Arianna Scala
ABSTRACT
Proteinuria represents an increase in the urinary excretion of proteins. It could also follow kidney transplantation and affects more than 40% of kidney transplant patients per year. It results from protein increases in their filtered load, due to alterations in the selectivity of the glomerular capillary wall, or from defects in their tubular uptake. Different parameters are associated with the various stages of proteinuria and therefore allow characterizing of its severity. For this purpose, the variation of proteinuria was evaluated by loading input two parameters: glycemia and the blood level of the m-Tor inhibitor. Through combination of data with different machine learning algorithms, the goal of this research work was to evaluate how blood glucose values and the use of immunosuppressive drugs can lead to prediction proteinuria classification in patients.
- P. Topham, ‘Proteinuric renal disease’, Clin. Med., vol. 9, no. 3, pp. 284–287, Jun. 2009, doi: 10.7861/clinmedicine.9-3-284.8Google Scholar
Cross Ref
- K. Zandi-Nejad, A. A. Eddy, R. J. Glassock, and B. M. Brenner, ‘Why is proteinuria an ominous biomarker of progressive kidney disease?’, Kidney Int., vol. 66, pp. S76–S89, Nov. 2004, doi: 10.1111/j.1523-1755.2004.09220.x.Google ScholarCross Ref
- B. A. Julian, H. Suzuki, Y. Suzuki, Y. Tomino, G. Spasovski, and J. Novak, ‘Sources of Urinary Proteins and their Analysis by Urinary Proteomics for the Detection of Biomarkers of Disease’, Proteomics Clin. Appl., vol. 3, no. 9, pp. 1029–1043, Aug. 2009, doi: 10.1002/prca.200800243Google ScholarCross Ref
- P. A. Peterson, P. E. Evrin, and I. Berggård, ‘Differentiation of glomerular, tubular, and normal proteinuria: determinations of urinary excretion of beta-2-macroglobulin, albumin, and total protein’, J. Clin. Invest., vol. 48, no. 7, pp. 1189–1198, Jul. 1969, doi: 10.1172/JCI106083.Google ScholarCross Ref
- W. A. Wilmer, B. H. Rovin, C. J. Hebert, S. V. Rao, K. Kumor, and L. A. Hebert, ‘Management of glomerular proteinuria: a commentary’, J. Am. Soc. Nephrol. JASN, vol. 14, no. 12, pp. 3217–3232, Dec. 2003, doi: 10.1097/01.asn.0000100145.27188.33.Google ScholarCross Ref
- W. J. Marshall, M. Lapsley, A. Day, and R. Ayling, Clinical Biochemistry E-Book: Metabolic and Clinical Aspects. Elsevier Health Sciences, 2014.Google Scholar
- L. A. Stevens and A. S. Levey, ‘Measurement of Kidney Function’, Med. Clin., vol. 89, no. 3, pp. 457–473, May 2005, doi: 10.1016/j.mcna.2004.11.009.Google Scholar
- J. Bamoulid , ‘Immunosuppression and Results in Renal Transplantation’, Eur. Urol. Suppl., vol. 15, no. 9, pp. 415–429, Dec. 2016, doi: 10.1016/j.eursup.2016.04.011.Google ScholarCross Ref
- M. Naesens, D. R. J. Kuypers, and M. Sarwal, ‘Calcineurin Inhibitor Nephrotoxicity’, Clin. J. Am. Soc. Nephrol., vol. 4, no. 2, pp. 481–508, Feb. 2009, doi: 10.2215/CJN.04800908.Google ScholarCross Ref
- M. A. Lim, J. Kohli, and R. D. Bloom, ‘Immunosuppression for kidney transplantation: Where are we now and where are we going?’, Transplant. Rev. Orlando Fla, vol. 31, no. 1, pp. 10–17, Jan. 2017, doi: 10.1016/j.trre.2016.10.006.Google ScholarCross Ref
- V. Bumbea , ‘Long-term results in renal transplant patients with allograft dysfunction after switching from calcineurin inhibitors to sirolimus’, Nephrol. Dial. Transplant. Off. Publ. Eur. Dial. Transpl. Assoc. - Eur. Ren. Assoc., vol. 20, no. 11, pp. 2517–2523, Nov. 2005, doi: 10.1093/ndt/gfh957.Google Scholar
- M. L. Suárez Fernández and F. G-Cosío, ‘Causes and consequences of proteinuria following kidney transplantation’, Nefrol. Engl. Ed., vol. 31, no. 4, pp. 404–414, Jul. 2011, doi: 10.3265/Nefrologia.pre2011.May.10972.Google Scholar
- Y.-M. Jiang , ‘Effect of renin-angiotensin system inhibitors on survival in kidney transplant recipients: A systematic review and meta-analysis’, Kaohsiung J. Med. Sci., vol. 34, no. 1, pp. 1–13, Jan. 2018, doi: 10.1016/j.kjms.2017.07.007.Google ScholarCross Ref
