title: Is it possible to predict one-year survival in centenarians? A neural network study.
abstract: BACKGROUND
  Human life expectancy is constantly increasing: the challenge for modern geriatric medicine is to identify the means to reach successfully extreme longevity.
  
  
  OBJECTIVE
  To determine which are the survival determinants in centenarians using a neural network.
  
  
  METHODS
  Sample of 110 centenarians living in Rome, mean age 101.6 years (SD=1.8) with a sex ratio males:females of 1:3. We administered an extensive health interview (lasting 1-2 h) to each subject. The questionnaire, carried out according to the Geriatric Multidimensional Assessment, is made up of 100 items including a comprehensive health and psychosocial assessment aimed at various topics of general health and well-being and some scales used in geriatric practice. We applied several three-layered feed-forward neural networks by mixing in different ways the most important of the 100 items.
  
  
  RESULTS
  The most predicting powered net is the one constructed with 23 variables regarding comorbidity, cardiovascular risk factors, cognitive status, mood, functional status and social interactions, which therefore are strictly related to survival in centenarians.
  
  
  CONCLUSIONS
  Survival in longevity is a complex biological phenomenon, which is an ideal field for using the neural network as a statistic method. The net shows us that the maintenance of social relationships even in presence of disability is of major importance for survival in the oldest old.
authors.affiliation: Department of Aging Science, University La Sapienza, Rome, Italy. laura_tafaro@virgilio.it
authors.forename: Laura
authors.initials: L
authors.lastname: Tafaro
authors.forename: Paolo
authors.initials: P
authors.lastname: Cicconetti
authors.forename: Gianfranco
authors.initials: G
authors.lastname: Piccirillo
authors.forename: Evaristo
authors.initials: E
authors.lastname: Ettorre
authors.forename: Vincenzo
authors.initials: V
authors.lastname: Marigliano
authors.forename: Mauro
authors.initials: M
authors.lastname: Cacciafesta
mesh_terms: D000369:Aged, 80 and over; D000465:Algorithms; D004522:Educational Status; D005260:Female; D015577:Geriatric Assessment / Q000379:methods* / Q000706:statistics & numerical data; D006304:Health Status; D006686:Hobbies; D006801:Humans; D008017:Life Expectancy; D008019:Life Style; D008297:Male; D016571:Neural Networks, Computer; D011788:Quality of Life; D012307:Risk Factors; D012384:Rome; D012959:Socioeconomic Factors