Journals of Gerontology: Medical Sciences cite as: J Gerontol A Biol Sci Med Sci, 2016, Vol. 71, No. 5, 649–655 doi:10.1093/gerona/glv228 Advance Access publication January 11, 2016
Research Article
Characterizing Frailty Status in the Systolic Blood Pressure Intervention Trial Nicholas M. Pajewski,1 Jeff D. Williamson2,* William B. Applegate,2 Dan R. Berlowitz,3,4 Linda P. Bolin,5 Glenn M. Chertow,6 Marie A. Krousel-Wood,7,8,9 Nieves Lopez-Barrera,10 James R. Powell,11 Christianne L. Roumie,12,13 Carolyn Still,14,15 Kaycee M. Sink,2 Rocky Tang,16 Clinton B. Wright,17 and Mark A. Supiano18,19,*; for the SPRINT Study Research Group Department of Biostatistical Sciences, Division of Public Health Sciences and 2Department of Internal Medicine, Section on Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina. 3Bedford Veterans Affairs Hospital, Massachusetts. 4School of Public Health, Boston University, Massachusetts. 5College of Nursing, East Carolina University, Greenville, North Carolina. 6Department of Nephrology, Stanford University School of Medicine, Palo Alto, California. 7Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana. 8Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana. 9Research Division, Ochsner Clinic Foundation, New Orleans, Louisiana. 10Department of Nephrology, University of Illinois at Chicago. 11Department of Internal Medicine, Division of General Internal Medicine, East Carolina University, Greenville, North Carolina. 12 Veterans Health Administration-Tennessee Valley Healthcare System Geriatric Research Education Clinical Center (GRECC), HSR&D Center, Nashville. 13Department of Medicine, Vanderbilt University, Nashville, Tennessee. 14Division of Nephrology and Hypertension, University Hospitals Case Medical Center, Cleveland, Ohio. 15Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, Ohio. 16Department of Surgery, Columbia University, New York. 17Evelyn F. McKnight Brain Institute, Departments of Neurology and Public Health Sciences, Leonard M. Miller School of Medicine, University of Miami, Florida. 18Division of Geriatrics, School of Medicine, University of Utah, Salt Lake City. 19Veterans Affairs Salt Lake City, Geriatric Research, Education, and Clinical Center, Utah. 1
*These authors contributed equally to this work. Address correspondence to Nicholas M. Pajewski, PhD, Department of Biostatistical Sciences, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157. Email: [email protected] Received September 28, 2015; Accepted November 30, 2015 Decision Editor: Stephen Kritchevsky, PhD
Abstract Background: The Systolic Blood Pressure Intervention Trial (SPRINT) is testing whether a lower systolic blood pressure (BP) target of 120 mm Hg leads to a reduction in cardiovascular morbidity and mortality among hypertensive, nondiabetic adults. Because there may be detrimental effects of intensive BP control, particularly in older, frail adults, we sought to characterize frailty within SPRINT to address ongoing questions about the ability of large-scale trials to enroll representative samples of noninstitutionalized, community-dwelling, older adults. Methods: We constructed a 36-item frailty index (FI) in 9,306 SPRINT participants, classifying participants as fit (FI ≤ 0.10), less fit (0.10 < FI ≤ 0.21), or frail (FI > 0.21). Recurrent event models were used to evaluate the association of the FI with the incidence of self-reported falls, injurious falls, and all-cause hospitalizations. Results: The distribution of the FI was comparable with what has been observed in population studies, with 2,570 (27.6%) participants classified as frail. The median FI was 0.18 (interquartile range = 0.14 to 0.24) in participants aged 80 years and older (N = 1,159), similar to the median FI of 0.17 reported for participants in the Hypertension in the Very Elderly Trial. In multivariable analyses, a 1% increase in the FI was associated with increased risk for self-reported falls (hazard ratio [HR] = 1.030), injurious falls (HR = 1.035), and all-cause hospitalizations (HR = 1.038) (all p values < .0001). Conclusions: Large clinical trials assessing treatments to reduce cardiovascular disease risk, such as SPRINT, can enroll heterogeneous populations of older adults, including the frail elderly, comparable with general population cohorts. Key Words: Falls—Frailty—Multimorbidities
© The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: [email protected].