- C. Ponticelli and G. Graziani, ‘Proteinuria after kidney transplantation’, Transpl. Int. Off. J. Eur Soc. Organ Transplant., vol. 25, no. 9, pp. 909–917, Sep. 2012, doi: 10.1111/j.1432-2277.2012.01500.x.Google Scholar
- Kidney Disease: Improving Global Outcomes (KDIGO) Transplant Work Group, ‘KDIGO clinical practice guideline for the care of kidney transplant recipients’, Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg., vol. 9 Suppl 3, pp. S1-155, Nov. 2009, doi: 10.1111/j.1600-6143.2009.02834.x.Google Scholar
- F.-Y. Hsu, F.-J. Lin, H.-T. Ou, S.-H. Huang, and C.-C. Wang, ‘Renoprotective Effect of Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Receptor Blockers in Diabetic Patients with Proteinuria’, Kidney Blood Press. Res., vol. 42, no. 2, pp. 358–368, 2017, doi: 10.1159/000477946.Google ScholarCross Ref
- J. Galle, ‘Reduction of proteinuria with angiotensin receptor blockers’, Nat. Clin. Pract. Cardiovasc. Med., vol. 5 Suppl 1, pp. S36-43, Jul. 2008, doi: 10.1038/ncpcardio0806.Google ScholarCross Ref
- A. M. Ponsiglione , ‘A Six Sigma DMAIC methodology as a support tool for Health Technology Assessment of two antibiotics’, Math. Biosci. Eng., vol. 18, no. 4, Art. no. mbe-18-04-174, 2021, doi: 10.3934/mbe.2021174.Google Scholar
- R. Kaboré, M. C. Haller, J. Harambat, G. Heinze, and K. Leffondré, ‘Risk prediction models for graft failure in kidney transplantation: a systematic review’, Nephrol. Dial. Transplant., vol. 32, no. suppl_2, pp. ii68–ii76, Apr. 2017, doi: 10.1093/ndt/gfw405.Google ScholarCross Ref
- S. A. Zenios, ‘Modeling the transplant waiting list: A queueing model with reneging’, Queueing Syst., vol. 31, no. 3, pp. 239–251, Jul. 1999, doi: 10.1023/A:1019162331525.Google ScholarDigital Library
- G. Improta , ‘Agile six sigma in healthcare: Case study at santobono pediatric hospital’, Int. J. Environ. Res. Public. Health, vol. 17, no. 3, 2020, doi: 10.3390/ijerph17031052.Google ScholarCross Ref
- A. Scala , ‘Lean Six Sigma Approach for Reducing Length of Hospital Stay for Patients with Femur Fracture in a University Hospital’, Int. J. Environ. Res. Public. Health, vol. 18, no. 6, Art. no. 6, Jan. 2021, doi: 10.3390/ijerph18062843.Google Scholar
- G. Improta, C. Ricciardi, A. Borrelli, A. D'alessandro, C. Verdoliva, and M. Cesarelli, ‘The application of six sigma to reduce the pre-operative length of hospital stay at the hospital Antonio Cardarelli’, Int. J. Lean Six Sigma, 2019, doi: 10.1108/IJLSS-02-2019-0014.Google ScholarCross Ref
- C. Ricciardi, A. M. Ponsiglione, G. Converso, I. Santalucia, M. Triassi, and G. Improta, ‘Implementation and validation of a new method to model voluntary departures from emergency departments’, Math. Biosci. Eng., vol. 18, no. 1, Art. no. mbe-18-01-013, 2021, doi: 10.3934/mbe.2021013.Google Scholar
- G. Improta , ‘Evaluation of Medical Training Courses Satisfaction: Qualitative Analysis and Analytic Hierarchy Process’, in 8th European Medical and Biological Engineering Conference, Cham, 2021, pp. 518–526, doi: 10.1007/978-3-030-64610-3_59.Google ScholarCross Ref
- G. Converso, G. Improta, M. Mignano, and L. C. Santillo, ‘A simulation approach for agile production logic implementation in a hospital emergency unit’, in International Conference on Intelligent Software Methodologies, Tools, and Techniques, 2015, pp. 623–634.Google ScholarCross Ref
- A. El-Baz, G. Gimel'farb, and M. A. El-Ghar, ‘Image analysis approach for identification of renal transplant rejection’, in 2008 19th International Conference on Pattern Recognition, Dec. 2008, pp. 1–4, doi: 10.1109/ICPR.2008.4761694.Google ScholarCross Ref
- G. Improta, C. Ricciardi, F. Amato, G. D'Addio, M. Cesarelli, and M. Romano, ‘Efficacy of machine learning in predicting the kind of delivery by cardiotocography’, in IFMBE Proc., 2020, vol. 76, pp. 793–799, doi: 10.1007/978-3-030-31635-8_95.Google ScholarCross Ref
