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OCTOBER 2014–VOL. 62, NO. 10
obstructive pulmonary disease, and congestive heart failure exacerbation.
THE INTERVENTION The readmission prevention team was an interdisciplinary team of physicians, physical therapists, nurses, and administrators. Data from several successful readmission prevention programs, specifically Project RED and BOOST5,6 were reviewed. It was recognized that the resources to duplicate these interventions were not available. Instead, it was possible to repurpose (redesign the roles of) existing staff members. Home health professionals were also conceptualized as “virtual staff” of the hospital; for example, rather than hiring physical therapists that the hospital employed to visit patients at home, home health physical therapy was used. Finally, because this was a 25-bed facility, it was possible to streamline the approach to serve all inpatients, eliminating the need for a mechanism to identify individuals at high risk of readmission. It was simply assumed that the majority of rural inpatients are at high risk for readmission. Interventions that were used in the redesign of discharge and postdischarge follow-up were telephone calls to check in within 72 hours of discharge, home health resources after discharge (nursing for review of medicines upon discharge home, physical and occupational therapy), scheduled follow-up visits with primary care provider no more than 1 week after discharge, and patient education using the “teach back technique” used by the BOOST program.
DATA There were 701 discharges during the year before the intervention and 497 in the year after. Figure 1 shows readmission rates (given in %) for the year before and the year after the intervention. The 30-day readmission rate was 9.6% in the year before the intervention and 6.2% in the year after (P = .04).
LETTERS TO THE EDITOR
1999
It can be efficient and cost effective to reduce the 30-day readmission rate of elderly adults in a rural critical access hospital. Arsheeya Mashaw, MD Department of Geriatrics, Page Memorial Hospital, Luray Virginia
ACKNOWLEDGMENTS Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the author and has determined that the author has no financial or any other kind of personal conflicts with this paper. Author Contributions: Arsheeya Mashaw is responsible for the entire content of this paper. Sponsor’s Role: None.
REFERENCES 1. Oddone EZ, Weinberger M, Horner M et al. Classifying general medicine readmissions: Are they preventable? J Gen Intern Med 1996;11:597– 605. 2. U.S. Census Bureau. Current Population Survey, 2008 and 2010 Annual Social and Economic Supplements [on-line] 2010. Available at http://www. census.gov/hhes/www/hlthins/data/incpovhlth/2009/tab 9.pdf Accessed January 3, 2014. 3. Arbaje AI, Wolff JL, Yu Q et al. Postdischarge environmental and socioeconomic factors and the likelihood of early hospital readmission among community-dwelling Medicare beneficiaries. Gerontologist 2008;48:495– 504. 4. Weeks W, Lee R, Wallace A et al. Do older rural and urban veterans experience different rates of unplanned readmission to VA and non-VA hospitals? J Rural Health 2008;25:62–69. 5. Jack BW, Chetty VK, Anthony D et al. A reengineered hospital discharge program to decrease rehospitalization: A randomized trial. Ann Intern Med 2009;150:178–187. 6. Hansen LO, Greenwald JL, Budnitz T et al. Project BOOST: Effectiveness of a multihospital effort to reduce rehospitalization. J Hosp Med 2013;8:421–427.
AT WHAT AGE DO YOU THINK YOU WILL STOP DRIVING? VIEWS OF OLDER U.S. ADULTS
DISCUSSION Despite lack of resources in a rural area, it was possible to decrease the rate of 30-day readmission using a simple redesign of the discharge process and postdischarge follow-up protocols. Specific interventions that were found to be particularly effective were making sure that people followed up with their primary care provider within 1 week of discharge, telephone call follow-up within 72 hours of discharge, and aggressive use of home health services. An interesting effect of having such a small hospital population was the ability to review each readmission case. Approximately 10% of individuals with 30-day readmissions were in their last year of life; many were readmitted three or four times within the last 6 to 8 months of life. Therefore, the next planned intervention will be to implement pre- and postdischarge discussions for all hospitalized individuals regarding goals of care.
