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J O U R N A L O F T H E AM E R I C A N C O L L E G E O F C A R D I O L O G Y V O L . 6 8 , N O . 1 3 , 2 0 1 6

P U B L I S H E D B Y E L S E V I E R O N B E H A L F O F T H E AM E R I C A N C O L L E G E O F

C A R D I O L O G Y F O U N D A T I O N

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ORIGINAL INVESTIGATIONS

Association of Systolic Blood PressureVariability With Mortality,Coronary Heart Disease, Stroke,and Renal Disease

Elvira O. Gosmanova, MD,a,b Margit K. Mikkelsen, BS,c Miklos Z. Molnar, MD, PHD,d Jun L. Lu, MD,d

Lenar T. Yessayan, MD, MS,e Kamyar Kalantar-Zadeh, MD, MPH, PHD,f Csaba P. Kovesdy, MDd,g

ABSTRACT

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BACKGROUND Intraindividual blood pressure (BP) fluctuates dynamically over time. Previous studies suggested an

adverse link between greater visit-to-visit variability in systolic blood pressure (SBP) and various outcomes. However,

these studies have significant limitations, such as a small size, inclusion of selected populations, and restricted outcomes.

OBJECTIVES This study investigated the association of increased visit-to-visit variability and all-cause mortality,

cardiovascular events, and end-stage renal disease (ESRD) in a large cohort of U.S. veterans.

METHODS From among 3,285,684 U.S. veterans with and without hypertension and normal estimated glomerular

filtration rates (eGFR) during 2005 and 2006, we identified 2,865,157 patients who had 8 or more outpatient BP

measurements. Systolic blood pressure variability (SBPV) was measured using the SD of all SBP values (normally

distributed) in 1 individual. Associations of SD quartiles (<10.3, 10.3 to 12.7, 12.7 to 15.6, and $15.6 mm Hg) with

all-cause mortality, incident coronary heart disease (CHD), stroke, and ESRD was examined using Cox models adjusted for

sociodemographic characteristics, baseline eGFR, comorbidities, body mass index, SBP, diastolic BP, and antihypertensive

medication use.

RESULTS Several sociodemographic variables (older age, male sex, African-American race, divorced or widowed status)

and clinical characteristics (lower baseline eGFR, higher SBP and diastolic BP), and comorbidities (presence of diabetes,

hypertension, cardiovascular disease, and lung disease) were all associated with higher intraindividual SBPV. The

multivariable adjusted hazard ratios and 95% confidence intervals for SD quartiles 2 through 4 (compared with the

first quartile) associated with all-cause mortality, CHD, stroke, and ESRD were incrementally higher.

CONCLUSIONS Higher SBPV in individuals with and without hypertension was associated with increased risks of

all-cause mortality, CHD, stroke, and ESRD. Further studies are needed to determine interventions that can lower SBPV

and their impact on adverse health outcomes. (J Am Coll Cardiol 2016;68:1375–86) Published by Elsevier on behalf of

the American College of Cardiology Foundation.

m the aNephrology Section, Stratton Veterans Affairs (VA) Medical Center, Albany, New York; bDivision of Nephrology, Albany

dical College, Albany, New York; cUniversity of Texas Health Science Center, San Antonio, Texas; dDivision of Nephrology,

iversity of Tennessee Health Science Center, Memphis, Tennessee; eDivision of Nephrology, University of Michigan, Ann Arbor,

chigan; fHarold Simmons Center for Chronic Disease Research and Epidemiology, Division of Nephrology and Hypertension,

iversity of California, Irvine, California; and the gNephrology Section, Memphis VA Medical Center, Memphis, Tennessee. This

dy was supported by grant R01DK096920 to Drs. Kalantar-Zadeh and Kovesdy and is the result of work supported with re-

rces and the use of facilities at the Memphis VA Medical Center and the Long Beach VAMedical Center. Support for VA/Centers

Medicare andMedicaid Services (CMS) data is provided by the Department of Veterans Affairs, Veterans Health Administration,

ABBR EV I A T I ON S

AND ACRONYMS

BMI = body mass index

BP = blood pressure

CHD = coronary heart disease

CHF = congestive heart failure

CI = confidence interval

CPT = Current Procedural

Terminology

eGFR = estimated glomerular

filtration rate

ESRD = end-stage renal

disease

HR = hazard ratio

ICD-9 = International

Classification of Diseases, Ninth

Revision

PDC = proportion of days

covered

SBP = systolic blood pressure

SBPV = systolic blood pressure

variability

SD = standard deviation

VA = Veterans Affairs

VISNs = Veterans Integrated

Service Networks

VVV = visit-to-visit variabi

Office of R

Numbers S

opinion of

Departmen

disclose. D

Manuscript

Gosmanova et al. J A C C V O L . 6 8 , N O . 1 3 , 2 0 1 6

Systolic BP Variability and Clinical Outcomes S E P T E M B E R 2 7 , 2 0 1 6 : 1 3 7 5 – 8 6

1376

E levated blood pressure (BP) is themost common chronic medical dis-ease observed in a variety of popula-

tions (1). It has been consistentlydemonstrated that higher baseline BP—bothuntreated and treated—is associated with ahigher risk for all-cause mortality, cardiovas-cular morbidity and mortality, and end-stagerenal disease (ESRD) (2–8). However, BPdoes not remain steady, but instead fluctu-ates continually, within a 24-h period, fromday to day, and from month to month (9).Furthermore, these fluctuations are notrandom and tend to remain consistent withinpatients (10,11); therefore, the traditional cor-relation between baseline BP and outcomes ofinterest has a potential to underestimate thetrue risk of elevated BP. This phenomenon,known as “regression dilution,” arises fromthe combined effects of measurement errorsand both short-term (diurnal and seasonal)and long-term (changes in BPwith aging, anti-hypertensive medication use, and adherenceto antihypertensive medications) within-individual variability of BP (12). A largemeta-analysis from the Prospective StudyGroup unequivocally demonstrated that the

mean or “usual” BP, corrected for regression dilution,was strongly and linearly related to increased risk ofmortality from coronary heart disease (CHD), stroke,and other vascular causes (13). However, even aver-aging BP over longer periods of follow-upmay not fullyaddress risks associated with BP variations. Visit-to-visit variability (VVV) of BP is being increasinglyconsidered as a newer method to evaluate intraindi-vidual BP fluctuations.

