A Multicenter Randomized Double-Blind Trial of Chrocare CDSS for Integrated Management of Type 2 Diabetes (Chrocare-DM)
A Randomized, Double-Blind, Parallel-Group, Multicenter Clinical Trial to Evaluate the Effectiveness and Safety of a Chrocare Large Model-Based Clinical Decision Support System for Integrated Management of Patients With Type 2 Diabetes
This is a multicenter, randomized, double-blind, parallel-group clinical study for adults with type 2 diabetes and early diabetic kidney or retinal complications. A total of 1,200 eligible participants will be randomly divided into two groups at a 1:1 ratio.
Participants in the intervention group will receive clinical management suggestions generated by the Chrocare large language model clinical decision support system, while those in the control group will receive standard diabetes care formulated by experienced endocrinologists.
All participants will complete 3 study visits over 6 months to test blood glucose, glycated hemoglobin, blood lipids, kidney and eye indicators, and record hypoglycemia or other adverse events.
The main goal of this research is to compare whether the AI-assisted management can help more patients reach target blood glucose levels, as well as evaluate the safety of this AI system for long-term diabetes integrated treatment.
Studienübersicht
Status
Detaillierte Beschreibung
This trial is a prospective, multicenter, randomized, double-blind, parallel-controlled clinical trial conducted in about 40 MMC (Metabolic Management Center) hospitals across China, enrolling 1200 adult patients aged ≥18 years with type 2 diabetes mellitus, baseline HbA1c 7.0%-9.0%, combined with mild-to-moderate diabetic nephropathy (UACR 3.39-33.9 mg/mmol) or non-proliferative diabetic retinopathy. Subjects will be assigned 1:1 via central stratified randomization to the Chrocare AI clinical decision support system intervention group or routine physician care control group. The double-blind design separates physicians into two independent roles: one clinician interacts with patients and performs follow-up, another clinician generates treatment plans without direct patient contact, masking group allocation throughout the trial period. The Chrocare large language model automatically extracts multi-dimensional clinical data from hospital EHR, LIS and imaging systems, and generates individualized hypoglycemic, hypotensive and lipid-lowering regimens complying with domestic and international diabetes guidelines, alongside risk assessment of diabetic microvascular complications. The control group's treatment plans are developed by a panel of attending endocrinologists with more than 5 years of clinical experience through double independent review. All participants will undergo fixed visits at baseline, month 3 and month 6. Collected indicators include glycated hemoglobin, fasting and postprandial blood glucose, blood pressure, BMI, visceral fat, blood lipid profile, liver and renal function, urine albumin-creatinine ratio, and retinal grading. All hypoglycemia, cardiovascular events, hospitalizations and other adverse events will be fully documented for safety evaluation. The primary efficacy endpoint is the proportion of participants achieving HbA1c less than 7.0% at month 6. Secondary endpoints include changes in metabolic indicators, improvement of microvascular complications, guideline adherence rate of treatment plans, and incidence of inappropriate medication. Safety endpoints cover all adverse events and serious adverse events during the 6-month follow-up. Statistical analyses will be performed on the full analysis set, per-protocol set and safety set using SAS, SPSS and R software, with mixed-effect logistic regression and generalized estimating equations applied for between-group comparisons. This study is initiated and sponsored by the Principal Investigator from Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, and has obtained ethical approval from the institutional ethics committee prior to participant enrollment.