To the Editor: By 2030, it is estimated that one in five persons in the United States will be aged 65 or older.1 Although the vast majority of older adults prefer to age in place (grow old in their current homes), aging in place can present challenges, particularly when older adults begin to experience declines in mobility. Nearly 80% of older adults live in car-dependent suburban or rural communities, with most of these communities lacking alternative mobility options.2 Therefore, when older adults in these communities stop driving, they are left with few transportation options.3 To meet the transportation and mobility needs of aging populations, it will be necessary to have a clear understanding of when older adults expect to stop driving. The purpose of this study was to provide national prevalence estimates of the age at which older adults in the United States report they will stop driving. Data were obtained from the Second Injury Control and Risk Survey, Phase 2, a cross-sectional, random-digit-dialed
2000
LETTERS TO THE EDITOR
telephone survey that the Centers for Disease Control and Prevention conducted between March 2007 and May 2008. The study methodology has been detailed previously.4 This analysis was restricted to survey respondents aged 65 or older who reported being current drivers (N = 565). Information was collected on demographic characteristics, driving status, and views related to when they would stop driving. Nationally weighted estimates were calculated. When drivers were asked at what age they thought they would stop driving, 58.4% responded with a specific age, 26.1% gave a response other than an age (e.g., vision impairment), 10.6% said they would never stop driving, and 4.9% did not know or refused to answer. Of those who gave an age response, 49.2% of those aged 65 to 69 and 41.9% of those aged 70 to 74 said they would stop more than 20 years from the time of the survey (Figure 1), and 51.3% of those aged 75 to 79 and 41.5% of those aged 80 and older said they would stop driving 11 to 20 years from the time of the survey. Of drivers who gave another type of response, 36.0% (95% confidence interval (CI) = 26.1%–46.0%) said they would stop when their vision became impaired, and 26.3% (95% CI = 17.5%– 35.1%) said they would stop when they became unsafe or dangerous on the road. More than half of older adult drivers reported that they would stop driving sometime in their 90s, and one in 10 reported they would never stop driving. A previous study found that drivers aged 70 to 74 had an average driving life expectancy (time until driving cessation) of 11 years and an average life expectancy of 18 to 21 years.5 The authors estimated that, after driving cessation, older adult men would be dependent on alternative sources of transportation for approximately 7 years and women for approximately 10 years before death. The findings of the current study suggest that many older adults
OCTOBER 2014–VOL. 62, NO. 10
JAGS
might overestimate their driving life expectancy. Another study found that most older adults do not think about when they might have to stop driving, nor do they plan for it.6 Encouraging older adults to think about the possibility that they or a loved one might have to stop driving earlier than they had anticipated and encouraging discussions about how they would approach this transition will be important in helping older adults successfully plan for driving cessation. Counseling by healthcare professionals might be an approach to early intervention and planning. Research shows that 43% of single older drivers who live alone and 32% of married older drivers preferred that a physician or healthcare professional be the individual to discuss driving concerns with them.7 Moreover, focus groups with older drivers found they viewed advanced planning with physicians about alternative transportation (e.g., advance driving directives) as positive.8 Because healthcare providers are generally not trained on driver fitness or future transportation planning, the American Medical Association has developed an older driver curriculum for healthcare providers,9 which has been shown to increase providers’ knowledge of and confidence in strategies to counsel at-risk drivers and assist with mobility planning.10 Although this curriculum can help physicians facilitate these important conversations, innovative community-based programs are needed to then assist older adults in maintaining their community mobility in safe, accessible, and affordable ways. Rebecca B. Naumann, MSPH Bethany A. West, MPH Erin K. Sauber-Schatz, PhD, MPH National Center for Injury Prevention and Control Centers for Disease Control and Prevention, Atlanta Georgia
ACKNOWLEDGMENTS The findings and conclusions in this report are those of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention. Conflict of Interest: The authors have no financial or any other kind of personal conflicts of interest relevant to this research. Author Contributions: Naumann conceptualized the study and analyzed the data. Naumann, West, and SauberSchatz contributed to interpretation of data, manuscript preparation, and background literature review. Sponsor’s Role: None.
REFERENCES
Figure 1. Age that drivers report they will stop driving according to current age group, Second Injury Control and Risk Survey, Phase 2, 2007 to 2008. n = crude number in each age group; bars denote 1 standard error above and below the weighted percentage. Solid black bar: Will stop driving in 0 to 10 years. Horizontal stripe bar: Will stop driving in 11 to 20 years. Dotted bar: Will stop driving in >20 years.
