Cardiovascular
Machine learning profiles of cardiovascular risk in patients with diabetes mellitus: the Silesia Diabetes-Heart Project
Aug
IDF Diabetes atlas. 10th edition.
Rawshani A, Rawshani A, Franzén S, Eliasson B, Svensson A-M, Miftaraj M, et al. Mortality and cardiovascular disease in type 1 and type 2 diabetes. N Engl J Med. 2017;376(15):1407–18.
Google Scholar
Sarwar N, Gao P, Kondapally Seshasai SR, Gobin R, Kaptoge S, Di Angelantonio E, et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet (London, England). 2010;375(9733):2215–22.
Google Scholar
Rawshani A, Sattar N, Franzén S, Rawshani A, Hattersley AT, Svensson A-M, et al. Excess mortality and cardiovascular disease in young adults with type 1 diabetes in relation to age at onset: a nationwide, register-based cohort study. Lancet (London, England). 2018;392(10146):477–86.
Google Scholar
Conroy RM, Pyörälä K, Fitzgerald AP, Sans S, Menotti A, De Backer G, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003;24(11):987–1003.
Google Scholar
Hippisley-Cox J, Coupland C, Brindle P. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ. 2017;357: j2099.
Google Scholar
Echouffo-Tcheugui JB, Kengne AP. Comparative performance of diabetes-specific and general population-based cardiovascular risk assessment models in people with diabetes mellitus. Diabetes Metab. 2013;39(5):389–96.
Google Scholar
Kengne AP, Patel A, Colagiuri S, Heller S, Hamet P, Marre M, et al. The Framingham and UK Prospective Diabetes Study (UKPDS) risk equations do not reliably estimate the probability of cardiovascular events in a large ethnically diverse sample of patients with diabetes: the Action in Diabetes and Vascular Disease: Pretera. Diabetologia. 2010;53(5):821–31.
Google Scholar
Chamnan P, Simmons RK, Sharp SJ, Griffin SJ, Wareham NJ. Cardiovascular risk assessment scores for people with diabetes: a systematic review. Diabetologia. 2009;52(10):2001–14.
Google Scholar
Sofogianni A, Stalikas N, Antza C, Tziomalos K. Cardiovascular risk prediction models and scores in the era of personalized medicine. J Pers Med. 2022;12(7):1180.
Google Scholar
Mora D, Nieto JA, Mateo J, Bikdeli B, Barco S, Trujillo-Santos J, et al. Machine learning to predict outcomes in patients with acute pulmonary embolism who prematurely discontinued anticoagulant therapy. Thromb Haemost. 2022;122(4):570–7.
Google Scholar
Lip GYH, Genaidy A, Tran G, Marroquin P, Estes C, Sloop S. Improving stroke risk prediction in the general population: a comparative assessment of common clinical rules, a new multimorbid index, and machine-learning-based algorithms. Thromb Haemost. 2022;122(1):142–50.
Google Scholar
Nopp S, Spielvogel CP, Schmaldienst S, Klauser-Braun R, Lorenz M, Bauer BN, et al. Bleeding risk assessment in end-stage kidney disease: validation of existing risk scores and evaluation of a machine learning-based approach. Thromb Haemost. 2022;122(9):1558.
Jiang Y, Yang ZG, Wang J, Shi R, Han PL, Qian WL, et al. Unsupervised machine learning based on clinical factors for the detection of coronary artery atherosclerosis in type 2 diabetes mellitus. Cardiovasc Diabetol. 2022;21(1):1–10.
Google Scholar
Drożdż K, Nabrdalik K, Kwiendacz H, Hendel M, Olejarz A, Tomasik A, et al. Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: a machine learning approach. Cardiovasc Diabetol. 2022;21(1):240.
Google Scholar
Nabrdalik K, Kwiendacz H, Drożdż K, Irlik K, Hendel M, Wijata AM, et al. Machine learning predicts cardiovascular events in patients with diabetes: the Silesia diabetes-heart project. Curr Probl Cardiol. 2023;48:101694.
Google Scholar
Chowdhury M, Nevitt S, Eleftheriadou A, Kanagala P, Esa H, Cuthbertson DJ, et al. Cardiac autonomic neuropathy and risk of cardiovascular disease and mortality in type 1 and type 2 diabetes: a meta-analysis. BMJ Open Diab Res Care. 2021;9:2480.
