دورية أكاديمية
أعرض في EDS

Machine Learning Model in Obesity to Predict Weight Loss One Year after Bariatric Surgery: A Pilot Study.

التفاصيل البيبلوغرافية
العنوان: Machine Learning Model in Obesity to Predict Weight Loss One Year after Bariatric Surgery: A Pilot Study.
المؤلفون: Nadal E; Instituto Universitario de Ingeniería Mecánica y Biomecánica (I2MB), Universitat Politècnica de València, 46022 Valencia, Spain., Benito E; CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), 28040 Madrid, Spain., Ródenas-Navarro AM; Endocrinology and Nutrition Service, Clinical University Hospital of Valencia, 46010 Valencia, Spain., Palanca A; Endocrinology and Nutrition Service, Clinical University Hospital of Valencia, 46010 Valencia, Spain.; INCLIVA Biomedical Research Institute, 46010 Valencia, Spain., Martinez-Hervas S; CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), 28040 Madrid, Spain.; Endocrinology and Nutrition Service, Clinical University Hospital of Valencia, 46010 Valencia, Spain.; INCLIVA Biomedical Research Institute, 46010 Valencia, Spain.; Department of Medicine, University of Valencia, 46010 Valencia, Spain., Civera M; Endocrinology and Nutrition Service, Clinical University Hospital of Valencia, 46010 Valencia, Spain.; INCLIVA Biomedical Research Institute, 46010 Valencia, Spain., Ortega J; INCLIVA Biomedical Research Institute, 46010 Valencia, Spain.; General Surgery Service, University Hospital of Valencia, 46010 Valencia, Spain.; Department of Surgery, University of Valencia, 46010 Valencia, Spain., Alabadi B; CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), 28040 Madrid, Spain.; Endocrinology and Nutrition Service, Clinical University Hospital of Valencia, 46010 Valencia, Spain.; INCLIVA Biomedical Research Institute, 46010 Valencia, Spain., Piqueras L; CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), 28040 Madrid, Spain.; INCLIVA Biomedical Research Institute, 46010 Valencia, Spain.; Department of Pharmacology, University of Valencia, 46010 Valencia, Spain., Ródenas JJ; Instituto Universitario de Ingeniería Mecánica y Biomecánica (I2MB), Universitat Politècnica de València, 46022 Valencia, Spain., Real JT; CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), 28040 Madrid, Spain.; Endocrinology and Nutrition Service, Clinical University Hospital of Valencia, 46010 Valencia, Spain.; INCLIVA Biomedical Research Institute, 46010 Valencia, Spain.; Department of Medicine, University of Valencia, 46010 Valencia, Spain.
المصدر: Biomedicines [Biomedicines] 2024 May 25; Vol. 12 (6). Date of Electronic Publication: 2024 May 25.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: MDPI AG Country of Publication: Switzerland NLM ID: 101691304 Publication Model: Electronic Cited Medium: Print ISSN: 2227-9059 (Print) Linking ISSN: 22279059 NLM ISO Abbreviation: Biomedicines Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Basel, Switzerland : MDPI AG, [2013]-
مستخلص: Roux-en-Y gastric bypass (RYGB) is a treatment for severe obesity. However, many patients have insufficient total weight loss (TWL) after RYGB. Although multiple factors have been involved, their influence is incompletely known. The aim of this exploratory study was to evaluate the feasibility and reliability of the use of machine learning (ML) techniques to estimate the success in weight loss after RYGP, based on clinical, anthropometric and biochemical data, in order to identify morbidly obese patients with poor weight responses. We retrospectively analyzed 118 patients, who underwent RYGB at the Hospital Clínico Universitario of Valencia (Spain) between 2013 and 2017. We applied a ML approach using local linear embedding (LLE) as a tool for the evaluation and classification of the main parameters in conjunction with evolutionary algorithms for the optimization and adjustment of the parameter model. The variables associated with one-year postoperative %TWL were obstructive sleep apnea, osteoarthritis, insulin treatment, preoperative weight, insulin resistance index, apolipoprotein A, uric acid, complement component 3, and vitamin B12. The model correctly classified 71.4% of subjects with TWL < 30% although 36.4% with TWL ≥ 30% were incorrectly classified as "unsuccessful procedures". The ML-model processed moderate discriminatory precision in the validation set. Thus, in severe obesity, ML-models can be useful to assist in the selection of patients before bariatric surgery.
