Key findings

In this systematic review of 10 observational studies we found that frailty was common, occurring in approximately 30% of adult ICU admissions. We also found that frailty was associated with increased risk of hospital and long-term mortality and that frail patients were less likely to have home as a discharge destination. We found no significant difference among frail and non-frail patients in the receipt of mechanical ventilation, receipt of vasoactive therapy, or duration of ICU stay. Increasing severity of frailty was associated with worsened outcomes including hospital and long-term mortality and our findings were robust when we analyzed high quality studies, adjusted data, and in trial sequential analysis.

Context

Although frailty has been long recognized by geriatric medicine, it has only been recently identified as an important determinant of prognosis for critically ill patients and our systematic review supports this. Our findings are consistent with the observation that frail patients are at increased risk of poor outcomes in other settings and after healthcare interventions [41, 42]. Potential causes for poor outcomes experienced by critically ill frail patients include its underlying pathophysiology of neuromuscular weakness, sarcopenia, decreased oxygen utilization, inflammation, and immunosenescence [9, 18, 43] reflecting a wide range of age-related molecular and cellular deficits [44, 45]. These may increase susceptibility to inflammatory insults and nosocomial infections common in critical illness. Diminished reserve arising from the multisystem nature of frailty may increase adverse effects of critical illness treatments such as bed rest, sedation, polypharmacy, instrumentation, and MV. Additionally, the reduced resilience of frail patients and increased likelihood of comorbid conditions [46] may make their recovery more difficult [47] and prolonged with reduced probability of returning to baseline increasing the chances of institutionalization [5, 6, 18]. In our study, we found that frail ICU patients were at an increased risk of not being discharged home, although this was reported in only four studies.

We did not find significant differences in ICU LOS, although there was a non-statistically significant increase in hospital stay. The only study reporting duration of MV found no difference between frail and non-frail patients [28]. This is unexpected since there are many factors, including diminished resilience, that may increase recovery time in frail patients prolonging their ICU and hospital stays as compared to non-frail patients. For example, frail patients may be more difficult to wean from mechanical ventilation because of weakness, sarcopenia, and decreased oxygen uptake [9, 17, 18, 43]. Further, as a result of immunosenescence, frail patients may need more time to recover from infections including those nosocomially acquired [45]. Our results are not in keeping with data in surgical populations, which have demonstrated that frail patients have longer stays in hospital and recovery time [6]. Possible reasons for these results include incomplete reporting of data, impact of end-of-life care or limitations of care influenced by frailty status, and discharge practices. A further factor that may have influenced the LOS data and duration of organ support is survival bias. Frail patients may have died earlier than the non-frail and this may have been associated with reduced LOS, as well as the duration of organ support. Data which would have allowed examination of this, such as “days alive and free of organ support”, was rarely reported with only Bagshaw et al. finding that hospital LOS was prolonged in frail survivors. These data should be described in futures studies focused on frailty in ICU settings.

Implications for clinicians, policy, and research

An important aspect of this work is to determine if ICU processes of care can be modified to improve outcomes for those identified as frail. Examples of processes which may have differential impact in those who are frail include nutritional support, sedation practices, intensity of mobilization/rehabilitation etc. While research is being conducted on how to improve outcomes, ongoing awareness of frailty as a marker of risk is important and may lead to better advanced care planning. Implicit in this is the recognition that frailty is not only associated with the elderly but may even occur in younger ages [26, 32]. Moreover, frailty may provide a better method to evaluate the trajectory of chronic health and its determinants such as cognition, mobility, function, and social engagement leading to ICU admission. Currents methods such as co-morbidity indices and chronic health evaluations integrated into illness severity scores and mortality prediction models are likely insufficient given the incremental impact of frailty on outcomes after adjusting for illness. Our work supports the value for implementation of frailty screening at the time of ICU admission. Since all the scales used in the included studies correlated with worsened outcomes; after further validation, the CFS which is the most studied, least time intensive, and easy to apply would be the most promising candidate.

ICU researchers and clinicians, who routinely measure co-morbidities, may question why frailty should be additionally measured or measured instead. The value of frailty is that it is a reflection of overall function which is not the case for co-morbidity, although frailty and co-morbidities are inherently intertwined in relation to the degree of frailty [48]. Fried and colleagues attempted to “untangle” these constructs but there is considerable overlap which increases with age [11]. Work on defining health deficit accumulation through network modeling shows that what matters the most is the density of a deficits connections to other deficits which is not captured by simple counting of deficits [49–51]. As an individual ages and accumulates deficits, as would be the case in many older people who are critically ill, the more that frailty and co-morbidity are inextricably intertwined.

Limitations

Although the association between frailty and poor outcomes from critical illness is supported by its underlying pathophysiology, it should be emphasized that the studies in our review were observational, may have been prone to bias, and causation cannot be determined. Two key potential biases are selection and confirmation biases. None of the studies applied the gold standard for frailty determination which is a comprehensive geriatric assessment performed by a specialist in geriatric medicine [52]. All these studies identified patients after ICU admission and we have no data on frail and non-frail patients declined ICU admission. In addition, the perception and identification of frailty may have influenced care received and limitations of care. Similarly we are unable to ascertain the role of survival bias in our results. Furthermore, we were limited in our ability to pool adjusted data because of heterogeneity in its reporting. However, supporting the importance of frailty as a determinant of outcome was that high quality studies which controlled for age and other co-founders including illness severity found that frailty was independently associated with adverse outcomes. In addition, we found that frail and non-frail patients had similar rates of mechanical ventilation and use of vasopressors reducing the likelihood of care limitations. Moreover, in most of the studies there was a frailty dose response where increasing frailty correlated with increasingly worse outcomes.

An additional limitation is that the included studies used three different frailty measures: the CFS, FP, and FI. We included all of these studies since frailty measures generally correlate well with each other [13]. When we performed subgroup analysis the results remained similar across all measures of frailty. However, unanswered questions remain including which is the most appropriate measure in the ICU setting? Should there be an ICU-specific frailty measure? Does it matter which measure if they all show similar trends for outcome? If this is the case, the one that is least time consuming and most feasible may be a reasonable starting point. Limitations also include variable reporting of outcomes, data originating from different healthcare settings, and need to transform data for aggregation. Further, the late registry in PROSPERO, the addition of long-term mortality as an outcome, and lack of a published protocol with a statistical plan could all increase the risk of bias.