Google Scholar
Le Dinh T, Phi Thi Nguyen N, Thanh Thi Tran H, Luong Cong T, Ho Thi Nguyen L, Do Nhu B, et al. Diabetic peripheral neuropathy associated with cardiovascular risk factors and glucagon-like peptide-1 concentrations among newly diagnosed patients with type 2 diabetes mellitus. Diabetes Metab Syndr Obes. 2022. https://doi.org/10.2147/DMSO.S344532.
Google Scholar
Ashley EA. The precision medicine initiative: a new national effort. JAMA. 2015;313(21):2119–20.
Google Scholar
Guo Y. A new paradigm of “real-time” stroke risk prediction and integrated care management in the digital health era: innovations using machine learning and artificial intelligence approaches. Thromb Haemost. 2022;122:5–7.
Google Scholar
Kleindorfer DO, Towfighi A, Chaturvedi S, Cockroft KM, Gutierrez J, Lombardi-Hill D, et al. 2021 guideline for the prevention of stroke in patients with stroke and transient ischemic attack: a guideline from the American Heart Association/American Stroke Association. Stroke. 2021;52(7):e364-467.
Google Scholar
Little RR. Glycated hemoglobin standardization–National glycohemoglobin standardization program (NGSP) perspective. Clin Chem Lab Med. 2003;41(9):1191–8.
Google Scholar
Moore JF, Sharer JD. Methods for quantitative creatinine determination. Curr Protoc Hum Genet. 2017;93:A-3O.
Pugliese G, Solini A, Bonora E, Orsi E, Zerbini G, Giorgino F, et al. The chronic kidney disease epidemiology collaboration (CKD-EPI) equation provides a better definition of cardiovascular burden associated with CKD than the modification of diet in renal disease (MDRD) study formula in subjects with type 2 diabetes. Atherosclerosis. 2011;218(1):194–9.
Google Scholar
Sampson M, Wolska A, Cole J, Zubirán R, Otvos JD, Meeusen JW, et al. Accuracy and clinical impact of estimating low-density lipoprotein-cholesterol at high and low levels by different equations. Biomedicines. 2022;10(12):3156.
Google Scholar
Committee ADAPP. 11 Chronic kidney disease and risk management: standards of medical care in diabetes-2022. Diabetes Care. 2022;45(1):S175-84.
Feldman EL, Stevens MJ, Thomas PK, Brown MB, Canal N, Greene DA. A practical two-step quantitative clinical and electrophysiological assessment for the diagnosis and staging of diabetic neuropathy. Diabetes Care. 1994;17(11):1281–9.
Google Scholar
Edmonds M, Manu C, Vas P. The current burden of diabetic foot disease. J Clin Orthop trauma. 2021;17:88–93.
Google Scholar
Armstrong DG, Boulton AJM, Bus SA. Diabetic foot ulcers and their recurrence. N Engl J Med. 2017;376(24):2367–75.
Google Scholar
Vinik AI, Maser RE, Mitchell BD, Freeman R. Diabetic autonomic neuropathy. Diabetes Care. 2003;26(5):1553–79.
Google Scholar
Ewing DJ, Campbell IW, Clarke BF. Assessment of cardiovascular effects in diabetic autonomic neuropathy and prognostic implications. Ann Intern Med. 1980;92(2II):308–11.
Google Scholar
36-Item short form survey (SF-36) Scoring instructions. RAND.
Audigier V, Husson F, Josse J. A principal components method to impute missing values for mixed data. Adv Data Anal Classif. 2016;10(1):5–26.
Google Scholar
Unal I. Defining an optimal cut-point value in ROC analysis: an alternative approach. Comput Math Methods Med. 2017. https://doi.org/10.1155/2017/3762651.
Google Scholar
Fernández A, Gómez S. Versatile linkage: a family of space-conserving strategies for agglomerative hierarchical clustering. J Classif. 2020;37(3):584–97.
Google Scholar
Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987;20:53–65.
Google Scholar
Oliveira FHM, Machado ARP, Andrade AO. On the use of t-distributed stochastic neighbor embedding for data visualization and classification of individuals with Parkinson’s disease. Comput Math Methods Med. 2018;2018:8019232.
Google Scholar
Sethi Y, Patel N, Kaka N, Kaiwan O, Kar J, Moinuddin A, et al. Precision medicine and the future of cardiovascular diseases: a clinically oriented comprehensive review. J Clin Med. 2023;12(5):1799.
Google Scholar
Tromp J, Paniagua SMA, Lau ES, Allen NB, Blaha MJ, Gansevoort RT, et al. Age dependent associations of risk factors with heart failure: pooled population based cohort study. BMJ. 2021;372: n461.
