Corinne V Evans

Diagnostic and Predictive Accuracy of Blood Pressure Screening Methods With Consideration of Rescreening Intervals: A Systematic Review for the U.S. Preventive Services Task Force

Nearly 1 in 3 U.S. adults has high blood pressure (BP), including two thirds of those aged 60 years or older (1). Elevated BP is the largest contributing risk factor to all-cause and cardiovascular mortality (2). Despite the clear importance of accurate diagnosis of high BP, recommendations for BP measurement protocols and rescreening intervals are not based on systematic reviews of the literature (3, 4), and recommended protocols, such as repeated measurements, are rarely followed in routine health care settings (59). To help address these issues, newer measurement methods have been developed to reduce error, simplify performance of repeated measurements, evaluate BP throughout the 24-hour cycle, and allow use in nonmedical settings. Evidence-based measurement methods and rescreening intervals could improve the benefits and efficiency of BP screening. In 2007, the U.S. Preventive Services Task Force (USPSTF) reaffirmed its 2003 A recommendation to screen for high BP in adults aged 18 years or older (10). In 2003, a synthesis of indirect evidence for BP screening found good-quality evidence that treatment of high BP in adults substantially decreases the incidence of cardiovascular events (11). Both reviews found that screening and treatment for high BP cause few major harms (11, 12). Given the strong evidence base for the previous recommendations and recently updated guidelines for BP control (4, 13), the USPSTF did not believe that updating the indirect evidence path was necessary. However, the previous systematic reviews did not identify a BP measurement reference standard, address diagnostic accuracy of BP measurement methods and protocols, or determine the most appropriate rescreening interval. Our evidence review was designed to address these important aspects of screening for high BP and update the direct evidence of benefits and harms of screening. Methods To conduct this review, we developed an analytic framework with 5 key questions (Appendix Figure 1) that examined direct evidence for the benefits and harms of screening for high BP (key questions 1 and 5, respectively), diagnostic accuracy of office BP measurement (OBPM) (key question 2), prediction of cardiovascular events by BP method and diagnostic accuracy of nonoffice measurement (key question 3), and rescreening interval (key question 4). Detailed methods are available in our full evidence report (14). The analytic framework, review questions, and methods for locating and qualifying evidence were posted on the USPSTF Web site for public comment before we started the review, and the final versions reflect public input. Appendix Figure 1. Analytic framework. ABPM = ambulatory blood pressure monitoring; BP = blood pressure; CHD = coronary heart disease; CVD = cardiovascular disease; ESKD = end-stage kidney disease; HBPM = home blood pressure monitoring; HF = heart failure. * Defined as the threshold for pharmacologic treatment. Data Sources and Searches We searched MEDLINE, PubMed, the Cochrane Central Register of Controlled Trials, and CINAHL from 2003 through 8 August 2014 to update benefits and harms of screening for high BP. We searched the same databases (excluding CINAHL) through 24 February 2014 as follows: starting in 1992 (to allow for implementation of the first guidelines for validation of BP monitoring devices [15]) for prediction of cardiovascular events by BP method and diagnostic accuracy of nonoffice measurement, and starting in 1966 (the beginning of MEDLINE) for rescreening interval. On the basis of the findings from these updated searches, we did not further update them because any studies we found would probably not have changed the overall conclusions. We also searched bibliographies of relevant reviews, included studies, and publication lists of highly referenced studies. Study Selection Two investigators independently reviewed abstracts and full-text articles against prespecified inclusion and exclusion criteria (14). We required all studies to have enrolled untreated adults and to have been conducted in countries rated as very high on the 2013 Human Development Index (16). For prediction of cardiovascular events, we allowed studies that included treated patients because a proportion of persons followed over time would inevitably begin treatment. Ambulatory BP monitoring (ABPM) and home BP monitoring (HBPM) devices were eligible for use in confirming an initially elevated OBPM result. For screening benefits and harms, cardiovascular events we analyzed included fatal or nonfatal myocardial infarction; sudden cardiac death; stroke; heart failure; atrial fibrillation; transient ischemic attack; end-stage kidney disease; or a composite of any of the aforementioned events, excluding cardiovascular symptoms, angina, revascularization, carotid intimamedia thickness, and left ventricular hypertrophy. For diagnostic accuracy of OBPM, we