Jeffrey A. Tice

Gastric banding or bypass: a systematic review comparing the two most popular bariatric procedures

OBJECTIVE Bariatric surgical procedures have increased exponentially in the United States. Laparoscopic adjustable gastric banding is now promoted as a safer, potentially reversible and effective alternative to Roux-en-Y gastric bypass, the current standard of care. This study evaluated the balance of patient-oriented clinical outcomes for laparoscopic adjustable gastric banding and Roux-en-Y gastric bypass. METHODS The MEDLINE database (1966 to January 2007), Cochrane clinical trials database, Cochrane reviews database, and Database of Abstracts of Reviews of Effects were searched using the key terms gastroplasty, gastric bypass, laparoscopy, Swedish band, and gastric banding. Studies with at least 1 year of follow-up that directly compared laparoscopic adjustable gastric banding with Roux-en-Y gastric bypass were included. Resolution of obesity-related comorbidities, percentage of excess body weight loss, quality of life, perioperative complications, and long-term adverse events were the abstracted outcomes. RESULTS The search identified 14 comparative studies (1 randomized trial). Few studies reported outcomes beyond 1 year. Excess body weight loss at 1 year was consistently greater for Roux-en-Y gastric bypass than laparoscopic adjustable gastric banding (median difference, 26%; range, 19%-34%; P < .001). Resolution of comorbidities was greater after Roux-en-Y gastric bypass. In the highest-quality study, excess body weight loss was 76% with Roux-en-Y gastric bypass versus 48% with laparoscopic adjustable gastric banding, and diabetes resolved in 78% versus 50% of cases, respectively. Both operating room time and length of hospitalization were shorter for those undergoing laparoscopic adjustable gastric banding. Adverse events were inconsistently reported. Operative mortality was less than 0.5% for both procedures. Perioperative complications were more common with Roux-en-Y gastric bypass (9% vs 5%), whereas long-term reoperation rates were lower after Roux-en-Y gastric bypass (16% vs 24%). Patient satisfaction favored Roux-en-Y gastric bypass (P=.006). CONCLUSION Weight loss outcomes strongly favored Roux-en-Y gastric bypass over laparoscopic adjustable gastric banding. Patients treated with laparoscopic adjustable gastric banding had lower short-term morbidity than those treated with Roux-en-Y gastric bypass, but reoperation rates were higher among patients who received laparoscopic adjustable gastric banding. Gastric bypass should remain the primary bariatric procedure used to treat obesity in the United States.

Using Clinical Factors and Mammographic Breast Density to Estimate Breast Cancer Risk: Development and Validation of a New Predictive Model