1. U.S. Census Bureau. National Population Projections (Based on Census 2010). Washington, DC: U.S. Census Bureau, 2013 [on-line]. Available at http://www.census.gov/population/projections/data/national/2012/summary tables.html Accessed December 19, 2013. 2. Rosenbloom S. The Mobility Needs of Older Americans: Implications for Transportation Reauthorization. Washington, DC: Brookings Institute, 2003. 3. Bailey L. Aging Americans: Stranded without Options. Washington, DC: Surface Transportation Policy Project, 2004. 4. Chen J, Kresnow MJ, Simon TR et al. Injury-prevention counseling and behavior among US children: Results from the second Injury Control and Risk Survey. Pediatrics 2007;119:e958–e965.
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OCTOBER 2014–VOL. 62, NO. 10
5. Foley DJ, Heimovitz HK, Guralnik JM et al. Driving life expectancy of persons aged 70 years and older in the United States. Am J Public Health 2002;92:1284–1289. 6. Kostyniuk LP, Shope JT. Driving and alternatives: Older drivers in Michigan. J Saf Res 2003;34:407–414. 7. Coughlin JF, Mohyde M, D’Ambrosio LA et al. Who Drives Older Driver Decisions? Cambridge, MA: MIT AgeLab, 2004. 8. Betz ME, Jones J, Petroff E et al. “I wish we could normalize driving health”: A qualitative study of clinician discussions with older drivers. J Gen Intern Med 2013;28:1573–1580. 9. Carr DB, Schwartzberg JG, Manning L, Sempek J. Physician’s Guide to Assessing and Counseling Older Drivers, 2nd ed. Washington, DC: NHTSA, 2010. 10. Meuser TM, Carr DB, Irmiter C et al. The American Medical Association older driver curriculum for health professionals: Changes in trainee confidence, attitudes, and practice behavior. Gerontol Geriatr Educ 2010;31:290–309.
INSULIN-LIKE GROWTH FACTOR-1 AS A PREDICTOR OF MORTALITY IN FRAIL EGYPTIAN ELDERLY ADULTS To the Editor: Frailty has generally been associated with poor survival. Frail elderly adults have a 15% to 50% greater mortality risk than those who are not frail.1–3 Insulin-like growth factor-1 (IGF-1) is an important hormone in the growth hormone–IGF-1 axis. Lower serum IGF-1 level is associated with progressive disability, poor muscle strength, slow walking speed, and mortality, suggesting a potential role for the decrease in IGF-1 level as a major endocrine dysregulation that has been implicated in frailty, disability, and mortality in older adults.4–6 The 4-year Mortality Index for Older Adults is a validated prognostic index that incorporates age, sex, selfreported comorbid conditions, and functional measures.7 The aim of the current study was to investigate the association between IGF-1 levels and mortality in frail elderly adults.
METHODS This was a case–control study. The study sample comprised 90 participants aged 60 and older selected from the Ain Shams University hospital (Cairo, Egypt). Cases included 30 frail elderly women and 30 frail elderly men diagnosed according to the Fried criteria as applied in a previous study.8 Controls included 30 healthy age-matched elderly adults (15 male, 15 female) with no apparent evidence of disease according to a full medical history and physical examination. Any individuals with an acute infection and any who were taking drugs that have anti-inflammatory effects were excluded from this study. Each participant then underwent a comprehensive geriatric assessment, and their 4-year Mortality Index for Older Adults was calculated.7 Glycosylated hemoglobin (HbA1c) was assayed using ion-exchange chromatographic separation and a colorimetric detection kit (Biosystems, SA, Barcelona, Spain). Quantitation of highsensitivity C-reactive protein (hs-CRP) was performed using an immunoturbidimetric assay in a hs-CRP kit (Biosystems, SA). IGF-1 was measured using enzyme-linked
LETTERS TO THE EDITOR
2001
Table 1. Pearson Correlations Between the 4-Year Mortality Index and the Study Variables in the Frail and Control Groups Correlation Coefficient, P-Value Variable
Age Body mass index Triglycerides High-density lipoprotein cholesterol Low-density lipoprotein cholesterol Total cholesterol High-sensitivity C-reactive protein Glycosylated hemoglobin Insulin like growth factor-1
Frail Group
0.211, 0.358, 0.273, 0.215, 0.116, 0.175, 0.069, 0.002, 0.326,
.10 .005 .03 .10 .38 .17 .60 .98 .01
Control Group
0.516, 0.129, 0.360, 0.229, 0.404, 0.266, 0.290, 0.208, 0.168,
.004 .50 .05 .22 .03 .15 .12 .27 .38
immunosorbent assay kits (DRG International, Springfield, NJ). Lipid profiles were determined in the central laboratory of the Ain Shams University teaching hospital.
