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Effect of Boarding on Mortality in ICUs

Stretch, Robert, MD1; Della Penna, Nicolás, BA2; Celi, Leo Anthony, MD, MS, MPH3; Landon, Bruce E., MD, MBA, MSc4,5

doi: 10.1097/CCM.0000000000002905
Clinical Investigations
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Objectives: Hospitals use a variety of strategies to maximize the availability of limited ICU beds. Boarding, which involves assigning patients to an open bed in a different subspecialty ICU, is one such practice employed when ICU occupancy levels are high, and beds in a particular unit are unavailable. Boarding disrupts the normal geographic colocation of patients and care teams, exposing patients to nursing staff with different training and expertise to those caring for nonboarders. We analyzed whether medical ICU patients boarding in alternative specialty ICUs are at increased risk of mortality.

Design: Retrospective cohort study using an instrumental variable analysis to control for unmeasured confounding. A semiparametric bivariate probit estimation strategy was employed for the instrumental model. Propensity score matching and standard logistic regression (generalized linear modeling) were used as robustness checks.

Setting: The medical ICU of a tertiary care nonprofit hospital in the United States between 2002 and 2012.

Patients: All medical ICU admissions during the specified time period.

Interventions: None.

Measurements and Main Results: The study population consisted of 8,429 patients of whom 1,871 were boarders. The instrumental variable model demonstrated a relative risk of 1.18 (95% CI, 1.01–1.38) for ICU stay mortality for boarders. The relative risk of in-hospital mortality among boarders was 1.22 (95% CI, 1.00–1.49). GLM and propensity score matching without use of the instrument yielded similar estimates. Instrumental variable estimates are for marginal patients, whereas generalized linear modeling and propensity score matching yield population average effects.

Conclusions: Mortality increased with boarding of critically ill patients. Further research is needed to identify safer practices for managing patients during periods of high ICU occupancy.

1Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA.

2Research School of Computer Science, Australian National University, Canberra, ACT, Australia.

3Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA.

4Department of Health Care Policy, Harvard Medical School, Boston, MA.

5Division of General Medicine and Primary Care, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA.

Drs. Stretch, Della Penna, Celi, and Landon participated equally in the design of the study. Drs. Stretch and Della Penna gathered and analyzed the data. Dr. Della Penna performed the statistical analysis. Drs. Stretch, Della Penna, Celi, and Landon drafted the article. All authors read and approved the final article.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (http://journals.lww.com/ccmjournal).

Dr. Celi is funded by the National Institute of Health through the grant R01 EB017205-01A1. Dr. Landon received funding from the Center for Health Care Delivery Science at Beth Israel Deaconess Medical Center. Drs. Celi and Landon received support for article research from the National Institutes of Health. The remaining authors have disclosed that they do not have any potential conflicts of interest.

For information regarding this article, E-mail: landon@hcp.med.harvard.edu

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