Studientyp
Einschreibung (Geschätzt)
Phase
- Unzutreffend
Kontakte und Standorte
Studienkontakt
- Name: Weiqing Wang
- Telefonnummer: 021-64370045-671803
- E-Mail: wqingw@shsmu.edu.cn
Studieren Sie die Kontaktsicherung
- Name: Yifei Zhang
- E-Mail: feifei-a@163.com
Studienorte
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Shanghai, China
- Ruijin Hospital, Shanghai Jiaotong University School of Medicine
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Kontakt:
- Weiqing Wang, MD, PhD
- Telefonnummer: 021-64370045-671803
- E-Mail: wqingw@shsmu.edu.cn
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Teilnahmekriterien
Zulassungskriterien
Studienberechtigtes Alter
Akzeptiert gesunde Freiwillige
Studienberechtigte Geschlechter
Beschreibung
Inclusion Criteria:
- Aged ≥ 18 years old;
- Clinically diagnosed with Type 2 Diabetes Mellitus;
- Baseline glycated hemoglobin (HbA1c) ranges from 7.0% to 9.0%;
- Complicated with mild-to-moderate diabetic nephropathy (UACR 3.39-33.9 mg/mmol) or mild non-proliferative diabetic retinopathy;
- Voluntarily sign written informed consent form;
- Able to complete all scheduled 6-month follow-up visits.
Exclusion Criteria:
- Type 1 diabetes, monogenic diabetes or secondary diabetes;
- Severe cardiac, hepatic or renal dysfunction; proliferative diabetic retinopathy;
- History of diabetic acute complications within 3 months before screening; active infectious diseases;
- Pregnant, breastfeeding or planning pregnancy during the trial period;
- Participated in other interventional clinical trials within the past 3 months;
- Investigators judge the subject unsuitable for enrollment for any other reason.
Studienplan
Wie ist die Studie aufgebaut?
Designdetails
- Hauptzweck: Behandlung
- Zuteilung: Zufällig
- Interventionsmodell: Parallele Zuordnung
- Maskierung: Doppelt
Waffen und Interventionen
Teilnehmergruppe / Arm |
Intervention / Behandlung |
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Experimental: Chrocare-Intervention Group
Participants receive integrated type 2 diabetes management recommendations generated by the Chrocare large language model clinical decision support system. The model automatically extracts multi-source clinical data, generates individualized hypoglycemic, hypotensive and lipid-lowering regimens consistent with diabetes guidelines, and assesses microvascular complication risks. All participants complete standardized follow-up visits at baseline, Month 3 and Month 6 with unified laboratory and imaging tests. |
This artificial intelligence large language model system automatically extracts electronic medical, laboratory and imaging data of type 2 diabetes patients, generates individualized hypoglycemic, hypotensive and lipid-lowering treatment plans complying with domestic and international diabetes guidelines, and provides risk assessment of diabetic microvascular complications for integrated chronic disease management during 6-month standardized follow-up. |
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Aktiver Komparator: Routine-Control Group
Participants receive standard integrated type 2 diabetes care formulated by experienced attending endocrinologists through double independent review. Physicians develop individualized treatment plans based on identical clinical data sources used in the intervention group. All participants complete the same 3 standardized follow-up visits and unified laboratory/imaging examinations within 6 months. |
Individualized 2-type diabetes comprehensive management schemes formulated by attending endocrinologists through double independent review, with identical 6-month unified follow-up and test indicators as the AI intervention group. |
Was misst die Studie?
Primäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
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Proportion of participants achieving HbA1c less than 7.0% at Month 6
Zeitfenster: 6 months after participant enrollment
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The percentage of subjects whose HbA1c level is below 7.0% after 6 months of intervention, compared between the Chrocare AI clinical decision support system group and the routine physician care control group. This indicator reflects the overall blood glucose control effect of integrated diabetes management. |
6 months after participant enrollment
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Sekundäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
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Change in HbA1c from baseline to Month 3 and Month 6
Zeitfenster: 3 months and 6 months after participant enrollment
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Absolute change of HbA1c value at Month 3 and Month 6 compared with baseline level, to evaluate the short-term and medium-term glucose-lowering effect of two management strategies. |
3 months and 6 months after participant enrollment
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Change in urine albumin-creatinine ratio (UACR) from baseline to Month 6
Zeitfenster: 6 months after participant enrollment
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Variation of UACR after 6-month intervention, reflecting the improvement status of early diabetic nephropathy in participants. |
6 months after participant enrollment
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Change in diabetic retinopathy from baseline to Month 6
Zeitfenster: 6 months after participant enrollment
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Variation of diabetic retinopathy after 6-month intervention, reflecting the improvement status of early diabetic retinopathy in participants. |
6 months after participant enrollment
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Proportion of treatment plans complying with domestic and international diabetes guidelines
Zeitfenster: Throughout the 6-month intervention period
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The percentage of individualized hypoglycemic, hypotensive and lipid-lowering regimens consistent with clinical practice guidelines for type 2 diabetes, compared between two groups. |
Throughout the 6-month intervention period
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Incidence of hypoglycemia and all adverse events during follow-up
Zeitfenster: From baseline visit to Month 6 follow-up visit
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Count and compare the occurrence rate of hypoglycemia, cardiovascular events and other adverse events in two groups within 6 months for safety evaluation. |
From baseline visit to Month 6 follow-up visit
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Accuracy of preliminary diagnosis of diabetic microvascular complications (diabetic retinopathy, diabetic nephropathy) at baseline assessment
Zeitfenster: Baseline visit (at participant enrollment)
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Compare the preliminary diagnostic accuracy rate of diabetic complications including retinopathy and nephropathy between the AI intervention group and routine physician control group when conducting baseline evaluation for enrolled participants. |
Baseline visit (at participant enrollment)
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Mitarbeiter und Ermittler
Ermittler
- Hauptermittler: Weiqing Wang, Ruijin Hospital: Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital
Publikationen und hilfreiche Links
Allgemeine Veröffentlichungen
- Lean ME, Leslie WS, Barnes AC, Brosnahan N, Thom G, McCombie L, Peters C, Zhyzhneuskaya S, Al-Mrabeh A, Hollingsworth KG, Rodrigues AM, Rehackova L, Adamson AJ, Sniehotta FF, Mathers JC, Ross HM, McIlvenna Y, Stefanetti R, Trenell M, Welsh P, Kean S, Ford I, McConnachie A, Sattar N, Taylor R. Primary care-led weight management for remission of type 2 diabetes (DiRECT): an open-label, cluster-randomised trial. Lancet. 2018 Feb 10;391(10120):541-551. doi: 10.1016/S0140-6736(17)33102-1. Epub 2017 Dec 5.
- Xu Y, Wang L, He J, Bi Y, Li M, Wang T, Wang L, Jiang Y, Dai M, Lu J, Xu M, Li Y, Hu N, Li J, Mi S, Chen CS, Li G, Mu Y, Zhao J, Kong L, Chen J, Lai S, Wang W, Zhao W, Ning G; 2010 China Noncommunicable Disease Surveillance Group. Prevalence and control of diabetes in Chinese adults. JAMA. 2013 Sep 4;310(9):948-59. doi: 10.1001/jama.2013.168118.
- Leslie WS, Ford I, Sattar N, Hollingsworth KG, Adamson A, Sniehotta FF, McCombie L, Brosnahan N, Ross H, Mathers JC, Peters C, Thom G, Barnes A, Kean S, McIlvenna Y, Rodrigues A, Rehackova L, Zhyzhneuskaya S, Taylor R, Lean ME. The Diabetes Remission Clinical Trial (DiRECT): protocol for a cluster randomised trial. BMC Fam Pract. 2016 Feb 16;17:20. doi: 10.1186/s12875-016-0406-2.
- Wang L, Gao P, Zhang M, Huang Z, Zhang D, Deng Q, Li Y, Zhao Z, Qin X, Jin D, Zhou M, Tang X, Hu Y, Wang L. Prevalence and Ethnic Pattern of Diabetes and Prediabetes in China in 2013. JAMA. 2017 Jun 27;317(24):2515-2523. doi: 10.1001/jama.2017.7596.
- Zhang Y, Wang W, Ning G. Metabolic Management Center: An innovation project for the management of metabolic diseases and complications in China. J Diabetes. 2019 Jan;11(1):11-13. doi: 10.1111/1753-0407.12847. Epub 2018 Oct 3. No abstract available.
