Frailty Evaluation in the Hospital

Study Design and Population

As described in detail elsewhere,[14] this was a prospective cohort study that enrolled adult patients (any age older than 18 years) at the time of discharge back to the community from 7 general internal medicine wards in 2 teaching hospitals in Edmonton, Alberta between October 2013 and November 2014. We excluded patients admitted from, or being discharged back to, long‐term care facilities or other acute care hospitals, or from out of the province; patients who were unable to communicate in English; patients with moderate or severe cognitive impairment (scoring 5 or more on the Short Portable Mental Status Questionnaire); or patients with projected life expectancy of less than 3 months. All patients provided written consent, and the study was approved by the Health Research Ethics board of the University of Alberta (project ID Pro00036880).

We assessed the degree of frailty within 24 hours of discharge in 3 ways. First, we used the CFS[9, 15] with patients being asked to rate their best functional status in the week prior to admission. As per the CFS validations studies, scores 5 were defined as frail.[9, 15] Second, we used the TUGT as a proxy for slow gait speed/dysmobility (with >20 seconds defined as abnormal).[13] The TUGT was recorded as the shortest recorded time of the 2 timed trials to get up from a seated position, walk 10 feet and back, and then sit in the chair again. Third, we also determined their Fried score[6] (using the modifications outlined below) and categorized the patients as frail if they scored 3 or more. Of the 5 Fried categories, we assessed weakness by grip strength in their dominant hand using a Jamar handheld dynamometer and weight loss of 10 lb or more in the past year based on patient self‐report; these are identical to the original Fried scale description. Grip strength in the lowest quintile for sex and body mass index was defined as weak grip strength as per convention in the literature, which corresponded to less than 28.5 kg for men and less than 18.5 kg for women.[16, 17] We assessed the other 3 Fried categories in modified fashion as follows. For slow gait, rather than assessing time to walk 15 feet as in the original study and assigning a point to those testing in the lowest quintile for their age/sex, we used the TUGT, because our research personnel were already trained in this test, and we were doing it already as part of the discharge package for all patients.[13] For the Fried category of low activity, we based this on patient self‐report using the relevant questions in the EuroQoL Questionnaire (EQ‐5D); the Fried score used self‐report with a different questionnaire. Finally, for self‐reported exhaustion we used the questions in the Patient Health Questionnaire 9 (PHQ‐9)[18] analogous to those used from the Center for Epidemiological Studies depression scale in the original Fried description. We did this as we were evaluating the PHQ‐9 in our cohort already, and did not want to increase responder burden by presenting them with 2 depression questionnaires.

We followed all patients until 30 days after discharge, and outcome data (all‐cause mortality or all‐cause readmission) were collected by research personnel blinded to the patient's frailty status at discharge using patient/caregiver self‐report and analysis of the provincial electronic health record. We included deaths in or out of the hospital, and all readmissions were unplanned.

We examined the correlation between the CFS score (5 vs <5) and (1) the modified Fried score (3 vs <3) and (2) TUGT (20 seconds vs >20 seconds) using chance corrected kappa coefficients. In our previous article[14] we reported the association between the CFS and readmissions/hospitalizations within 30 days of discharge. In this article we examine whether either the Fried score or TUGT accurately and independently predict postdischarge readmissions/deaths, and whether they add additional prognostic information to the CFS assessment by comparing models with/without each definition using the C statistic and the Integrated Discrimination Improvement index. All analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC), with P values of <0.05 considered statistically significant. Subgroup analysis was done in patients older than 65 years.

Prevalence of Frailty According to Different Definitions

Although the CFS assessment resulted in 162 (33%) patients being deemed frail, only 82 (51%) of those patients also met the phenotype frailty definition using either the Fried model or the TUGT, and 49 (10%) patients who were not classified as frail on the CFS met either of the phenotypic definitions of frailty (Figure 1). Overall, 211 (43%) patients were frail according to at least 1 assessment, and 46 (9%) met all 3 frailty definitions. In the subgroup of 245 patients older than 65 years, 137 (56%) were frail according to at least 1 assessment, 38 (16%) met all 3 frailty definitions, and 27 (11%) of those patients classified as not frail on the CFS met either phenotypic definition of frailty. Agreement between TUGT and CFS or CFS and Fried was relatively poor with kappas of 0.31 (95% confidence interval [CI]: 0.23‐0.40) and 0.33 (95% CI: 0.25‐0.42), respectively. It is noteworthy that some patients deemed nonfrail on the CFS had slow gait speeds, and most CFS‐frail patients had gait speeds in the nonfrail range (Figure 2).