lity

SEE PAGE 1387

Higher systolic blood pressure (SBP) variability(SBPV) has been shown to be a better predictor of all-cause and cardiovascular mortality (14–16), stroke(17,18), and cardiac disease (5,19–21) compared withaverage SBP. Nevertheless, a strong adverse associa-tion between increased SBPV and cardiovasculardisease and stroke was not confirmed in all studies(21,22); additionally, some studies found associations

esearch and Development, Health Services Research and Deve

DR 02-237 and 98-004). Opinions expressed in this paper are thos

the Department of Veterans Affairs. Drs. Gosmanova, Kalanta

t of Veterans Affairs. All authors have reported that they have no r

r. Gosmanova and Ms. Mikkelsen contributed equally to this wor

received May 22, 2016; revised manuscript received June 13, 201

between higher SBPV with all-cause mortality, but notwith stroke or coronary events (20). Furthermore,these previous clinical observations have importantlimitations, such as being restricted to very specific orhigh-risk populations, having a small number of BPmeasurements and a small sample size, or assessingonly selected outcomes. Therefore, we conducted alarge cohort study involving 2,865,157 U.S. veteranswho had at least 8 outpatient BP measurements toexamine the prognostic significance of increased VVVof SBP on all-cause mortality, CHD, stroke, and ESRD.

METHODS

We used data from a historic cohort study examiningrisk factors in patients with incident chronickidney disease consisting of 3,582,478 patients withan estimated glomerular filtration rate (eGFR) of$60 ml/min/1.73 m2, based on serum creatininemeasurements performed during October 1, 2004,through September 30, 2006, in any U.S. Departmentof Veterans Affairs (VA) facility (23–25). Of the3,492,943 patients with any available SBP measure-ments, we excluded 5,466 with only inpatient SBPmeasurements and 622,320 patients who had #7 SBPrecords during follow-up. Our final analytic sampleconsisted of 2,865,157 patients (Online Figure 1).

SOCIODEMOGRAPHIC CHARACTERISTICS AND

COMORBIDITIES. Cohort entry was defined as the dateof the first eGFR measurement of $60 ml/min/1.73 m2

between October 1, 2004, and September 30, 2006.Information about baseline age, race, sex, maritalstatus, per capita income, comorbid conditions, bodymass index (BMI), service connectedness (indicatingwhether comorbidities were directly caused by mili-tary service, resulting in certain privileges such aspreferential access to care and lower copayments),and receipt of influenza vaccinations during thecohort entry period, and frequency of health careencounters (defined as the number of health carevisits/year throughout the entire follow-up period)were obtained from national VA research data files, aspreviously described (26–28). Race was determined bycombining information from VA sources with thoseobtained from Medicare through the VA-Medicaredata merge project (29). In case of discrepancies, we

lopment, VA Information Resource Center (Project

e of the authors and do not necessarily represent the

r-Zadeh, and Kovesdy are employees of the U.S.

elationships relevant to the contents of this paper to

k.

6, accepted June 21, 2016.

J A C C V O L . 6 8 , N O . 1 3 , 2 0 1 6 Gosmanova et al.S E P T E M B E R 2 7 , 2 0 1 6 : 1 3 7 5 – 8 6 Systolic BP Variability and Clinical Outcomes

1377

used the race determination fromMedicare because ofits more accurate nature (30). Comorbidities andclinical events were assessed from the VA Inpatientand Outpatient Medical SAS Datasets using Interna-tional Classification of Diseases-Ninth Revision(ICD-9) diagnostic and procedure codes and CurrentProcedural Terminology (CPT) codes (23). Baseline(prevalent) comorbidities were defined as the pres-ence of relevant ICD-9 and CPT codes recorded duringthe cohort entry period. In addition to VA data, weextracted select socioeconomic indicators using 2004county typology codes (housing stress, low education,low employment, and persistent poverty) based onpatients’ home addresses, obtained from the AreaHealth Resources Files system. Geographic variationwas examined by grouping Veterans IntegratedService Networks (VISNs) into 4 regions: northeast(VISNs 1, 2, 4), midwest (VISNs 10, 12, 15, 23), south(VISNs 5–9, 16, 17), and west (VISNs 19–22) (31).

BLOOD PRESSURE AND MEDICATION USE. Informa-tion about BP was collected from the date of cohortentry until the end of follow-up (death, last VA con-tact, or July 26, 2013). All BP values measured duringoutpatient clinical encounters in any VA facilitythroughout follow-up were recorded. In the case ofmultiple BP measurements taken on the same day, weselected only the last BP value. We expressed visit-to-visit SBP variability as the SD of the longitudinalintraindividual SBP measurements in each patient.VA pharmacy dispensation records (32) were used toassess baseline exposure to different classes of anti-hypertensive medications and to statin type ofcholesterol-lowering medications.

OUTCOMES. Outcomes of interest were all-causemortality, incident CHD, ischemic strokes, andESRD. Deaths were identified from the VA Vital StatusFiles, the sensitivity and specificity of which (usingthe U.S. National Death index as gold standard) are98.3% and 99.8%, respectively (33). Incident CHD wasdefined as the composite of a first occurrence of anICD-9 or CPT code for acute myocardial infarction,percutaneous angioplasty, or coronary artery bypassgrafting after October 1, 2006, in patients withoutsuch diagnoses before this date. Incident ischemicstroke was defined as the first occurrence of an ICD-9code for ischemic stroke after October 1, 2006, inpatients without such diagnosis before this date. In-formation on ESRD (defined as initiation of renalreplacement therapy) was obtained from the UnitedStates Renal Data System.

STATISTICAL ANALYSIS. Data were expressed asmean � SD, median (interquartile range), and pro-portions, and examined across SD quartiles. Because

of the large sample size, traditional statistical testingof differences in baseline characteristics was notcarried out. The start of the follow-up period was thedate of cohort entry for analyses ofmortality andESRD,and October 1, 2006, for incident CHD and stroke.Patients were followed until death or were censoredat the date of the last health care or administrativeVA encounter; or on July 26, 2013, for mortality, CHD,and stroke; and December 31, 2011, for ESRD.

