Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloArtificial Neural Networks in the Outcome Prediction of Adjustable Gastric Banding in Obese Women.
Anno di pubblicazione2010
Autore/iPiaggi P; Lippi C; Fierabracci P; Maffei M; Calderone A; Mauri M; Anselmino M; Cassano GB; Vitti P; Pinchera; Landi A; Santini F.
Affiliazioni autoriDep Engineering, University of Pisa (Piaggi, Landi) Dep Endocrinology University Hospital Pisa (Lippi, Fierabracci, Vitti, Pinchera, Santini, Maffei) Dulbecco Telethon Institute (Maffei) ISA CNR (Maffei) Dep Psychiatry Univ of Pisa (Calderone, Mauri, Cassano) Dep Surgery University Hospital Pisa (Anselmino)
Autori CNR e affiliazioni
  • inglese
AbstractBackground Obesity is unanimously regarded as a global epidemic and a major contributing factor to the development of many common illnesses. Laparoscopic Adjustable Gastric Banding (LAGB) is one of the most popular surgical approaches worldwide. Yet, substantial variability in the results and significant rate of failure can be expected, and it is still debated which categories of patients are better suited to this type of bariatric procedure. The aim of this study was to build a statistical model based on both psychological and physical data to predict weight loss in obese patients treated by LAGB, and to provide a valuable instrument for the selection of patients that may benefit from this procedure. Methodology/Principal Findings The study population consisted of 172 obese women, with a mean±SD presurgical and postsurgical Body Mass Index (BMI) of 42.5±5.1 and 32.4±4.8 kg/m2, respectively. Subjects were administered the comprehensive test of psychopathology Minnesota Multiphasic Personality Inventory-2 (MMPI-2). Main goal of the study was to use presurgical data to predict individual therapeutical outcome in terms of Excess Weight Loss (EWL) after 2 years. Multiple linear regression analysis using the MMPI-2 scores, BMI and age was performed to determine the variables that best predicted the EWL. Based on the selected variables including age, and 3 psychometric scales, Artificial Neural Networks (ANNs) were employed to improve the goodness of prediction. Linear and non linear models were compared in their classification and prediction tasks: non linear model resulted to be better at data fitting (36% vs. 10% variance explained, respectively) and provided more reliable parameters for accuracy and mis-classification rates (70% and 30% vs. 66% and 34%, respectively). Conclusions/Significance ANN models can be successfully applied for prediction of weight loss in obese women treated by LAGB. This approach may constitute a valuable tool for selection of the best candidates for surgery, taking advantage of an integrated multidisciplinary approach.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine dae13624
Pagine a-
Pagine totali12
RivistaPloS one
Attiva dal 2006
Editore: Public Library of Science - San Francisco, CA
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 1932-6203
Titolo chiave: PloS one
Titolo proprio: PloS one
Titolo abbreviato: PLoS ONE
Titoli alternativi:
  • Public Library of Science one
  • PLoS 1
Numero volume della rivista5
Fascicolo della rivista10
Verificato da refereeSì: Internazionale
Stato della pubblicazione-
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000283537000017)
Parole chiavebariatric surgery, obesity, compliance, principal components.
Link (URL, URI)
Titolo parallelo-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • ISA — Istituto di Scienze dell'Alimentazione
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
articolo pubblicato
Tipo documento: application/pdf