included studies that compared different office-based devices or measurement protocols and reported sensitivity, specificity, predictive values, or concordance (for example, ). For diagnostic accuracy of confirmatory BP measurement methods, eligible study populations had an initial elevated office BP at screening, which allowed for reporting or calculation of the positive predictive value (PPV). For prediction of cardiovascular events, eligible studies followed a cohort of patients over time and reported the associations (hazard or risk ratios) of BP as a continuous variable, measured by at least 2 methods at baseline, with data on overall mortality or cardiovascular events collected during follow-up. For rescreening interval, we included studies that followed cohorts of initially nonhypertensive adults over time and reported hypertension incidence at rescreening intervals of up to 6 years. Data Extraction and Quality Assessment One investigator abstracted data from all included studies, and a second checked for accuracy. Two investigators independently assessed the quality of included studies by using predefined, design-specific criteria (1719). We rated study quality as good, fair, or poor and excluded all poor-quality studies (17). We resolved disagreements about quality through discussion with a third investigator. Where reported, studies with various threats to internal validity were downgraded to fair-quality according to USPSTF standards (17). Data Synthesis and Analysis We qualitatively described the results on the benefits and harms of screening. Per our protocol, we first calculated the diagnostic accuracy of OBPM by using the recommendations of the American Heart Association as the reference standard because there is no gold standard for BP measurement (3). With the subsequent identification of ABPM as the best predictor of cardiovascular events, we calculated the diagnostic accuracy of OBPM and confirmatory BP measurement methods by using ABPM as the reference standard where possible. We qualitatively described all diagnostic accuracy results because data were insufficient for quantitative synthesis. For prediction of cardiovascular events, we combined fatal and nonfatal events within outcome categories (cardiovascular, stroke, and cardiac). Risk was most commonly reported as the hazard ratio associated with each 10mm Hg increase in systolic BP and each 5mm Hg increase in diastolic BP. We converted hazard ratios to these common increments if they were reported differently (14). We depicted the hazard ratios in forest plots for qualitative evaluation; because of the small numbers of studies for each outcome and heterogeneity across studies, we did not calculate summary meta-analytic estimates of risk to determine the best BP measurement method for prediction. We conducted exploratory meta-analyses to compare ABPM protocols (24-hour, daytime, and nighttime) by generating estimates of cardiovascular events or mortality risk for each protocol by using the DerSimonianLaird random-effects method (20). In sensitivity analyses, these results were compared to estimates generated by using profile likelihood (21) and KnappHartung methods (22). For rescreening, we pooled reported incidence rates across all studies to generate a weighted mean incidence at yearly intervals (reported within0.5 year). We qualitatively examined within-study comparisons among a priori subgroups of age, BP, sex, body mass index (BMI), smoking status, and race/ethnicity (14). When constructing the overall summary of evidence (Appendix Table 1), we evaluated included studies within the context of each review question for consistency of results for important outcomes and relevance to primary care. Appendix Table 1. Overall Summary of Evidence, by Key Question Role of the Funding Source Staff from the Agency for Healthcare Research and Quality (AHRQ) provided oversight for the project and assisted in external review of the companion draft evidence synthesis. Liaisons for the USPSTF helped to resolve issues about the scope of the review but were not involved in the conduct of the review. Results We reviewed 19309 abstracts and 1171 articles for possible inclusion (Appendix Figure 2). Appendix Figure 2. Summary of evidence search and selection. KQ = key question. * Surveillance search results through August 2014 for trials reporting direct benefits of screening were not included; no additional trials were identified. Benefits of Screening for High BP For direct evidence of screening benefit, we included only randomized, controlled trials (RCTs) that reported changes in health outcomes as a result of screening for hypertension compared with no screening. We identified 1 good-quality cluster RCT of a community pharmacybased BP screening program targeting adults aged 65 years or older (23). Trained volunteer health educators also provided participants with educational materials and resources to support self-management. This trial found fewer annual composite cardiovascular-related hospitalizations in the intervention group than in t

