Goals: To develop and validate a model for probability of hospital mortality for cancer patients at 72 h of intensive care unit (ICU) management.
Patients and methods: This is an inception cohort study performed at 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 827 patients and then validated on an additional 415 patients. Multiple logistic regression modeling was used to develop the models, which were subsequently evaluated for discrimination and calibration. The main outcome measure is in-hospital death.
Results: A probability of mortality model, which incorporates ten discrete categorical variables, was developed and validated. All variables were collected at 72 h of ICU care. Variables included evidence of disease progression, performance status before hospitalization, heart rate >100 beats/min, Glasgow coma score </=5, mechanical ventilation, arterial oxygen pressure/fractional inspiratory oxygen ( PaO(2)/FiO(2)) ratio <250, platelets <100 k/ micro l, serum bicarbonate (HCO(3))<20 mEq/l, blood urea nitrogen (BUN) >40 mg/dl, and a urine output of <150 ml for any 8 h in the previous 24 h. The p values for the fit of the preliminary and validation models were 0.535 and 0.354 respectively, and the areas under the receiver operating characteristic (ROC) curves were 0.809 and 0.820.
Conclusions: We report a multivariable logistic regression model to estimate the probability of hospital mortality in critically ill cancer patients at 72 h of ICU care. The model is comprised of ten unambiguous and readily available variables. When used in conjunction with clinical judgment, this model should improve discussions about goals of care of these patients. Additional validation in a community hospital setting is warranted.