A prognostic model for 6-month mortality in elderly survivors of critical illness

Abstract

Background: Although 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.

Methods: We 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.

Results: Six-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).

Conclusions: Clinical 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.

Figure 1.

Derivation and validation cohorts. *First medical ICU admission specifically refers to the first Columbia or Cornell medical ICU admission with no previous Cornell or Columbia ICU admission in 2005.

Figure 2.

Independent percent contribution of predictor variables from the logistic regression model used to predict 6-mo mortality in elderly (≥ 65 y) ICU survivors that includes SAPS-II laboratory scores derived from the first 24 h after ICU admission and during the last 3 d of hospitalization. We determined the contribution of each risk factor to the final model’s predictive power by dividing the difference in the −2 log likelihood between the null model and a final model without the variable of interest by the difference in the −2 log likelihood between the null and final model. We then normalized these results to determine the percent contribution of each predictor variable when added last to the model. Comorbidity Score represents the Charlson Comorbidity Index. Admission Source represents admission from a skilled-care facility or other short-term acute care hospital. DNR = do not resuscitate; SAPS = Simplified Acute Physiology Score.

Figure 3.

Comparison of observed and predicted 6-mo mortality for patients divided into five equal-sized groups from the derivation and validation cohorts.