Machine learning models using non-linear techniques improve the prediction of resting energy expenditure in individuals receiving hemodialysis

References

• United States Renal Data System. Annual data report, executive summary. 2019 [cited 2023 May 20]. Available from: https://www.usrds.org/media/2371/2019-executive-summary.pdf
   Google Scholar
• National Kidney Foundation. KDOQI. clinical practice guidelines for chronic kidney disease: evaluation, classification and stratification. 2002 [cited 2023 May 20]. Available from: https://www.kidney.org/sites/default/files/docs/ckd_evaluation_classification_stratification.pdf. Accessed May 20 2023.
   Google Scholar
• Caron N, Peyrot N, Caderby T, et al. Energy expenditure in people with diabetes mellitus: a review. Front Nutr. 2016;3:1. doi: 10.3389/fnut.2016.00056.
   PubMedGoogle Scholar
• Fouque D, Kalantar-Zadeh K, Kopple J, et al. A proposed nomenclature and diagnostic criteria for protein-energy wasting in acute and chronic kidney disease. Kidney Int. 2008;73(4):391–15. doi: 10.1038/sj.ki.5002585.
   PubMed Web of Science ®Google Scholar
• Carrero JJ, Stenvinkel P, Cuppari L, et al. Etiology of the protein-energy wasting syndrome in chronic kidney disease: a consensus statement from the international society of renal nutrition and metabolism (ISRNM). J Ren Nutr. 2013;23(2):77–90. doi: 10.1053/j.jrn.2013.01.001.
   PubMed Web of Science ®Google Scholar
• Herselman M, Moosa MR, Kotze TJ, et al. Protein-energy malnutrition as a risk factor for increased morbidity in long-term hemodialysis patients. J Ren Nutr. 2000;10(1):7–15. doi: 10.1016/s1051-2276(00)90017-7.
   PubMedGoogle Scholar
• de Mutsert R, Grootendorst DC, Axelsson J, et al. Excess mortality due to interaction between protein-energy wasting, inflammation and cardiovascular disease in chronic dialysis patients. Nephrol Dial Transplant. 2008;23(9):2957–2964. doi: 10.1093/ndt/gfn167.
   PubMed Web of Science ®Google Scholar
• Avesani CM, Draibe SA, Kamimura MA, et al. Resting energy expenditure of chronic kidney disease patients: influence of renal function and subclinical inflammation. Am J Kidney Dis. 2004;44(6):1008–1016. doi: 10.1053/j.ajkd.2004.08.023.
   PubMed Web of Science ®Google Scholar
• Kamimura MA, Draibe SA, Dalboni MA, et al. Serum and cellular interleukin-6 in haemodialysis patients: relationship with energy expenditure. Nephrol Dial Transplant. 2007;22(3):839–844. doi: 10.1093/ndt/gfl705.
   PubMed Web of Science ®Google Scholar
• Ikizler TA, Cano NJ, Franch H, et al. Prevention and treatment of protein energy wasting in chronic kidney disease patients: a consensus statement by the international society of renal nutrition and metabolism. Kidney Int. 2013;84(6):1096–1107. doi: 10.1038/ki.2013.147.
   PubMed Web of Science ®Google Scholar
• Singer MA. Of mice and men and elephants: metabolic rate sets glomerular filtration rate. Am J Kidney Dis. 2001;37(1):164–178. doi: 10.1016/S0272-6386(01)80073-1.
   PubMed Web of Science ®Google Scholar
• Morton AR, Singer MA. The problem with Kt/V: dialysis dose should be normalized to metabolic rate not volume. Semin Dial. 2007;20(1):12–15. doi: 10.1111/j.1525-139X.2007.00232.x.
   PubMed Web of Science ®Google Scholar
• Schadewaldt P, Nowotny B, Strassburger K, et al. Indirect calorimetry in humans: a postcalorimetric evaluation procedure for correction of metabolic monitor variability. Am J Clin Nutr. 2013;97(4):763–773. doi: 10.3945/ajcn.112.035014.
   PubMed Web of Science ®Google Scholar
• Oshima T, Berger MM, De Waele E, et al. Indirect calorimetry in nutritional therapy. A position paper by the ICALIC study group. Clin Nutr. 2017;36(3):651–662. doi: 10.1016/j.clnu.2016.06.010.
   PubMed Web of Science ®Google Scholar
• Harris JA, Benedict FG. A biometric study of human basal metabolism. Proc Natl Acad Sci U S A. 1918;4(12):370–373. doi: 10.1073/pnas.4.12.370.
   PubMedGoogle Scholar
• Mifflin MD, St Jeor ST, Hill LA, et al. A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr. 1990;51(2):241–247. doi: 10.1093/ajcn/51.2.241.
   PubMed Web of Science ®Google Scholar
• Kamimura MA, Avesani CM, Bazanelli AP, et al. Are predictive equations reliable for estimating resting energy expenditure in chronic kidney disease patients? Nephrol Dial Transplant. 2011;26(2):544–550. doi: 10.1093/ndt/gfq452.