The association of SBP SD categories with theoutcomes of interest was examined in crude (model 1)and multivariable adjusted Cox models. Models wereadjusted sequentially based on a priori considerationsfor baseline values of age, sex, race, and baselineeGFR (model 2); baseline comorbidities such as dia-betes mellitus, hypertension, cardiovascular disease,congestive heart failure (CHF), cerebrovascular dis-ease, peripheral arterial disease, chronic lung disease,dementia, rheumatoid diseases, peptic ulcer disease,chronic liver disease, hemiplegia, malignancies, hu-man immunodeficiency virus/acquired immunodefi-ciency syndrome, depression, and BMI (model 3); percapita income, marital status, service connectedness,receipt of statins and influenza vaccination(s), fre-quency of health care visits, and living in areas withhigh housing stress, low education, low employmentand persistent poverty, and medical diagnosis ofnonadherence (the V15.81 code from ICD-9) (model4); and baseline SBP and diastolic BP, and receiptof different antihypertensive medication classesat baseline (renin-angiotensin system inhibitors,beta blockers, alpha blockers, vasodilators, thiazidediuretic agents, loop diuretic agents, potassium-sparing diuretic agents, and centrally acting alphaagonists), and geographic region (model 5). A total of2,322,759 patients (81% of the total sample) hadcomplete data for analysis in the final multivariablemodels. We used unimputed (complete data) analysesand substituted missing data by performing multipleimputations. Sensitivity analyses were performed toexamine the association of SBP SD with all outcomesof interest in subgroups of patients with differentnumber of SBP measurements available for SDcalculation and in various subgroups. Adherence toantihypertensive medications was estimated by pro-portion of days covered (PDC, defined as percentage ofdays a subject had medication available) (33–35). PDCwas calculated in a subgroup of individuals pre-scribed antihypertensive medications (n ¼ 611,249).Good adherence was considered as PDC $80%(n ¼ 487,886; 79.8%) and inadequate adherenceas <80% (n ¼ 123,363; 20.2%). The results of multi-variable adjusted models including adjustment forPDC among antihypertensive drug users were similar

TABLE 1 Baseline Characteristics

Overall(N ¼ 2,865,157)

SD of Systolic BP (mm Hg)

<10.3(n ¼ 716,288)

10.3-12.7(n ¼ 716,292)

12.8-15.5(n ¼ 716,288)

$15.6(n ¼ 716,289)

Age, yrs 59.3 � 13.1 56.4 � 14.5 58.6 � 13.1 61.1 � 12.1 63.7 � 11.6

Male 2,690,181 (93.9) 655,512 (91.5) 668,327 (93.3) 679,509 (94.9) 686,833 (95.9)

Race

White 2,082,512 (77.8) 516,298 (79.8) 526,070 (78.8) 528,409 (78.0) 511,735 (74.9)

African American 474,403 (17.7) 98,760 (15.2) 111,589 (16.7) 120,584 (17.8) 143,470 (21.0)

Hispanic 62,965 (2.4) 16,137 (2.5) 15,547 (2.3) 15,223 (2.2) 16,058 (2.3)

Other 56,173 (2.1) 15,848 (2.5) 14,721 (2.2) 13,342 (2.0) 12,262 (1.8)

Marital status

Married 1,520,945 (55.0) 424,870 (61.4) 391,081 (56.5) 367,192 (53.1) 337,802 (48.8)

Single 296,923 (10.7) 80,835 (11.7) 76,783 (11.1) 71,985 (10.4) 67,320 (9.7)

Divorced 748,305 (27.0) 152,402 (22.0) 181,822 (26.3) 198,426 (28.7) 215,655 (31.2)

Widowed 201,784 (7.3) 33,696 (4.9) 41,877 (6.1) 54,359 (7.8) 71,579 (10.3)

Service connection 1,251,338 (43.7) 329,960 (46.1) 328,662 (45.9) 310,563 (43.4) 283,153 (39.4)

Number of provider visits per month

#1 152,792 (40.3) 417, 836 (58.4) 290,438 (40.6) 234,298 (32.8) 210,221 (29.4)

2–3 1,515,625 (53.0) 281,819 (29.4) 386,018 (54.0) 420,156 (58.7) 427,632 (59.8)

$4 192,594 (6.7) 15,707 (2.2) 38,903 (5.4) 60,772 (8.5) 77,212 (10.8)

Influenza vaccination 917,194 (32.0) 195,106 (27.2) 226,001 (31.6) 245,198 (34.2) 250,889 (35.0)

Housing stress 960, 116 (34.9) 230,642 (33.7) 240,875 (35.0) 244,386 (35,5) 244,213 (35.5)

Low education 303,339 (11.0) 71,083 (10.4) 75,049 (10.9) 77,527 (11.3) 79,680 (11.6)

Low employment 267,074 (9.7) 64,100 (9.4) 66,915 (9.7) 67,489 (9.8) 68,570 (10.0)

Persistent poverty 140, 288 (5.1) 34,861 (5.1) 35,268 (5.1) 34,902 (5.1) 35,257 (5.1)

eGFR, ml/min/1.73 m2 83.9 � 15.4 85.1 � 15.7 84.5 � 15.4 83.5 � 15.2 82.3 � 15.2

BMI, kg/m2 29.3 � 5.7 29.1 � 5.1 29.5 � 5.6 29.5 � 6.0 29.3 � 6.2

Income, $ 22,362(11,659–34,347)

25,026(12,480–40,380)

23,285(11,998–35,022)

21,893(11,568–35,022)

19,666(11,022–31,640)

Geographic region

Northeast 416,946 (15.2) 105,240 (15.4) 105,377 (15.3) 106,218 (15.5) 100,129 (14.7)

Midwest 478,204 (17.4) 113,820 (16.6) 115,653 (16.9) 120,275 (17.6) 128,456 (18.8)

South 1,269,442 (46.3) 325,926 (47.5) 318,743 (46.5) 313,356 (45.7) 311,417 (45.6)

West 574,972 (20.1) 140,716 (20.5) 145,906 (21.3) 145,247 (21.2) 143,103 (21.0)

