Intensive care unit prognostic scoring systems to predict death: a cost-effectiveness analysis.

OBJECTIVE

To evaluate the cost-effectiveness, using the technique of decision analysis, of withdrawing care from patients in the intensive care unit (ICU) who are predicted to have a high probability of death (>90%) after 48 hrs using a mortality risk estimate based on daily Acute Physiology and Chronic Health Evaluation (APACHE) III scores.

MATERIALS AND METHODS

A decision tree model was constructed to compare the cost-effectiveness of two clinical strategies. In the first strategy, patients receive ICU care until they were discharged, died, or had care withdrawn based on subjective clinical criteria. In the second strategy, patients remained in the ICU until they were either discharged, died, or had life-sustaining care withdrawn based on subjective criteria or if they were predicted to have a >90% risk of mortality after 48 hrs by a prognostic scoring system. Transition probabilities were based on a retrospective data analysis of 4,106 noncardiac ICU patients admitted to a tertiary surgical ICU over a 9-yr period. Cost estimates were based on daily Therapeutic Intervention Scoring System (TISS) scores from our database and using published data on the estimated production cost for a TISS point. The sensitivity (16.6%) and specificity (99.6%) of the mortality risk estimate at 48 hrs (using the >90% decision point) based on daily APACHE III scores were derived from published data.

RESULTS

In the base case analysis, we assumed that the sensitivity and specificity of the prognostic risk estimate are unchanged when exported to a new environment. Not using a prognostic scoring system as the basis for withdrawing care resulted in a slightly higher survival rate (87.2% vs. 86.85%) at a cost-per-death prevented (CPDP) of $263,700. Since prognostic scoring systems have not been shown to retain the same predictive power when exported to new databases, we chose to explore the effect of varying the specificity of the scoring system on CPDP. Decreasing the specificity from .996 (baseline) to .98 causes the CPDP to drop to $53,300. Changing the specificity to .95 results in a CPDP prevented of $21,700. Using one-way sensitivity analysis, the CPDP is shown to be relatively insensitive to delaying the decision point from ICU day 3 to day 7. Sensitivity analysis also indicates that CPDP increases rapidly with hospital death rate. For a death rate of 30%, the CPDP increases to $768,600 (in the base case, the death rate is 12.8%); when the specificity is decreased to .95, the CPDP drops to $62,100.

CONCLUSION

Unless daily mortality risk estimates based on APACHE III can be shown to retain the same level of predictive power in ICUs outside the development database, it is unlikely that the incremental cost-effectiveness gained by using them as the basis to withdraw care is sufficient to justify their use in this manner.