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Evid Based Nurs 15:26-27 doi:10.1136/ebn.2011.100249
  • Care of the older person
  • Cohort Study

Improving the ability to predict falls among older adults following inpatient rehabilitation

  1. Laura M Wagner
  1. College of Nursing, Hartford Institute for Geriatric Nursing, New York University, New York, USA
  1. Correspondence to Laura M Wagner
    College of Nursing, Hartford Institute for Geriatric Nursing, New York University, 726 Broadway, 10th floor, NY 10003; USA; lmw9{at}nyu.edu

Commentary on: [CrossRef][Medline]Google Scholar

Implications for practice and research

  • The simple tool furthers our understanding of the key variables that could be predictive of falls following a rehabilitation stay.

  • Collaboration with other members of the healthcare team may be necessary to complete the tool such as sway assessment since nurses may not be readily trained in how to complete one.

  • Future research should focus on testing the predicted probability of falling in high-risk groups after being discharged from rehabilitation such as stroke survivors and those with cognitive impairments.

Context

Falls in the older people continue to challenge healthcare providers in all care settings. This is especially true in rehabilitation settings where the goals focus on restoration of the clients' function and independence. As a result, clients are encouraged to be as mobile as possible which also increases their risk for falling, especially if their mobility status is compromised. One challenge continually facing healthcare providers is how to best identify and predict those at greatest risk for falling both during a rehabilitation stay and after discharge.

Methods

This study was conducted in the rehabilitation wards in two Australian hospitals. Baseline data on 15 common predictor variables (eg, fall history) were collected and a total of 431 older patients were followed up at least monthly for 3 months after discharge to identify incidence of falls.

Findings

In total, 34% of the participants fell during this time with 16% falling repeatedly. The presence of three risk factors: male gender, prescription of central nervous system (CNS) medications and increased postural sway (using a swaymeter) has a 68% probability of falling within 3 months after rehabilitation stay.

Commentary

Sherrington et al have developed a simple tool to predict falls in patients following inpatient rehabilitation. The strengths of this study include (1) the rigourous analytic approaches and (2) the validation of internal validity using sophisticated statistical procedures, which increases the likelihood that the tool is replicable. Yet, one is left to wonder whether the participants are truly representative of the aged rehabilitation population, especially given that only 20% of the participants had a general decrease in their mobility and half of the eligible participants did not enrol in the study. As the authors point out, before the tool can be widely disseminated into clinical practice, external validation must be done to ensure the generalisability of its use (ie, external validity).

The data from this study are built on previous studies predicting falls on postdischarge participants, which were based solely on the routinely available discharge medical records. However, the accuracy of the data is extremely difficult to ascertain in the rehabilitation population.1 2 Other variables such as housing arrangements and levels of social support after discharge with activities of daily living, may play a greater role in affecting falls after discharge from rehabilitation. Also, the level of postdischarge therapy received by the client may also play a role. Furthermore, another limitation of this tool, as is with many other prediction tools, is the inability to account for environmental conditions present that could be contributors to the actual fall.

Perell et al3 recommend a list of criteria for choosing the most appropriate assessment tool. These criteria include high sensitivity, specificity and inter-rater reliability; similarity of resident population to the one in which the instrument was developed or studied; written procedures explicitly outlining appropriate use of the tool; reasonable time required to administer the scale; and established thresholds identifying when to initiate interventions. Further testing of this prediction tool needs to include reliability assessment and methods of scale administration and use, especially given that sway measurement may not be an activity in which nurses have proficiency. A discussion of how the prediction tool will be useful to guide care planning in clients following a rehabilitation stay is also needed. Nonetheless, the present study is a step in the right direction to advance the science of predicting falls among older adults following inpatient rehabilitation.

Footnotes

  • Competing interests None.

References

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