PURPOSE: To develop prospectively and validate a model for probability of hospital survival at admission to the intensive care unit (ICU) of patients with malignancy. PATIENTS AND METHODS: This was an inception cohort study in the setting of four ICUs of academic medical centers in the United States. Defined continuous and categorical variables were collected on consecutive patients with cancer admitted to the ICU. A preliminary model was developed from 1,483 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 receiver operating characteristic (ROC) analysis. The main outcome measure was hospital survival after ICU admission. RESULTS: The observed hospital mortality rate was 42%. Continuous variables used in the ICU admission model are PaO2/FiO2 ratio, platelet count, respiratory rate, systolic blood pressure, and days of hospitalization pre-ICU. Categorical entries include presence of intracranial mass effect, allogeneic bone marrow transplantation, recurrent or progressive cancer, albumin less than 2.5 g/dL, bilirubin > or = 2 mg/dL, Glasgow Coma Score less than 6, prothrombin time greater than 15 seconds, blood urea nitrogen (BUN) greater than 50 mg/dL, intubation, performance status before hospitalization, and cardiopulmonary resuscitation (CPR). The P values for the fit of the preliminary and validation models are .939 and .314, respectively, and the areas under the ROC curves are .812 and .802. CONCLUSION: We report a disease-specific multivariable logistic regression model to estimate the probability of hospital mortality in a cohort of critically ill cancer patients admitted to the ICU. The model consists of 16 unambiguous and readily available variables. This model should move the discussion regarding appropriate use of ICU resources forward. Additional validation in a community hospital setting is warranted.
PURPOSE: To develop prospectively and validate a model for probability of hospital survival at admission to the intensive care unit (ICU) of patients with malignancy. PATIENTS AND METHODS: This was an inception cohort study in the setting of four ICUs of academic medical centers in the United States. Defined continuous and categorical variables were collected on consecutive patients with cancer admitted to the ICU. A preliminary model was developed from 1,483 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 receiver operating characteristic (ROC) analysis. The main outcome measure was hospital survival after ICU admission. RESULTS: The observed hospital mortality rate was 42%. Continuous variables used in the ICU admission model are PaO2/FiO2 ratio, platelet count, respiratory rate, systolic blood pressure, and days of hospitalization pre-ICU. Categorical entries include presence of intracranial mass effect, allogeneic bone marrow transplantation, recurrent or progressive cancer, albumin less than 2.5 g/dL, bilirubin > or = 2 mg/dL, Glasgow Coma Score less than 6, prothrombin time greater than 15 seconds, blood ureanitrogen (BUN) greater than 50 mg/dL, intubation, performance status before hospitalization, and cardiopulmonary resuscitation (CPR). The P values for the fit of the preliminary and validation models are .939 and .314, respectively, and the areas under the ROC curves are .812 and .802. CONCLUSION: We report a disease-specific multivariable logistic regression model to estimate the probability of hospital mortality in a cohort of critically ill cancerpatients admitted to the ICU. The model consists of 16 unambiguous and readily available variables. This model should move the discussion regarding appropriate use of ICU resources forward. Additional validation in a community hospital setting is warranted.
Authors: Stephen M Pastores; Debra A Goldman; David J Shaz; Natalie Kostelecky; Ryan J Daley; Tim J Peterson; Kay See Tan; Neil A Halpern Journal: Cancer Date: 2018-05-04 Impact factor: 6.860
Authors: Peter Schellongowski; Michael Benesch; Thomas Lang; Friederike Traunmüller; Christian Zauner; Klaus Laczika; Gottfried J Locker; Michael Frass; Thomas Staudinger Journal: Intensive Care Med Date: 2003-11-04 Impact factor: 17.440
Authors: Peter A Hampshire; Catherine A Welch; Lawrence A McCrossan; Katharine Francis; David A Harrison Journal: Crit Care Date: 2009-08-25 Impact factor: 9.097