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Measuring and Analyzing Length of Stay in Critical Care Trials

Harhay, Michael O. PhD*,†; Ratcliffe, Sarah J. PhD; Small, Dylan S. PhD†,§; Suttner, Leah H. MS*; Crowther, Michael J. PhD; Halpern, Scott D. MD, PhD*,†,¶,#

doi: 10.1097/MLR.0000000000001059
Online Article: Applied Methods

Background: In randomized clinical trials among critically ill patients, it is uncertain how choices regarding the measurement and analysis of nonmortal outcomes measured in terms of duration, such as intensive care unit (ICU) length of stay (LOS), affect studies’ conclusions.

Objectives: Assess the definitions and analytic methods used for ICU LOS analyses in published randomized clinical trials.

Research Design: This is a systematic review and statistical simulation study.

Results: Among the 80 of 150 trials providing sufficient information regarding the chosen definition of ICU LOS, 3 different start times (ICU admission, trial enrollment/randomization, receipt of intervention) and 2 end times (discharge readiness, actual discharge) were used. In roughly three quarters of these studies, ICU LOS was compared using approaches that did not explicitly account for death, either by ignoring it entirely or stratifying the analyses by survival status. The remaining studies used time-to-event (discharge) models censoring at death or applied a fixed LOS value to patients who died. In statistical simulations, we showed that each analytic approach tested a different question regarding ICU LOS, and that approaches that do not explicitly account for death often produce misleading or ambiguous conclusions when treatments produce small effects on mortality, even if those are not detected as significant in the trial.

Conclusions: There is considerable variability in how ICU LOS is measured and analyzed which impairs the ability to compare results across trials and can produce spurious conclusions. Analyses of duration-based outcomes such as LOS should jointly assess the impact of the intervention on mortality to yield correct interpretations.

*Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine

Palliative and Advanced Illness Research (PAIR) Center, University of Pennsylvania, Philadelphia, PA

Department of Public Health Sciences, University of Virginia, Division of Biostatistics, Charlottesville, VA

§Department of Statistics, Wharton School, University of Pennsylvania, Philadelphia, PA

Biostatistics Research Group, Department of Health Sciences, Centre for Medicine, University of Leicester, Leicester, UK

Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine

#Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA

Supported by the National Heart, Lung, and Blood Institute (F31-HL127947 and K99-HL141678 to M.O.H.) and the National Institute of General Medical Sciences (R01-GM104470 to S.J.R.) of the National Institutes of Health.

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

The authors declare no conflict of interest.

Reprints: Michael O. Harhay, PhD, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, 304 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021. E-mail:

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