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Evid Based Nurs doi:10.1136/eb-2012-100830
  • Adult nursing
  • Quantitative other

Predicting outcome at the bedside: the surgical intensive care unit: optimal mobility score

  1. Anne C Mosenthal
  1. Department of Surgery, UMDNJ-New Jersey Medical School, Newark, New Jersey, USA
  1. Correspondence to: Dr Anne C Mosenthal
    UMDNJ-NJMS, Surgery, 185 S. Orange Ave, MSB G506, Newark, NJ 07103, USA; mosentac{at}umdnj.edu

Commentary on: Kasotakis G, Schmidt U, Perry D, et al. The surgical intensive care unit optimal mobility score predicts mortality and length of stay. Crit Care Med 2012;40:1122–8

Implications for practice and research

  • A simple outcome prediction tool that can be used at the bedside in the surgical intensive care unit (SICU) by all members of the critical care team.

  • More research is needed to validate this tool in other SICU populations and determine how it can be used to inform clinical decision–making.

Context

The science of predicting outcomes in the intensive care unit (ICU) has long been the subject of debate. The hope of developing a prognostication model based on data that could be used to support clinical decision-making has not been realised. Many models are used to stratify surgical patients, and compare outcomes across ICUs, but have not proven useful for clinical decision-making in real time, particularly when contemplating transitions to palliative care and withdrawal of life support. Clinical judgement is superior to any prognostication model; however, there is wide variability between clinicians in their judgements, and how they use them to make clinical decisions about the withdrawal or withholding of life support.1

Methods

Kasotakis and colleagues have described a model for predicting Surgical Intensive Care Unit length of stay and mortality based on the Surgical Intensive Care Unit Optimal Mobility Score (SOMS), a nursing assessment of mobility capacity carried out on first day after admission to the SICU.2 They studied this in a single SICU population, with no pre-existing functional impairment. Two nurses assessed each patient for mobility capacity, and a physiotherapist assessed grip strength. A multivariate analysis was performed to determine predictors of mortality and length of stay.

Findings

SOMS was the only independent predictor of inhospital mortality, and was as good as Acute Physiology and Chronic Health Evaluation II (APACHE II) in predicting length of stay. The authors conclude that in surgical ICU patients the SOMS is a reliable and valid tool to predict inhospital mortality and length of stay.

Commentary

The search continues for reliable prognostication tools in the SICU that can be used to inform clinical decisions, particularly around the end of life. The authors may have changed this paradigm with their recent study. By combining the science of outcome prediction with the art of bedside assessment of mobility into a simple algorithm, they have described a model for predicting ICU outcomes that can be used in real-time, influence care and facilitate communication with families about prognosis during discussions around goals of care.

Why is this important? Our ability to predict outcomes, and make decisions in face of uncertainty are critical elements in end-of-life decision-making. While our judgment is superior to any models, it is highly variable. Interestingly, families of critically ill patients rely little on what the doctor says about the prognosis, but rather on their own perceptions of the patient's illness, physical appearance, as well as personal variables such as strength of character and faith.3 Thus, the interdisciplinary nature of the SOMS is crucial, as it represents an objective way to predict outcome that is based on the nurse and therapist clinical assessment, not the physician's, that is easily visible and observable by the family as well as all members of the team. This may improve communication with families and surrogates about prognosis, by aligning their perception of illness with observable conditions, and prediction of likely outcomes.

This study has validated SOMS to predict inhospital outcome in a single institution with a primarily elective surgical population in the SICU. Whether SOMS has the same power in other populations is unclear and requires further study in multiple centres. However, as a tool to improve our clinical ability to prognosticate at the bedside, communicate this among all disciplines of the care team and family, it holds exciting possibilities.

Footnotes

  • Funding None.

  • Competing interests None.

References

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