References: J Diabetes Res. 2020 Jun 12;2020:4168340. (PMID: 32626780)
Obes Surg. 2019 Mar;29(3):811-818. (PMID: 30560312)
Obes Rev. 2012 Apr;13(4):388-92. (PMID: 22151906)
Diabetes Care. 2014 Aug;37(8):2351-8. (PMID: 24804699)
JAMA. 2018 Apr 24;319(16):1723-1725. (PMID: 29570750)
Comput Struct Biotechnol J. 2020 Jun 17;18:1466-1473. (PMID: 32637044)
Ann Intern Med. 2005 Apr 5;142(7):547-59. (PMID: 15809466)
Eur J Clin Invest. 2012 May;42(5):503-9. (PMID: 21985442)
Surg Obes Relat Dis. 2019 Jan;15(1):146-151. (PMID: 30425002)
Obes Surg. 2020 Jun;30(6):2124-2130. (PMID: 32009214)
Rev Esp Cardiol (Engl Ed). 2016 Jun;69(6):579-87. (PMID: 27133458)
J Clin Med. 2019 Mar 14;8(3):. (PMID: 30875745)
Obes Surg. 2021 Jul;31(7):3279-3290. (PMID: 33893610)
Cochrane Database Syst Rev. 2014 Aug 08;(8):CD003641. (PMID: 25105982)
Eur Heart J. 2020 Jan 14;41(3):359-367. (PMID: 31513271)
Obes Surg. 2020 Mar;30(3):975-981. (PMID: 31848986)
Nat Mach Intell. 2019 Jul;1(7):296-306. (PMID: 31701088)
Obes Surg. 2012 Jan;22(1):70-89. (PMID: 21833817)
Obes Surg. 2017 Jun;27(6):1446-1452. (PMID: 27943095)
Surg Endosc. 2021 Jan;35(1):182-191. (PMID: 31953733)
Stat Med. 2008 Jan 30;27(2):157-72; discussion 207-12. (PMID: 17569110)
J Clin Med. 2019 May 12;8(5):. (PMID: 31083643)
Am J Physiol Heart Circ Physiol. 2005 Dec;289(6):H2649-56. (PMID: 16085680)
Obes Surg. 2011 Mar;21(3):367-72. (PMID: 20683784)
Surg Obes Relat Dis. 2011 Jan-Feb;7(1):45-9. (PMID: 21111688)
JAMA Surg. 2016 Nov 1;151(11):1056-1062. (PMID: 27532274)
Value Health. 2019 May;22(5):580-586. (PMID: 31104738)
Surg Obes Relat Dis. 2014 Jul-Aug;10(4):697-704. (PMID: 24913590)
Diabetes Care. 2014 Jul;37(7):1900-9. (PMID: 24760264)
Obes Surg. 2013 Jan;23(1):31-8. (PMID: 22923313)
Surg Endosc. 2016 Feb;30(2):480-488. (PMID: 26017908)
BMC Endocr Disord. 2020 Feb 5;20(1):20. (PMID: 32024495)
Science. 2000 Dec 22;290(5500):2323-6. (PMID: 11125150)
Cir Esp. 2017 Jan;95(1):4-16. (PMID: 27979315)
J Diabetes Metab Disord. 2017 Dec 28;16:49. (PMID: 29299442)
Surg Endosc. 2012 Jun;26(6):1744-50. (PMID: 22234587)
J Med Imaging Radiat Sci. 2019 Dec;50(4):477-487. (PMID: 31601480)
Curr Drug Targets. 2020;21(12):1208-1215. (PMID: 32640954)
BMC Med Res Methodol. 2018 Nov 16;18(1):137. (PMID: 30445910)
Nutrients. 2019 Feb 25;11(2):. (PMID: 30823595)
Diabetes Res Clin Pract. 2014 Aug;105(2):141-50. (PMID: 24798950)
Hum Brain Mapp. 2020 Apr 15;41(6):1435-1444. (PMID: 31804003)
معلومات مُعتمدة: PI15/00082 Instituto de Salud Carlos III; PI18/00209 Instituto de Salud Carlos III; PIE15/00013 Instituto de Salud Carlos III; AICO 2019/250 Generalitat Valenciana; DPI2017-89816-R Ministerio de Economía, Industria y Competitividad; SAF2014-57845-R Ministerio de Economía, Industria y Competitividad; Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas; CIBER de Bioingenieria, Biomateriales y Nanomateriales
فهرسة مساهمة: Keywords: RYGB; bariatric surgery; locally linear embedding; machine learning; obesity; predictive model; total weight loss
تواريخ الأحداث: Date Created: 20240627 Latest Revision: 20240629
رمز التحديث: 20240629
مُعرف محوري في PubMed: PMC11200726
DOI: 10.3390/biomedicines12061175
PMID: 38927382
قاعدة البيانات: MEDLINE
الوصف
تدمد:2227-9059
DOI:10.3390/biomedicines12061175