Figure 1

Figure 2

Characteristics According to Frailty Status

Although frail patients were generally similar across definitions (Table 1) in that they were older, had more comorbidities, more hospitalizations in the prior year, and longer index hospitalization lengths of stay than nonfrail patients, patients meeting phenotypic definitions of frailty but not classified as frail on the CFS were younger, had lower Charlson scores, higher EQ‐5D scores, and were discharged with less medications (Table 1).

Outcomes According to Frailty Status

The overall rate of 30‐day death or hospital readmission was 17.1% (85 patients), primarily as a result of hospital readmissions (81, 16.4%) (Table 2). Although patients classified as frail on the CFS exhibited significantly higher 30‐day readmission/death rates (24.1% vs 13.8% for not frail, P = 0.005) even after adjusting for age and sex (adjusted odds ratio [aOR]: 2.02, 95% CI: 1.19‐3.41) (Table 3), patients meeting either of the phenotypic definitions for frailty but not the CFS definition were not at higher risk for 30‐day readmission/death (aOR: 0.87, 95% CI: 0.34‐2.19) (Table 3). The group at highest risk for 30‐day readmissions/death were those meeting both the CFS and either phenotypic definition of frailty (25.6% vs 13.8% for those not frail, aOR: 2.15, 95% CI: 1.10‐4.19) (Tables 2 and 3). None of the Integrated Discrimination Improvement indices (for modified Fried added to CFS or TUGT added to CFS) were statistically significant, suggesting no net new information was added to predictive models, and there were no appreciable changes in C statistics (Table 3). Neither the modified Fried score nor the TUGT on their own added independent prognostic information to age/sex alone as predictors of postdischarge outcomes. It is noteworthy that the areas under the curve for models using any combination of the frailty definitions plus age and sex were not high (all ranged between 0.55 and 0.60 for the overall cohort and from 0.52 and 0.65 in the elderly). If the frailty definitions were examined as continuous variables rather than dichotomized into frail/not frail, the C statistics were not appreciably better: 0.65 for CFS, 0.58 for TUGT, and 0.60 for modified Fried. Of note, the CFS score with the published cutoff of 5 demonstrated the highest kappa, sensitivity, specificity, and positive predictive value in relation to outcomes.

Outcomes According to Frailty Status in the Elderly Subgroup

Although absolute risks of readmission or death were higher in elderly patients than younger patients, the excess risk was largely seen in those elderly patients classified as frail on the CFS. In fact, all of the associations reported above for the entire cohort were in the same direction in the elderly subgroup (Tables 2 and 3).

Strengths and Limitations

Although this was a prospective cohort study with blinded ascertainment of endpoints (30‐day outcome data were collected by observers who were unaware of the patients' CFS or phenotypic model scores), it is not without limitations. First, the only postdischarge outcomes we assessed were readmission and death, and it would be interesting to evaluate which frailty tools best predict those who are most likely to benefit from home‐care services in the community. Second, as we were interested in 30‐day readmission rates, we excluded long‐term care residents from our study and patients who had foreshortened life expectancy, in essence, the frailest of the frail. Although this reduced the size of any association between frailty and adverse outcomes, we focused this study on the situations where there is clinical equipoise and there is rarely a diagnostic dilemma around the identification of frailty and need for increased services in palliative or long‐term care patients. Third, we did not use exactly the same questionnaires or gait speed assessments as used in the original Fried score description, but as outlined in the Methods section, we used analogous questions on closely related questionnaires to extract the same information. Fourth, some might consider our comparisons biased toward the CFS, as it reflects gestalt clinical impressions (informed by patients and proxies) of frailty status before hospital admission while the Fried score and TUGT were based on patient status just prior to discharge, it may be that the former is a better measure of eventual recovery (and ongoing risk) than the latter measures. If this is the case, for the purposes of targeting interventions to prevent postdischarge complications, it would suggest to us that the CFS is better suited, whereas phenotype tools can be reserved for the postdischarge phase of recovery. By the same token, perhaps serial measures of the CFS and phenotypic tools are more important, as the trajectory of recovery may be most informative for risk prediction.[7] Certainly, if one were interested in changes in functional status during hospitalization,[29] then objective phenotypic measures such as grip strength or TUGT times would seem more appropriate choices. Fifth, some may perceive it as a weakness that we did not restrict our cohort to elderly patients; however, we actually view this as a strength, because frailty is not exclusive to older patients. Sixth, although we restricted this study to patients being discharged from general internal medicine wards, it is worth mentioning that previous studies have shown similar associations between frailty and outcomes in nonmedical hospitalized patients.[19, 22, 23, 24]