Systolic BP, mm Hg 133.1 � 17.6 127.1 � 12.2 129.9 � 14.5 133.8 � 16.7 141.5 � 21.9

Diastolic BP, mm Hg 77.5 � 11.8 75.7 � 10.1 76.6 � 10.8 77.7 � 11.7 79.8 � 13.7

Diabetes 731,960 (25.6) 124,361 (17.4) 164,563 (23.0) 201,526 (28.2) 241,510 (33.8)

Hypertension 1,797,718 (62.9) 317,750 (44.5) 401,786 (56.2) 491,009 (68.7) 587,173 (82.2)

Cardiovascular disease 347,084 (12.1) 53,368 (7.5) 74,240 (10.4) 97,064 (13.6) 122,412 (17.1)

Cerebrovascular disease 182,696 (6.4) 22,291 (3.1) 33,496 (4.7) 48,857 (6.8) 78,052 (10.9)

Peripheral artery disease 167,480 (5.9) 18,896 (2.6) 29,653 (4.2) 45,332 (6.3) 73,599 (10.3)

Chronic lung disease 551,927 (19.3) 99,195 (13.9) 129,819 (18.2) 153,778 (21.5) 169,135 (23.7)

Dementia 21,795 (0.8) 1,964 (0.3) 3,403 (0.5) 5,932 (0.8) 10,496 (1.5)

Rheumatologic disease 42,874 (1.5) 8,301 (1.2) 10,970 (1.5) 11,934 (1.7) 11,669 (1.6)

Liver disease 36,231 (1.3) 4,537 (0.6) 7,919 (1.1) 10,978 (1.5) 12,797 (1.8)

Malignancies 296,585 (10.4) 51,299 (7.2) 66,268 (9.3) 81,934 (11.5) 97,084 (13.6)

AIDS/HIV 20,161 (0.7) 5,037 (0.7) 5,844 (0.8) 5,115 (0.7) 4,165 (0.6)

Depression 298,440 (10.4) 59,148 (8.3) 79,292 (11.1) 83,343 (11.7) 76,657 (10.7)

Nonadherence diagnosis 206,211 (7.2) 24,452 (3.4) 40,336 (5.6) 57,771 (8.1) 83,652 (11.7)

Statin 435,410 (15.2) 99,458 (13.9) 106,106 (14.8) 112,568 (15.7) 117,278 (16.4)

ACEI or ARB 667,744 (23.3) 111,395 (15.6) 142,578 (19.9) 180,323 (25.2) 233,448 (32.6)

Beta blockers 487,408 (17.0) 81,270 (11.4) 104,238 (14.6) 131.218 (18.3) 170,681 (23.8)

CCB 328,115 (11.5) 56,824 (7.9) 68,547 (9.6) 86,156 (12.0) 116,588 (16.3)

Alpha blockers 208,646 (7.3) 38,810 (5.4) 46,815 (6.5) 55,640 (7.8) 67,381 (9.4)

Vasodilators 6,577 (0.2) 549 (0.1) 816 (0.1) 1,471 (0.2) 3,741 (0.5)

Thiazide diuretic agents 292,421 (10.2) 48,143 (6.7) 61,960 (8.7) 78,569 (11.0) 103,722 (14.5)

Loop diuretic agents 102,995 (3.6) 14,010 (2.0) 21,113 (3.0) 29,310 (4.1) 38,562 (5.4)

Continued on the next page

Gosmanova et al. J A C C V O L . 6 8 , N O . 1 3 , 2 0 1 6

Systolic BP Variability and Clinical Outcomes S E P T E M B E R 2 7 , 2 0 1 6 : 1 3 7 5 – 8 6

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TABLE 1 Continued

Overall(N ¼ 2,865,157)

SD of Systolic BP (mm Hg)

<10.3(n ¼ 716,288)

10.3-12.7(n ¼ 716,292)

12.8-15.5(n ¼ 716,288)

$15.6(n ¼ 716,289)

Potassium-sparing diuretic agents 73,762 (2.6) 13,783 (1.9) 16,881 (2.4) 19,826 (2.8) 23,272 (3.3)

Central alpha agonist 26,165 (0.9) 2,014 (0.3) 3,429 (0.5) 5,890 (0.8) 14,832 (2.1)

Adherence $80% amongantihypertensive drug users

487,886 (79.8) 63,202 (84.3) 98,899 (82.3) 137,672 (80.5) 188,110 (76.8)

Values are mean � SD, n (%), or median (interquartile range).

ACEI ¼ angiotensin-converting enzyme inhibitor; AIDS ¼ acquired immunodeficiency disease; ARB ¼ angiotensin receptor blocker; BMI ¼ body mass index; BP ¼ bloodpressure; CCB ¼ calcium-channel blockers; eGFR ¼ estimated glomerular filtration rate; HIV ¼ human immunodeficiency virus.

J A C C V O L . 6 8 , N O . 1 3 , 2 0 1 6 Gosmanova et al.S E P T E M B E R 2 7 , 2 0 1 6 : 1 3 7 5 – 8 6 Systolic BP Variability and Clinical Outcomes

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to the main analyses (Online Table 1). We performeda sensitivity analysis in which VVV of SBP wasassessed as the SD of at least 3 longitudinal intra-individual SBP measurements in each patient duringthe initial 12-month period of their follow-up. Statis-tical analyses were performed using STATA MP, ver-sions 13 and 14 (STATA Corporation, College Station,Texas). The study protocol was approved by theResearch and Development Committees at the Mem-phis VA Medical Center and Long Beach VA MedicalCenter.

RESULTS

The mean age of the cohort was 60 � 13 years, 94%were male, 78% white, and 18% African American.Patients had a median of 24 (25th to 75th percentile:15 to 42) SBP measurements. The mean baseline SBPwas 133 � 18 mm Hg overall, and 127 � 12, 130 � 15,134 � 17, and 142 � 22 mm Hg for SD quartiles 1, 2, 3,and 4, respectively. Baseline characteristics of theentire cohort as well as the patients categorized bySD quartiles, are shown in Table 1. Patients withhigher SBPV were older, more likely to be male, Af-rican American, and unmarried; had lower incomeand higher prevalence of comorbid conditions; andmore frequently used all classes of antihypertensivemedications.

