Asian Pacific Journal of Cancer Prevention

Asian Pacific Journal of Cancer Prevention (아시아태평양암예방학회)
권호 표지
DOI QR코드

DOI QR Code

Prediction of Length of ICU Stay Using Data-mining Techniques: an Example of Old Critically Ill Postoperative Gastric Cancer Patients

  • Zhang, Xiao-Chun (Department of Intensive Care Unit, The First Affiliated Hospital) ;
  • Zhang, Zhi-Dan (Department of Intensive Care Unit, The First Affiliated Hospital) ;
  • Huang, De-Sheng (Department of Mathematics, College of Basic Medical Sciences, China Medical University)
  • Published : 2012.01.31
Download PDF
⟨ Previous Next ⟩

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, eight 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.

Keywords

References

  1. Abbott RR, Setter M, Chan S, et al (1991). APACHE II: prediction of outcome of 451 ICU oncology admissions in a community hospital. Ann Oncol, 2, 571-4. https://doi.org/10.1093/oxfordjournals.annonc.a058022
  2. Beenen E, Brown L, Connor S (2011). A comparison of the hospital costs of open vs. minimally invasive surgical management of necrotizing pancreatitis. HPB (Oxford), 13, 178-84. https://doi.org/10.1111/j.1477-2574.2010.00267.x
  3. Berney SC, Gordon IR, Opdam HI, et al (2011). A classification and regression tree to assist clinical decision making in airway management for patients with cervical spinal cord injury. Spinal Cord, 49, 244-50. https://doi.org/10.1038/sc.2010.97
  4. Carr DD (2009). Building collaborative partnerships in critical care: the RN case manager/social work dyad in critical care. Prof Case Manag, 14, 121-32, quiz 133-34. https://doi.org/10.1097/NCM.0b013e3181a774c3
  5. Dossett LA, Redhage LA, Sawyer RG, et al (2009). Revisiting the validity of APACHE II in the trauma ICU: improved risk stratification in critically injured adults. Injury, 40, 993-8. https://doi.org/10.1016/j.injury.2009.03.004
  6. Du B, Xi X, Chen D, et al (2010). Clinical review: critical care medicine in mainland China. Crit Care, 14, 206. https://doi.org/10.1186/cc8222
  7. Faulk JF, Savitz LA (2009). Intensive care nurses' interest in clinical personal digital assistants. Crit Care Nurse, 29, 58-64.
  8. Guan P, Huang D, Guo J, et al (2008). Bacillary dysentery and meteorological factors in northeastern China: a historical review based on classification and regression trees. Jpn J Infect Dis, 61, 356-60.
  9. Kamangar F, Dores GM, Anderson WF (2006). Patterns of cancer incidence, mortality, and prevalence across five continents: defining priorities to reduce cancer disparities in different geographic regions of the world. J Clin Oncol, 24, 2137-50. https://doi.org/10.1200/JCO.2005.05.2308
  10. Lapinsky SE, Holt D, Hallett D, et al (2008). Survey of information technology in Intensive Care Units in Ontario, Canada. BMC Med Inform Decis Mak, 8, 5. https://doi.org/10.1186/1472-6947-8-5
  11. Marik PE, Hedman L (2000). What's in a day? Determining intensive care unit length of stay. Crit Care Med, 28, 2090-3. https://doi.org/10.1097/00003246-200006000-00071
  12. Miccolo MA, Spanier AH (1993). Critical care management in the 1990s. Making collaborative practice work. Crit Care Clin, 9, 443-53.
  13. Pittoni GM, Scatto A (2009). Economics and outcome in the intensive care unit. Curr Opin Anaesthesiol, 22, 232-6. https://doi.org/10.1097/ACO.0b013e328323f9a8
  14. Rapoport J, Teres D, Zhao Y, et al (2003). Length of stay data as a guide to hospital economic performance for ICU patients. Med Care, 41, 386-97.
  15. Render ML, Kim HM, Deddens J, et al (2005). Variation in outcomes in Veterans Affairs intensive care units with a computerized severity measure. Crit Care Med, 33, 930-9. https://doi.org/10.1097/01.CCM.0000162497.86229.E9
  16. Stricker K, Rothen HU, Takala J (2003). Resource use in the ICU: short- vs. long-term patients. Acta Anaesthesiol Scand, 47, 508-15. https://doi.org/10.1034/j.1399-6576.2003.00083.x
  17. Suistomaa M, Niskanen M, Kari A, et al (2002). Customized prediction models based on APACHE II and SAPS II scores in patients with prolonged length of stay in the ICU. Intensive Care Med, 28, 479-85. https://doi.org/10.1007/s00134-002-1214-9
  18. Terra SM (2007). An evidence-based approach to case management model selection for an acute care facility: is there really a preferred model? Prof Case Manag, 12, 147- 57, quiz 158-59. https://doi.org/10.1097/01.PCAMA.0000271365.01171.b6
  19. Weissman C (1997). Analyzing intensive care unit length of stay data: problems and possible solutions. Crit Care Med, 25, 1594-600. https://doi.org/10.1097/00003246-199709000-00031
  20. Yang L (2006). Incidence and mortality of gastric cancer in China. World J Gastroenterol, 12, 17-20. https://doi.org/10.3748/wjg.v12.i1.17
  21. Zalon ML, Sandhaus S, Valenti D, et al (2010). Using PDAs to detect cognitive change in the hospitalized elderly patient. Appl Nurs Res, 23, e21-7. https://doi.org/10.1016/j.apnr.2009.10.002

Cited by

  1. Data mining: Potential applications in research on nutrition and health vol.74, pp.1, 2017, https://doi.org/10.1111/1747-0080.12337