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650
Despite decades of research on strategies for the control of hypertension, there remains considerable uncertainty about optimal blood pressure (BP) targets for older adults (aged 60 years and older) and the relative safety of various targets (1,2). Reservations about the generalizability of results from clinical trials contribute to this uncertainty in clinical practice (3). For example, the generalizability of the Hypertension in the Very Elderly Trial (HYVET) results to older adults living in Western, developed countries, particularly those with frailty, has been questioned in that the majority of HYVET’s participants were generally healthy subjects recruited from Eastern Europe and China (4). Thus, an important consideration is whether clinical trials involving elderly persons have a sufficient range of frail and nonfrail elderly persons to commend implementation of their results in community-based clinical practices. In addition, recent studies have suggested an attenuation of the association between elevated BP and mortality when analyses are stratified according to gait speed (a surrogate marker for frailty) or other measures of functional status (5–7). Other epidemiologic studies have also demonstrated interactions among elevated systolic BP and surrogate markers of frailty on the risk of stroke and cardiovascular events (8,9). The Systolic Blood Pressure Intervention Trial (SPRINT, clinicaltrials.gov identifier: NCT01206062) aims to test the hypothesis that lowering systolic BP to less than 120 mmHg reduces cardiovascular morbidity and mortality versus a target of less than 140 mmHg among hypertensive, nondiabetic adults (10). SPRINT was specifically designed with a consideration of older persons, recruiting more than 2,600 participants 75 years of age and older and also by incorporating health measures particularly important in aging, for example, neuropsychological testing of cognitive function and gait speed. The SPRINT study design did not include a specific assessment of frailty, either by way of a frailty phenotype assessment or a validated frailty index (FI). Consequently, characterizing the range of frailty status within the SPRINT cohort is needed both to address its generalizability with respect to community-dwelling populations and to evaluate frailty-related effect modification of the SPRINT intervention on cardiovascular morbidity and mortality. Recent work on frailty assessment has moved toward a deficit accumulation approach, creating FIs that reflect the ratio of the sum of the number of symptoms, diseases, abnormal clinical or laboratory findings, or other impairments a particular individual has (numerator) to the total number of items included in the index (denominator) (11). Importantly, it appears that the precise deficits included in building a FI are not critical, provided that included deficits are appropriately associated with aging, are not extremely rare nor saturated within the population, and that the FI contains at least 30 items (12). Of relevance to SPRINT, Warwick and colleagues recently examined the relation between frailty and antihypertensive treatment in the Hypertension in the Very Elderly Trial (HYVET) trial (13). Using a 60-item FI, their results demonstrated (i) that the distribution of the FI in the HYVET cohort was similar to that reported in population studies and (ii) a consistent benefit of antihypertensive treatment on stroke, cardiovascular events, and total mortality irrespective of baseline frailty status. Given these results and concerns about adverse consequences of antihypertensive therapy in observational studies of older adults, a critical direction for SPRINT will be to examine its FI distribution in relation to the general population and whether frailty modifies the effect of antihypertensive therapy intensity on cardiovascular morbidity and mortality. As a precursor to such an analysis, we
Journals of Gerontology: MEDICAL SCIENCES, 2016, Vol. 71, No. 5
describe the construction of a FI for the SPRINT cohort based on relevant data available at the time of randomization. In addition, to examine the longitudinal predictive validity of the FI, we examine its association with three incident outcomes expected to correlate with frailty: self-reported falls (noninjurious), injurious falls, and all-cause hospitalizations. Such results may also add to our understanding of the ability of large-scale clinical trials to recruit a representative range of noninstitutionalized, community-dwelling, older adults.