Behavioral Counseling to Promote a Healthy Lifestyle in Persons With Cardiovascular Risk Factors: A Systematic Review for the U.S. Preventive Services Task Force

Decreases in cardiovascular mortality rates in recent decades have been attributed, in part, to improvements in modifiable risk factors (1). A substantial portion of the U.S. population has at least one modifiable risk factor for cardiovascular disease (CVD) (such as hypertension, dyslipidemia, impaired fasting glucose, the metabolic syndrome, and cigarette smoking) (27). Despite convincing evidence that healthy diet and physical activity are associated with important health outcomes, including reduction in cardiovascular events and mortality rates (817), U.S. adults are not meeting recommendations for healthy diet and physical activity (1820). Likewise, nutrition and exercise counseling practices in primary care remain suboptimal, even for persons at high risk for CVD (2124). In 2012, the U.S. Preventive Services Task Force (USPSTF) recommended that clinicians consider selectively providing or referring adults without preexisting CVD or risk factors for intensive behavioral counseling interventions (C recommendation) (25). The USPSTF subsequently recommended that clinicians screen all adults for obesity and offer or refer obese patients to intensive, multicomponent behavioral interventions (B recommendation) (26). This systematic review was designed to complement the existing reviews that supported the 2012 USPSTF recommendations and to support the USPSTF in updating its 2002 and 2003 recommendations on healthy diet and physical activity counseling in persons with known cardiovascular risk factors (27, 28). To conduct this review, we developed an analytic framework with 4 key questions (Supplement 1) that included the effect of dietary or physical activity counseling on patient health outcomes (question 1), intermediate cardiovascular diseaserelated outcomes (question 2), behavioral outcomes (question 3), and the harms of counseling (question 4). Supplement 1. Analytic Framework Methods Detailed methods, including search strategies; detailed inclusion criteria; and excluded studies are publically available in our full evidence report (29). Data Sources and Searches We searched MEDLINE, PubMed, PsycINFO, the Database of Abstracts of Reviews of Effects, and the Cochrane Central Register of Controlled Trials from January 2001 to October 2013. We supplemented our searches with suggestions from experts and reference lists from other relevant systematic reviews. Study Selection Two investigators independently reviewed 7218 abstracts and 553 full-text articles against a priorispecified inclusion criteria (Supplement 2). We included studies in adults who had at least 1 cardiovascular risk factor, including hypertension, dyslipidemia, impaired fasting glucose or glucose tolerance, the metabolic syndrome, and cigarette smoking. We excluded studies limited to persons with known diabetes (considered a CVD risk equivalent), coronary artery disease, cerebrovascular disease, peripheral artery disease, or severe chronic kidney disease. We also excluded populations at increased risk for CVD (such as those who are obese, physically inactive, and prehypertensive) but without other CVD risk factors because these bodies of evidence were considered in previous reviews (30, 31) and USPSTF recommendations (25, 26). We included behaviorally based counseling interventions to promote a healthy diet or physical activity, delivered alone or as part of a multicomponent intervention. We excluded interventions that provided controlled diets or supervised exercise, as opposed to interventions aimed at evaluating whether counseling could change behavior. Supplement 2. Literature Flow Diagram We limited studies of efficacy or effectiveness to fair- or good-quality randomized, controlled trials or controlled clinical trials that had at least 6 months of follow-up, were done in developed countries, and published their results in 1990 or later. Included trials had to have a control group (such as usual care, a minimal intervention, or attention control). We examined health outcomes (such as morbidity or mortality related to CVD), intermediate health outcomes (such as physiologic measures of blood pressure, lipid and glucose, and weight; diabetes incidence; medication use; and composite CVD risk scores), and behavioral outcomes (such as self-reported dietary intake and physical activity or objectively measured markers of behavior change [such as VO2max or urinary sodium]). We also included observational studies that reported serious harms (that is, adverse events resulting in unexpected or unwanted medical attention). Data Extraction and Quality Assessment One reviewer extracted population characteristics, study design elements, intervention and control characteristics, and study results into standardized evidence tables. A second reviewer checked the data for accuracy. Articles that met our inclusion criteria were critically appraised by 2 reviewers independently