title: Prediction of length of ICU stay using data-mining techniques: an example of old critically Ill postoperative gastric cancer patients.
abstract: OBJECTIVE
  With the background of aging population in China and advances in clinical medicine, the amount of operations on old patients increases correspondingly, which imposes increasing challenges to critical care medicine and geriatrics. The study was designed to describe information on the length of ICU stay from a single institution experience of old critically ill gastric cancer patients after surgery and the framework of incorporating data-mining techniques into the prediction.
  
  
  METHODS
  A retrospective design was adopted to collect the consecutive data about patients aged 60 or over with a gastric cancer diagnosis after surgery in an adult intensive care unit in a medical university hospital in Shenyang, China, from January 2010 to March 2011. Characteristics of patients and the length their ICU stay were gathered for analysis by univariate and multivariate Cox regression to examine the relationship with potential candidate factors. A regression tree was constructed to predict the length of ICU stay and explore the important indicators.
  
  
  RESULTS
  Multivariate Cox analysis found that shock and nutrition support need were statistically significant risk factors for prolonged length of ICU stay. Altogether, seven variables entered the regression model, including age, APACHE II score, SOFA score, shock, respiratory system dysfunction, circulation system dysfunction, diabetes and nutrition support need. The regression tree indicated comorbidity of two or more kinds of shock as the most important factor for prolonged length of ICU stay in the studied sample.
  
  
  CONCLUSIONS
  Comorbidity of two or more kinds of shock is the most important factor of length of ICU stay in the studied sample. Since there are differences of ICU patient characteristics between wards and hospitals, consideration of the data-mining technique should be given by the intensivists as a length of ICU stay prediction tool.
authors.affiliation: Department of Intensive Care Unit, The First Affiliated Hospital, Shenyang, China.
authors.forename: Xiao-Chun
authors.initials: XC
authors.lastname: Zhang
authors.forename: Zhi-Dan
authors.initials: ZD
authors.lastname: Zhang
authors.forename: De-Sheng
authors.initials: DS
authors.lastname: Huang
mesh_terms: D018806:APACHE; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015897:Comorbidity; D016638:Critical Illness; D057225:Data Mining; D005260:Female; D006801:Humans; D007362:Intensive Care Units; D007902:Length of Stay; D008297:Male; D008875:Middle Aged; D018529:Nutritional Support; D011184:Postoperative Period; D011379:Prognosis; D012189:Retrospective Studies; D012307:Risk Factors; D012720:Severity of Illness Index; D013274:Stomach Neoplasms / Q000601:surgery*