Google Scholar
Boyko EJ, Ahroni JH, Smith DG, Davignon D. Increased mortality associated with diabetic foot ulcer. Diabet Med. 1996;13(11):967–72.
Google Scholar
Chammas NK, Hill RLR, Edmonds ME. Increased mortality in diabetic foot ulcer patients: the significance of ulcer type. J Diabetes Res. 2016;2016:2879809.
Google Scholar
Morbach S, Furchert H, Gröblinghoff U, Hoffmeier H, Kersten K, Klauke G-T, et al. Long-term prognosis of diabetic foot patients and their limbs: amputation and death over the course of a decade. Diabetes Care. 2012;35(10):2021–7.
Google Scholar
Dietrich I, Braga GA, de Melo FG, da Costa AC. The diabetic foot as a proxy for cardiovascular events and mortality review. Curr Atheroscler Rep. 2017;19(11):44.
Google Scholar
Meloni M, Bellia A, Giurato L, Lauro D, Uccioli L. Below-the-ankle arterial disease: a new marker of coronary artery disease in patients with diabetes and foot ulcers. Acta Diabetol. 2022;59(10):1331–8.
Google Scholar
Balasubramanian GV, Chockalingam N, Naemi R. The role of cutaneous microcirculatory responses in tissue injury, inflammation and repair at the foot in diabetes. Front Bioeng Biotechnol. 2021;9: 732753.
Google Scholar
Jensen J, Poulsen MK, Petersen PW, Gerdes B, Rossing K, Schou M. Prevalence of heart failure phenotypes and current use of therapies in primary care: results from a nationwide study. ESC Hear Fail. 2023. https://doi.org/10.1002/ehf2.14324.
Google Scholar
Green JB, Bethel MA, Armstrong PW, Buse JB, Engel SS, Garg J, et al. Effect of sitagliptin on cardiovascular outcomes in type 2 diabetes. N Engl J Med. 2015;373(3):232–42.
Google Scholar
Stone GW, Kappetein AP, Sabik JF, Pocock SJ, Morice M-C, Puskas J, et al. Five-year outcomes after PCI or CABG for left main coronary disease. N Engl J Med. 2019;381(19):1820–30.
Google Scholar
Sharma A, Zheng Y, Ezekowitz JA, Westerhout CM, Udell JA, Goodman SG, et al. Cluster analysis of cardiovascular phenotypes in patients with type 2 diabetes and established atherosclerotic cardiovascular disease: a potential approach to precision medicine. Diabetes Care. 2021;45(1):204–12. https://doi.org/10.2337/dc20-2806.
Google Scholar
ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, et al. 11. Chronic kidney disease and risk management: standards of care in diabetes-2023. Diabetes Care. 2023;46(1):S191-202.
Google Scholar
Goretzko D, Heumann C, Bühner M. Investigating parallel analysis in the context of missing data: a simulation study comparing six missing data methods. Educ Psychol Meas. 2020;80(4):756–74.
Google Scholar
Slade E, Naylor MG. A fair comparison of tree-based and parametric methods in multiple imputation by chained equations. Stat Med. 2020;39(8):1156–66.
Google Scholar
Anand V, Downs SM. Probabilistic asthma case finding: a noisy or reformulation. AMIA Annu Symp Proc. 2008;2008:6–10.
Google Scholar
Kotowski K, Kucharski D, Machura B, Adamski S, Gutierrez Becker B, Krason A, et al. Detecting liver cirrhosis in computed tomography scans using clinically-inspired and radiomic features. Comput Biol Med. 2023;152: 106378.
Google Scholar
Salman I, Vomlel J. Learning the structure of Bayesian networks from incomplete data using a mixture model. Informatica. 2023. https://doi.org/10.31449/inf.v47i1.4497.
Google Scholar
Subramani S, Varshney N, Anand MV, Soudagar MEM, Al-Keridis LA, Upadhyay TK, et al. Cardiovascular diseases prediction by machine learning incorporation with deep learning. Front Med. 2023;10:1150933.
Google Scholar
Dubel R, Wijata AM, Nalepa J. On the impact of noisy labels on supervised classification models. In: Mikyška J, de Mulatier C, Paszynski M, Krzhizhanovskaya VV, Dongarra JJ, Sloot PMA, editors. BT, computational science, ICCS 2023. Cham: Springer; 2023. p. 111–9.
Google Scholar
