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Benchmark for intensive care unit length of stay: One step forward, several more to go.

Over the last few decades, we have witnessed numerous research findings and new technologies that help us provide better care to our patients. Unfortunately, the quality of health care is far from optimal (1). There have been urgent calls highlighting the need to close the quality gap (2). We all agree that the best available evidence should guide the process of care and that good process of care is likely to lead to good performance, including reduced mortality and efficient resource utilization. U.S. News and World Report has for several years given report cards ranking hospitals based on their performance. The Solucient and VISICU group has announced the best 100 hospitals based on their superior intensive care unit (ICU) clinical, operational, and financial performances (3). Hospitals fortunate enough to be ranked high in such reports use the information for advertisement. Accurate information about the performance status of ICUs is important not only for healthcare organizations but also for government and private insurers and most importantly for patients. Realizing the importance of accurate outcome measurement in the ICU, the Joint Commission on Healthcare Organizations has discussed risk-adjusted mortality and length of stay to measure performance. The critical care specialty has been ahead of the game when it comes to riskadjusted benchmarks. In this issue of Critical Care Medicine (4), Dr. Zimmerman and colleagues report a risk-adjusted prediction model for ICU length of stay. We have been using the adult ICU outcome prediction models for 2 decades worldwide (5–7). Most of these models have focused on ICU and hospital mortality. The first risk-adjusted Acute Physiology and Chronic Health Evaluation (APACHE) benchmark for ICU length of stay was developed using patient data collected 15 yrs ago (8). Although the pressure to decrease healthcare cost in the early and mid1990s reduced the hospital length of stay, it had no significant effect on the ICU length of stay, resulting in acceptable agreement between the mean APACHE III predicted and observed ICU length of stay (9). The APACHE III ICU length of stay prediction model was based on the acute physiology score, age, comorbid conditions, surgery status (emergency or elective), and patient’s location and length of hospital stay before ICU admission (8). Some variables with profound positive and negative impact on the ICU length of stay (10), such as mechanical ventilation and do-not-resuscitate status, were not included in the APACHE III ICU length of stay prediction model. ICU mortality and length of stay benchmarks have several potential applications. They can be used to highlight the weaknesses and strengths of individual ICUs (11) and identify processes of care associated with the best practice (10, 12, 13). Accreditation organizations such as the Joint Commission on Accreditation of Healthcare Organizations and government and private insurers may use such benchmarks to evaluate the performance of hospitals. For patients who are able to choose their providers, data based on well-validated and peer-reviewed benchmarks are more informative than data created for public relations purposes based on subjective and inadequately adjusted analyses. But we need to have a reliable tool to measure the adjusted length of stay. A reliable benchmark tool for the ICU length of stay needs to capture important predictor variables. In the last 10 yrs, the critical care community has developed several treatment options with impacts on clinical outcome (14–20). As a result of these interventions, the morbidity and mortality of critical illness have changed. To be useful, the ICU outcome benchmarks have to reflect these changes. In a recent publication in this journal (21a), Dr. Zimmerman and colleagues showed that the APACHE IV mortality prediction model had an excellent performance, with an area under the receiver operating characteristic curve of 0.88 and good calibration, in selected ICUs. In this issue of Critical Care Medicine (4), the same group report the performance of the APACHE IV ICU length of stay benchmark. In the revised and updated APACHE IV model for predicting ICU length of stay, they included important new predictor variables and applied refined statistical methodology. They randomly selected 60% of the database for the development of the model and validated it on the remaining 40%. The model cannot be used for individual patient ICU length of stay prediction. However, its overall performance was excellent, with the difference between the mean observed and predicted ICU length of stay of only 1.9 hrs! For 93% of the ICU admission diagnoses groups, there were no significant differences between the mean observed and mean predicted ICU lengths of stay. The APACHE IV ICU length of stay benchmark is a huge step forward as a tool for assessing the performance of an ICU. ICUs can use such tools not only to compare themselves with and learn from others but also to assess the impact of changes they may introduce to improve the quality of care. Can we use the APACHE IV benchmark in all ICUs? I believe we are not there yet. The APACHE IV benchmark study was performed in 104 ICUs of 45 hospitals in the United States. The absence of even a single ICU outside this country that participated in the study makes the model inapplicable elsewhere. Even for the United States, the performance of the APACHE IV prediction model needs to be validated before we endorse its use nationally. There are thousands of hospitals with ICUs in the United States (21) of which only a small fraction participated in the study. Even among the hospitals that participated in the study, some of their ICUs may not have been included. The ICUs included in the *See also p. 2517.