In conclusion, we looked at 3 different ways of screening for frailty, 1 being a subjective but well‐validated tool (the CFS) and the other 2 being objective assessments that look at specific phenotypic characteristics. There is a compelling need to find a standardized assessment to determine frailty in both research and clinical settings, and our study provides support for use of the CFS over the Fried or TUGT as screening tools. Standardized frailty assessments should be part of the discharge planning for all medical patients so that extra resources can be properly targeted at those patients at greatest risk for suboptimal transition back to community living.

Study Design and Population

As described in detail elsewhere,[14] this was a prospective cohort study that enrolled adult patients (any age older than 18 years) at the time of discharge back to the community from 7 general internal medicine wards in 2 teaching hospitals in Edmonton, Alberta between October 2013 and November 2014. We excluded patients admitted from, or being discharged back to, long‐term care facilities or other acute care hospitals, or from out of the province; patients who were unable to communicate in English; patients with moderate or severe cognitive impairment (scoring 5 or more on the Short Portable Mental Status Questionnaire); or patients with projected life expectancy of less than 3 months. All patients provided written consent, and the study was approved by the Health Research Ethics board of the University of Alberta (project ID Pro00036880).

We assessed the degree of frailty within 24 hours of discharge in 3 ways. First, we used the CFS[9, 15] with patients being asked to rate their best functional status in the week prior to admission. As per the CFS validations studies, scores 5 were defined as frail.[9, 15] Second, we used the TUGT as a proxy for slow gait speed/dysmobility (with >20 seconds defined as abnormal).[13] The TUGT was recorded as the shortest recorded time of the 2 timed trials to get up from a seated position, walk 10 feet and back, and then sit in the chair again. Third, we also determined their Fried score[6] (using the modifications outlined below) and categorized the patients as frail if they scored 3 or more. Of the 5 Fried categories, we assessed weakness by grip strength in their dominant hand using a Jamar handheld dynamometer and weight loss of 10 lb or more in the past year based on patient self‐report; these are identical to the original Fried scale description. Grip strength in the lowest quintile for sex and body mass index was defined as weak grip strength as per convention in the literature, which corresponded to less than 28.5 kg for men and less than 18.5 kg for women.[16, 17] We assessed the other 3 Fried categories in modified fashion as follows. For slow gait, rather than assessing time to walk 15 feet as in the original study and assigning a point to those testing in the lowest quintile for their age/sex, we used the TUGT, because our research personnel were already trained in this test, and we were doing it already as part of the discharge package for all patients.[13] For the Fried category of low activity, we based this on patient self‐report using the relevant questions in the EuroQoL Questionnaire (EQ‐5D); the Fried score used self‐report with a different questionnaire. Finally, for self‐reported exhaustion we used the questions in the Patient Health Questionnaire 9 (PHQ‐9)[18] analogous to those used from the Center for Epidemiological Studies depression scale in the original Fried description. We did this as we were evaluating the PHQ‐9 in our cohort already, and did not want to increase responder burden by presenting them with 2 depression questionnaires.

We followed all patients until 30 days after discharge, and outcome data (all‐cause mortality or all‐cause readmission) were collected by research personnel blinded to the patient's frailty status at discharge using patient/caregiver self‐report and analysis of the provincial electronic health record. We included deaths in or out of the hospital, and all readmissions were unplanned.