   PubMed Web of Science ®Google Scholar
• Dias Rodrigues JC, Lamarca F, Lacroix de Oliveira C, et al. Agreement between prediction equations and indirect calorimetry to estimate energy expenditure in elderly patients on hemodialysis. Espen J. 2014;9(2):e91–e96. doi: 10.1016/j.clnme.2013.12.002.
   Google Scholar
• Wu PY, Chen YT, Wong TC, et al. Energy requirement of patients undergoing hemodialysis: a Cross-Sectional study in multiple centers. Biochem Res Int. 2020;2020:2054265. doi: 10.1155/2020/2054265.
   PubMed Web of Science ®Google Scholar
• Fernandes T, Avesani C, Kamimura M, et al. Estimating resting energy expenditure of patients on dialysis: development and validation of a predictive equation. Nutrition. 2019;67–68:110527. doi: 10.1016/j.nut.2019.06.008.
   PubMed Web of Science ®Google Scholar
• Byham-Gray LD, Parrott JS, Peters EN, et al. Modeling a predictive energy equation specific for maintenance hemodialysis. JPEN J Parenter Enteral Nutr. 2018;42(3):587–596.
   PubMed Web of Science ®Google Scholar
• Vilar E, Machado A, Garrett A, et al. Disease-specific predictive formulas for energy expenditure in the dialysis population. J Ren Nutr. 2014;24(4):243–251. doi: 10.1053/j.jrn.2014.03.001.
   PubMed Web of Science ®Google Scholar
• Byham-Gray L, Parrott JS, Ho WY, et al. Development of a predictive energy equation for maintenance hemodialysis patients: a pilot study. J Ren Nutr. 2014;24(1):32–41. doi: 10.1053/j.jrn.2013.10.005.
   PubMed Web of Science ®Google Scholar
• Oliveira B, Sridharan S, Farrington K, et al. Comparison of resting energy equations and total energy expenditure in haemodialysis patients and body composition measured by multi-frequency bioimpedance. Nephrology. 2018;23(8):748–754. doi: 10.1111/nep.13112.
   PubMed Web of Science ®Google Scholar
• Morrow EA, Marcus A, Byham-Gray L. Comparison of a handheld indirect calorimetry device and predictive energy equations among individuals on maintenance hemodialysis. J Ren Nutr. 2017;27(6):402–411. doi: 10.1053/j.jrn.2017.06.011.
   PubMed Web of Science ®Google Scholar
• Hung R, Sridharan S, Farrington K, et al. Comparison of estimates of resting energy expenditure equations in haemodialysis patients. Int J Artif Organs. 2017;40(3):96–101. doi: 10.5301/ijao.5000575.
   PubMed Web of Science ®Google Scholar
• Bailey A, Brody R, Sackey J, et al. Current methods for developing predictive energy equations in maintenance dialysis are imprecise. Ann Med. 2022;54(1):909–920. doi: 10.1080/07853890.2022.2057581.
   PubMed Web of Science ®Google Scholar
• Ponce D, de Goes CR, de Andrade LGM. Proposal of a new equation for estimating resting energy expenditure of acute kidney injury patients on dialysis: a machine learning approach. Nutr Metab. 2020;17(1):96. doi: 10.1186/s12986-020-00519-y.
   Google Scholar
• Siga MM, Ducher M, Florens N, et al. Prediction of all-cause mortality in haemodialysis patients using a bayesian network. Nephrol Dial Transplant. 2020;35(8):1420–1425. doi: 10.1093/ndt/gfz295.
   PubMed Web of Science ®Google Scholar
• Xiong CZ, Su M, Jiang Z, et al. Prediction of hemodialysis timing based on LVW feature selection and ensemble learning. J Med Syst. 2018;43(1):18. Published 2018 Dec 13. doi: 10.1007/s10916-018-1136-x.
   PubMed Web of Science ®Google Scholar
• Sheng K, Zhang P, Yao X, et al. Prognostic machine learning models for first-year mortality in incident hemodialysis patients: development and validation study. JMIR Med Inform. 2020;8(10):e20578. doi: 10.2196/20578.
   PubMed Web of Science ®Google Scholar
• Mezzatesta S, Torino C, Meo P, et al. A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis. Comput Methods Programs Biomed. 2019;177:9–15. doi: 10.1016/j.cmpb.2019.05.005.
   PubMed Web of Science ®Google Scholar
• Avesani CM, Kamimura MA, Cuppari L. Energy expenditure in chronic kidney disease patients. J Ren Nutr. 2011;21(1):27–30. doi: 10.1053/j.jrn.2010.10.013.
   PubMed Web of Science ®Google Scholar
• Mullainathan S, Spiess J. Machine learning: an applied econometric approach. J Econ Perspect. 2017;31(2):87–106. doi: 10.1257/jep.31.2.87.
   Web of Science ®Google Scholar
• Obermeyer Z, Emanuel EJ. Predicting the future – big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216–1219. doi: 10.1056/NEJMp1606181.
