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Variability in duration of stay in pediatric intensive care units: a multiinstitutional study
1Department of Pediatrics, George Washington University School of Medicine, Washington, DC, USA.
Insights
A new statistical model predicts pediatric intensive care unit (PICU) length of stay (LOS) by adjusting for patient risk factors. Organizational factors like intensivist presence and care coordination shorten LOS, while larger PICU size may increase it.
Area of Science:
- Pediatric critical care medicine
- Health services research
- Biostatistics
Background:
- Length of stay (LOS) in pediatric intensive care units (PICUs) is a key metric for resource utilization and quality assessment.
- Predicting and understanding factors influencing LOS is crucial for effective hospital management and patient care.
- Existing models may not adequately account for patient-specific risk factors at admission.
Purpose of the Study:
- To develop and validate a statistical model for predicting pediatric intensive care unit (PICU) length of stay (LOS).
- To adjust LOS predictions for patient-related risk factors identified at admission.
- To identify institutional factors that influence LOS in PICUs.
Main Methods:
- A prospective study involving 5415 admissions across 16 pediatric intensive care units (PICUs) selected through stratified cluster sampling.
- Data collected included patient demographics, Pediatric Risk of Mortality (PRISM) scores, diagnoses, pre-admission care, first-day critical care modalities, and LOS.
- Log-logistic regression analysis was employed to model LOS based on patient and institutional factors.
Main Results:
- Patient-related predictors of LOS included PRISM score, diagnostic groups, pre-admission factors (operative status, inpatient/outpatient, prior PICU admission), and mechanical ventilation use.
- PICU factors associated with shorter LOS (5-11%) were the presence of an intensivist, residents, and care coordination.
- Increased ratio of PICU beds to hospital beds was linked to longer LOS; medical school affiliation and admission volume did not significantly affect LOS when adjusted for patient conditions.
Conclusions:
- The developed statistical model effectively adjusts PICU LOS for patient-specific risk factors, facilitating comparisons of resource utilization across institutions.
- Organizational factors promoting team-oriented care, such as intensivist presence and care coordination, are associated with reduced LOS.
- Larger relative PICU size may inadvertently incentivize longer bed occupancy, impacting overall resource management.
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.