuous glucose monitoring (CGM) data analysis will provide a new perspective to analyze factors related to diabetic retinopathy (DR). However, the problem of visualizing CGM data and automatically predicting the incidence of DR from CGM is still controversial. Here, we explored the feasibility of using CGM profiles to predict DR in type 2 diabetes (T2D) by deep learning approach. This study fused deep learning with a regularized nomogram to construct a novel deep learning nomogram from CGM profiles to identify patients at high risk of DR. Specifically, a deep learning network was employed to mine the nonlinear relationship between CGM profiles and DR. Moreover, a novel nomogram combining CGM deep factors with basic information was established to score the patients' DR risk. This dataset consists of 788 patients belonging to two cohorts: 494 in the training cohort and 294 in the testing cohort. The area under the curve (AUC) values of our deep learning nomogram were 0.82 and 0.80 in the training cohort and testing cohort, respectively. By incorporating basic clinical factors, the deep learning nomogram achieved an AUC of 0.86 in the training cohort and 0.85 in the testing cohort. The calibration plot and decision curve showed that the deep learning nomogram had the potential for clinical application. This analysis method of CGM profiles can be extended to other diabetic complications by further investigation.
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Affiliation(s)
- Rui Tao
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Xia Yu
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Jingyi Lu
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, 600 Yishan Road, Shanghai, 200233, China
| | - Yaxin Wang
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, 600 Yishan Road, Shanghai, 200233, China
| | - Wei Lu
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, 600 Yishan Road, Shanghai, 200233, China
| | - Zhanhu Zhang
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Hongru Li
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Jian Zhou
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, 600 Yishan Road, Shanghai, 200233, China.
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Development of a Computer System for Automatically Generating a Laser Photocoagulation Plan to Improve the Retinal Coagulation Quality in the Treatment of Diabetic Retinopathy. Symmetry (Basel) 2023. [DOI: 10.3390/sym15020287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
In this article, the development of a computer system for high-tech medical uses in ophthalmology is proposed. An overview of the main methods and algorithms that formed the basis of the coagulation plan planning system is presented. The system provides the formation of a more effective plan for laser coagulation in comparison with the use of existing coagulation techniques. An analysis of monopulse- and pattern-based laser coagulation techniques in the treatment of diabetic retinopathy has shown that modern treatment methods do not provide the required efficacy of medical laser coagulation procedures, as the laser energy is nonuniformly distributed across the pigment epithelium and may exert an excessive effect on parts of the retina and anatomical elements. The analysis has shown that the efficacy of retinal laser coagulation for the treatment of diabetic retinopathy is determined by the relative position of coagulates and parameters of laser exposure. In the course of the development of the computer system proposed herein, main stages of processing diagnostic data were identified. They are as follows: the allocation of the laser exposure zone, the evaluation of laser pulse parameters that would be safe for the fundus, mapping a coagulation plan in the laser exposure zone, followed by the analysis of the generated plan for predicting the therapeutic effect. In the course of the study, it was found that the developed algorithms for placing coagulates in the area of laser exposure provide a more uniform distribution of laser energy across the pigment epithelium when compared to monopulse- and pattern-based laser coagulation techniques.
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State-of-the-Art Research on Diabetic Retinopathy. J Clin Med 2022; 11:jcm11133790. [PMID: 35807075 PMCID: PMC9267317 DOI: 10.3390/jcm11133790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 06/29/2022] [Indexed: 11/30/2022] Open
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Gurupur VP, Miao Z. A brief analysis of challenges in implementing telehealth in a rural setting. Mhealth 2022; 8:17. [PMID: 35449506 PMCID: PMC9014233 DOI: 10.21037/mhealth-21-38] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 12/16/2021] [Indexed: 11/06/2022] Open
Abstract
Available literature clearly indicates that successful implementation of telemedicine and telehealth has been a challenge. This challenge is further amplified if the reader must consider this implementation in a rural setting. In this article the authors discuss some of the key challenges associated with this implementation. The article sheds light on a few key studies and commentaries associated with the use of telehealth in a rural setting. Critically, the article summarizes these critical findings; thereby, informing the reader on the bottlenecks associated with the use of telehealth in a geographically rural area. Also, briefly summarizing the existing body of knowledge on this topic of study. Furthermore, a case study briefly narrating the use of telemedicine and telehealth for rural Oklahoma is presented to advance our understanding of the situation in this field. Some of the critical details associated with this case study provides insights on some of the key challenges associated with the implementation of telehealth in a rural setting. This case study also provides insights on key workflow processes that helped the implementation of telehealth. Finally, the authors summarize the key challenges in the implementation of telehealth based on their perspective. Here it is important to inform the readers that this article is not a scientific review on the topic instead presents an opinion backed by facts and existing literature. Overall, the authors present a key discussion that can lead to advances in research and required innovations that might help in providing easy access to healthcare through telehealth.