   PubMed Web of Science ®Google Scholar
• Olejnik LA, Peters EN, Parrott JS, et al. Abbreviated steady state intervals for measuring resting energy expenditure in patients on maintenance hemodialysis. JPEN J Parenter Enteral Nutr. 2017;41(8):1348–1355. doi: 10.1177/0148607116660981.
   PubMed Web of Science ®Google Scholar
• Koo T, Li M. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15(2):155–163. doi: 10.1016/j.jcm.2016.02.012.
   PubMed Web of Science ®Google Scholar
• Altman DG, Bland JM. Measurement in medicine: the analysis of method comparison studies. Statistician. 1983;32(3):307–317. doi: 10.2307/2987937.
   Google Scholar
• Frankenfield D, Roth-Yousey L, Compher C. Comparison of predictive equations for resting metabolic rate in healthy nonobese and obese adults: a systematic review. J Am Diet Assoc. 2005;105(5):775–789. doi: 10.1016/j.jada.2005.02.005.
   PubMedGoogle Scholar
• Essadik R, Msaad R, Lebrazi H, et al. Assessing the prevalence of protein-energy wasting in haemodialysis patients: a cross-sectional monocentric study. Nephrol Ther. 2017;13(7):537–543. doi: 10.1016/j.nephro.2017.02.013.
   PubMed Web of Science ®Google Scholar
• Koefoed M, Kromann CB, Juliussen SR, et al. Nutritional status of maintenance dialysis patients: low lean body mass index and obesity are common, protein-energy wasting is uncommon. PLOS One. 2016;11(2):e0150012. doi: 10.1371/journal.pone.015001240,4.
   PubMed Web of Science ®Google Scholar
• Bailey JL, Wang X, England BK, et al. The acidosis of chronic renal failure activates muscle proteolysis in rats by augmenting transcription of genes encoding proteins of the ATP-dependent ubiquitin-proteasome pathway. J Clin Invest. 1996;97(6):1447–1453. doi: 10.1172/JCI118566.
   PubMed Web of Science ®Google Scholar
• Graham KA, Reaich D, Channon SM, et al. Correction of acidosis in hemodialysis decreases whole body protein degradation. J Am Soc Nephrol. 1997;8(4):632–637. doi: 10.1681/ASN.V84632.
   PubMed Web of Science ®Google Scholar
• Goldstein SL, Ikizler TA, Zappitelli M, et al. Non-infected hemodialysis catheters are associated with increased inflammation compared to arteriovenous fistulas. Kidney Int. 2009;76(10):1063–1069. doi: 10.1038/ki.2009.303.
   PubMed Web of Science ®Google Scholar
• Lacquaniti A, Campo S, Falliti G, et al. Free light chains, high mobility group box 1, and mortality in hemodialysis patients. J Clin Med. 2022;11(23):6904. doi: 10.3390/jcm11236904.
   PubMed Web of Science ®Google Scholar
• Campo S, Lacquaniti A, Trombetta D, et al. Immune system dysfunction and inflammation in hemodialysis patients: two sides of the same coin. J Clin Med. 2022;11(13):3759. doi: 10.3390/jcm11133759.
   PubMed Web of Science ®Google Scholar
• Monardo P, Lacquaniti A, Campo S, et al. Updates on hemodialysis techniques with a common denominator: the personalization of the dialytic therapy. Semin Dial. 2021;34(3):183–195. doi: 10.1111/sdi.12956.
   PubMed Web of Science ®Google Scholar
• Deger SM, Sundell MB, Siew ED, et al. Insulin resistance and protein metabolism in chronic hemodialysis patients. J Ren Nutr. 2013;23(3):e59–e66. ED doi: 10.1053/j.jrn.2012.08.013.
   PubMed Web of Science ®Google Scholar
• Hume R. Prediction of lean body mass from height and weight. J Clin Pathol. 1966;19(4):389–391. doi: 10.1136/jcp.19.4.389.
   PubMed Web of Science ®Google Scholar
• Deurenberg P, Weststrate JA, Hautvast JG. Changes in fat-free mass during weight loss measured by bioelectrical impedance and by densitometry. Am J Clin Nutr. 1989;49(1):33–36. doi: 10.1093/ajcn/49.1.33.
   PubMed Web of Science ®Google Scholar
• Salazar-Robles E, Lerma A, Calderón-Juárez M, et al. Assessment of factors related to diminished appetite in hemodialysis patients with a new adapted and validated questionnaire. Nutrients. 2021;13(4):1371. doi: 10.3390/nu13041371.
   PubMed Web of Science ®Google Scholar
• Kalantar-Zadeh K, Block G, McAllister CJ, et al. Appetite and inflammation, nutrition, anemia, and clinical outcome in hemodialysis patients. Am J Clin Nutr. 2004;80 (2):299–307. doi: 10.1093/ajcn/80.2.299.
   PubMed Web of Science ®Google Scholar
• Detsky AS, McLaughlin JR, Baker JP, et al. What is subjective global assessment of nutritional status? JPEN J Parenter Enteral Nutr. 1987;11(1):8–13. doi: 10.1177/014860718701100108.
   PubMed Web of Science ®Google Scholar