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| Name | Class |
|---|---|
| Silesian University of Technology | OTHER |
| Liverpool Heart and Chest Hospital NHS Foundation Trust | OTHER |
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The project is an observational one which undertakes different, easy to obtain in everyday clinical practice, demographical, laboratory and clinical parameters of patients with diabetes in Silesian Region in Poland to predict cardiovascular disease, cardiovascular events and neuropathy using machine learning approach.
The study is a prospective, observational one in which it is planned to obtain demographical, laboratory and clinical parameters of patients who are hospitalized in one the the main diabetology sites in Silesia region in Poland and to follow them prospectively for 10 years in order to collect information related to new cardiovascular events. Telephone contact will be performed every 12 months following hospital discharge. Any procedures related to the patients hospitalized in the diabetology ward will be the routine ones and the bioethics committee of Medical University of Silesia gave the permission for the study but waved the necessity of informed consent to be signed.Moreover there will be subgroup analysis of patients recruited from the outpatient diabetology clinics in Silesia region in order to participate in the observational study collecting data related to to vitamin D concentration, densitometry, fibroscan, carotid ultrasound examination, vascular stiffness, electrocardiography, peripheral and cardiovascular autonomic neuropathy and fundus imaging. Patients who are treated in outpatient diabetology clinics in Silesia region and are included into the study must have the informed consent signed and bioethics committee agreement has been obtained.
Machine learning approach will be implemented to discover the association between easy to obtain in everyday practice parameters, namely clinical, biochemical, and demographical ones to identify patients at the highest risk of cardiovascular disease.
The secondary aim of the study is to utilize fundus imaging, electrocardiography, alongside demographic and clinical data in order to potentially diagnose neuropathy.
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| Measure | Description | Time Frame |
|---|---|---|
| The risk of cardiovascular disease and events among patients with diabetes | To predict the risk of cardiovascular disease and events among patients with diabetes using machine learning approach | 5 years |
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Inclusion Criteria:
Exclusion Criteria:
For the group of hospitalized patients:
For the subgroup of patients recruited form outpatient diabetology clinics:
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Patients hospitalized in diabetology ward in Zabrze. Patients treated in outpatient diabetology clinics in Silesia region.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Katarzyna Nabrdalik, PhD, prof. | Contact | 0048697592954 | knabrdalik@sum.edu.pl | |
| Hanna Kwiendacz, PhD | Contact | 0048509774849 | hkwiendacz@sum.edu.pl |
| Name | Affiliation | Role |
|---|---|---|
| Katarzyna Nabrdalik, PhD,prof. | Medical University of Silesia | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Department of Internal Diseases, Diabetology and Nephrology | Recruiting | Zabrze | 41-800 | Poland |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 42374510 | Derived | Janota-Sosinska O, Yu Q, Irlik K, Kwiendacz H, Wlosowicz-Momot A, Pabis P, Wojcik W, Olejarz A, Piasnik J, Alam U, Zheng Y, Gumprecht J, Lip GYH, Nabrdalik K. Deep learning analysis of ECGs detects Cardiovascular-Kidney-Metabolic syndrome burden in people with diabetes: a report from the Silesia Diabetes-Heart Project. Cardiovasc Diabetol. 2026 Jun 29. doi: 10.1186/s12933-026-03246-5. Online ahead of print. | |
| 39227929 |
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| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| D002318 | Cardiovascular Diseases |
| ID | Term |
|---|---|
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
| D004700 | Endocrine System Diseases |
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| Derived |
| Janota O, Mantovani M, Kwiendacz H, Irlik K, Bucci T, Lam SHM, Huang B, Alam U, Boriani G, Hendel M, Piasnik J, Olejarz A, Wlosowicz A, Pabis P, Wojcik W, Gumprecht J, Lip GYH, Nabrdalik K. Metabolically "extremely unhealthy" obese and non-obese people with diabetes and the risk of cardiovascular adverse events: the Silesia Diabetes - Heart Project. Cardiovasc Diabetol. 2024 Sep 3;23(1):326. doi: 10.1186/s12933-024-02420-x. |
| 39127709 | Derived | Nabrdalik K, Irlik K, Meng Y, Kwiendacz H, Piasnik J, Hendel M, Ignacy P, Kulpa J, Kegler K, Herba M, Boczek S, Hashim EB, Gao Z, Gumprecht J, Zheng Y, Lip GYH, Alam U. Artificial intelligence-based classification of cardiac autonomic neuropathy from retinal fundus images in patients with diabetes: The Silesia Diabetes Heart Study. Cardiovasc Diabetol. 2024 Aug 10;23(1):296. doi: 10.1186/s12933-024-02367-z. |
| 38603589 | Derived | Irlik K, Aldosari H, Hendel M, Kwiendacz H, Piasnik J, Kulpa J, Ignacy P, Boczek S, Herba M, Kegler K, Coenen F, Gumprecht J, Zheng Y, Lip GYH, Alam U, Nabrdalik K. Artificial intelligence-enhanced electrocardiogram analysis for identifying cardiac autonomic neuropathy in patients with diabetes. Diabetes Obes Metab. 2024 Jul;26(7):2624-2633. doi: 10.1111/dom.15578. Epub 2024 Apr 11. |
| 37985994 | Derived | Nabrdalik K, Kwiendacz H, Irlik K, Hendel M, Drozdz K, Wijata AM, Nalepa J, Janota O, Wojcik W, Gumprecht J, Lip GYH. Machine learning identification of risk factors for heart failure in patients with diabetes mellitus with metabolic dysfunction associated steatotic liver disease (MASLD): the Silesia Diabetes-Heart Project. Cardiovasc Diabetol. 2023 Nov 20;22(1):318. doi: 10.1186/s12933-023-02014-z. |