Methods Study Cohort The design and baseline characteristics of SPRINT have been described previously (10). Briefly, 9,361 participants were randomized to either a standard systolic BP target of less than 140 mm Hg or a lower target of less than 120 mm Hg. Participants were eligible for SPRINT if they were at least 50 years of age and had a systolic BP at a screening visit within the range of 130–180, 130– 170, 130–160, or 130–150 mm Hg while being on no more than 0/1, 2, 3, or 4 antihypertensive medications respectively. In addition, participants had to be at increased risk for cardiovascular events, meeting at least one of the following criteria: (i) presence of clinical or subclinical cardiovascular disease other than stroke, (ii) chronic kidney disease, defined as an estimated glomerular filtration rate between 20 and 59 mL/min/1.73 m2, (iii) 10-year Framingham risk score for cardiovascular disease risk ≥ 15% based on clinical features and laboratory results within the past 12 months, or (iv) age ≥ 75 years. The SPRINT study protocol was approved by Institutional Review Boards at each of the trial’s sites, and all participants provided informed consent.
Variables Included in Frailty Index In constructing a measure of frailty, we adopted a deficit accumulation approach (11). We started by modifying a 25-item FI developed in the African-American Health Study based on items available in the SPRINT cohort (16 items) (14). We then added another 20 items available in SPRINT similar to items included in the FI applied to HYVET (13). The proposed list of 36 total items and scoring scheme is described in Supplementary Table 1. The FI includes information on global cognitive function based on the Montreal Cognitive Assessment (15), self-ratings of health from the Veterans RAND 12-Item Health Survey (VR-12) (16), self-ratings of depressive symptoms from the nine item-Patient Health Questionnaire (PHQ-9) (17), two other cognitive screening instruments, laboratory measurements, BP measurements, and self-reported comorbidities. Finally, among the subset of participants 75 years of age and older, gait speed was also measured based on a 4-m walk test and was included in the FI, yielding 37 items in this subgroup (see Supplementary Material).
Scoring of the Frailty Index The proposed FI was calculated as the sum of the score for each deficit, divided by the total number of non-missing items. We weighted each item equally, excluded participants who did not have at least 30 non-missing items, and classified participants as fit (FI ≤ 0.10), less fit (0.10 < FI ≤ 0.21), or frail (0.21 < FI) (18–20).
Longitudinal Outcomes Participants were queried by clinic staff at each quarterly visit for the occurrence of serious adverse events and selected adverse events.
Journals of Gerontology: MEDICAL SCIENCES, 2016, Vol. 71, No. 5
In addition, participants could spontaneously report the occurrence of serious adverse events to clinic staff between quarterly visits. Serious adverse events were then reported to the Coordinating Center Medical Safety Officer within 72 hours of knowledge of the event. At each ascertainment, participants self-reported whether they had fallen and landed on the floor or ground, or hit an object like a table or stair. If the participant reported that a fall required treatment in an emergency room or required hospitalization, then the event was classified as an injurious fall. This was defined as a sudden, unintentional change in position in which the participant comes to rest on the ground, floor, or a lower level, not as a result of syncope or overwhelming external force. Hospitalizations were defined as an overnight stay in an acute care hospital, for any reason.
Statistical Analysis We compared participant characteristics by baseline frailty status using analysis of variance for continuous variables and chi-square tests for categorical variables. We modeled the relationship between the FI and age, separately for each sex, using local polynomial regression (21). For the longitudinal outcomes of falls and hospitalization, because both events can recur with some frequency, we examined the occurrence of these events by baseline frailty status using the mean cumulative count (MCC) estimator (22). The MCC estimates the average number of events per individual (incident or recurrent) over a specified time interval. Standard errors for the MCC were estimated using bootstrap resampling (N = 1,000). To address potential confounding effects, we fit a recurrent events regression model for falls and hospitalizations using the gap time formulation of the Prentice, Williams, and Peterson model (PWP-GT) (23).
