Power and limitations of daily prognostications of death in the medical intensive care unit

Crit Care Med. 2011 Mar;39(3):474-9. doi: 10.1097/CCM.0b013e318205df9b.


Objective: We tested the accuracy of predictions of impending death for medical intensive care unit patients, offered daily by their professional medical caretakers.

Design: For 560 medical intensive care unit patients, on each medical intensive care unit day, we asked their attending physicians, fellows, residents, and registered nurses one question: "Do you think this patient will die in the hospital or survive to be discharged?"

Results: We obtained>6,000 predictions on 2018 medical intensive care unit patient days. Seventy-five percent of MICU patients who stayed≥4 days had discordant predictions; that is, at least one caretaker predicted survival, whereas others predicted death before discharge. Only 107 of 206 (52%) patients with a prediction of "death before discharge" actually died in hospital. This number rose to 66% (96 of 145) for patients with 1 day of corroborated (i.e., >1) prediction of "death," and to 84% (79 of 94) with at least 1 unanimous day of predictions of death. However, although positive predictive value rose with increasingly stringent prediction criteria, sensitivity fell so that the area under the receiver-operator characteristic curve did not differ for single, corroborated, or unanimous predictions of death. Subsets of older (>65 yrs) and ventilated medical intensive care unit patients revealed parallel findings.

Conclusions: 1) Roughly half of all medical intensive care unit patients predicted to die in hospital survived to discharge nonetheless. 2) More highly corroborated predictions had better predictive value; although, approximately 15% of patients survived unexpectedly, even when predicted to die by all medical caretakers.

MeSH terms

  • Age Factors
  • Aged
  • Analysis of Variance
  • Female
  • Hospital Mortality*
  • Humans
  • Intensive Care Units* / statistics & numerical data
  • Internship and Residency
  • Length of Stay
  • Linear Models
  • Male
  • Medical Staff, Hospital
  • Middle Aged
  • Nursing Staff, Hospital
  • Patient Discharge / statistics & numerical data
  • Prognosis
  • ROC Curve
  • Sensitivity and Specificity
  • Withholding Treatment / statistics & numerical data