ALL-CAUSE MORTALITY. There were 484,887 deathsduring a median follow-up of 8 years (16.9%; mor-tality rate: 22.87 [95% CI: 22.80 to 22.94] per 1,000patient-years) in the entire cohort. An incrementallyhigher all-cause mortality was observed with higherSD quartiles in unadjusted (Figure 1A) and adjustedmodels (Figure 2A). In the fully adjusted model, SDquartiles 2 through 4 (compared with the first quar-tile) were associated with all-cause mortality hazardratios (HR) of 1.10 (95% confidence interval [CI]: 1.08to 1.11), 1.32 (95% CI: 1.30 to 1.34), and 1.80(95% CI: 1.78 to 1.82), respectively. The results

remained essentially the same when missing valueswere imputed (Figure 2A) and in analyses using SBPVcalculated from measurements performed during thefirst year of follow-up (Online Table 2).

OTHER OUTCOMES. There were 67,227 CHD eventsduring a median follow-up of 8 years (2.7%; incidentrate: 3.63 [95% CI: 3.60 to 3.66] per 1,000 patient-years). Higher SBPV was associated with signifi-cantly higher risk of incident CHD in unadjusted(Figure 1B) and all adjusted models (Figure 2B). In thefully adjusted model, HRs of CHD for SD quartiles 2through 4 (compared with SD quartile 1) were: 2.11(95% CI: 2.02 to 2.19); 3.59 (95% CI: 3.45 to 3.72), and5.92 (95% CI: 5.70 to 6.14), respectively. Similartrends were seen in imputed analysis (Figure 2B) aswell as in analyses using SBPV calculated from mea-surements performed during the first year of follow-up (Online Table 2).

There were 62,523 incident stroke events during amedian follow-up of 8 years (2.3%; incident rate: 3.16[95% CI: 3.14 to 3.19] per 1,000 patient-years). HigherSBPV was associated with significantly higher risk ofincident stroke in all models (Figures 1C and 2C). Inthe fully adjusted model, HRs of stroke for SDquartiles 2 through 4 (compared with SD quartile 1)were 2.05 (95% CI: 1.95 to 2.14), 3.63 (95% CI: 3.47 to3.79), and 6.60 (95% CI: 6.32 to 6.89), respectively.The results remained similar in imputed analysis(Figure 2C), and in analyses using SBPV calculatedfrom measurements performed during the first yearof follow-up (Online Table 2).

There were 6,710 new ESRD cases during a medianfollow-up of 4.9 years (0.23%; incident rate: 0.39[95% CI: 0.38 to 0.40] per 1,000 patient-years).Similar to the previous outcomes, higher SBPV wasassociated with higher risk of ESRD in unadjusted(Figure 1D) and all adjusted analyses (Figure 2D). Inthe fully adjusted model, HRs of ESRD for SD quar-tiles 2 through 4 (compared with SD quartile 1) were:1.33 (95% CI: 1.10 to 1.60), 2.56 (95% CI: 2.16 to 3.03),

FIGURE 1 Kaplan-Meier Curves of Various Clinical Outcomes Associated With Different SD Quartiles of Mean SBP

0 1 2 3 4 5 6 7 8 9

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Analysis Time (Years)

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All-cause mortality Incident CHD

Incident ischemic stroke ESRD

SD quartile 1 SD quartile 2 SD quartile 3 SD quartile 4

A B

C D

(A) Kaplan-Meier survival plots for all-cause mortality. (B) Kaplan-Meier survival plots for incident CHD. (C) Kaplan-Meier survival plots for incident ischemic

stroke. (D) Kaplan-Meier survival plots for end-stage renal disease. CHD ¼ coronary heart disease; SBP ¼ systolic blood pressure; SD ¼ standard deviation.

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and 10.59 (95% CI: 9.02 to 12.43), respectively. Theassociation between higher SBPV and ESRD wassimilar in imputed analysis and in analyses usingSBPV calculated from measurements performed dur-ing the first year of follow-up (Online Table 2).

SUBGROUP ANALYSES. The results of fully adjustedanalyses in various subgroups are shown in Figure 3.Higher SBPV remained predictive of worse survival(Figure 3A) and increased risk of incident CHD(Figure 3B), stroke (Figure 3C), and ESRD (Figure 3D) inall studied subgroups, including different geographicregions (Online Figure 2). The exception was patientswith CHF (4.7% of the whole cohort). In this sub-group, no increased mortality was observed in asso-ciation with higher SD quartiles. However, similarlyto the whole cohort, the adverse associations be-tween higher SD quartiles and other outcomes (CHD,stroke, and ESRD) were present in patients with CHF(Figures 3A to 3D).

DISCUSSION

To the best of our knowledge, this is the largest studyto date examining the association between long-termvisit-to-visit systolic blood pressure variability andall-cause mortality and cardiovascular and renaloutcomes. Increased SBPV, measured via higher SDsof intraindividual SBP assessed over 8 years, wasassociated with a graded increase in the risks of all-cause mortality, incident CHD, stroke, and ESRDindependent from baseline sociodemographic char-acteristics and comorbidities, including hyperten-sion, BP, and antihypertensive medication use.

These findings confirm and strengthen the impor-tance of long-term variability in SBP for health-related outcomes. Previously, higher VVV in SBPwas linked to increased all-cause mortality among956 enrollees of the third National Health and Nutri-tion Examination Survey (14) and among 8,811 par-ticipants of the ADVANCE (Action in Diabetes and

FIGURE 2 HRs and 95% CIs of Outcomes of Interest Associated With Different SD Quartiles of Mean SBP in Unadjusted and Various Adjusted Analyses

Model 1 Model 2 Model 3 Model 4 Model 5 ImputedAnalysis

4.5

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Model 1 Model 2 Model 3 Model 4 Model 5 ImputedAnalysis

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Analysis

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Model 1 Model 2 Model 3 Model 4 Model 5 ImputedAnalysis

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(A) HRs and 95% CIs of all-cause mortality associated with different SD quartiles of mean blood pressure in unadjusted and various adjusted analyses. (B) HRs and 95%