651
In these models, we examined the FI both as a continuous predictor and categorized as fit, less fit, or frail. We included adjustments for age, sex, race/ethnicity, education, alcohol consumption, and treatment arm. All analyses were performed using the R Statistical Computing Environment (R Core Team, Vienna, Austria) or SAS v9.4 (SAS, Cary, NC).
Comparisons With Other Studies We compared the distribution of the SPRINT FI in participants 80 years of age and older at the time of randomization to the distribution of the FI used in HYVET (13). In addition, we compared the sex-specific relationship between the FI and biologic age to published regression estimates based on pooled data from the 1994 and 1999 waves of the National Long Term Care Survey (NLTCS) (24). We also compared the relationship between the FI and biologic age with mean FI values reported by 10-year age groups from Wave 1 (2004–2005) of the Survey of Health, Ageing, and Retirement in Europe (SHARE), a study of 29,905 adults aged 50 and older from 11 countries in Europe and Israel (25).
Results Based on the requirement of at least 30 non-missing items, we were able to calculate the FI in 9,306 (99.4%) of randomized SPRINT participants, with 9,207 (98.9%) participants having two missing items or less. The most frequently missing item was blood urea nitrogen (31.9%), followed by urine albumin to creatinine ratio (4.6%), potassium (3.5%), and sodium (2.4%). Table 1 shows the demographic and behavioral characteristics for the 9,306 SPRINT participants in whom we could calculate the FI, stratified by baseline frailty status.
Table 1. Characteristics of 9,306 SPRINT Participants by Baseline Frailty Status
Age (years) Female sex Race / ethnicity White Black Hispanic Other Education 12 years Alcohol consumption Nondrinker Light drinker Moderate drinker Heavy drinker Unknown Number of antihypertensive medications Zero One Two Three or more Note: FI = frailty index.
Frail
Less Fit
Fit
FI > 0.21
0.10 < FI ≤ 0.21
FI ≤ 0.10
N = 2,560
N = 4,999
N = 1,747
p Value
68.3 ± 10.3 1,067 (41.7)
68.2 ± 9.3 1,744 (34.9)
66.6 ± 8.3 498 (28.5)
Research Article
Characterizing Frailty Status in the Systolic Blood Pressure Intervention Trial Nicholas M. Pajewski,1 Jeff D. Williamson2,* William B. Applegate,2 Dan R. Berlowitz,3,4 Linda P. Bolin,5 Glenn M. Chertow,6 Marie A. Krousel-Wood,7,8,9 Nieves Lopez-Barrera,10 James R. Powell,11 Christianne L. Roumie,12,13 Carolyn Still,14,15 Kaycee M. Sink,2 Rocky Tang,16 Clinton B. Wright,17 and Mark A. Supiano18,19,*; for the SPRINT Study Research Group Department of Biostatistical Sciences, Division of Public Health Sciences and 2Department of Internal Medicine, Section on Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina. 3Bedford Veterans Affairs Hospital, Massachusetts. 4School of Public Health, Boston University, Massachusetts. 5College of Nursing, East Carolina University, Greenville, North Carolina. 6Department of Nephrology, Stanford University School of Medicine, Palo Alto, California. 7Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana. 8Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana. 9Research Division, Ochsner Clinic Foundation, New Orleans, Louisiana. 10Department of Nephrology, University of Illinois at Chicago. 11Department of Internal Medicine, Division of General Internal Medicine, East Carolina University, Greenville, North Carolina. 12 Veterans Health Administration-Tennessee Valley Healthcare System Geriatric Research Education Clinical Center (GRECC), HSR&D Center, Nashville. 13Department of Medicine, Vanderbilt University, Nashville, Tennessee. 14Division of Nephrology and Hypertension, University Hospitals Case Medical Center, Cleveland, Ohio. 15Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, Ohio. 16Department of Surgery, Columbia University, New York. 17Evelyn F. McKnight Brain Institute, Departments of Neurology and Public Health Sciences, Leonard M. Miller School of Medicine, University of Miami, Florida. 18Division of Geriatrics, School of Medicine, University of Utah, Salt Lake City. 19Veterans Affairs Salt Lake City, Geriatric Research, Education, and Clinical Center, Utah. 1
*These authors contributed equally to this work. Address correspondence to Nicholas M. Pajewski, PhD, Department of Biostatistical Sciences, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157. Email: [email protected] Received September 28, 2015; Accepted November 30, 2015 Decision Editor: Stephen Kritchevsky, PhD