We examined the correlation between the CFS score (5 vs <5) and (1) the modified Fried score (3 vs <3) and (2) TUGT (20 seconds vs >20 seconds) using chance corrected kappa coefficients. In our previous article[14] we reported the association between the CFS and readmissions/hospitalizations within 30 days of discharge. In this article we examine whether either the Fried score or TUGT accurately and independently predict postdischarge readmissions/deaths, and whether they add additional prognostic information to the CFS assessment by comparing models with/without each definition using the C statistic and the Integrated Discrimination Improvement index. All analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC), with P values of <0.05 considered statistically significant. Subgroup analysis was done in patients older than 65 years.

Prevalence of Frailty According to Different Definitions

Although the CFS assessment resulted in 162 (33%) patients being deemed frail, only 82 (51%) of those patients also met the phenotype frailty definition using either the Fried model or the TUGT, and 49 (10%) patients who were not classified as frail on the CFS met either of the phenotypic definitions of frailty (Figure 1). Overall, 211 (43%) patients were frail according to at least 1 assessment, and 46 (9%) met all 3 frailty definitions. In the subgroup of 245 patients older than 65 years, 137 (56%) were frail according to at least 1 assessment, 38 (16%) met all 3 frailty definitions, and 27 (11%) of those patients classified as not frail on the CFS met either phenotypic definition of frailty. Agreement between TUGT and CFS or CFS and Fried was relatively poor with kappas of 0.31 (95% confidence interval [CI]: 0.23‐0.40) and 0.33 (95% CI: 0.25‐0.42), respectively. It is noteworthy that some patients deemed nonfrail on the CFS had slow gait speeds, and most CFS‐frail patients had gait speeds in the nonfrail range (Figure 2).

Figure 1

Figure 2

Characteristics According to Frailty Status

Although frail patients were generally similar across definitions (Table 1) in that they were older, had more comorbidities, more hospitalizations in the prior year, and longer index hospitalization lengths of stay than nonfrail patients, patients meeting phenotypic definitions of frailty but not classified as frail on the CFS were younger, had lower Charlson scores, higher EQ‐5D scores, and were discharged with less medications (Table 1).

Outcomes According to Frailty Status

The overall rate of 30‐day death or hospital readmission was 17.1% (85 patients), primarily as a result of hospital readmissions (81, 16.4%) (Table 2). Although patients classified as frail on the CFS exhibited significantly higher 30‐day readmission/death rates (24.1% vs 13.8% for not frail, P = 0.005) even after adjusting for age and sex (adjusted odds ratio [aOR]: 2.02, 95% CI: 1.19‐3.41) (Table 3), patients meeting either of the phenotypic definitions for frailty but not the CFS definition were not at higher risk for 30‐day readmission/death (aOR: 0.87, 95% CI: 0.34‐2.19) (Table 3). The group at highest risk for 30‐day readmissions/death were those meeting both the CFS and either phenotypic definition of frailty (25.6% vs 13.8% for those not frail, aOR: 2.15, 95% CI: 1.10‐4.19) (Tables 2 and 3). None of the Integrated Discrimination Improvement indices (for modified Fried added to CFS or TUGT added to CFS) were statistically significant, suggesting no net new information was added to predictive models, and there were no appreciable changes in C statistics (Table 3). Neither the modified Fried score nor the TUGT on their own added independent prognostic information to age/sex alone as predictors of postdischarge outcomes. It is noteworthy that the areas under the curve for models using any combination of the frailty definitions plus age and sex were not high (all ranged between 0.55 and 0.60 for the overall cohort and from 0.52 and 0.65 in the elderly). If the frailty definitions were examined as continuous variables rather than dichotomized into frail/not frail, the C statistics were not appreciably better: 0.65 for CFS, 0.58 for TUGT, and 0.60 for modified Fried. Of note, the CFS score with the published cutoff of 5 demonstrated the highest kappa, sensitivity, specificity, and positive predictive value in relation to outcomes.