CIs of incident coronary artery disease associated with different SD quartiles of mean blood pressure in unadjusted and various adjusted analyses. (C) HRs and 95% CIs of

incident stroke associated with different SD quartiles of mean blood pressure in unadjusted and various adjusted analyses. (D) HRs and 95% CIs of incident end-stage

renal disease associated with different SD quartiles of mean blood pressure in unadjusted and various adjusted analyses. Model 1 ¼ unadjusted analysis; Model 2 ¼adjusted analysis for age, sex, race, and baseline eGFR; Model 3 ¼ model 2 plus additional adjustment for baseline comorbidities such as diabetes mellitus, hypertension,

CVD, congestive heart failure, cerebrovascular disease, peripheral arterial disease, chronic lung disease, dementia, rheumatoid diseases, peptic ulcer disease, chronic liver

disease, hemiplegia, malignancies, HIV/AIDS, depression, and body mass index; Model 4 ¼ model 3 plus additional adjustment for per capita income, marital status,

service connectedness, receipt of statins and influenza vaccination(s), frequency of health care visits, and living in areas with high housing stress, low education, low

employment and persistent poverty, and medical diagnosis of nonadherence; Model 5 ¼model 4 plus additional adjustment for baseline SBP and DBP, receipt of different

antihypertensive medication classes at baseline (renin-angiotensin system inhibitors, beta blockers, alpha blockers, vasodilators, thiazide diuretics, loop diuretics, po-

tassium sparing diuretics, and centrally acting alpha agonists), and geographic region. AIDS ¼ acquired immunodeficiency disease; CI ¼ confidence interval; CVD ¼cardiovascular disease; DBP ¼ diastolic blood pressure; eGFR ¼ estimated glomerular filtration rate; HIV ¼ human immunodeficiency virus; HR ¼ hazard ratio; other

abbreviations as in Figure 1.

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Vascular Disease: Preterax and Diamicron ModifiedRelease Controlled Evaluation) study (36). TheADVANCE study also demonstrated a higher risk ofmacrovascular and microvascular outcomes withhigher VVV in SBP. Rothwell et al. (17) showed thatamong 2 cohorts of patients (n ¼ 2,006 and n ¼ 18,530,respectively) with previous transient ischemic attack,higher VVV in SBP was associated with higher risk ofsubsequent stroke independent of mean SBP. The riskof stroke was similarly higher during 5.4 years offollow-up with the higher VVV in SBP among 58,228women enrolled in the Women’s Health Initiative (18).Increased SBPV was also linked to adverse renal out-comes. One SD higher SBPV was associated with 15%(95% CI: 11% to 20%) higher incidence in renaldysfunction defined as new proteinuria on dipstick,

eGFR <60 ml/min1.73 m2, or rapid eGFR decline($3 ml/min/1.73 m2 per year) in a nationwide Japanesestudy including 48,587 participants (37). A post hocanalysis of the IDNT (Irbesartan Diabetic NephropathyTrial) and the RENAL (Reduction of End Points in Non-Insulin-Dependent Diabetes with the Angiotensin IIAntagonist Losartan) study that included 2,739 in-dividuals with baseline nephropathy demonstratedthat greater baseline VVV in SBP was associated withhigher risk of the composite outcome of doublingserum creatinine, ESRD, or death, but not with car-diovascular outcomes (38). In a subset of 21,245 pa-tients from the ALLHAT (Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial),higher VVV in SBP was also associated with increasedrisk of ESRD, 50% decline of eGFR, or both (39).

FIGURE 3 Adjusted HRs and 95% CIs of Outcomes of Interest Associated With Different SD Quartiles of Mean SBP in Various Subgroups

Overall

SubgroupsAge, years, <60

60

Race, White

Black

DM, Yes

No

HTN, Yes

No

CHF, Yes

No

eGFR, ml/min/1.73m2, <90

90

SBP, mm Hg, <140

140

AHD, Yes

No

# of SBP measurements, <24

24

≥

≥

≥

≥

0 2 4 6 8

Overall

SubgroupsAge, years, <60

60

Race, White

Black

DM, Yes

No

HTN, Yes

No

CHF, Yes

No

eGFR, ml/min/1.73m2, <90

90

SBP, mm Hg, <140

140

AHD, Yes

No

# of SBP measurements, <24

24

≥

≥

≥

≥

0.25 0.5 2 8 161 4 32 64

SD quartile 2 SD quartile 3 SD quartile 4

HR, 95% CI2.05 (1.95-2.14)3.63 (3.47-3.79)

6.60 (6.32-6.89)1.90 (1.79-2.02)3.31 (3.12-3.50)

6.26 (5.91-6.63)2.14 (1.99-2.29)3.72 (3.48-3.97)6.41 (6.00-6.83)2.08 (1.97-2.18)3.63 (3.46-3.81)6.54 (6.24-6.86)1.80 (1.60-2.02)3.37 (3.03-3.76)6.15 (5.53-6.85)2.00 (1.82-2.18)3.65 (3.36-3.97)6.75 (6.22-7.34)2.04 (1.94-2.16)3.55 (3.38-3.74)

6.36 (6.05-6.69)2.05 (1.93-2.19)3.67 (3.45-3.89)6.72 (3.45-3.89)1.99 (1.86-2.13)3.40 (3.19-3.63)5.96 (5.57-6.36)2.11 (1.66-2.69)3.32 (2.64-4.17)

5.10 (4.06-6.39)2.03 (1.93-2.12)3.60 (3.45-3.76)6.64 (6.36-6.94)2.06 (1.95-2.18)3.65 (3.46-3.85)6.55 (6.21-6.90)1.99 (1.84-2.15)3.50 (3.25-3.77)

6.48 (6.02-6.98)2.14 (2.03-2.25)3.72 (3.54-3.90)6.55 (6.24-6.87)1.65 (1.48-1.83)3.13 (2.84-3.46)5.92 (5.36-6.52)2.00 (1.89-2.12)3.48 (3.28-3.67)6.41 (6.06-6.78)2.00 (1.89-2.12)3.47 (3.28-3.67)6.41 (6.06-6.78)1.60 (1.47-1.74)2.33 (2.16-2.53)4.11 (3.80-4.44)1.93 (1.83-2.05)3.40 (3.22-3.58)6.15 (5.83-6.50)