- Davies MJ, Aroda VR, Collins BS, Gabbay RA, Green J, Maruthur NM, Rosas SE, Del Prato S, Mathieu C, Mingrone G, Rossing P, Tankova T, Tsapas A, Buse JB. Management of hyperglycaemia in type 2 diabetes, 2022. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia. 2022 Dec;65(12):1925-1966. doi: 10.1007/s00125-022-05787-2. Epub 2022 Sep 24.
- Cruz Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ; SPIRIT-AI and CONSORT-AI Working Group; SPIRIT-AI and CONSORT-AI Steering Group; SPIRIT-AI and CONSORT-AI Consensus Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med. 2020 Sep;26(9):1351-1363. doi: 10.1038/s41591-020-1037-7. Epub 2020 Sep 9.
- Taheri S, Zaghloul H, Chagoury O, Elhadad S, Ahmed SH, El Khatib N, Amona RA, El Nahas K, Suleiman N, Alnaama A, Al-Hamaq A, Charlson M, Wells MT, Al-Abdulla S, Abou-Samra AB. Effect of intensive lifestyle intervention on bodyweight and glycaemia in early type 2 diabetes (DIADEM-I): an open-label, parallel-group, randomised controlled trial. Lancet Diabetes Endocrinol. 2020 Jun;8(6):477-489. doi: 10.1016/S2213-8587(20)30117-0.
- GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023 Jul 15;402(10397):203-234. doi: 10.1016/S0140-6736(23)01301-6. Epub 2023 Jun 22.
- Look AHEAD Research Group; Wing RR, Bolin P, Brancati FL, Bray GA, Clark JM, Coday M, Crow RS, Curtis JM, Egan CM, Espeland MA, Evans M, Foreyt JP, Ghazarian S, Gregg EW, Harrison B, Hazuda HP, Hill JO, Horton ES, Hubbard VS, Jakicic JM, Jeffery RW, Johnson KC, Kahn SE, Kitabchi AE, Knowler WC, Lewis CE, Maschak-Carey BJ, Montez MG, Murillo A, Nathan DM, Patricio J, Peters A, Pi-Sunyer X, Pownall H, Reboussin D, Regensteiner JG, Rickman AD, Ryan DH, Safford M, Wadden TA, Wagenknecht LE, West DS, Williamson DF, Yanovski SZ. Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes. N Engl J Med. 2013 Jul 11;369(2):145-54. doi: 10.1056/NEJMoa1212914. Epub 2013 Jun 24.
- Ning G. Medical Education in Diabetes Management on the New Horizon: Insights From Metabolic Management Center. J Diabetes. 2025 Mar;17(3):e70075. doi: 10.1111/1753-0407.70075. No abstract available.
- Zhang Y, Shi J, Peng Y, Zhao Z, Zheng Q, Wang Z, Liu K, Jiao S, Qiu K, Zhou Z, Yan L, Zhao D, Jiang H, Dai Y, Su B, Gu P, Su H, Wan Q, Peng Y, Liu J, Hu L, Ke T, Chen L, Xu F, Dong Q, Terzopoulos D, Ning G, Xu X, Ding X, Wang W. Artificial intelligence-enabled screening for diabetic retinopathy: a real-world, multicenter and prospective study. BMJ Open Diabetes Res Care. 2020 Oct;8(1):e001596. doi: 10.1136/bmjdrc-2020-001596.
- Hu C, Zhang Y, Zhang J, Huo Y, Wan Q, Li M, Qi H, Du R, Zhu Y, Qin Y, Hu R, Shi L, Su Q, Yu X, Yan L, Qin G, Tang X, Chen G, Xu M, Wang T, Zhao Z, Chen Y, Gao Z, Wang G, Shen F, Luo Z, Chen L, Li Q, Ye Z, Zhang Y, Liu C, Wang Y, Wu S, Yang T, Deng H, Chen L, Zhao J, Mu Y, Wang W, Xu Y, Bi Y, Lu J, Ning G; REACTION Study Group. Age at menarche, ideal cardiovascular health metrics, and risk of diabetes in adulthood: Findings from the REACTION study. J Diabetes. 2021 Jun;13(6):458-468. doi: 10.1111/1753-0407.13128. Epub 2020 Dec 29.