Outcomes According to Frailty Status in the Elderly Subgroup

Although absolute risks of readmission or death were higher in elderly patients than younger patients, the excess risk was largely seen in those elderly patients classified as frail on the CFS. In fact, all of the associations reported above for the entire cohort were in the same direction in the elderly subgroup (Tables 2 and 3).

Strengths and Limitations

Although this was a prospective cohort study with blinded ascertainment of endpoints (30‐day outcome data were collected by observers who were unaware of the patients' CFS or phenotypic model scores), it is not without limitations. First, the only postdischarge outcomes we assessed were readmission and death, and it would be interesting to evaluate which frailty tools best predict those who are most likely to benefit from home‐care services in the community. Second, as we were interested in 30‐day readmission rates, we excluded long‐term care residents from our study and patients who had foreshortened life expectancy, in essence, the frailest of the frail. Although this reduced the size of any association between frailty and adverse outcomes, we focused this study on the situations where there is clinical equipoise and there is rarely a diagnostic dilemma around the identification of frailty and need for increased services in palliative or long‐term care patients. Third, we did not use exactly the same questionnaires or gait speed assessments as used in the original Fried score description, but as outlined in the Methods section, we used analogous questions on closely related questionnaires to extract the same information. Fourth, some might consider our comparisons biased toward the CFS, as it reflects gestalt clinical impressions (informed by patients and proxies) of frailty status before hospital admission while the Fried score and TUGT were based on patient status just prior to discharge, it may be that the former is a better measure of eventual recovery (and ongoing risk) than the latter measures. If this is the case, for the purposes of targeting interventions to prevent postdischarge complications, it would suggest to us that the CFS is better suited, whereas phenotype tools can be reserved for the postdischarge phase of recovery. By the same token, perhaps serial measures of the CFS and phenotypic tools are more important, as the trajectory of recovery may be most informative for risk prediction.[7] Certainly, if one were interested in changes in functional status during hospitalization,[29] then objective phenotypic measures such as grip strength or TUGT times would seem more appropriate choices. Fifth, some may perceive it as a weakness that we did not restrict our cohort to elderly patients; however, we actually view this as a strength, because frailty is not exclusive to older patients. Sixth, although we restricted this study to patients being discharged from general internal medicine wards, it is worth mentioning that previous studies have shown similar associations between frailty and outcomes in nonmedical hospitalized patients.[19, 22, 23, 24]

In conclusion, we looked at 3 different ways of screening for frailty, 1 being a subjective but well‐validated tool (the CFS) and the other 2 being objective assessments that look at specific phenotypic characteristics. There is a compelling need to find a standardized assessment to determine frailty in both research and clinical settings, and our study provides support for use of the CFS over the Fried or TUGT as screening tools. Standardized frailty assessments should be part of the discharge planning for all medical patients so that extra resources can be properly targeted at those patients at greatest risk for suboptimal transition back to community living.

Study Design and Population

As described in detail elsewhere,[14] this was a prospective cohort study that enrolled adult patients (any age older than 18 years) at the time of discharge back to the community from 7 general internal medicine wards in 2 teaching hospitals in Edmonton, Alberta between October 2013 and November 2014. We excluded patients admitted from, or being discharged back to, long‐term care facilities or other acute care hospitals, or from out of the province; patients who were unable to communicate in English; patients with moderate or severe cognitive impairment (scoring 5 or more on the Short Portable Mental Status Questionnaire); or patients with projected life expectancy of less than 3 months. All patients provided written consent, and the study was approved by the Health Research Ethics board of the University of Alberta (project ID Pro00036880).

