CONTEXT: Very elderly critically ill patients have three possible hospital outcomes: discharge to home, discharge to a skilled nursing or rehabilitation facility, or death. The factors associated with these outcomes are unknown. OBJECTIVE: To develop a three-outcome prediction model for very elderly critically ill patients. DESIGN: Retrospective chart abstraction with ordered logistic regression analysis. SETTING: Academic medical center. PATIENTS: Four hundred and fifty-five patients 85 yrs or older admitted to intensive care units (ICU) during 1996 and 1997. MEASUREMENTS AND MAIN RESULTS: A fitted ordinal logistic regression predictive model was developed using data from 243 patients hospitalized in 1996, and validated on data from 212 patients hospitalized in 1997. Model variables include age, gender, baseline support level, type of ICU, heart rate at ICU admission, use of mechanical ventilation, vasopressors or a pulmonary artery catheter during the ICU stay, and the development of respiratory, neurologic or hematologic failure or sepsis while in the ICU. When tested on the 1997 data, the model was well calibrated and had a high discriminant index. CONCLUSIONS: This mathematical model can be used to predict the risks of these three hospital outcomes for this population of patients. These predictions can provide a context when discussing goals and expectations with patients, families, and other healthcare providers and to aid in hospital discharge planning.
CONTEXT: Very elderly critically ill patients have three possible hospital outcomes: discharge to home, discharge to a skilled nursing or rehabilitation facility, or death. The factors associated with these outcomes are unknown. OBJECTIVE: To develop a three-outcome prediction model for very elderly critically ill patients. DESIGN: Retrospective chart abstraction with ordered logistic regression analysis. SETTING: Academic medical center. PATIENTS: Four hundred and fifty-five patients 85 yrs or older admitted to intensive care units (ICU) during 1996 and 1997. MEASUREMENTS AND MAIN RESULTS: A fitted ordinal logistic regression predictive model was developed using data from 243 patients hospitalized in 1996, and validated on data from 212 patients hospitalized in 1997. Model variables include age, gender, baseline support level, type of ICU, heart rate at ICU admission, use of mechanical ventilation, vasopressors or a pulmonary artery catheter during the ICU stay, and the development of respiratory, neurologic or hematologic failure or sepsis while in the ICU. When tested on the 1997 data, the model was well calibrated and had a high discriminant index. CONCLUSIONS: This mathematical model can be used to predict the risks of these three hospital outcomes for this population of patients. These predictions can provide a context when discussing goals and expectations with patients, families, and other healthcare providers and to aid in hospital discharge planning.
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