using the USPSTF and National Institute for Health and Care Excellence criteria (32, 33). We rated articles as good-, fair-, or poor-quality. Good-quality studies generally met all criteria, whereas fair-quality studies did not meet all criteria but had no known important limitation that could invalidate its results. Poor-quality studies had important limitations that were considered fatal flaws (for example, more than 40% attrition with or without differential attrition between intervention groups; lack of randomization with biased assignment of participants to intervention groups, often with differences in baseline characteristics or no reporting of baseline characteristics; per protocol analyses only; and description of methods that did not allow adequate assessment of quality). These studies were excluded from this review. Data Synthesis and Analysis Because of the clinical heterogeneity across this body of evidence, we stratified our analyses according to the type of intervention (that is, a focus on dietary counseling alone, physical activity alone, or combined diet and physical activity counseling) and according to how study populations were targeted or defined (that is, dyslipidemia, hypertension, impaired fasting glucose or glucose tolerance, or mixed risk factors). We did random-effects meta-analyses for 5 or more studies using the DerSimonianLaird method to estimate the effect size of counseling on intermediate health outcomes (that is, systolic and diastolic blood pressure; total, high-density lipoprotein, and low-density lipoprotein cholesterol; triglycerides; fasting blood glucose; diabetes incidence; and weight or body mass index) (34). We did qualitative synthesis for health outcomes, behavioral outcomes, and harms. Outcome analyses were also stratified by length of follow-up after randomization (short term was less than 12 months, intermediate term was 12 to 24 months, and long term was greater than 24 months). We used stratified analyses, visual inspection of forest plots arranged by effect size, and/or meta-regressions to examine the effect of a priorispecified primary sources of heterogeneity on effect size: study population, intervention type, overall intervention intensity (low was less than 30 minutes of total contact, medium was 30 to 360 minutes, and high was more than 360 minutes), number of intervention contacts, duration of intervention, length of follow-up, overall study quality, year of publication, country setting, type of control group, and population risk (including average age; percentage of persons who smoke or have hypertension, dyslipidemia, or diabetes; average systolic blood pressure; average low-density lipoprotein cholesterol level; average body mass index; and use of medications). We assessed the presence of statistical heterogeneity among the studies using standard chi-square tests, and the magnitude of heterogeneity was estimated using the I 2 statistic (35). In instances of 10 or more studies, we formally assessed for publication bias and whether the distribution of the effect sizes was symmetrical with respect to the precision measure by using funnel plots and the Egger linear regression method (36, 37). We did all analyses using Stata, version 11.2. Role of the Funding Source Agency for Healthcare Research and Quality staff oversaw the project and assisted in external review of the companion draft evidence synthesis. Liaisons for the USPSTF helped to resolve issues about the scope of the review but were not involved in the conduct of the review. Results Description of Included Trials Seventy-four fair- or good-quality healthy lifestyle counseling trials in persons with cardiovascular risk factors met our inclusion criteria (Supplements 3 and 4). Forty-nine trials evaluated combined lifestyle counseling interventions, 18 diet-only interventions, and 10 physical activityonly interventions. Of the interventions evaluated, only 2 were low-intensity, 48 were medium-intensity, and 37 were high-intensity. Medium-intensity interventions had a median of 5 contacts (interquartile range [IQR], 3 to 8 contacts) and a median duration of 9 months (IQR, 4 to 11 months). High-intensity interventions had a median of 16 contacts (IQR, 9 to 31 contacts) and a median duration of 12 months (IQR, 8 to 18 months). Counseling interventions included didactic education as well as individualized care plans, problem-solving skills, and audit and feedback. Many trials included weight loss or weight goals for participants who were overweight. Some counseling interventions included cointerventions (such as smoking cessation counseling when applicable, protocols for medication adjustment, and provision of free or low-cost exercise options). Interventions were delivered by dietitians, nutritionists, physiotherapists, exercise professionals or consultants, or trained interventionists (such as health educators, psychologists, nurses, or case managers). Supplement 3. Trial Results in Health Outcomes (Key Question 1) Supplement 4. Included Studies and Outcomes, by Intervention