We assessed the degree of frailty within 24 hours of discharge in 3 ways. First, we used the CFS[9, 15] with patients being asked to rate their best functional status in the week prior to admission. As per the CFS validations studies, scores 5 were defined as frail.[9, 15] Second, we used the TUGT as a proxy for slow gait speed/dysmobility (with >20 seconds defined as abnormal).[13] The TUGT was recorded as the shortest recorded time of the 2 timed trials to get up from a seated position, walk 10 feet and back, and then sit in the chair again. Third, we also determined their Fried score[6] (using the modifications outlined below) and categorized the patients as frail if they scored 3 or more. Of the 5 Fried categories, we assessed weakness by grip strength in their dominant hand using a Jamar handheld dynamometer and weight loss of 10 lb or more in the past year based on patient self‐report; these are identical to the original Fried scale description. Grip strength in the lowest quintile for sex and body mass index was defined as weak grip strength as per convention in the literature, which corresponded to less than 28.5 kg for men and less than 18.5 kg for women.[16, 17] We assessed the other 3 Fried categories in modified fashion as follows. For slow gait, rather than assessing time to walk 15 feet as in the original study and assigning a point to those testing in the lowest quintile for their age/sex, we used the TUGT, because our research personnel were already trained in this test, and we were doing it already as part of the discharge package for all patients.[13] For the Fried category of low activity, we based this on patient self‐report using the relevant questions in the EuroQoL Questionnaire (EQ‐5D); the Fried score used self‐report with a different questionnaire. Finally, for self‐reported exhaustion we used the questions in the Patient Health Questionnaire 9 (PHQ‐9)[18] analogous to those used from the Center for Epidemiological Studies depression scale in the original Fried description. We did this as we were evaluating the PHQ‐9 in our cohort already, and did not want to increase responder burden by presenting them with 2 depression questionnaires.

We followed all patients until 30 days after discharge, and outcome data (all‐cause mortality or all‐cause readmission) were collected by research personnel blinded to the patient's frailty status at discharge using patient/caregiver self‐report and analysis of the provincial electronic health record. We included deaths in or out of the hospital, and all readmissions were unplanned.

We examined the correlation between the CFS score (5 vs <5) and (1) the modified Fried score (3 vs <3) and (2) TUGT (20 seconds vs >20 seconds) using chance corrected kappa coefficients. In our previous article[14] we reported the association between the CFS and readmissions/hospitalizations within 30 days of discharge. In this article we examine whether either the Fried score or TUGT accurately and independently predict postdischarge readmissions/deaths, and whether they add additional prognostic information to the CFS assessment by comparing models with/without each definition using the C statistic and the Integrated Discrimination Improvement index. All analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC), with P values of <0.05 considered statistically significant. Subgroup analysis was done in patients older than 65 years.

Prevalence of Frailty According to Different Definitions

Although the CFS assessment resulted in 162 (33%) patients being deemed frail, only 82 (51%) of those patients also met the phenotype frailty definition using either the Fried model or the TUGT, and 49 (10%) patients who were not classified as frail on the CFS met either of the phenotypic definitions of frailty (Figure 1). Overall, 211 (43%) patients were frail according to at least 1 assessment, and 46 (9%) met all 3 frailty definitions. In the subgroup of 245 patients older than 65 years, 137 (56%) were frail according to at least 1 assessment, 38 (16%) met all 3 frailty definitions, and 27 (11%) of those patients classified as not frail on the CFS met either phenotypic definition of frailty. Agreement between TUGT and CFS or CFS and Fried was relatively poor with kappas of 0.31 (95% confidence interval [CI]: 0.23‐0.40) and 0.33 (95% CI: 0.25‐0.42), respectively. It is noteworthy that some patients deemed nonfrail on the CFS had slow gait speeds, and most CFS‐frail patients had gait speeds in the nonfrail range (Figure 2).

Figure 1

Figure 2

Characteristics According to Frailty Status

Although frail patients were generally similar across definitions (Table 1) in that they were older, had more comorbidities, more hospitalizations in the prior year, and longer index hospitalization lengths of stay than nonfrail patients, patients meeting phenotypic definitions of frailty but not classified as frail on the CFS were younger, had lower Charlson scores, higher EQ‐5D scores, and were discharged with less medications (Table 1).

