Estimating long-term survival of critically ill patients: the PREDICT model

Abstract

Background: Long-term survival outcome of critically ill patients is important in assessing effectiveness of new treatments and making treatment decisions. We developed a prognostic model for estimation of long-term survival of critically ill patients.

Methodology and principal findings: This was a retrospective linked data cohort study involving 11,930 critically ill patients who survived more than 5 days in a university teaching hospital in Western Australia. Older age, male gender, co-morbidities, severe acute illness as measured by Acute Physiology and Chronic Health Evaluation II predicted mortality, and more days of vasopressor or inotropic support, mechanical ventilation, and hemofiltration within the first 5 days of intensive care unit admission were associated with a worse long-term survival up to 15 years after the onset of critical illness. Among these seven pre-selected predictors, age (explained 50% of the variability of the model, hazard ratio [HR] between 80 and 60 years old = 1.95) and co-morbidity (explained 27% of the variability, HR between Charlson co-morbidity index 5 and 0 = 2.15) were the most important determinants. A nomogram based on the pre-selected predictors is provided to allow estimation of the median survival time and also the 1-year, 3-year, 5-year, 10-year, and 15-year survival probabilities for a patient. The discrimination (adjusted c-index = 0.757, 95% confidence interval 0.745-0.769) and calibration of this prognostic model were acceptable.

Significance: Age, gender, co-morbidities, severity of acute illness, and the intensity and duration of intensive care therapy can be used to estimate long-term survival of critically ill patients. Age and co-morbidity are the most important determinants of long-term prognosis of critically ill patients.

Figure 1. The proportional hazards assumption of the predictors in the Cox model was assessed by plotting the logarithm of the negative logarithm of the Kaplan Meier survivor estimates and the assumption was found to be acceptable for the three pre-selected continuous predictors; APACHE II predicted mortality, Charlson co-morbidity index, and age.

(a) Graph assessing the proportionality of hazards associated with severity of acute illness measured by the APACHE II predicted mortality risk categories (0–20%, 20–40%, 40–60%, 60–80%, 80–100%). (b) Graph assessing the proportionality of hazards associated with co-morbidities measured by Charlson co-morbidity index categories (0, 1, 2, 3, 4–5, >5). (c) Graph assessing the proportionality of hazards associated with age measured by age categories (16–30, 30–50, 50–60, 60–70, 70–80, >80 years old)

Figure 2. Contribution of each predictor in predicting the survival time in the Cox proportional hazards model.

Figure 3. The relationship between relative hazard and each predictor after adjusting for other predictors in the model.

Figure 4. The estimated (adjusted) hazard ratios and multilevel confidence bars (0.70 as illustrated by the black bar to 0.99 as illustrated by the orange bar) for the effects of predictors in the model are summarized in the figure below.

An increase of 20 years of age and an increase in Charlson co-morbidity index from 0 to 5 approximately doubled the risk of death. Doubling the APACHE II predicted mortality from 20% to 40% increased the relative risk of death by about 30 to 40%. Similarly, increased the number of days of intensive care therapy from 1 to 5 increased the relative risk of death by between 10% and 50%.

Figure 5. Nomogram for predicting long-term survival probabilities and median survival time.

Note: gender: 2 = female, 1 = male. Predicted.mortality = APACHE II predicted mortality in %.

Figure 6. Bootstrap estimate of calibration accuracy for 15-year estimates from the Cox proportional hazards model.

Dots correspond to apparent predictive accuracy and x marks the bootstrap-corrected estimates.