• Hannah Wunsch, David J Lederer, Neil W Schluger, Matthew R Baldwin, Wazim R Narain, Joseph T Cooke, Mathew S Maurer, John W Rowe, and Peter B Bach.
• Division of Pulmonary, Allergy, and Critical Care, College of Physicians and Surgeons, Columbia University, NY, USA. mrb45@columbia.edu
• Chest. 2013 Apr 1;143(4):910-9.

BackgroundAlthough 1.4 million elderly Americans survive hospitalization involving intensive care annually, many are at risk for early mortality following discharge. No models that predict the likelihood of death after discharge exist explicitly for this population. Therefore, we derived and externally validated a 6-month postdischarge mortality prediction model for elderly ICU survivors.MethodsWe derived the model from medical record and claims data for 1,526 consecutive patients aged ≥ 65 years who had their first medical ICU admission in 2006 to 2009 at a tertiary-care hospital and survived to discharge (excluding those patients discharged to hospice). We then validated the model in 1,010 patients from a different tertiary-care hospital.ResultsSix-month mortality was 27.3% and 30.2% in the derivation and validation cohorts, respectively. Independent predictors of mortality (in descending order of contribution to the model's predictive power) were a do-not-resuscitate order, older age, burden of comorbidity, admission from or discharge to a skilled-care facility, hospital length of stay, principal diagnoses of sepsis and hematologic malignancy, and male sex. For the derivation and external validation cohorts, the area under the receiver operating characteristic curve was 0.80 (SE, 0.01) and 0.71 (SE, 0.02), respectively, with good calibration for both (P = 0.31 and 0.43).ConclusionsClinical variables available at hospital discharge can help predict 6-month mortality for elderly ICU survivors. Variables that capture elements of frailty, disability, the burden of comorbidity, and patient preferences regarding resuscitation during the hospitalization contribute most to this model's predictive power. The model could aid providers in counseling elderly ICU survivors at high risk of death and their families.

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