Screening for Obesity and Intervention for Weight Management in Children and Adolescents: Evidence Report and Systematic Review for the US Preventive Services Task Force

Importance Obesity is common in children and adolescents in the United States, is associated with negative health effects, and increases the likelihood of obesity in adulthood. Objective To systematically review the benefits and harms of screening and treatment for obesity and overweight in children and adolescents to inform the US Preventive Services Task Force. Data Sources MEDLINE, PubMed, PsycINFO, Cochrane Collaboration Registry of Controlled Trials, and the Education Resources Information Center through January 22, 2016; references of relevant publications; government websites. Surveillance continued through December 5, 2016. Study Selection English-language trials of benefits or harms of screening or treatment (behavior-based, orlistat, metformin) for overweight or obesity in children aged 2 through 18 years, conducted in or recruited from health care settings. Data Extraction and Synthesis Two investigators independently reviewed abstracts and full-text articles, then extracted data from fair- and good-quality trials. Random-effects meta-analysis was used to estimate the benefits of lifestyle-based programs and metformin. Main Outcomes and Measures Weight or excess weight (eg, body mass index [BMI]; BMI z score, measuring the number of standard deviations from the median BMI for age and sex), cardiometabolic outcomes, quality of life, other health outcomes, harms. Results There was no direct evidence on the benefits or harms of screening children and adolescents for excess weight. Among 42 trials of lifestyle-based interventions to reduce excess weight (N = 6956), those with an estimated 26 hours or more of contact consistently demonstrated mean reductions in excess weight compared with usual care or other control groups after 6 to 12 months, with no evidence of causing harm. Generally, intervention groups showed absolute reductions in BMI z score of 0.20 or more and maintained their baseline weight within a mean of approximately 5 lb, while control groups showed small increases or no change in BMI z score, typically gaining a mean of 5 to 17 lb. Only 3 of 26 interventions with fewer contact hours showed a benefit in weight reduction. Use of metformin (8 studies, n = 616) and orlistat (3 studies, n = 779) were associated with greater BMI reductions compared with placebo: −0.86 (95% CI, −1.44 to −0.29; 6 studies; I2 = 0%) for metformin and −0.50 to −0.94 for orlistat. Groups receiving lifestyle-based interventions offering 52 or more hours of contact showed greater improvements in blood pressure than control groups: −6.4 mm Hg (95% CI, −8.6 to −4.2; 6 studies; I2 = 51%) for systolic blood pressure and −4.0 mm Hg (95% CI, −5.6 to −2.5; 6 studies; I2 = 17%) for diastolic blood pressure. There were mixed findings for insulin or glucose measures and no benefit for lipids. Medications showed small or no benefit for cardiometabolic outcomes, including fasting glucose level. Nonserious harms were common with medication use, although discontinuation due to adverse effects was usually less than 5%. Conclusions and Relevance Lifestyle-based weight loss interventions with 26 or more hours of intervention contact are likely to help reduce excess weight in children and adolescents. The clinical significance of the small benefit of medication use is unclear.

