Real-world outcomes of a clinical decision support system for diabetic retinopathy in Spain

Romero-Aroca, Pedro; Verges, Raquel; Maarof, Najlaa; Vallas-Mateu, Aida; Latorre, Alex; Moreno, Antonio; Sagarra-Alamo, Ramon; Basora-Gallisa, Josep; Cristiano, Julián; Baget-Bernaldiz, Marc

doi:10.1136/bmjophth-2022-000974

Cited by 3 publications

(3 citation statements)

References 28 publications

(45 reference statements)

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“…A total of 16 risk factors were considered: Current age, age at diagnosis of DM, sex, type of DM, body mass index, duration of DM, DM treatment, smoker, control of high blood pressure, diastolic pressure rate , systolic blood pressure rate, HbA1c%, creatinine, estimated glomerular filtration rate (eGFR) measured by CKD-EPI, total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides and microalbuminuria [13,14].Of all those risk factors, the decision tree statistical system with fuzzy rules was used to generate the first two DRPAs (RETIPROGRAM 1.0 and 2.0). An improved version 3.0, which has been piloted at Hospital Universitario Sant Joan [10] includes the following factors: 1. Sex.…”

Section: Construction Of the Algorithmmentioning

confidence: 99%

“…To address this, we have developed a diabetic retinopathy risk algorithm [9] that we are piloting in our clinical practice [10]. The present study evaluates the effectiveness of that algorithm in a population of 120,389 patients with T2DM whom we have followed over 11 years from 2010 to 2021, and for whom we have the necessary clinical data.…”

Section: Introductionmentioning

confidence: 99%

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Title Referable Diabetic Retinopathy Prediction Algorithm Applied to a Population of 120389 Type 2 Diabetics over 11 Years Follow-Up.

ROMERO-AROCA,

Verges,

Pascual-Fontanilles

et al. 2024

Preprint

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(1) Background Diabetic Retinopathy (DR) is the primary cause of poor vision in young adults. Screening for DR is important but many patients do not attend; (2) Methods: We validated an algorithm for predicting DR and its progression by conducting a retrospective study of 11 children in a population of 120,389 T2DM (3) Results: By applying the algorithm to the population showed an AUC of 0.93 (95% CI, 0.92–0.94) for any-DR and 0.90 (95% CI, 0.89–0.91) for referable-DR(4) Conclusions: The algorithm is useful for predicting patients who could develop referable forms of DR and also for any-DR. This would allow a personalized screening plan to be drawn up for each patient.

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Section: Construction Of the Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Title Referable Diabetic Retinopathy Prediction Algorithm Applied to a Population of 120389 Type 2 Diabetics over 11 Years Follow-Up.

ROMERO-AROCA,

Verges,

Pascual-Fontanilles

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Valid studies, n (%) a,b Characteristic and categories for each characteristic Observational studies (n=18, 21%) [23,29,72,97,107] 5 (28) Prospective cohort study [46,54,55,70,86,87,91,101,102] 9 (50) Retrospective cohort study [65] 1 (6) Ambispective cohort study [103,105,106] 3 (17) Cross-sectional study RCTs c (n=36, 42%) [28, 30-33, 35, 36, 38-40, 42, 43, 49, 56, 61, 63, 74, 75, 80, 83, 84, 93, 98] 23 (64) Parallel design [69,71,88] 3 (8) Cross-over design [41, 47, 50-53, 66, 81, 85, 89] 10 (28) Cluster RCT [44,45,59,78,108] 5 (6) PCT d [34, 37, 48, 57, 58, 62, 64, 67, 68, 73, 76, 77, 79, 92, 94-96, 99, 100, 104, 109, 111] 22 (26) Single-arm trial [60,82,90,110] 4 ( 5) Other e a Number of studies and percentages were presented. The percentages of valid, unspecified, and unapplicable studies were calculated within the total studies (N=85), while the percentages for each characteristic were calculated within the valid studies.…”

