Abstract
Background
The concept of patient frailty was developed as a means to describe why certain individuals of the same age have a higher state of disease vulnerability. Within the community of geriatric investigators, there is some disagreement in about how best to quantify frailty, with some advocating for simple, convenient tests, and others arguing that a comprehensive battery is needed to get an accurate measure of an individual’s frailty. Among the metrics includes the Frailty Index, an assessment of 70 clinical deficits (McDowell et al., 2001), the FRAIL scale, which only requires answers to 5 simple questions (Abellan van Kan, Rolland, Bergman, et al., 2008; Abellan van Kan, Rolland, Morley, & Vellas, 2008), the Rockwood Scale (Newman et al., 2001; Rockwood et al., 2000), and walk speed (Abellan van Kan et al., 2009; Afilalo et al., 2014; Cesari et al., 2005; Studenski et al., 2011). Assessments of cognitive status have also been shown in the elderly to be predictive of adverse outcomes. The mini-Cog test is a well-described, simple, method for assessing cognitive status, and has been shown to be a useful screen for dementia and predicting adverse outcomes (Agarwal et al., 2016; Fage et al., 2015; Heng et al., 2016; Tsoi et al., 2015). In addition to predictive accuracy, practical application of these approaches remains a key issue, which we sought to examine within the setting of a cardiac device clinic visit.
Overall, implantation of cardiovascular implantable electrical devices (CIEDs) has increased dramatically in the past few decades. As the overall functionality of these devices has improved, so has potential for use of data collected by the device in management of patients. In addition to providing treatment through pacemaker and defibrillator functions, CIEDs are capable of collecting a wide range of data parameters on the individuals in whom they are implanted. Among the types of information stored and tracked on CIEDs includes information about heart rate, history of cardiac arrhythmias and device therapies, and activity measures. All modern CIEDs have accelerometers, as well as biometric impedance monitors, and adjustable algorithms for monitoring minute-to-minute activity, which can be stored for customizable durations within the device, as well as uploaded to remote monitoring systems. Most modern CIEDs are radiofrequency-capable, meaning that patients seldom have to manually transmit device parameters over the wired telephone or using a modem as in the past. As such, use of CIED data creates an opportunity for monitoring patient data in a manner previously unavailable, and relevant to this investigation, the opportunity to measure with greater precision the daily activities of patients.
In this pilot investigation, we examined the feasibility of examining frailty and mental status, as well as daily activity recorded by CIEDs, in individuals seen for routine device clinic follow-up in cardiology clinic.
Methods
Population
Between September 2017 and March 2018, we recruited 49 consecutive patients who were seen in the device clinic who were over age 65, and willing and able to provide informed consent, and willing to take part in the walk-time, survey/questionnaire assessment, or mini-Cog assessment. We had planned to examine CIED activity data for patients obtained from their respective CIED manufacturers, although we were only able to obtain data from Boston Scientific (N = 9 patients) in an analyzable format.
Clinical Assessment
The outcomes measured in this investigation included daily activity obtained from CIED interrogation, clinical frailty assessment and cognitive status assessment using the 4-meter walk time (Abellan van Kan et al., 2009; Afilalo et al., 2014; Cesari et al., 2005; Studenski et al., 2011)., FRAIL scale questionnaire (Malmstrom et al., 2014) (see appended), the Rockwood Clinical Frailty Scale (Newman et al., 2001; Rockwood et al., 2000) (https://www.dal.ca/sites/gmr/our-tools/clinical-frailty-scale.html) (see appendix), and the mini-Cog cognitive status assessment (Agarwal et al., 2016; Fage et al., 2015; Heng et al., 2016; Tsoi et al., 2015). Additional information was collected at the visit, including demographic data; past medical history, focused on cardiac history; falls and fall history; medications; and relevant social history.
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CIED Activity Analysis
Activity time series containing daily activity measured in minutes per day was obtained for all subjects with Boston Scientific devices (N = 9). For each subject, the mean, standard deviation, kurtosis, skew, minimum and maximum minutes of activity per day was calculated. A linear model was fit to identify the long-term trend, and the slope and intercept were also stored. To capture autocorrelation structure, the autocorrelation function (ACF) and partial autocorrelation function (PACF) were collected for lags of 1, 2, 3, 7, and 14 days. To predict future activity measures at 7, 14, 30, 60, and 90 days, a seasonal autoregressive integrated moving average (ARIMA) (1, 0, 1) (1, 0, 1)7 model was fit to each time series. The coefficients for each subjects’ model (Seasonal AR1, seasonal MA1, AR1, MA1) were also stored for analysis.
