Making ICU prognostication patient centered: Is there a role for dynamic information?

Critical Care Medicine

Volumn 41, Issue 4, 2013, Pages 1136-1138

  Ehlenbach, William J ^a   Cooke, Colin R ^b  

^a UNIVERSITY OF WISCONSIN SCHOOL OF MEDICINE AND PUBLIC HEALTH   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

Author keywords

critical care; long term survivors; prognosis; severity of illness index; statistical models

Indexed keywords

APACHE; EDITORIAL; HEALTH CARE UTILIZATION; HEALTH SERVICE; INTENSIVE CARE UNIT; MEDICAL DECISION MAKING; MEDICAL INFORMATION; PATIENT CARE; PRIORITY JOURNAL; QUALITY OF LIFE;

CRITICAL ILLNESS; FEMALE; HOSPITAL MORTALITY; HUMANS; HYPOTENSION; INTENSIVE CARE UNITS; MALE; SEPSIS;

EID: 84875800749     PISSN: 00903493     EISSN: 15300293     Source Type: Journal    
DOI: 10.1097/CCM.0b013e31827c03eb     Document Type: Editorial

References (17)

1. Knaus WA, Draper EA, Wagner DP, et al.: APACHE II: A severity of disease classification system. Crit Care Med 1985; 13:818-829
2. Cooke CR: The siren song of simple tools that predict mortality. Respir Care 2011; 56:533-535
3. Mayaud L, Lai PS, Clifford GD, et al.: Dynamic Data During Hypotension Episode Improves Mortality Predictions Among Patients With Sepsis and Hypotension. Crit Care Med 2013; 41:954-962
4. Saeed M, Villarroel M, Reisner AT, et al.: Multiparameter Intelligent Monitoring in Intensive Care II: A public-access intensive care unit database. Crit Care Med 2011; 39:952-960
5. Vincent JL, Moreno R, Takala J, et al.: The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med 1996; 22:707-710
6. Marshall JC, Cook DJ, Christou NV, et al.: Multiple organ dysfunction score: A reliable descriptor of a complex clinical outcome. Crit Care Med 1995; 23:1638-1652
7. Minne L, Abu-Hanna A, de Jonge E: Evaluation of SOFA-based models for predicting mortality in the ICU: A systematic review. Crit Care 2008; 12:R161
8. Ferreira FL, Bota DP, Bross A, et al.: Serial evaluation of the SOFA score to predict outcome in critically ill patients. JAMA 2001; 286:1754-1758
9. Wagner DP, Knaus WA, Harrell FE, et al.: Daily prognostic estimates for critically ill adults in intensive care units: Results from a prospective, multicenter, inception cohort analysis. Crit Care Med 1994; 22:1359-1372
10. Clermont G, Angus DC, DiRusso SM, et al.: Predicting hospital mortality for patients in the intensive care unit: A comparison of artificial neural networks with logistic regression models. Crit Care Med 2001; 29: 291-296
11. Kim S, Kim W, Park RW: A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining Techniques. Healthc Inform Res 2011; 17:232-243
12. Altman DG, Vergouwe Y, Royston P, et al.: Prognosis and prognostic research: Validating a prognostic model. BMJ 2009; 338:b605
13. A controlled trial to improve care for seriously ill hospitalized patients. The study to understand prognoses and preferences for outcomes and risks of treatments (SUPPORT). The SUPPORT Principal Investigators. JAMA 1995,274(20) 1591-1598.
14. Zier LS, Sottile PD, Hong SY, et al.: Surrogate decision makers' interpretation of prognostic information: A mixed-methods study. Ann Intern Med 2012; 156:360-366
15. Ehlenbach WJ, Hough CL, Crane PK, et al.: Association between acute care and critical illness hospitalization and cognitive function in older adults. JAMA 2010; 303:763-770
16. Iwashyna TJ, Ely EW, Smith DM, et al.: Long-term cognitive impairment and functional disability among survivors of severe sepsis. JAMA 2010; 304:1787-1794
17. Fried TR, Bradley EH, Towle VR, et al.: Understanding the treatment preferences of seriously ill patients. N Engl J Med 2002; 346:1061-1066