Nontraditional Risk Factors in Cardiovascular Disease Risk Assessment: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force

Importance Incorporating nontraditional risk factors may improve the performance of traditional multivariable risk assessment for cardiovascular disease (CVD). Objective To systematically review evidence for the US Preventive Services Task Force on the benefits and harms of 3 nontraditional risk factors in cardiovascular risk assessment: the ankle-brachial index (ABI), high-sensitivity C-reactive protein (hsCRP) level, and coronary artery calcium (CAC) score. Data Sources MEDLINE, PubMed, and the Cochrane Central Register of Controlled Trials for studies published through May 22, 2017. Surveillance continued through February 7, 2018. Study Selection Studies of asymptomatic adults with no known cardiovascular disease. Data Extraction and Synthesis Independent critical appraisal and data abstraction by 2 reviewers. Main Outcomes and Measures Cardiovascular events, mortality, risk assessment performance measures (calibration, discrimination, or risk reclassification), and serious adverse events. Results Forty-three studies (N = 267 244) were included. No adequately powered trials have evaluated the clinical effect of risk assessment with nontraditional risk factors on patient health outcomes. The addition of the ABI (10 studies), hsCRP level (25 studies), or CAC score (19 studies) can improve both discrimination and reclassification; the magnitude and consistency of improvement varies by nontraditional risk factor. For the ABI, improvements in performance were the greatest for women, in whom traditional risk assessment has poor discrimination (C statistic change of 0.112 and net reclassification index [NRI] of 0.096). Results were inconsistent for hsCRP level, with the largest analysis (n = 166 596) showing a minimal effect on risk prediction (C statistic change of 0.0039, NRI of 0.0152). The largest improvements in discrimination (C statistic change ranging from 0.018 to 0.144) and reclassification (NRI ranging from 0.084 to 0.35) were seen for CAC score, although CAC score may inappropriately reclassify individuals not having cardiovascular events into higher-risk categories, as determined by negative nonevent NRI. Evidence for the harms of nontraditional risk factor assessment was limited to computed tomography imaging for CAC scoring (8 studies) and showed that radiation exposure is low but may result in additional testing. Conclusions and Relevance There are insufficient adequately powered clinical trials evaluating the incremental effect of the ABI, hsCRP level, or CAC score in risk assessment and initiation of preventive therapy. Furthermore, the clinical meaning of improvements in measures of calibration, discrimination, and reclassification risk prediction studies is uncertain.

An approach to addressing subpopulation considerations in systematic reviews: the experience of reviewers supporting the U.S. Preventive Services Task Force

BackgroundGuideline developers and other users of systematic reviews need information about whether a medical or preventive intervention is likely to benefit or harm some patients more (or less) than the average in order to make clinical practice recommendations tailored to these populations. However, guidance is lacking on how to include patient subpopulation considerations into the systematic reviews upon which guidelines are often based. In this article, we describe methods developed to consistently consider the evidence for relevant subpopulations in systematic reviews conducted to support primary care clinical preventive service recommendations made by the U.S. Preventive Services Task Force (USPSTF).Proposed approachOur approach is grounded in our experience conducting systematic reviews for the USPSTF and informed by a review of existing guidance on subgroup analysis and subpopulation issues. We developed and refined our approach based on feedback from the Subpopulation Workgroup of the USPSTF and pilot testing on reviews being conducted for the USPSTF. This paper provides processes and tools for incorporating evidence-based identification of important sources of potential heterogeneity of intervention effects into all phases of systematic reviews. Key components of our proposed approach include targeted literature searches and key informant interviews to identify the most important subpopulations a priori during topic scoping, a framework for assessing the credibility of subgroup analyses reported in studies, and structured investigation of sources of heterogeneity of intervention effects.ConclusionsFurther testing and evaluation are necessary to refine this proposed approach and demonstrate its utility to the producers and users of systematic reviews beyond the context of the USPSTF. Gaps in the evidence on important subpopulations identified by routinely applying this process in systematic reviews will also inform future research needs.

