Because of the likelihood that aging-related changes in physiology, functionality, and disease states may contribute to frailty, some authors have developed models of frailty that include measures of these domains. For example, Rockwood et al. (1999) developed and validated a brief clinical instrument to classify frailty in hospitalized patients that included measures of function, cognition, walking, and bladder/bowel function as determining factors. Using Canadian provincial sampling frames from 9008 randomly collected subjects, those who had no abnormalities in these areas were deemed nonfrail, and those with incontinence, dementia, and dependency in activities of daily living were deemed frail (Rockwood et al., 1999). This was further validated by demonstrating a greatly increased risk of nursing home placement in the frail as compared to nonfrail group (Rockwood et al., 1999).
Studenski et al. (2004) recently developed and is validating a Clinical Global Impression Measure for Frailty that includes most intrinsic domains represented in the physical frailty models earlier, but also includes extrinsic or consequential domains of healthcare outcomes and complexity, functionality, and psychosocial status (see Table 57.1). An ongoing study of each of these domains in order to determine which if any of these domains best predicts frailty and other poor health outcomes, and in order to be able to measure change in frailty status over time, is important. Once validated, this model could play a potentially important role in measuring responses to interventions. Advantages of these models are that they both can be performed by clinicians rather than trained research technicians (Studenski et al.,
2004; Rockwood et al., 1999). In addition, both of these models incorporate components of cognition, health status, and functional domains that may be able to capture additional information about frailty or subtypes of frailty with further study. However, the mixing of outcomes and intrinsic factors in frailty may also make it more difficult to identify common clusters of signs and symptoms that could inform biologic studies (Fried et al., 2005).
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