A mortality model for critically ill cancer patients Academic Article uri icon


MeSH Major

  • Colorectal Neoplasms
  • Hepatectomy
  • Liver Neoplasms
  • Patient Readmission


  • Purpose: ICU outcome scoring systems have not been validated for patients with malignancies. We present a prospectively developed and validated outcome model for patients with cancer. Methods: Beginning July 1994, distinct continuous or categorical variables were collected on consecutive patients with cancer admitted to the ICU at MSKCC1, City of Hope National Medical Center4, Duarte, CA, Mount Sinai Medical Center2, New York, NY, and MD Anderson Cancer Center3, Houston, TX to develop a model for probability of hospital survival at ICU admission. Variables recorded were both cancer and critical illness related. A preliminary model was developed from 1483 patients and then validated on an additional 230 patients. Multiple logistic regression modeling was used to develop the models and subsequently evaluated by Goodness-of-fit and ROC analysis. Results: The observed hospital mortality rate was 37.7%. Continuous variables used in the ICU admission model are PaO2/FiO2 ratio, platelet count, respiratory rate, systolic blood pressure and days of pre-ICU hospitalization. Categorical entries include presence of intracranial mass effect, allogeneic bone marrow transplantation, recurrent or progressive cancer, albumin <2.5 g/dl, bilirubin ≥2 mg/dl, Glasgow Coma Score <6, protime >15 sec, BUN >50 mg/dl, intubation, performance status (ECOG) before hospitalization and CPR. The p-values for the fit of the preliminary and validation models are 0.939 and 0.314 respectively and the areas under the ROC curves .812 and .802. Conclusions: An ICU admission mortality model now exists for critically ill cancer patients. Clinical Implications: Cancer patient and oncologist alike can now be provided with an objective probability of a patients' prospects of hospital survival when admitted to an ICU. Additionally, we can stratify patients for clinical research and can compare hospitals on the ratio of observed to expected deaths based on this model.

publication date

  • October 1996



  • Academic Article

Additional Document Info

start page

  • 1S

end page

  • 234S


  • 110


  • 4 SUPPL.