Statistical Analysis
Categorical variables were compared using a chi-square test, and continuous variables were compared using a Student’s T-test. Analysis of CIED activity data was performed using RStudio (version 1.2.5019), and other analysis was performed using Stata IC (version 16, Stata, Inc., College Station, TX).
Results
Over a period of approximately 6 months, we found that 25 of 49 patients (51.0%) over age 65 failed at least one of the frailty or cognitive assessments (Table 1). Both clinical assessments (FRAIL score and Rockwood assessment) had complete overlap with the 4-meter walk test, and no patients who were deemed frail by the FRAIL score or Rockwood assessment, or both, had a normal 4-meter walk time. There was some overlap between an abnormal mini-Cog assessment and the three measures of frailty, although six of nine patients (66.7%) had only an abnormal mini-Cog, with no frailty detected using other measures.
Population Demographics, by Frailty Measure.
Note. *Frailty based on having at least one abnormal study from frailty and minicog assessment. T test used to compare continuous measures and Chi-Squared used for categorical.
Patients with at least one abnormal frailty or mini-Cog assessment tended to be older (79.6 ± 8.4 vs. 71.3 ± 5.5 years), with more medical conditions and were more likely to be on over 10 medications, although fewer had a history of ventricular arrhythmias (12.0% vs. 45.8%) or an ICD implanted (20% vs. 58.3%) (Table 1). As shown in Table 2, the range of patients failing each assessment was between 18.4%, for the FRAIL score, and 35.3%, for the Rockwood Frailty assessment, with most assessments passing roughly 2/3 to 4/5 of the tests.
Frailty Measures.
Note. *Average 4-meter walk time among all individuals was 4.2 ± 1.4 seconds. **Average Rockwood score among all individuals was 3.6 ± 1.3.
All but one subject in whom activity data was available via the device’s internal accelerometer had at least 1 year of data, with one subject having only 12 days of data available for analysis (Table 3). Among the various summary measures compiled, we found that patients having failed at least one assessment were more active on average (148.3 ± 63.8 vs. 100.1 ± 56.2 minutes of activity/day) and had a higher single day of activity (Activity max: 356.0 ± 69.7 vs. 194.0 ± 90.5 minutes/day) than those who passed all assessments, but also had more variability in activity across days than those who failed at least one test (Standard deviation of activity: 52.6 ± 11.9 vs. 31.4 ± 10.4 minutes/day). Time series modeling applied to the activity data did not indicate any evidence of a negative trend, or future forecasted activity at 30 or 90 days that was lower among the patients with at least one abnormal assessment, indicating that the activity in subjects determined to have increased frailty or cognitive impairment was not a reliable determinant.
Activity Summary Measures Versus Frailty.
Note. *p value corresponds to t-test comparing failed any test to failed none.
Discussion
In this single-center, feasibility pilot study of subjects over age 65 seen in routine follow-up in a cardiology CIED/device clinic, who were consecutively evaluated for frailty and cognitive assessment, we found that the overall rate of frailty or cognitive dysfunction was relatively high (over 50%). A number of studies have shown feasibility for assessment of frailty using technological devices, and a number of monitors and measures are being developed to test for frailty (Hollewand et al., 2016). Hewson and colleagues used a smartphone app that processed information from a grip ball, triaxial accelerometer, and scale to develop a model for predicting frailty (Hewson et al., 2013). Dunn et al. used an accelerometer to measure activity compared with clinical assessment in liver transplant candidates and found that self-assessments and provider assessments of physical activity do not reliably indicate actual performance (Dunn et al., 2016). One study looked at the DynaPort accelerometer for measuring activity in the home, but the authors did not find acceptable sensitivity or specificity for detection of activity in frail elderly individuals (Groningen Frailty Indicator (GFI) score ≥4, ≥75 years) (Hollewand et al., 2016). In an older population with diabetes and peripheral neuropathy (age, 77 ± 7 years old), a wearable triaxial accelerometer device was predictive of activity and falls (Najafi et al., 2013).
