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Long-stay patients in the pediatric intensive care unit
J P Marcin1, A D Slonim, M M Pollack
1Section of Critical Care Medicine, Department of Pediatrics, University of California, Davis, Sacramento, CA, USA.
Insights
Long-stay patients (LSPs) in the pediatric intensive care unit (PICU) represent a significant portion of resource utilization. Identifying these patients early can inform targeted interventions for improved outcomes and cost savings.
Area of Science:
- Pediatric critical care medicine
- Health services research
- Clinical informatics
Background:
- Length of stay in the pediatric intensive care unit (PICU) is a key indicator of patient severity, health status, and unit performance.
- Long-stay patients (LSPs) consume a disproportionate amount of PICU resources, impacting overall care efficiency and costs.
Purpose of the Study:
- To determine the clinical profiles and resource utilization patterns of LSPs in the PICU.
- To develop and validate a predictive model for early identification of LSPs to enable targeted interventions.
Main Methods:
- A nonconcurrent cohort study was conducted across 32 pediatric intensive care units (16 randomly selected, 16 volunteer).
- Data from 11,165 consecutive admissions were analyzed using logistic regression to identify predictors of prolonged stays (>12 days).
- A predictive algorithm was developed and validated to assess its performance in identifying LSPs.
Main Results:
- Long-stay patients constituted 4.7% of admissions but accounted for 36.1% of total PICU days of care.
- Predictive factors for LSPs included younger age (<12 months), prior ICU admission, emergency admission, chronic care needs (TPN, tracheostomy), specific respiratory diagnoses, and need for ventilatory support.
- The developed prediction algorithm demonstrated good discrimination (AUC 0.83-0.85) and calibration for identifying LSPs.
Conclusions:
- Long-stay patients in the PICU experience less favorable outcomes and consume significantly more resources.
- Key clinical characteristics, including age and reliance on chronic care devices, are associated with prolonged PICU stays.
- A validated predictive algorithm can effectively identify high-risk patients for timely, resource-optimizing interventions.
Objective:
Length of stay in the pediatric intensive care unit (PICU) is a reflection of patient severity of illness and health status, as well as PICU quality and performance. We determined the clinical profiles and relative resource use of long-stay patients (LSPs) and developed a prediction model to identify LSPs for early quality and cost saving interventions.
Design:
Nonconcurrent cohort study.
Setting:
A total of 16 randomly selected PICUs and 16 volunteer PICUs.
Patients:
A total of 11,165 consecutive admissions to the 32 PICUs.
Interventions:
None.
Measurements And Main Results:
LSPs were defined as patients having a length of stay greater than the 95th percentile (>12 days). Logistic regression analysis was used to determine which clinical characteristics, available within the first 24 hrs after admission, were associated with LSPs and to create a predictive algorithm. Overall, LSPs were 4.7% of the population but represented 36.1% of the days of care. Multivariate analysis indicated that the following factors are predictive of long stays: age <12 months, previous ICU admission, emergency admission, no CPR before admission, admission from another ICU or intermediate care unit, chronic care requirements (total parenteral nutrition and tracheostomy), specific diagnoses including acquired cardiac disease, pneumonia, and other respiratory disorders, having never been discharged from the hospital, need for ventilatory support or an intracranial catheter, and a Pediatric Risk of Mortality III score between 10 and 33. The performance of the prediction algorithm in both the training and validation samples for identifying LSPs was good for both discrimination (area under the receiver operating characteristics curve of 0.83 and 0.85, respectively), and calibration (goodness of fit, p = .33 and p = .16, respectively). LSPs comprised from 2.1% to 8.1% of individual ICU patients and occupied from 15.2% to 57.8% of individual ICU bed days.
Conclusions:
LSPs have less favorable outcomes and use more resources than non-LSPs. The clinical profile of LSPs includes those who are younger and those that require chronic care devices. A predictive algorithm could help identify patients at high risk of prolonged stays appropriate for specific interventions.
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