An official American Thoracic Society systematic review: The effect of nighttime intensivist staffing on mortality and length of stay among intensive care unit patients

BACKGROUND: Studies of nighttime intensivist staffing have yielded mixed results.

GOALS: To review the association of nighttime intensivist staffing with outcomes of intensive care unit (ICU) patients.

METHODS: We searched five databases (2000-2016) for studies comparing in-hospital nighttime intensivist staffing with other nighttime staffing models in adult ICUs and reporting mortality or length of stay. We abstracted data on staffing models, outcomes, and study characteristics and assessed study quality, using standardized tools. Meta-analyses used random effects models.

RESULTS: Eighteen studies met inclusion criteria: one randomized controlled trial and 17 observational studies. Overall methodologic quality was high. Studies included academic hospitals (n = 10), community hospitals (n = 2), or both (n = 6). Baseline clinician staffing included residents (n = 9), fellows (n = 4), and nurse practitioners or physician assistants (n = 2). Studies included both general and specialty ICUs and were geographically diverse. Meta-analysis (one randomized controlled trial; three nonrandomized studies with exposure limited to nighttime intensivist staffing with adjusted estimates of effect) demonstrated no association with mortality (odds ratio, 0.99; 95% confidence interval, 0.75-1.29). Secondary analyses including studies without risk adjustment, with a composite exposure of organizational factors, stratified by intensity of daytime staffing and by ICU type, yielded similar results. Minimal or no differences were observed in ICU and hospital length of stay and several other secondary outcomes.

CONCLUSIONS: Notwithstanding limitations of the predominantly observational evidence, our systematic review and meta-analysis suggests nighttime intensivist staffing is not associated with reduced ICU patient mortality. Other outcomes and alternative staffing models should be evaluated to further guide staffing decisions.