Length of stay and efficiency in pediatric intensive care units.

OBJECTIVE: Assessment of pediatric intensive care unit (PICU) efficiency with a length of stay prediction model and validation of this assessment by an efficiency measure based on daily use of intensive care unit-specific therapies. DESIGN: Inception cohort study of data acquired between 1989 and 1994. SETTING: Thirty-two PICUs, 16 selected randomly and 16 volunteering. SUBJECTS: Consecutive admissions of 10,658 patients (466 deaths) who stayed at least 2 hours and up to 12 days in the PICU. MEASUREMENTS: Length of stay and its prediction from a model with admission day data (PRISM III-24, diagnostic factors, mechanical ventilation). For validation 11 PICUs recorded each patient's "efficient" days, that is, days when at least one PICU-specific therapy was given. PICU efficiency was computed as either the ratio of the observed efficient days or the days accounted for by the predictor variables to the total care days, and the agreement was assessed by Spearman's rank correlation analysis. RESULTS: The total care days provided by each PICU (n = 32) were well predicted by the length of stay model (r = 0.946). The agreement in 11 validation PICUs between therapy-based efficiency (range 0.30 to 0.67) and predictor-based efficiency (range 0.31 to 0.63) was excellent (rank correlation r = 0.936, p < 0.0001). CONCLUSION: PICU efficiency comparisons with either method are nearly equivalent. Predictor-based efficiency has the advantage that it can be computed from admission day data only.

The Pediatric Risk of Mortality III--Acute Physiology Score (PRISM III-APS): a method of assessing physiologic instability for pediatric intensive care unit patients.

OBJECTIVE: To develop a physiology-based measure of physiologic instability for use in pediatric patients that has an expanded scale compared with the Pediatric Risk of Mortality (PRISM) III score. STUDY DESIGN: Data were collected from consecutive admissions to 32 pediatric ICUs (11,165 admission, 543 deaths). Patient-level data included physiologic data, outcomes, descriptive information, and diagnoses. Physiologic data included the most abnormal values in the first 24 hours of pediatric ICU stay from 27 variables. Initially, ranges of each physiologic variable were evaluated for their association with mortality. A multi-variate logistic regression analysis was used to determine the final variables and their ranges. Integer scores reflecting the relative contribution to mortality risk were assigned to the variable ranges. RESULTS: A total of 59 ranges of 21 physiologic variables were selected. This score is called the Pediatric Risk of Mortality III--Acute Physiology Score (PRISM III-APS). Mortality increased as the PRISM III-APS score increased. Most patients have PRISM III-APS scores less than 10, and these patients have a mortality risk of less than 1%. At the other extreme, the mortality rate of the 137 patients with a PRISM III-APS score of greater than 80 was greater than 97%. CONCLUSION: The PRISM III-APS score is an expanded measure of physiologic instability that has been validated against mortality. Compared with PRISM III, PRISM III-APS should be more sensitive to small changes in physiologic status.

Variability in duration of stay in pediatric intensive care units: a multiinstitutional study.

OBJECTIVE: Development of a statistical model to predict length of stay (LOS) in a pediatric intensive care unit (PICU) that adjusts for patient-related risk factors at admission. DESIGN: Randomized selection of sites by cluster sampling from a 1989 national survey of all hospitals with PICUs, stratified for four quality-of-care factors into 16 clusters (size, presence of an intensive care specialist, medical school affiliation, coordination of care). The data collection was prospective in the selected units. PATIENTS: 5415 consecutive medical, surgical, or emergency admissions to 16 PICUs. MEASUREMENTS: Patients: Pediatric Risk of Mortality (PRISM) score for the initial 24 hours, admission diagnosis classified into system and cause of the primary dysfunction, operative status, preadmission care, critical care modalities required during the first 24 hours, age, sex, PICU length of stay, and outcome. PICU sites: admission volume, coordination of care, presence of an intensivist, presence of residents, and number of pediatric ICU and pediatric hospital beds. METHODS: Log-logistic regression analysis of LOS on patient-related and institution-related factors. RESULTS: Significant (p < 0.05) patient-related predictors of LOS included PRISM, 10 diagnostic groups, 3 preadmission factors (operative status, inpatient/outpatient, previous PICU admission), and first-day use of mechanical ventilation. The ratio of observed to predicted LOS varied among PICUs from 0.83 to 1.25, with three PICUs displaying significantly (p < 0.05) shorter and three PICUs longer LOS. The PICU factors associated (p < 0.05) with shorter (5% to 11%) LOS were presence of an intensivist, presence of residents, and coordination of care, whereas an increased ratio of PICU to hospital beds was associated with longer (p < 0.05) LOS. Medical school affiliation, admission volume, number of pediatric hospital beds, and PICU mortality rates did not have statistically significant effects on LOS when adjusted for patient conditions. CONCLUSIONS: The predictor can be used to adjust LOS in PICUs for patient-related risk factors, enabling the comparison of resource utilization among different institutions. Organizational factors known to foster team-oriented care are associated with shorter LOS, whereas increased relative PICU size may pose an incentive to keep PICU beds occupied longer.