JOURNAL ARTICLE
REVIEW
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A review of the revised Functional Capacity Index as a predictor of 12 month outcomes following injury.

Injury 2017 January 4
The measurement of functional outcomes following severe trauma has been widely recognised as a priority for countries with developed trauma systems. In this respect, the Functional Capacity Index (FCI), a multi-attribute index which has been incorporated into the most recent Abbreviated Injury Scale (AIS) dictionary, is potentially attractive as it offers 12-month functional outcome predictions for patients captured by existing AIS-coded datasets. This review paper outlines the development, construction and validation of the predictive form of the FCI (termed the pFCI), the modifications made which produced the currently available 'revised' pFCI, and the extent to which the revised pFCI has been validated and used. The original pFCI performed poorly in validation studies. The revised pFCI does not address many of the identified limitations of the original version, and despite the ready availability of a truncated version in the AIS dictionary, it has only been used in a handful of studies since its introduction several years ago. Additionally, there is little evidence for its validity. It is suggested that the pFCI should be better validated, whether in the narrow population group of young, healthy individuals for which it was developed, or in the wider population of severely injured patients. Methods for accounting for the presence of multiple injures (of which two have currently been used) should also be evaluated. Many factors other than anatomical injury are known to affect functional outcomes following trauma. However, it is intuitive that any model which attempts to predict the ongoing morbidity burden in a trauma population should consider the effects of the injuries sustained. Although the revised pFCI potentially offers a low-cost assessment of likely functional limitations resulting from anatomical injury, it must be more rigorously evaluated before more comprehensive predictive tools can be developed from it.

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