RESULTS The frail group had significantly lower triglyceride, low-density lipoprotein cholesterol, and IGF-1 levels and significantly higher serum hs-CRP and HbA1c levels. Frail participants had a significantly higher 4-year mortality index than the control group (P = .04). There was no difference between the two groups in age, smoking status, body mass index (BMI), total cholesterol, high-density lipoprotein level, and the presence of chronic diseases. Table 1 shows the Pearson correlation analysis of the association between the 4-year mortality index and the study variables in both groups. The 4-year mortality index was positively correlated with IGF-1 and negatively correlated with BMI and serum triglyceride levels in the frail group and was positively correlated with age in the control group. Multiple regression analysis was performed with 4-year mortality index as the dependent variable and BMI, IGF-1, and triglyceride level as independent variables in the frail group. IGF-1 was independently associated with the 4-year mortality index after adjustment for other factors (P = .02).
DISCUSSION Serum IGF-1 levels were significantly lower in frail elderly adults than in healthy controls, indicating that frail elderly adults have lower anabolic hormone levels than nonfrail controls of a similar age. This has also been demonstrated in several other studies.6,9 As expected, frail elderly adults had a higher 4-year mortality index than nonfrail controls. Mortality indices are important tools to characterize disease burden and complexity; they are essential for clinical assessments and decision-making, especially in vulnerable groups. Multivariate analysis indicated that IGF-1 level was an independent risk factor associated with the 4-year mortality index in the frail group after adjusting for the other factors,
Copyright of Journal of the American Geriatrics Society is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
OCTOBER 2014–VOL. 62, NO. 10
obstructive pulmonary disease, and congestive heart failure exacerbation.
THE INTERVENTION The readmission prevention team was an interdisciplinary team of physicians, physical therapists, nurses, and administrators. Data from several successful readmission prevention programs, specifically Project RED and BOOST5,6 were reviewed. It was recognized that the resources to duplicate these interventions were not available. Instead, it was possible to repurpose (redesign the roles of) existing staff members. Home health professionals were also conceptualized as “virtual staff” of the hospital; for example, rather than hiring physical therapists that the hospital employed to visit patients at home, home health physical therapy was used. Finally, because this was a 25-bed facility, it was possible to streamline the approach to serve all inpatients, eliminating the need for a mechanism to identify individuals at high risk of readmission. It was simply assumed that the majority of rural inpatients are at high risk for readmission. Interventions that were used in the redesign of discharge and postdischarge follow-up were telephone calls to check in within 72 hours of discharge, home health resources after discharge (nursing for review of medicines upon discharge home, physical and occupational therapy), scheduled follow-up visits with primary care provider no more than 1 week after discharge, and patient education using the “teach back technique” used by the BOOST program.
DATA There were 701 discharges during the year before the intervention and 497 in the year after. Figure 1 shows readmission rates (given in %) for the year before and the year after the intervention. The 30-day readmission rate was 9.6% in the year before the intervention and 6.2% in the year after (P = .04).
LETTERS TO THE EDITOR
1999
It can be efficient and cost effective to reduce the 30-day readmission rate of elderly adults in a rural critical access hospital. Arsheeya Mashaw, MD Department of Geriatrics, Page Memorial Hospital, Luray Virginia
ACKNOWLEDGMENTS Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the author and has determined that the author has no financial or any other kind of personal conflicts with this paper. Author Contributions: Arsheeya Mashaw is responsible for the entire content of this paper. Sponsor’s Role: None.