- Wang T, Lu J, Su Q, Chen Y, Bi Y, Mu Y, Chen L, Hu R, Tang X, Yu X, Li M, Xu M, Xu Y, Zhao Z, Yan L, Qin G, Wan Q, Chen G, Dai M, Zhang D, Gao Z, Wang G, Shen F, Luo Z, Qin Y, Chen L, Huo Y, Li Q, Ye Z, Zhang Y, Liu C, Wang Y, Wu S, Yang T, Deng H, Li D, Lai S, Bloomgarden ZT, Shi L, Ning G, Zhao J, Wang W; 4C Study Group. Ideal Cardiovascular Health Metrics and Major Cardiovascular Events in Patients With Prediabetes and Diabetes. JAMA Cardiol. 2019 Sep 1;4(9):874-883. doi: 10.1001/jamacardio.2019.2499.
- Martindale APL, Llewellyn CD, de Visser RO, Ng B, Ngai V, Kale AU, di Ruffano LF, Golub RM, Collins GS, Moher D, McCradden MD, Oakden-Rayner L, Rivera SC, Calvert M, Kelly CJ, Lee CS, Yau C, Chan AW, Keane PA, Beam AL, Denniston AK, Liu X. Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines. Nat Commun. 2024 Feb 22;15(1):1619. doi: 10.1038/s41467-024-45355-3.
- Show us the evidence for the value of medical AI. Nat Med. 2026 Apr;32(4):1163. doi: 10.1038/s41591-026-04389-4. No abstract available.
- Nie J, Haft C, Xia A, Wang X. AI-Powered Diabetes Precision Health: From Data to Action. NEJM AI. 2025 Sep;2(9):10.1056/AIp2500475. doi: 10.1056/AIp2500475. Epub 2025 Aug 28.
- Ahmad E, Lim S, Lamptey R, Webb DR, Davies MJ. Type 2 diabetes. Lancet. 2022 Nov 19;400(10365):1803-1820. doi: 10.1016/S0140-6736(22)01655-5. Epub 2022 Nov 1.
Studienaufzeichnungsdaten
Haupttermine studieren
Studienbeginn (Geschätzt)
Primärer Abschluss (Geschätzt)
Studienabschluss (Geschätzt)
Studienanmeldedaten
Zuerst eingereicht
Zuerst eingereicht, das die QC-Kriterien erfüllt hat
Zuerst gepostet (Tatsächlich)
Studienaufzeichnungsaktualisierungen
Letztes Update gepostet (Tatsächlich)
Letztes eingereichtes Update, das die QC-Kriterien erfüllt
Zuletzt verifiziert
Mehr Informationen
Begriffe im Zusammenhang mit dieser Studie
Zusätzliche relevante MeSH-Bedingungen
- Urogenitale Erkrankungen
- Erkrankungen des endokrinen Systems
- Gefäßerkrankungen
- Herz-Kreislauf-Erkrankungen
- Männliche Urogenitalerkrankungen
- Nierenerkrankungen
- Urologische Erkrankungen
- Weibliche Urogenitalerkrankungen
- Weibliche Urogenitalerkrankungen und Schwangerschaftskomplikationen
- Stoffwechselerkrankungen
- Störungen des Glukosestoffwechsels
- Diabetes Mellitus
- Augenkrankheiten
- Diabetische Angiopathien
- Diabetes-Komplikationen
- Erkrankungen der Netzhaut
- Ernährungs- und Stoffwechselerkrankungen
- Diabetische Retinopathie
- Diabetes mellitus, Typ 2
- Diabetische Nephropathien
Andere Studien-ID-Nummern
- EC-2026-392
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