Abstract Background: The Systolic Blood Pressure Intervention Trial (SPRINT) is testing whether a lower systolic blood pressure (BP) target of 120 mm Hg leads to a reduction in cardiovascular morbidity and mortality among hypertensive, nondiabetic adults. Because there may be detrimental effects of intensive BP control, particularly in older, frail adults, we sought to characterize frailty within SPRINT to address ongoing questions about the ability of large-scale trials to enroll representative samples of noninstitutionalized, community-dwelling, older adults. Methods: We constructed a 36-item frailty index (FI) in 9,306 SPRINT participants, classifying participants as fit (FI ≤ 0.10), less fit (0.10 < FI ≤ 0.21), or frail (FI > 0.21). Recurrent event models were used to evaluate the association of the FI with the incidence of self-reported falls, injurious falls, and all-cause hospitalizations. Results: The distribution of the FI was comparable with what has been observed in population studies, with 2,570 (27.6%) participants classified as frail. The median FI was 0.18 (interquartile range = 0.14 to 0.24) in participants aged 80 years and older (N = 1,159), similar to the median FI of 0.17 reported for participants in the Hypertension in the Very Elderly Trial. In multivariable analyses, a 1% increase in the FI was associated with increased risk for self-reported falls (hazard ratio [HR] = 1.030), injurious falls (HR = 1.035), and all-cause hospitalizations (HR = 1.038) (all p values < .0001). Conclusions: Large clinical trials assessing treatments to reduce cardiovascular disease risk, such as SPRINT, can enroll heterogeneous populations of older adults, including the frail elderly, comparable with general population cohorts. Key Words: Falls—Frailty—Multimorbidities
© The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: [email protected].
649
650
Despite decades of research on strategies for the control of hypertension, there remains considerable uncertainty about optimal blood pressure (BP) targets for older adults (aged 60 years and older) and the relative safety of various targets (1,2). Reservations about the generalizability of results from clinical trials contribute to this uncertainty in clinical practice (3). For example, the generalizability of the Hypertension in the Very Elderly Trial (HYVET) results to older adults living in Western, developed countries, particularly those with frailty, has been questioned in that the majority of HYVET’s participants were generally healthy subjects recruited from Eastern Europe and China (4). Thus, an important consideration is whether clinical trials involving elderly persons have a sufficient range of frail and nonfrail elderly persons to commend implementation of their results in community-based clinical practices. In addition, recent studies have suggested an attenuation of the association between elevated BP and mortality when analyses are stratified according to gait speed (a surrogate marker for frailty) or other measures of functional status (5–7). Other epidemiologic studies have also demonstrated interactions among elevated systolic BP and surrogate markers of frailty on the risk of stroke and cardiovascular events (8,9). The Systolic Blood Pressure Intervention Trial (SPRINT, clinicaltrials.gov identifier: NCT01206062) aims to test the hypothesis that lowering systolic BP to less than 120 mmHg reduces cardiovascular morbidity and mortality versus a target of less than 140 mmHg among hypertensive, nondiabetic adults (10). SPRINT was specifically designed with a consideration of older persons, recruiting more than 2,600 participants 75 years of age and older and also by incorporating health measures particularly important in aging, for example, neuropsychological testing of cognitive function and gait speed. The SPRINT study design did not include a specific assessment of frailty, either by way of a frailty phenotype assessment or a validated frailty index (FI). Consequently, characterizing the range of frailty status within the SPRINT cohort is needed both to address its generalizability with respect to community-dwelling populations and to evaluate frailty-related effect modification of the SPRINT intervention on cardiovascular morbidity and mortality. Recent