Screening for Peripheral Artery Disease Using the Ankle-Brachial Index: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force

Importance Peripheral artery disease (PAD) is associated with a high risk for cardiovascular events and poor ambulatory function, even in the absence of symptoms. Screening for PAD with the ankle-brachial index (ABI) may identify patients in need of treatment to improve health outcomes. Objective To systematically review evidence for the US Preventive Services Task Force on PAD screening with the ABI, the diagnostic accuracy of the test, and the benefits and harms of treatment of screen-detected PAD. Data Sources MEDLINE, PubMed, and the Cochrane Central Register of Controlled Trials for relevant English-language studies published between January 2012 and May 2, 2017. Surveillance continued through February 7, 2018. Study Selection Studies of unselected or generally asymptomatic adults with no known cardiovascular disease. Data Extraction and Synthesis Independent critical appraisal and data abstraction by 2 reviewers. Main Outcomes and Measures Cardiovascular morbidity; PAD morbidity; mortality; health-related quality of life; diagnostic accuracy; and serious adverse events. Results Five studies (N = 5864 participants) were included that examined the indirect evidence for the benefits and harms of screening and treatment of screen-detected PAD. No population-based screening trials evaluated the direct benefits or harms of PAD screening with the ABI alone. A single diagnostic accuracy study of the ABI compared with magnetic resonance angiography gold-standard imaging (n = 306) found low sensitivity (7%-34%) and high specificity (96%-100%) in a screening population. Two adequately powered trials (n = 4626) in asymptomatic populations with and without diabetes with a variably defined low ABI (⩽0.95 or ⩽0.99) showed no statistically significant effect of aspirin (100 mg daily) for composite CVD outcomes (adjusted hazard ratio [HR], 1.00 [95% CI, 0.81-1.23] and HR, 0.98 [95% CI, 0.76-1.26]). One trial (n = 3350) demonstrated no statistically significant increase in major bleeding events with the use of aspirin (adjusted HR, 1.71 [95% CI, 0.99- 2.97]) and no statistically significant increase in major gastrointestinal bleeding (relative risk, 1.13 [95% CI, 0.44-2.91]). Two exercise trials (n = 932) in screen-relevant populations reported no differences in quality of life, Walking Impairment Questionnaire walking distance, or symptoms at 12 and 52 weeks; no harms were reported. Conclusions and Relevance There was no direct evidence and limited indirect evidence on the benefits of PAD screening with the ABI in unselected or asymptomatic populations. Available studies suggest low sensitivity and lack of beneficial effect on health outcomes, but these studies have important limitations.

Framework for Using Risk Stratification to Improve Clinical Preventive Service Guidelines

People should only receive a preventive service if the potential benefits of the service outweigh the potential harms. Both benefits and risks may vary for different populations. Thus, it is clinically important to understand when and how guidelines for preventive services should be stratified according to the underlying risk of the population. For example, preventive services may be risk stratified with specific clinical recommendations based on age, sex, race/ethnicity, family history, genotype, behavior risks, or comorbidities. This paper articulates the conceptual approach and practical tools that were developed for consideration by the U.S. Preventive Services Task Force to determine if and how risk stratification should be incorporated into clinical guidelines. This approach is described in an algorithm with six sequential questions: (1) Are there clinically relevant subpopulations? (2) Are there credible subgroup analyses for these subpopulations? (3) Do subgroup analyses show clinically important differences? (4) Do these differences result in variation of net benefit, or does the evidence only exist in persons with a narrow spectrum of risk? (5) Can the subpopulations be easily identified? and (6) Does a well-validated multivariate risk tool improve identification of clinically relevant subpopulations compared with a simpler approach? This framework allows for a systematic approach to determine if and how to incorporate evidence for specific populations, a consistent application of critical thinking about this evidence, and transparent communication about the derivation of risk-stratified recommendations or evidence gaps.