Activity monitors have been used in CIEDs to moderate pacing to activity level (so-called “rate-responsive pacing”) for over 20 years, and have been validated against clinical measurements and external monitors by each of the major manufacturers (Garrigue et al., 2002; McAlister et al., 1989; Padeletti et al., 2006; Roberts et al., 1995), although there is less evidence for comparing these monitors against frailty or hard endpoints. Kramer and colleagues examined CIED activity measures in a remote monitoring database and found that decreased device-measured activity was inversely correlated with mortality for individuals with both ICDs (Kramer et al., 2015) and Cardiac Resynchronization Therapy (CRT) devices (Kramer et al., 2017). These initial studies provide important feasibility that activity data obtained from a CIED might provide a high-quality assessment of frailty.
Despite the limited scale of this pilot investigation, our results suggest several findings that could be useful in planning larger studies of frailty or cognitive assessment within the community of older adults with CIEDs implanted. First, although we did not do a formal reliability assessment, we found the 4-meter walk time to have the most agreement with other measures of frailty designation. It is possible that additional assessments, through use of the FRAIL scale or through contacting other treating providers for information, could potentially be avoided if this simple assessment could be performed. In our study, the 4-meter walk time was incorporated into bringing patients back to the room, and thus caused minimal interruption of the visit.
Second, type 2 diabetes mellitus and hypertension were more likely to be found among patients with markers of frailty, which is to be expected. Past studies have shown that those with hypertension is more likely to be found among frail individuals (Aprahamian et al., 2018) and that the insulin resistance found in type 2 diabetics likely confounds markers of frailty with its contributions to compromised vascular function and impaired skeletal muscle function (Assar et al., 2019). Further analysis is needed to examine the overall contributing role that each comorbidity has on frailty.
Third, we found that patients with an abnormal cognitive or frailty assessment were less likely to have an ICD implanted, despite a greater number of comorbidities and medical problems. This finding is reassuring and suggests that, at least in this population, providers are being thoughtful about weighing the impact of life-prolonging therapies in these patients. Finally, we found that use of activity information from a small number of individuals in whom the device company was willing and able to share analyzable data was not predictive of the frailty assessment results, which raises the question of whether it is worth the challenges of obtaining this data at the level in which it can be analyzed for patterns in activity over time. Interestingly, patients who were deemed frail or with cognitive deficiency had more daily activity on average than those who were not, and although the variability was higher, this result is unexpected given that daily activity is generally viewed as a marker of greater health.
It is interesting to note that there was no difference in the mean daily activity of patients with a normal or abnormal cognitive assessment (148.3 ± 31.9 vs. 100.1 ± 25.1 min/day, p = .27). While underpowered due to only nine patients being available to analyze from the Boston Scientific dataset, the patients with an abnormal frailty assessment had a standard deviation of daily activity (52.6 ± 5.9 vs. 31.4 ± 4.7 min/day, p = .03). These findings suggest that in those patients with abnormal assessments of frailty, the daily activity may fluctuate more drastically and be a greater prognostic indicator than the mean daily activity. Those who had results suggestive of frailty were more active on average and, unexpectedly, had more variability in the activity across days. This may suggest that those who are frail have an inconsistent level of activity on a day to day basis. Previous studies by Kramer et al suggested that a decrease in device measured activity was inversely correlated with mortality (Kramer, Tsai, et al., 2017). A larger, more recent study of frail patients in various settings also found that activity and frailty to be inversely related, however cardiac device data was not used to ascertain this (da Silva et al., 2019). This finding will need to be examined further with a larger dataset and with different devices.
While we hope to have demonstrated the feasibility of integrating frailty data indicators with information from ICDs, it is important to note that further studies will be necessary with larger patient cohorts to confirm the links found in this study and in others.
Conclusion
In conclusion, in this pilot investigation we found that frailty assessment was feasible and practical within the context of a device clinic follow-up visit, and that the relatively simple measure of gait speed captured the majority of patients determined to be frail using other measures. We found that while analysis of activity time series data from CIEDs had some potential for identifying frail subjects, the specific measure identified in this study lacks any clear clinical correlation, and that practical barriers existed to obtaining this information from device companies for analysis. Further work is needed to examine the role of CIED-derived activity analysis for frailty assessment.
Limitations
Our pilot study is limited by the sample size, as only nine patient had CIED data available for our analysis. In addition, only Boston Scientific provided the data on their devices, which limits our analysis and applicability of our data to other types of CIED. Future studies on this topic would benefit from a larger sample size across various different CIED companies.
Footnotes
Appendix
Author’s Contribution
All authors contributed in the study.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Funding is by Dr. Rosenberg’s K Grant K23 (5K23HL127296).
Ethical Approval and Consent to Participate
-University of Colorado IRB (# 16-2587) with consent of all those who are participating.
Availability of Supporting Data
Upon request.