work on frailty assessment has moved toward a deficit accumulation approach, creating FIs that reflect the ratio of the sum of the number of symptoms, diseases, abnormal clinical or laboratory findings, or other impairments a particular individual has (numerator) to the total number of items included in the index (denominator) (11). Importantly, it appears that the precise deficits included in building a FI are not critical, provided that included deficits are appropriately associated with aging, are not extremely rare nor saturated within the population, and that the FI contains at least 30 items (12). Of relevance to SPRINT, Warwick and colleagues recently examined the relation between frailty and antihypertensive treatment in the Hypertension in the Very Elderly Trial (HYVET) trial (13). Using a 60-item FI, their results demonstrated (i) that the distribution of the FI in the HYVET cohort was similar to that reported in population studies and (ii) a consistent benefit of antihypertensive treatment on stroke, cardiovascular events, and total mortality irrespective of baseline frailty status. Given these results and concerns about adverse consequences of antihypertensive therapy in observational studies of older adults, a critical direction for SPRINT will be to examine its FI distribution in relation to the general population and whether frailty modifies the effect of antihypertensive therapy intensity on cardiovascular morbidity and mortality. As a precursor to such an analysis, we
Journals of Gerontology: MEDICAL SCIENCES, 2016, Vol. 71, No. 5
describe the construction of a FI for the SPRINT cohort based on relevant data available at the time of randomization. In addition, to examine the longitudinal predictive validity of the FI, we examine its association with three incident outcomes expected to correlate with frailty: self-reported falls (noninjurious), injurious falls, and all-cause hospitalizations. Such results may also add to our understanding of the ability of large-scale clinical trials to recruit a representative range of noninstitutionalized, community-dwelling, older adults.
Methods Study Cohort The design and baseline characteristics of SPRINT have been described previously (10). Briefly, 9,361 participants were randomized to either a standard systolic BP target of less than 140 mm Hg or a lower target of less than 120 mm Hg. Participants were eligible for SPRINT if they were at least 50 years of age and had a systolic BP at a screening visit within the range of 130–180, 130– 170, 130–160, or 130–150 mm Hg while being on no more than 0/1, 2, 3, or 4 antihypertensive medications respectively. In addition, participants had to be at increased risk for cardiovascular events, meeting at least one of the following criteria: (i) presence of clinical or subclinical cardiovascular disease other than stroke, (ii) chronic kidney disease, defined as an estimated glomerular filtration rate between 20 and 59 mL/min/1.73 m2, (iii) 10-year Framingham risk score for cardiovascular disease risk ≥ 15% based on clinical features and laboratory results within the past 12 months, or (iv) age ≥ 75 years. The SPRINT study protocol was approved by Institutional Review Boards at each of the trial’s sites, and all participants provided informed consent.
Variables Included in Frailty Index In constructing a measure of frailty, we adopted a deficit accumulation approach (11). We started by modifying a 25-item FI developed in the African-American Health Study based on items available in the SPRINT cohort (16 items) (14). We then added another 20 items available in SPRINT similar to items included in the FI applied to HYVET (13). The proposed list of 36 total items and scoring scheme is described in Supplementary Table 1. The FI includes information on global cognitive function based on the Montreal Cognitive Assessment (15), self-ratings of health from the Veterans RAND 12-Item Health Survey (VR-12) (16), self-ratings of depressive symptoms from the nine item-Patient Health Questionnaire (PHQ-9) (17), two other cognitive screening instruments, laboratory measurements, BP measurements, and self-reported comorbidities. Finally, among the subset of participants 75 years of age and older, gait speed was also measured based on a 4-m walk test and was included in the FI, yielding 37 items in this subgroup (see Supplementary Material).