HR, 95% CI1.33 (1.10-1.60)2.56 (2.16-3.03)

10.59 (9.02-12.40)1.57 (1.13-2.18)

4.37 (3.26-5.87)26.01 (19.61-34.49)

1.16 (0.93-1.46)1.62 (1.31-1.99)4.11 (3.38-5.01)1.28 (1.04-1.58)2.18 (1.81-2.63)7.83 (6.55-9.36)1.44 (0.91-2.30)4.06 (2.69-6.13)

20.63 (13.86-30.70)1.10 (0.84-1.44)2.15 (1.69-2.73)

10.97 (8.75-13.75)1.51 (1.17-1.96)2.92 (2.31-3.71)

8.26 (6.57-10.38)1.06 (0.86-1.32)1.88 (1.55-2.28)7.61 (6.34-9.14)1.72 (1.20-2.47)3.92 (2.81-5.48)

18.46 (13.45-25.34)0.72 (0.42-1.21)1.22 (0.77-1.94)3.52 (2.26-5.50)1.36 (1.12-1.66)2.60 (2.17-3.12)

11.44 (9.64-13.58)1.28 (1.04-1.59)2.45 (2.02-2.98)9.26 (7.70-11.12)1.38 (0.94-2.02)2.58 (1.83-3.65)

13.00 (9.36-18.04)1.36 (1.10-1.68)2.77 (2.28-3.37)

10.82 (9.00-13.00)1.09 (0.74-1.58)1.86 (1.31-2.64)8.31 (5.95-11.62)1.08 (0.83-1.41)2.01 (1.59-2.53)6.94 (5.56-8.65)1.48 (1.14-1.94)2.85 (2.24-3.64)

14.60 (11.60-18.38)1.48 (1.18-1.85)2.35 (1.91-2.90)5.35 (4.40-6.50)2.24 (1.53-3.27)

5.98 (4.20-8.52)30.97 (21.88-43.86)

C D

Overall

SubgroupsAge, years, <60

60

Race, White

Black

DM, Yes

No

HTN, Yes

No

CHF, Yes

No

eGFR, ml/min/1.73m2, <90

90

SBP, mm Hg, <140

140

AHD, Yes

No

# of SBP measurements, <24

24

≥

≥

≥

≥

Overall

SubgroupsAge, years, <60

60

Race, White

Black

DM, Yes

No

HTN, Yes

No

CHF, Yes

No

eGFR, ml/min/1.73m2, <90

90

SBP, mm Hg, <140

140

AHD, Yes

No

# of SBP measurements, <24

24

≥

≥

≥

≥

0.0 0.5 1.0 1.5 2.0 2.5 3.0 0 2 4 6 8

HR, 95% CI1.10 (1.08-1.11)1.32 (1.30-1.34)1.80 (1.78-1.82)1.27 (1.24-1.30)1.70 (1.67-1.74)

2.59 (2.53-2.65)1.03 (1.01-1.04)1.18 (1.16-1.19)1.54 (1.52-1.56)1.09 (1.08-1.11)1.30 (1.28-1.31)1.76 (1.74-1.78)1.15 (1.10-1.19)1.45 (1.40-1.51)

2.02 (1.94-2.09)1.02 (1.00-1.05)1.19 (1.17-1.22)1.65 (1.61-1.68)1.11 (1.10-1.13)

1.32 (1.33-1.37)1.83 (1.80-1.85)1.01 (0.99-1.02)1.18 (1.16-1.19)

1.62 (1.59-1.64)1.17 (1.15-1.19)

1.47 (1.45-1.50)2.05 (2.01-2.09)0.85 (0.82-0.88)0.84 (0.81-0.87)1.02 (0.98-1.06)1.10 (1.09-1.12)1.35 (1.33-1.37)1.87 (1.85-1.89)1.06 (1.05-1.08)1.25 (1.23-1.27)1.68 (1.66-1.70)1.20 (1.17-1.23)1.52 (1.49-1.56)2.14 (2.09-2.19)1.10 (1.09-1.12)1.32 (1.30-1.34)1.71 (1.69-1.73)

1.02 (0.99-1.05)1.24 (1.21-1.28)1.79 (1.75-1.84)

1.00 (0.98-1.02)1.15 (1.13-1.17)

1.56 (1.53-1.58)1.16 (1.14-1.18)1.45 (1.43-1.48)2.00 (1.97-2.04)

1.17 (1.15-1.18)1.42 (1.40-1.45)1.93 (1.90-1.96)1.48 (1.45-1.52)

2.04 (2.00-2.09)2.93 (2.86-3.00)

HR, 95% CI2.11 (2.02-2.19)3.59 (3.45-3.72)5.91 (5.70-6.14)2.00 (1.90-2.10)3.26 (3.11-3.42)

5.29 (5.04-5.54)2.16 (2.01-2.31)3.72 (3.49-3.96)6.11 (5.74-6.51)

2.09 (2.00-2.18)3.51 (3.37-3.66)5.70 (5.47-5.93)2.23 (1.96-2.55)4.19 (3.70-4.75)7.42 (6.56-8.40)1.99 (1.84-2.15)3.44 (3.21-3.70)5.69 (5.30-6.11)2.13 (2.01-2.26)3.58 (3.42-3.74)5.88 (5.62-6.15)2.05 (1.94-2.17)3.51 (3.33-3.70)5.90 (5.60-6.21)2.13 (2.01-2.26)3.53 (3.34-3.73)5.41 (5.11-5.74)1.88 (1.48-2.40)2.66 (2.12-3.36)3.97 (3.16-4.99)2.10 (2.02-2.19)3.60 (3.47-3.74)5.98 (5.76-6.21)2.09 (1.99-2.20)3.60 (3.44-3.77)5.95 (5.68-6.24)2.09 (1.96-2.24)3.45 (3.24-3.68)5.65 (5.30-6.02)2.13 (2.05-2.24)3.61 (3.46-3.77)5.79 (5.55-6.04)2.04 (1.84-2.25)3.64 (3.32-4.00)6.33 (5.77-6.95)1.99 (1.86-2.12)3.39 (3.19-3.61)5.59 (5.26-5.93)2.13 (2.03-2.25)3.60 (3.43-3.78)5.94 (5.66-6.24)1.66 (1.54-1.78)2.36 (2.20-2.53)3.75 (3.50-4.02)1.99 (1.89-2.09)3.35 (3.19-3.51)5.50 (5.24-5.77)

A B

SD quartile 1 served as a reference group. (A) Adjusted HRs and 95% CIs of all-cause mortality associated with different SD quartiles of mean blood pressure

in various subgroups. (B) Adjusted HRs and 95% CIs of incident coronary artery disease associated with different SD quartiles of mean blood pressure in

various subgroups. (C) Adjusted HRs and 95% CIs of incident stroke associated with different SD quartiles of mean blood pressure in various subgroups.