REFERENCES 1. Oddone EZ, Weinberger M, Horner M et al. Classifying general medicine readmissions: Are they preventable? J Gen Intern Med 1996;11:597– 605. 2. U.S. Census Bureau. Current Population Survey, 2008 and 2010 Annual Social and Economic Supplements [on-line] 2010. Available at http://www. census.gov/hhes/www/hlthins/data/incpovhlth/2009/tab 9.pdf Accessed January 3, 2014. 3. Arbaje AI, Wolff JL, Yu Q et al. Postdischarge environmental and socioeconomic factors and the likelihood of early hospital readmission among community-dwelling Medicare beneficiaries. Gerontologist 2008;48:495– 504. 4. Weeks W, Lee R, Wallace A et al. Do older rural and urban veterans experience different rates of unplanned readmission to VA and non-VA hospitals? J Rural Health 2008;25:62–69. 5. Jack BW, Chetty VK, Anthony D et al. A reengineered hospital discharge program to decrease rehospitalization: A randomized trial. Ann Intern Med 2009;150:178–187. 6. Hansen LO, Greenwald JL, Budnitz T et al. Project BOOST: Effectiveness of a multihospital effort to reduce rehospitalization. J Hosp Med 2013;8:421–427.
AT WHAT AGE DO YOU THINK YOU WILL STOP DRIVING? VIEWS OF OLDER U.S. ADULTS
DISCUSSION Despite lack of resources in a rural area, it was possible to decrease the rate of 30-day readmission using a simple redesign of the discharge process and postdischarge follow-up protocols. Specific interventions that were found to be particularly effective were making sure that people followed up with their primary care provider within 1 week of discharge, telephone call follow-up within 72 hours of discharge, and aggressive use of home health services. An interesting effect of having such a small hospital population was the ability to review each readmission case. Approximately 10% of individuals with 30-day readmissions were in their last year of life; many were readmitted three or four times within the last 6 to 8 months of life. Therefore, the next planned intervention will be to implement pre- and postdischarge discussions for all hospitalized individuals regarding goals of care.
To the Editor: By 2030, it is estimated that one in five persons in the United States will be aged 65 or older.1 Although the vast majority of older adults prefer to age in place (grow old in their current homes), aging in place can present challenges, particularly when older adults begin to experience declines in mobility. Nearly 80% of older adults live in car-dependent suburban or rural communities, with most of these communities lacking alternative mobility options.2 Therefore, when older adults in these communities stop driving, they are left with few transportation options.3 To meet the transportation and mobility needs of aging populations, it will be necessary to have a clear understanding of when older adults expect to stop driving. The purpose of this study was to provide national prevalence estimates of the age at which older adults in the United States report they will stop driving. Data were obtained from the Second Injury Control and Risk Survey, Phase 2, a cross-sectional, random-digit-dialed
2000
LETTERS TO THE EDITOR
telephone survey that the Centers for Disease Control and Prevention conducted between March 2007 and May 2008. The study methodology has been detailed previously.4 This analysis was restricted to survey respondents aged 65 or older who reported being current drivers (N = 565). Information was collected on demographic characteristics, driving status, and views related to when they would stop driving. Nationally weighted estimates were calculated. When drivers were asked at what age they thought they would stop driving, 58.4% responded with a specific age, 26.1% gave a response other than an age (e.g., vision impairment), 10.6% said they would never stop driving, and 4.9% did not know or refused to answer. Of those who gave an age response, 49.2% of those aged 65 to 69 and 41.9% of those aged 70 to 74 said they would stop more than 20 years from the time of the survey (Figure 1), and 51.3% of those aged 75 to 79 and 41.5% of those aged 80 and older said they would stop driving 11 to 20 years from the time of the survey. Of drivers who gave another type of response, 36.0% (95% confidence interval (CI) = 26.1%–46.0%) said they would stop when their vision became impaired, and 26.3% (95% CI = 17.5%– 35.1%) said they would stop when they became unsafe or dangerous on the road. More than half of older adult drivers reported that they would stop driving sometime in their 90s, and one in 10 reported they would never stop driving. A previous study found that drivers aged 70 to 74 had an average driving life expectancy (time until driving cessation) of 11 years and an average life expectancy of 18 to 21 years.5 The authors estimated that, after driving cessation, older adult men would be dependent on alternative sources of transportation for approximately 7 years and women for approximately 10 years before death. The findings of the current study suggest that many older adults