Outcomes According to Frailty Status

The overall rate of 30‐day death or hospital readmission was 17.1% (85 patients), primarily as a result of hospital readmissions (81, 16.4%) (Table 2). Although patients classified as frail on the CFS exhibited significantly higher 30‐day readmission/death rates (24.1% vs 13.8% for not frail, P = 0.005) even after adjusting for age and sex (adjusted odds ratio [aOR]: 2.02, 95% CI: 1.19‐3.41) (Table 3), patients meeting either of the phenotypic definitions for frailty but not the CFS definition were not at higher risk for 30‐day readmission/death (aOR: 0.87, 95% CI: 0.34‐2.19) (Table 3). The group at highest risk for 30‐day readmissions/death were those meeting both the CFS and either phenotypic definition of frailty (25.6% vs 13.8% for those not frail, aOR: 2.15, 95% CI: 1.10‐4.19) (Tables 2 and 3). None of the Integrated Discrimination Improvement indices (for modified Fried added to CFS or TUGT added to CFS) were statistically significant, suggesting no net new information was added to predictive models, and there were no appreciable changes in C statistics (Table 3). Neither the modified Fried score nor the TUGT on their own added independent prognostic information to age/sex alone as predictors of postdischarge outcomes. It is noteworthy that the areas under the curve for models using any combination of the frailty definitions plus age and sex were not high (all ranged between 0.55 and 0.60 for the overall cohort and from 0.52 and 0.65 in the elderly). If the frailty definitions were examined as continuous variables rather than dichotomized into frail/not frail, the C statistics were not appreciably better: 0.65 for CFS, 0.58 for TUGT, and 0.60 for modified Fried. Of note, the CFS score with the published cutoff of 5 demonstrated the highest kappa, sensitivity, specificity, and positive predictive value in relation to outcomes.

Outcomes According to Frailty Status in the Elderly Subgroup

Although absolute risks of readmission or death were higher in elderly patients than younger patients, the excess risk was largely seen in those elderly patients classified as frail on the CFS. In fact, all of the associations reported above for the entire cohort were in the same direction in the elderly subgroup (Tables 2 and 3).

Strengths and Limitations

Although this was a prospective cohort study with blinded ascertainment of endpoints (30‐day outcome data were collected by observers who were unaware of the patients' CFS or phenotypic model scores), it is not without limitations. First, the only postdischarge outcomes we assessed were readmission and death, and it would be interesting to evaluate which frailty tools best predict those who are most likely to benefit from home‐care services in the community. Second, as we were interested in 30‐day readmission rates, we excluded long‐term care residents from our study and patients who had foreshortened life expectancy, in essence, the frailest of the frail. Although this reduced the size of any association between frailty and adverse outcomes, we focused this study on the situations where there is clinical equipoise and there is rarely a diagnostic dilemma around the identification of frailty and need for increased services in palliative or long‐term care patients. Third, we did not use exactly the same questionnaires or gait speed assessments as used in the original Fried score description, but as outlined in the Methods section, we used analogous questions on closely related questionnaires to extract the same information. Fourth, some might consider our comparisons biased toward the CFS, as it reflects gestalt clinical impressions (informed by patients and proxies) of frailty status before hospital admission while the Fried score and TUGT were based on patient status just prior to discharge, it may be that the former is a better measure of eventual recovery (and ongoing risk) than the latter measures. If this is the case, for the purposes of targeting interventions to prevent postdischarge complications, it would suggest to us that the CFS is better suited, whereas phenotype tools can be reserved for the postdischarge phase of recovery. By the same token, perhaps serial measures of the CFS and phenotypic tools are more important, as the trajectory of recovery may be most informative for risk prediction.[7] Certainly, if one were interested in changes in functional status during hospitalization,[29] then objective phenotypic measures such as grip strength or TUGT times would seem more appropriate choices. Fifth, some may perceive it as a weakness that we did not restrict our cohort to elderly patients; however, we actually view this as a strength, because frailty is not exclusive to older patients. Sixth, although we restricted this study to patients being discharged from general internal medicine wards, it is worth mentioning that previous studies have shown similar associations between frailty and outcomes in nonmedical hospitalized patients.[19, 22, 23, 24]

In conclusion, we looked at 3 different ways of screening for frailty, 1 being a subjective but well‐validated tool (the CFS) and the other 2 being objective assessments that look at specific phenotypic characteristics. There is a compelling need to find a standardized assessment to determine frailty in both research and clinical settings, and our study provides support for use of the CFS over the Fried or TUGT as screening tools. Standardized frailty assessments should be part of the discharge planning for all medical patients so that extra resources can be properly targeted at those patients at greatest risk for suboptimal transition back to community living.