PICU Volume and Outcome: A Severity-Adjusted Analysis*

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To determine the relationship between PICU volume and severity-adjusted mortality in a large, national dataset.


Retrospective cohort study.


The VPS database (VPS, LLC, Los Angeles, CA), a national multicenter clinical PICU database.


All patients with discharge dates between September 2009 and March 2012 and valid Pediatric Index of Mortality 2 and Pediatric Risk of Mortality III scores, who were not transferred to another ICU and were seen in an ICU that collected at least three quarters of data.



Measurements and Main Results:

Anonymized data received included ICU mortality, hospital and patient demographics, and Pediatric Index of Mortality 2 and Pediatric Risk of Mortality III scores. PICU volume/quarter was determined (VPS sites submit data quarterly) per PICU and was divided by 100 to assess the impact per 100 discharges per quarter (volume). A mixed-effects logistic regression model accounting for repeated measures of patients within ICUs was performed to assess the association of volume on severity-adjusted mortality, adjusting for patient and unit characteristics. Multiplicative interactions between volume and severity of illness were also modeled. We analyzed 186,643 patients from 92 PICUs, with an overall ICU mortality rate of 2.6%. Volume ranged from 0.24 to 8.89 per ICU per quarter; the mean volume was 2.61. The mixed-effects logistic regression model found a small but nonlinear relationship between volume and mortality that varied based on the severity of illness. When severity of illness is low, there is no clear relationship between volume and mortality up to a Pediatric Index of Mortality 2 risk of mortality of 10%; for patients with a higher severity of illness, severity of illness-adjusted mortality is directly proportional to a unit’s volume.


For patients with low severity of illness, ICU volume is not associated with mortality. As patient severity of illness rises, higher volume units have higher severity of illness-adjusted mortality. This may be related to differences in quality of care, issues with unmeasured confounding, or calibration of existing severity of illness scores.

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