OCTOBER 2014–VOL. 62, NO. 10
JAGS
might overestimate their driving life expectancy. Another study found that most older adults do not think about when they might have to stop driving, nor do they plan for it.6 Encouraging older adults to think about the possibility that they or a loved one might have to stop driving earlier than they had anticipated and encouraging discussions about how they would approach this transition will be important in helping older adults successfully plan for driving cessation. Counseling by healthcare professionals might be an approach to early intervention and planning. Research shows that 43% of single older drivers who live alone and 32% of married older drivers preferred that a physician or healthcare professional be the individual to discuss driving concerns with them.7 Moreover, focus groups with older drivers found they viewed advanced planning with physicians about alternative transportation (e.g., advance driving directives) as positive.8 Because healthcare providers are generally not trained on driver fitness or future transportation planning, the American Medical Association has developed an older driver curriculum for healthcare providers,9 which has been shown to increase providers’ knowledge of and confidence in strategies to counsel at-risk drivers and assist with mobility planning.10 Although this curriculum can help physicians facilitate these important conversations, innovative community-based programs are needed to then assist older adults in maintaining their community mobility in safe, accessible, and affordable ways. Rebecca B. Naumann, MSPH Bethany A. West, MPH Erin K. Sauber-Schatz, PhD, MPH National Center for Injury Prevention and Control Centers for Disease Control and Prevention, Atlanta Georgia
ACKNOWLEDGMENTS The findings and conclusions in this report are those of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention. Conflict of Interest: The authors have no financial or any other kind of personal conflicts of interest relevant to this research. Author Contributions: Naumann conceptualized the study and analyzed the data. Naumann, West, and SauberSchatz contributed to interpretation of data, manuscript preparation, and background literature review. Sponsor’s Role: None.
REFERENCES
Figure 1. Age that drivers report they will stop driving according to current age group, Second Injury Control and Risk Survey, Phase 2, 2007 to 2008. n = crude number in each age group; bars denote 1 standard error above and below the weighted percentage. Solid black bar: Will stop driving in 0 to 10 years. Horizontal stripe bar: Will stop driving in 11 to 20 years. Dotted bar: Will stop driving in >20 years.
1. U.S. Census Bureau. National Population Projections (Based on Census 2010). Washington, DC: U.S. Census Bureau, 2013 [on-line]. Available at http://www.census.gov/population/projections/data/national/2012/summary tables.html Accessed December 19, 2013. 2. Rosenbloom S. The Mobility Needs of Older Americans: Implications for Transportation Reauthorization. Washington, DC: Brookings Institute, 2003. 3. Bailey L. Aging Americans: Stranded without Options. Washington, DC: Surface Transportation Policy Project, 2004. 4. Chen J, Kresnow MJ, Simon TR et al. Injury-prevention counseling and behavior among US children: Results from the second Injury Control and Risk Survey. Pediatrics 2007;119:e958–e965.
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OCTOBER 2014–VOL. 62, NO. 10
5. Foley DJ, Heimovitz HK, Guralnik JM et al. Driving life expectancy of persons aged 70 years and older in the United States. Am J Public Health 2002;92:1284–1289. 6. Kostyniuk LP, Shope JT. Driving and alternatives: Older drivers in Michigan. J Saf Res 2003;34:407–414. 7. Coughlin JF, Mohyde M, D’Ambrosio LA et al. Who Drives Older Driver Decisions? Cambridge, MA: MIT AgeLab, 2004. 8. Betz ME, Jones J, Petroff E et al. “I wish we could normalize driving health”: A qualitative study of clinician discussions with older drivers. J Gen Intern Med 2013;28:1573–1580. 9. Carr DB, Schwartzberg JG, Manning L, Sempek J. Physician’s Guide to Assessing and Counseling Older Drivers, 2nd ed. Washington, DC: NHTSA, 2010. 10. Meuser TM, Carr DB, Irmiter C et al. The American Medical Association older driver curriculum for health professionals: Changes in trainee confidence, attitudes, and practice behavior. Gerontol Geriatr Educ 2010;31:290–309.