(D) Adjusted HRs and 95% CIs of incident end-stage renal disease associated with different SD quartiles of mean blood pressure in various subgroups.

AHD ¼ antihypertensive drugs; CHF ¼ congestive heart failure; DM ¼ diabetes mellitus; HTN ¼ hypertension; other abbreviations as in Figures 1 and 2.

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CENTRAL ILLUSTRATION Systolic Blood Pressure Variability: Clinical Outcomes

Gosmanova, E.O. et al. J Am Coll Cardiol. 2016;68(13):1375–86.

A variety of intrinsic and extrinsic factors influence visit-to-visit systolic blood pressure variability, which was measured using SDs of normally

distributed SBP values. As SD quartiles rose, so too did risk of all-cause mortality, incident CHD, stroke, and ESRD. BP ¼ blood pressure;

ESRD ¼ end-stage renal disease; CHD ¼ coronary heart disease; CI ¼ confidence interval; Q ¼ quartile; SD ¼ standard deviation.

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PERSPECTIVES

COMPETENCY IN MEDICAL KNOWLEDGE:

Greater long-term SBPV is associated with increased

risks of all-cause mortality, coronary disease, stroke,

and end-stage renal disease, independent of diagnosis

or treatment of hypertension.

TRANSLATIONAL OUTLOOK: Additional research

is needed to understand the impact on clinical out-

comes of interventions that reduce SBPV.

Gosmanova et al. J A C C V O L . 6 8 , N O . 1 3 , 2 0 1 6

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The methods for assessing long-term or VVV in BPare not standardized. It has been argued that SD ofSBPV is directly related to BP levels; therefore, highSD would be seen in patients with higher BP. How-ever, it has been shown in several reports that theSD of intervisit SBP measurements correlates withother more complex methods of VVV assessment,which are independent from mean SBP, such asvariation independent of mean, average successivevariability (17), and SDreg-, the SD about the partici-pant’s regression line with regressed SBP acrossvisits (18).

VVV is more than a measurement error because itwas observed even in trials that aggressively stan-dardized BP determination techniques and moni-tored adherence. Moreover, VVV derived even fromroutine office “unstandardized” BP values was wellreproducible (11). The reasons behind increases inVVV are unclear but several mechanisms were pro-posed (Central Illustration), such as nonadherence toBP medications (40), changes in the elastic proper-ties of blood vessels and aortic distensibility (41),and disturbed baroreflex function leading to exag-gerated pressor response to physical and emotionalstimuli as well as social and lifestyle factors (42). Inturn, increased VVV caused greater stress on bloodvessels and endothelial dysfunction promoting earlytarget-organ damage (43). VVV was shown to corre-late positively with pulse wave velocity and nega-tively with ankle-brachial index, suggesting a linkbetween impaired vascular function and VVV (19).Interestingly, limited data suggested that antihy-pertensive drug classes are associated with differ-ential effects on VVV. A meta-analysis of 389randomized controlled trials of antihypertensivemedications demonstrated that when antihyperten-sive drug classes were compared with each other,calcium channel blockers and nonloop diureticagents were associated with respective reductions of19% (p < 0.001) and 13% (p ¼ 0.007) in VVV in SBP;whereas angiotensin-converting enzyme inhibitors,angiotensin receptor blockers, and beta blockerswere associated with increases in SBPV of 8%(p ¼ 0.008), 16% (p < 0.001), and 17% (p < 0.001),respectively (44). Only calcium channel blockers, asan antihypertensive drug class, were associated witha reduction in SBPV compared with placebo (44).Future studies are needed to better understand theeffects of antihypertensive medications on SBPV as apotential intervention to reduce the risks associatedwith high VVV in BP.

STUDY LIMITATIONS. The strengths of our studyincluded its large sample size of almost 3 million

individuals, the use of a large number of BP mea-surements to calculate SBPV variability, and itsrepresentativeness of veterans across the UnitedStates. However, several limitations of our analysisneed to be recognized. This was an observationalstudy; therefore, we only reported associations andcannot make inferences about the causality of SBPV.Although we used adjusted analyses accounting fornumerous baseline patient characteristics, we cannotexclude the effect of potential unmeasured con-founders on our results. Additionally, our cohortconsisted of predominantly male veterans; thus,these results may not be generalizable to women or togeneral populations. Previous studies have shownsome discrepancies in BP variation between men andwomen as well as, perhaps, different levels of prog-nostic significance between men and women (22). Notall potential confounders affecting outcomes wereincluded, such as smoking status and baseline pro-teinuria, because the data on these variables were notavailable in sufficient numbers for analysis. Westudied SBPV, but did not examine separately dia-stolic BP variability; however, it has been shown inseveral previous studies that VVV variability in dia-stolic BP had poor correlation with stroke (17) andall-cause mortality (14).

CONCLUSIONS

Greater SBPV was associated with higher risk ofall-cause mortality, CHD, stroke, and ESRD. In addi-tion to being an important prognostic indicator, SBPVmay also become a therapeutic target. Future studiesare needed to examine the effects of interventionsthat lower SBPV on clinical outcomes.

REPRINT REQUESTS AND CORRESPONDENCE: Dr.Csaba P. Kovesdy, Division of Nephrology, MemphisVA Medical Center, 1030 Jefferson Avenue, Memphis,Tennessee 38104. E-mail: ckovesdy@uthsc.edu.

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KEY WORDS hypertension, outcomes,visit-to-visit variability

APPENDIX For supplemental tables andfigures, please see the online version ofthis article.