- M. Romano, G. D'Addio, F. Clemente, A. M. Ponsiglione, G. Improta, and M. Cesarelli, ‘Symbolic dynamic and frequency analysis in foetal monitoring’, in 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Jun. 2014, pp. 1–5, doi: 10.1109/MeMeA.2014.6860122.Google ScholarCross Ref
- T. A. Trunfio, A. Scala, A. D. Vecchia, A. Marra, and A. Borrelli, ‘Multiple Regression Model to Predict Length of Hospital Stay for Patients Undergoing Femur Fracture Surgery at “San Giovanni di Dio e Ruggi d'Aragona” University Hospital’, in 8th European Medical and Biological Engineering Conference, Cham, 2021, pp. 840–847, doi: 10.1007/978-3-030-64610-3_94.Google Scholar
- C. Thongprayoon , ‘Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation’, J. Clin. Med., vol. 9, no. 4, Art. no. 4, Apr. 2020, doi: 10.3390/jcm9041107.Google Scholar
- G. D'Addio, C. Ricciardi, G. Improta, P. Bifulco, and M. Cesarelli, Feasibility of Machine Learning in Predicting Features Related to Congenital Nystagmus, vol. 76. Springer, 2020.Google ScholarCross Ref
- T. R. Srinivas , ‘Big Data, Predictive Analytics, and Quality Improvement in Kidney Transplantation: A Proof of Concept’, Am. J. Transplant., vol. 17, no. 3, pp. 671–681, 2017, doi: https://doi.org/10.1111/ajt.14099.Google ScholarCross Ref
- A. Burlacu , ‘Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review’, BioMed Res. Int., vol. 2020, p. e9867872, Jun. 2020, doi: 10.1155/2020/9867872.Google Scholar
- J. Chang, C. Ronco, and M. H. Rosner, ‘Computerized decision support systems: improving patient safety in nephrology’, Nat. Rev. Nephrol., vol. 7, no. 6, pp. 348–355, Jun. 2011, doi: 10.1038/nrneph.2011.50.Google ScholarCross Ref
- C. Yang, G. Kong, L. Wang, L. Zhang, and M.-H. Zhao, ‘Big data in nephrology: Are we ready for the change?’, Nephrol. Carlton Vic, vol. 24, no. 11, pp. 1097–1102, Nov. 2019, doi: 10.1111/nep.13636.Google ScholarCross Ref
- G. Improta, V. Mazzella, D. Vecchione, S. Santini, and M. Triassi, ‘Fuzzy logic-based clinical decision support system for the evaluation of renal function in post-Transplant Patients’, J. Eval. Clin. Pract., vol. 26, no. 4, pp. 1224–1234, Aug. 2020, doi: 10.1111/jep.13302.Google ScholarCross Ref
- G. C. Viberti, D. Mackintosh, R. W. Bilous, J. C. Pickup, and H. Keen, ‘Proteinuria in diabetes mellitus: role of spontaneous and experimental variation of glycemia’, Kidney Int., vol. 21, no. 5, pp. 714–720, May 1982, doi: 10.1038/ki.1982.87.Google ScholarCross Ref
- R. J. MacIsaac, G. Jerums, and E. I. Ekinci, ‘Effects of glycaemic management on diabetic kidney disease’, World J. Diabetes, vol. 8, no. 5, pp. 172–186, May 2017, doi: 10.4239/wjd.v8.i5.172.Google ScholarCross Ref
- J. L. Gross, M. J. de Azevedo, S. P. Silveiro, L. H. Canani, M. L. Caramori, and T. Zelmanovitz, ‘Diabetic Nephropathy: Diagnosis, Prevention, and Treatment’, Diabetes Care, vol. 28, no. 1, pp. 164–176, Jan. 2005, doi: 10.2337/diacare.28.1.164.Google ScholarCross Ref
- J. Wang, Z. Xu, B. Chen, S. Zheng, P. Xia, and Y. Cai, ‘The role of sirolimus in proteinuria in diabetic nephropathy rats’, Iran. J. Basic Med. Sci., vol. 20, no. 12, pp. 1339–1344, Dec. 2017, doi: 10.22038/IJBMS.2017.9618.Google Scholar
- G. K. Rangan, ‘Sirolimus-associated proteinuria and renal dysfunction’, Drug Saf., vol. 29, no. 12, pp. 1153–1161, 2006, doi: 10.2165/00002018-200629120-00006.Google ScholarCross Ref
- G. Stallone , ‘Sirolimus and proteinuria in renal transplant patients: evidence for a dose-dependent effect on slit diaphragm-associated proteins’, Transplantation, vol. 91, no. 9, pp. 997–1004, May 2011, doi: 10.1097/TP.0b013e318211d342.Google ScholarCross Ref
- Assessment of proteinuria level in nephrology patients using a machine learning approach
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