INSULIN-LIKE GROWTH FACTOR-1 AS A PREDICTOR OF MORTALITY IN FRAIL EGYPTIAN ELDERLY ADULTS To the Editor: Frailty has generally been associated with poor survival. Frail elderly adults have a 15% to 50% greater mortality risk than those who are not frail.1–3 Insulin-like growth factor-1 (IGF-1) is an important hormone in the growth hormone–IGF-1 axis. Lower serum IGF-1 level is associated with progressive disability, poor muscle strength, slow walking speed, and mortality, suggesting a potential role for the decrease in IGF-1 level as a major endocrine dysregulation that has been implicated in frailty, disability, and mortality in older adults.4–6 The 4-year Mortality Index for Older Adults is a validated prognostic index that incorporates age, sex, selfreported comorbid conditions, and functional measures.7 The aim of the current study was to investigate the association between IGF-1 levels and mortality in frail elderly adults.
METHODS This was a case–control study. The study sample comprised 90 participants aged 60 and older selected from the Ain Shams University hospital (Cairo, Egypt). Cases included 30 frail elderly women and 30 frail elderly men diagnosed according to the Fried criteria as applied in a previous study.8 Controls included 30 healthy age-matched elderly adults (15 male, 15 female) with no apparent evidence of disease according to a full medical history and physical examination. Any individuals with an acute infection and any who were taking drugs that have anti-inflammatory effects were excluded from this study. Each participant then underwent a comprehensive geriatric assessment, and their 4-year Mortality Index for Older Adults was calculated.7 Glycosylated hemoglobin (HbA1c) was assayed using ion-exchange chromatographic separation and a colorimetric detection kit (Biosystems, SA, Barcelona, Spain). Quantitation of highsensitivity C-reactive protein (hs-CRP) was performed using an immunoturbidimetric assay in a hs-CRP kit (Biosystems, SA). IGF-1 was measured using enzyme-linked
LETTERS TO THE EDITOR
2001
Table 1. Pearson Correlations Between the 4-Year Mortality Index and the Study Variables in the Frail and Control Groups Correlation Coefficient, P-Value Variable
Age Body mass index Triglycerides High-density lipoprotein cholesterol Low-density lipoprotein cholesterol Total cholesterol High-sensitivity C-reactive protein Glycosylated hemoglobin Insulin like growth factor-1
Frail Group
0.211, 0.358, 0.273, 0.215, 0.116, 0.175, 0.069, 0.002, 0.326,
.10 .005 .03 .10 .38 .17 .60 .98 .01
Control Group
0.516, 0.129, 0.360, 0.229, 0.404, 0.266, 0.290, 0.208, 0.168,
.004 .50 .05 .22 .03 .15 .12 .27 .38
immunosorbent assay kits (DRG International, Springfield, NJ). Lipid profiles were determined in the central laboratory of the Ain Shams University teaching hospital.
RESULTS The frail group had significantly lower triglyceride, low-density lipoprotein cholesterol, and IGF-1 levels and significantly higher serum hs-CRP and HbA1c levels. Frail participants had a significantly higher 4-year mortality index than the control group (P = .04). There was no difference between the two groups in age, smoking status, body mass index (BMI), total cholesterol, high-density lipoprotein level, and the presence of chronic diseases. Table 1 shows the Pearson correlation analysis of the association between the 4-year mortality index and the study variables in both groups. The 4-year mortality index was positively correlated with IGF-1 and negatively correlated with BMI and serum triglyceride levels in the frail group and was positively correlated with age in the control group. Multiple regression analysis was performed with 4-year mortality index as the dependent variable and BMI, IGF-1, and triglyceride level as independent variables in the frail group. IGF-1 was independently associated with the 4-year mortality index after adjustment for other factors (P = .02).
DISCUSSION Serum IGF-1 levels were significantly lower in frail elderly adults than in healthy controls, indicating that frail elderly adults have lower anabolic hormone levels than nonfrail controls of a similar age. This has also been demonstrated in several other studies.6,9 As expected, frail elderly adults had a higher 4-year mortality index than nonfrail controls. Mortality indices are important tools to characterize disease burden and complexity; they are essential for clinical assessments and decision-making, especially in vulnerable groups. Multivariate analysis indicated that IGF-1 level was an independent risk factor associated with the 4-year mortality index in the frail group after adjusting for the other factors,
Copyright of Journal of the American Geriatrics Society is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