Section: Referencesmentioning

confidence: 99%

Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review

Huang,

Liang,

et al. 2023

J Med Internet Res

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Background Providing comprehensive and individualized diabetes care remains a significant challenge in the face of the increasing complexity of diabetes management and a lack of specialized endocrinologists to support diabetes care. Clinical decision support systems (CDSSs) are progressively being used to improve diabetes care, while many health care providers lack awareness and knowledge about CDSSs in diabetes care. A comprehensive analysis of the applications of CDSSs in diabetes care is still lacking. Objective This review aimed to summarize the research landscape, clinical applications, and impact on both patients and physicians of CDSSs in diabetes care. Methods We conducted a scoping review following the Arksey and O’Malley framework. A search was conducted in 7 electronic databases to identify the clinical applications of CDSSs in diabetes care up to June 30, 2022. Additional searches were conducted for conference abstracts from the period of 2021-2022. Two researchers independently performed the screening and data charting processes. Results Of 11,569 retrieved studies, 85 (0.7%) were included for analysis. Research interest is growing in this field, with 45 (53%) of the 85 studies published in the past 5 years. Among the 58 (68%) out of 85 studies disclosing the underlying decision-making mechanism, most CDSSs (44/58, 76%) were knowledge based, while the number of non-knowledge-based systems has been increasing in recent years. Among the 81 (95%) out of 85 studies disclosing application scenarios, the majority of CDSSs were used for treatment recommendation (63/81, 78%). Among the 39 (46%) out of 85 studies disclosing physician user types, primary care physicians (20/39, 51%) were the most common, followed by endocrinologists (15/39, 39%) and nonendocrinology specialists (8/39, 21%). CDSSs significantly improved patients’ blood glucose, blood pressure, and lipid profiles in 71% (45/63), 67% (12/18), and 38% (8/21) of the studies, respectively, with no increase in the risk of hypoglycemia. Conclusions CDSSs are both effective and safe in improving diabetes care, implying that they could be a potentially reliable assistant in diabetes care, especially for physicians with limited experience and patients with limited access to medical resources. International Registered Report Identifier (IRRID) RR2-10.37766/inplasy2022.9.0061

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Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review (Preprint)

Huang¹,

Liang²,

Li³

et al. 2023

Preprint

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BACKGROUND Providing comprehensive and individualized diabetes care remains a significant challenge in the face of the increasing complexity of diabetes management and a lack of specialized endocrinologists to support diabetes care. Clinical Decision Support Systems (CDSSs) were progressively being used to improve diabetes care, while many healthcare providers (HCPs) lacked awareness and knowledge about CDSSs in diabetes care. A comprehensive analysis of the applications of CDSSs in diabetes care was still lacking. OBJECTIVE This review aimed to summarize the research landscape, clinical applications as well as the impact on both patients and physicians of CDSSs in diabetes care. METHODS We conducted a scoping review following the Arksey and O'Malley framework. A search was conducted in 7 electronic databases to identify clinical applications of CDSSs in diabetes care by June 30, 2022. Additional searches were conducted for conference abstracts in 2021-2022. Two researchers independently performed the screening and data charting process. RESULTS Out of 11,569 retrieved studies, 85 were included for analysis. Research interest is growing in this field, with 53% of the studies published in the last five years. Among 58 studies disclosed the underlying decision-making mechanism, most CDSSs (76%, 44/58) were knowledge-based, while the number of non-knowledge-based systems has been increasing in recent years. Among 81 studies disclosed application scenarios, the majority of CDSSs were used for treatment recommendation (78%, 63/81). For physician user types, primary care physicians (51%, 20/39) were the most common, followed by endocrinologists (38%, 15/39) and non-endocrinology specialists (21%, 8/39). CDSSs significantly improved patients’ blood glucose, blood pressure, and lipid profiles in 71% (45/63), 67% (12/18), and 38% (8/21) of the studies, respectively, with no increase in the risk of hypoglycemia. CONCLUSIONS It has been shown that CDSSs are both effective and safe in improving diabetes care, implying that CDSSs could be a potentially reliable assistant in diabetes care, especially for physicians with limited experience and patients with limited access to medical resources. INTERNATIONAL REGISTERED REPORT RR2-doi: 10.37766/inplasy2022.9.0061

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Real-world outcomes of a clinical decision support system for diabetic retinopathy in Spain

Cited by 3 publications

References 28 publications

Title Referable Diabetic Retinopathy Prediction Algorithm Applied to a Population of 120389 Type 2 Diabetics over 11 Years Follow-Up.

Title Referable Diabetic Retinopathy Prediction Algorithm Applied to a Population of 120389 Type 2 Diabetics over 11 Years Follow-Up.

Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review

Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review (Preprint)

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