Scoring of the Frailty Index The proposed FI was calculated as the sum of the score for each deficit, divided by the total number of non-missing items. We weighted each item equally, excluded participants who did not have at least 30 non-missing items, and classified participants as fit (FI ≤ 0.10), less fit (0.10 < FI ≤ 0.21), or frail (0.21 < FI) (18–20).
Longitudinal Outcomes Participants were queried by clinic staff at each quarterly visit for the occurrence of serious adverse events and selected adverse events.
Journals of Gerontology: MEDICAL SCIENCES, 2016, Vol. 71, No. 5
In addition, participants could spontaneously report the occurrence of serious adverse events to clinic staff between quarterly visits. Serious adverse events were then reported to the Coordinating Center Medical Safety Officer within 72 hours of knowledge of the event. At each ascertainment, participants self-reported whether they had fallen and landed on the floor or ground, or hit an object like a table or stair. If the participant reported that a fall required treatment in an emergency room or required hospitalization, then the event was classified as an injurious fall. This was defined as a sudden, unintentional change in position in which the participant comes to rest on the ground, floor, or a lower level, not as a result of syncope or overwhelming external force. Hospitalizations were defined as an overnight stay in an acute care hospital, for any reason.
Statistical Analysis We compared participant characteristics by baseline frailty status using analysis of variance for continuous variables and chi-square tests for categorical variables. We modeled the relationship between the FI and age, separately for each sex, using local polynomial regression (21). For the longitudinal outcomes of falls and hospitalization, because both events can recur with some frequency, we examined the occurrence of these events by baseline frailty status using the mean cumulative count (MCC) estimator (22). The MCC estimates the average number of events per individual (incident or recurrent) over a specified time interval. Standard errors for the MCC were estimated using bootstrap resampling (N = 1,000). To address potential confounding effects, we fit a recurrent events regression model for falls and hospitalizations using the gap time formulation of the Prentice, Williams, and Peterson model (PWP-GT) (23).
651
In these models, we examined the FI both as a continuous predictor and categorized as fit, less fit, or frail. We included adjustments for age, sex, race/ethnicity, education, alcohol consumption, and treatment arm. All analyses were performed using the R Statistical Computing Environment (R Core Team, Vienna, Austria) or SAS v9.4 (SAS, Cary, NC).
Comparisons With Other Studies We compared the distribution of the SPRINT FI in participants 80 years of age and older at the time of randomization to the distribution of the FI used in HYVET (13). In addition, we compared the sex-specific relationship between the FI and biologic age to published regression estimates based on pooled data from the 1994 and 1999 waves of the National Long Term Care Survey (NLTCS) (24). We also compared the relationship between the FI and biologic age with mean FI values reported by 10-year age groups from Wave 1 (2004–2005) of the Survey of Health, Ageing, and Retirement in Europe (SHARE), a study of 29,905 adults aged 50 and older from 11 countries in Europe and Israel (25).
Results Based on the requirement of at least 30 non-missing items, we were able to calculate the FI in 9,306 (99.4%) of randomized SPRINT participants, with 9,207 (98.9%) participants having two missing items or less. The most frequently missing item was blood urea nitrogen (31.9%), followed by urine albumin to creatinine ratio (4.6%), potassium (3.5%), and sodium (2.4%). Table 1 shows the demographic and behavioral characteristics for the 9,306 SPRINT participants in whom we could calculate the FI, stratified by baseline frailty status.
Table 1. Characteristics of 9,306 SPRINT Participants by Baseline Frailty Status
Age (years) Female sex Race / ethnicity White Black Hispanic Other Education 12 years Alcohol consumption Nondrinker Light drinker Moderate drinker Heavy drinker Unknown Number of antihypertensive medications Zero One Two Three or more Note: FI = frailty index.
Frail
Less Fit
Fit
FI > 0.21
0.10 < FI ≤ 0.21
FI ≤ 0.10
N = 2,560
N = 4,999
N = 1,747
p Value
68.3 ± 10.3 1,067 (41.7)
68.2 ± 9.3 1,744 (34.9)
66.6 ± 8.3 498 (28.5)
