Intensive care unit length of stay: Benchmarking based on Acute Physiology and Chronic Health Evaluation (APACHE) IV.

OBJECTIVE: To revise and update the Acute Physiology and Chronic Health Evaluation (APACHE) model for predicting intensive care unit (ICU) length of stay. DESIGN: Observational cohort study. SETTING: One hundred and four ICUs in 45 U.S. hospitals. PATIENTS: Patients included 131,618 consecutive ICU admissions during 2002 and 2003, of which 116,209 met inclusion criteria. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The APACHE IV model for predicting ICU length of stay was developed using ICU day 1 patient data and a multivariate linear regression procedure to estimate the precise ICU stay for randomly selected patients who comprised 60% of the database. New variables were added to the previous APACHE III model, and advanced statistical modeling techniques were used. Accuracy was assessed by comparing mean observed and mean predicted ICU stay for the excluded 40% of patients. Aggregate mean observed ICU stay was 3.86 days and mean predicted 3.78 days (p < .001), a difference of 1.9 hrs. For 108 (93%) of 116 diagnoses, there was no significant difference between mean observed and mean predicted ICU stay. The model accounted for 21.5% of the variation in ICU stay across individual patients and 62% across ICUs. Correspondence between mean observed and mean predicted length of stay was reduced for patients with a short (< or =1.7 days) or long (> or =9.4 days) ICU stay and a low (<20%) or high (>80%) risk of death on ICU day 1. CONCLUSIONS: The APACHE IV model provides clinically useful ICU length of stay predictions for critically ill patient groups, but its accuracy and utility are limited for individual patients. APACHE IV benchmarks for ICU stay are useful for assessing the efficiency of unit throughput and support examination of structural, managerial, and patient factors that affect ICU stay.

Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today's critically ill patients.

OBJECTIVE: To improve the accuracy of the Acute Physiology and Chronic Health Evaluation (APACHE) method for predicting hospital mortality among critically ill adults and to evaluate changes in the accuracy of earlier APACHE models. DESIGN: : Observational cohort study. SETTING: A total of 104 intensive care units (ICUs) in 45 U.S. hospitals. PATIENTS: A total of 131,618 consecutive ICU admissions during 2002 and 2003, of which 110,558 met inclusion criteria and had complete data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We developed APACHE IV using ICU day 1 information and a multivariate logistic regression procedure to estimate the probability of hospital death for randomly selected patients who comprised 60% of the database. Predictor variables were similar to those in APACHE III, but new variables were added and different statistical modeling used. We assessed the accuracy of APACHE IV predictions by comparing observed and predicted hospital mortality for the excluded patients (validation set). We tested discrimination and used multiple tests of calibration in aggregate and for patient subgroups. APACHE IV had good discrimination (area under the receiver operating characteristic curve = 0.88) and calibration (Hosmer-Lemeshow C statistic = 16.9, p = .08). For 90% of 116 ICU admission diagnoses, the ratio of observed to predicted mortality was not significantly different from 1.0. We also used the validation data set to compare the accuracy of APACHE IV predictions to those using APACHE III versions developed 7 and 14 yrs previously. There was little change in discrimination, but aggregate mortality was systematically overestimated as model age increased. When examined across disease, predictive accuracy was maintained for some diagnoses but for others seemed to reflect changes in practice or therapy. CONCLUSIONS: APACHE IV predictions of hospital mortality have good discrimination and calibration and should be useful for benchmarking performance in U.S. ICUs. The accuracy of predictive models is dynamic and should be periodically retested. When accuracy deteriorates they should be revised and updated.

Evaluating ventilator weaning best practice: a long-term acute care hospital system-wide quality initiative.

Long-term acute care (LTAC) hospitals and units are becoming increasingly important to the management of patients who have serious, complex critical illnesses and require mechanical ventilation for extended periods of time. Kindred Healthcare, Inc., a nation-wide system of LTAC hospitals embarked on a quality initiative to establish a Ventilator Management and Weaning Best Practice. The process steps included: measurement of performance of all hospitals in the system using a risk-adjusted methodology to evaluate clinical outcomes, identification of facilities with superior outcomes; structured evaluation of the characteristics, practices, and protocols of these Best Practice hospitals; and utilization of the information gleaned from these hospitals to establish evidence-based LTAC best practice ventilator management guidelines. Key characteristics of the Best Practice LTAC hospitals were: hospital-wide philosophy that "everybody weans"-that is, all disciplines actively participate and all patients are expected to wean; collaborative multidisciplinary plans of care; a consistent and a 24-hour-a-day approach to ventilator management and weaning; daily communication; mutual respect for the contributions of all disciplines to the weaning process; early, aggressive nutrition support and intervention by rehabilitation services; use of 24-hour in-hospital advance practice nurses, hospitalists, or physician assistants; and intervention by physiatrists.