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Affiliation(s)
- Varadraj P. Gurupur
- School of Global Health Management and Informatics, University of Central Florida, Orange County, FL, USA
| | - Zhuqi Miao
- Center for Health Sciences, Oklahoma State University, Stillwater, Payne County, OK, USA
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Maeda-Gutiérrez V, Galván-Tejada CE, Cruz M, Galván-Tejada JI, Gamboa-Rosales H, García-Hernández A, Luna-García H, Gonzalez-Curiel I, Martínez-Acuña M. Risk-Profile and Feature Selection Comparison in Diabetic Retinopathy. J Pers Med 2021; 11:1327. [PMID: 34945799 PMCID: PMC8705564 DOI: 10.3390/jpm11121327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 11/24/2021] [Accepted: 11/25/2021] [Indexed: 11/16/2022] Open
Abstract
One of the main microvascular complications presented in the Mexican population is diabetic retinopathy which affects 27.50% of individuals with type 2 diabetes. Therefore, the purpose of this study is to construct a predictive model to find out the risk factors of this complication. The dataset contained a total of 298 subjects, including clinical and paraclinical features. An analysis was constructed using machine learning techniques including Boruta as a feature selection method, and random forest as classification algorithm. The model was evaluated through a statistical test based on sensitivity, specificity, area under the curve (AUC), and receiving operating characteristic (ROC) curve. The results present significant values obtained by the model obtaining 69% of AUC. Moreover, a risk evaluation was incorporated to evaluate the impact of the predictors. The proposed method identifies creatinine, lipid treatment, glomerular filtration rate, waist hip ratio, total cholesterol, and high density lipoprotein as risk factors in Mexican subjects. The odds ratio increases by 3.5916 times for control patients which have high levels of cholesterol. It is possible to conclude that this proposed methodology is a preliminary computer-aided diagnosis tool for clinical decision-helping to identify the diagnosis of DR.
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Affiliation(s)
- Valeria Maeda-Gutiérrez
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro 98000, Mexico; (V.M.-G.); (J.I.G.-T.); (H.G.-R.); (A.G.-H.); (H.L.-G.)
| | - Carlos E. Galván-Tejada
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro 98000, Mexico; (V.M.-G.); (J.I.G.-T.); (H.G.-R.); (A.G.-H.); (H.L.-G.)
| | - Miguel Cruz
- Unidad de Investigación Médica en Bioquímica, Centro Médico Nacional Siglo XXI, Hospital de Especialidades, Instituto Mexicano del Seguro Social, Mexico City, Av. Cuauhtémoc 330, Col. Doctores, Del. Cuauhtémoc, Ciudad de Mexico 06720, Mexico;
| | - Jorge I. Galván-Tejada
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro 98000, Mexico; (V.M.-G.); (J.I.G.-T.); (H.G.-R.); (A.G.-H.); (H.L.-G.)
| | - Hamurabi Gamboa-Rosales
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro 98000, Mexico; (V.M.-G.); (J.I.G.-T.); (H.G.-R.); (A.G.-H.); (H.L.-G.)
| | - Alejandra García-Hernández
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro 98000, Mexico; (V.M.-G.); (J.I.G.-T.); (H.G.-R.); (A.G.-H.); (H.L.-G.)
| | - Huizilopoztli Luna-García
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro 98000, Mexico; (V.M.-G.); (J.I.G.-T.); (H.G.-R.); (A.G.-H.); (H.L.-G.)
| | - Irma Gonzalez-Curiel
- Unidad Académica de Ciencias Químicas, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro 98000, Mexico; (I.G.-C.); (M.M.-A.)
| | - Mónica Martínez-Acuña
- Unidad Académica de Ciencias Químicas, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro 98000, Mexico; (I.G.-C.); (M.M.-A.)
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Multiple Single Nucleotide Polymorphism Testing Improves the Prediction of Diabetic Retinopathy Risk with Type 2 Diabetes Mellitus. J Pers Med 2021; 11:jpm11080689. [PMID: 34442333 PMCID: PMC8398882 DOI: 10.3390/jpm11080689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 07/15/2021] [Accepted: 07/19/2021] [Indexed: 11/17/2022] Open
Abstract
Diabetic retinopathy (DR) is one of the most frequent causes of irreversible blindness, thus prevention and early detection of DR is crucial. The purpose of this study is to identify genetic determinants of DR in individuals with type 2 diabetic mellitus (T2DM). A total of 551 T2DM patients (254 with DR, 297 without DR) were included in this cross-sectional research. Thirteen T2DM-related single nucleotide polymorphisms (SNPs) were utilized for constructing genetic risk prediction model. With logistic regression analysis, genetic variations of the FTO (rs8050136) and PSMD6 (rs831571) polymorphisms were independently associated with a higher risk of DR. The area under the curve (AUC) calculated on known nongenetic risk variables was 0.704. Based on the five SNPs with the highest odds ratio (OR), the combined nongenetic and genetic prediction model improved the AUC to 0.722. The discriminative accuracy of our 5-SNP combined risk prediction model increased in patients who had more severe microalbuminuria (AUC = 0.731) or poor glycemic control (AUC = 0.746). In conclusion, we found a novel association for increased risk of DR at two T2DM-associated genetic loci, FTO (rs8050136) and PSMD6 (rs831571). Our predictive risk model presents new insights in DR development, which may assist in enabling timely intervention in reducing blindness in diabetic patients.
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