Context Existing breast cancer prediction tools do not account for breast density, a strong risk factor for breast cancer and have been studied in white women only. Contribution The authors developed a breast cancer risk prediction model that incorporates a measure of breast density routinely reported with mammography. Its predictions were accurate, but it had only modest ability to distinguish women who did not develop cancer from those who did, and it misclassified risk in some subgroups. Implication The model requires validation in additional populations. A breast cancer prediction model that incorporates breast density does well in some but not all domains of predicting risk. Its accuracy should be better characterized before it is used clinically. The Editors In 2007, breast cancer will have been diagnosed in more than 178000 women in the United States, and more than 40000 women will have died of breast cancer (1). Most of these women never had their risk for breast cancer assessed, and even fewer considered chemoprevention (25). Providing women with an estimate of their risk for breast cancer would provide an opportunity for them to consider options to decrease their risk. Women at low short-term risk for breast cancer may experience less anxiety about their health and would be less likely to benefit from prevention efforts. Women at very high risk may warrant additional screening tests, such as breast magnetic resonance imaging (6), and might benefit from chemoprevention of breast cancer with tamoxifen or raloxifene. The standard risk assessment model available to practitioners (the Gail model) (7) identifies only a minority of women who eventually develop breast cancer being at high risk (8). Better breast cancer risk prediction tools are needed (9). The radiographic appearance of the breast has been consistently shown to be a major risk factor for breast cancer, whether it is defined by a qualitative assessment of the parenchymal pattern or a quantitative measure of percentage of density (1012). Women in whom more than 50% of total breast area is mammographically dense have high breast density and are at 3- to 5-fold greater risk for breast cancer than women in whom breast density is less than 25% (10, 1316). The increased risk for breast cancer associated with breast density is due in part to the lower sensitivity of mammography in dense breasts (1719), but the association remains strong after accounting for masking (20, 21). Mammographically dense breast tissue is rich in epithelium and stroma (10), and the association could represent activation of epithelial cells or fibroblasts (2225). Recently, several models have been published that incorporate breast density: One uses a continuous measure of breast density that is not available to clinicians and has not been validated (26), and the other predicts 1-year risk for breast cancer (27). We previously demonstrated that a simple model based on age, ethnicity, and a categorical measure of breast density had predictive accuracy similar to that of the Gail model in a multiethnic cohort of women receiving screening mammograms in northern California (28). We expand on that work by using data from more than 1 million ethnically diverse women throughout the United States to develop and validate a risk assessment tool that incorporates breast density and therefore might improve breast cancer screening and prevention efforts. Methods Study Population We included 1095484 women age 35 years or older who had had at least 1 mammogram with breast density measured by using the Breast Imaging Reporting and Data System (BI-RADS) classification system in any of the 7 mammography registries participating in the National Cancer Institutefunded Breast Cancer Surveillance Consortium (BCSC) (available at breastscreening.cancer.gov) (29). The BCSC is a community-based, ethnically and geographically diverse sample that broadly represents the United States (30). We excluded women who had a diagnosis of breast cancer before their first eligible mammography examination. Because our goal was to develop a model of long-term risk for invasive breast cancer, we excluded women with cancer diagnosed in the first 6 months of follow-up to minimize the number of cases of cancer included in the model that were diagnosed on the basis of the mammogram used for risk assessment. Women were also excluded if they had breast implants. Women in whom ductal carcinoma in situ was diagnosed were censored at the time of diagnosis in the primary analysis. When women had several mammograms, we based our analysis on findings from the first mammogram. Each registry obtains annual approval from its institutional review board for consenting processes or a waiver of consent, enrollment of participants, and ongoing data linkage for research purposes. All registries have received a Certificate of Confidentiality from the federal government that protects the identities of research participants. Measurement of Risk Factors Patient information was obtained primarily from self-report at the time of mammography. We selected 2 risk factors in addition to breast density for inclusion in the model on the basis of simplicity (yes or no) and a high attributable risk: history of breast cancer in a first-degree relative and history of a breast biopsy. Body mass index was later considered for addition to the model, but it was excluded to maintain parsimony and because it had minimal effect on model discrimination (the increase in the concordance statistic [c-statistic] was only 0.003). For modeling and validation, missing data for relatives with breast cancer and number of breast biopsies were set to 0. The 5-year Gail risk was computed for each woman by using the algorithms provided by the National Cancer Institute to calculate the Gail model risk for individual women (31). For Gail model calculations, missing data were coded as specified by that model (age at menarche as14 years, age at first live birth as<20 years, number of breast biopsies as 0, and number of first-degree relatives as 0). Ethnicity was coded by using the expanded race and ethnicity definition currently used in the Surveillance, Epidemiology, and End Results (SEER) database and U.S. Vital Statistics (non-Hispanic White, non-Hispanic Black, Asian or Pacific Islander, Native American/Alaskan Native, Hispanic, or other). We classified women who self-identified as mixed or other race with participants who did not report race and ethnicity. Breast Density Community radiologists at each site classified breast density on screening mammograms as part of routine clinical practice by using the American College of Radiology BI-RADS density categories (32): almost entirely fat (category 1), scattered fibroglandular densities (category 2), heterogeneously dense (category 3), and extremely dense (category 4). The BI-RADS category 2 was used as the reference group for breast density because it formed the largest group. Ascertainment of Breast Cancer Cases Breast cancer outcomes (invasive cancer and ductal carcinoma in situ) were obtained at each site through linkage with the regional population-based SEER program, state tumor registries, and pathology databases. Vital Status Vital status was obtained through linkage to SEER registries, state tumor registries, and the individual state vital statistics or the National Death Index. Model Development We used a proportional hazards model of invasive breast cancer to estimate the hazard ratios for each BI-RADS breast density category. Women entered the model 6 months after the index mammogram and were censored at the time of death, diagnosis of ductal carcinoma in situ, or the end of follow-up. All models were adjusted for age (in 5-year intervals) and race and ethnicity. The strength of the breast density association with breast cancer was greater for women younger than age 65 years (P for interaction< 0.001). Thus, separate models were fitted for women younger than age 65 years and for women age 65 years or older. No other interaction terms were included in the final model. We calculated similar estimates for first-degree relatives with breast cancer (yes or no) and a personal history of breast biopsy (yes or no) from the BCSC. All predictors met the proportional hazards assumption that was assessed by loglog plots and by including interaction terms with time for each predictor variable. We then developed an absolute risk model by using methods described in the Appendix Figure. The model primarily estimates predicted incidence of invasive breast cancer by using age, race or ethnicity, and breast density. These estimates are then adjusted for family history and biopsy history if available. We based our estimates of breast cancer incidence on the SEER age- and ethnicity-specific risk for invasive breast cancer (1992 to 2002) (33). Age-specific incidence for each ethnic group was estimated by fitting a third-order polynomial model to the SEER data. Age-specific incidence rates for the Native American and Alaskan Native group were inconsistent in SEER, so we excluded this group from further analyses. We calculated the baseline risk for the model by adjusting SEER incidence for the populations attributable risk for each breast density subgroup. We estimated the age- and ethnicity-specific distribution of mammographic breast density needed for these calculations by using data from a larger set of 3343047 mammograms from the BCSC. The distribution of breast density varied statistically significantly by age and by race or ethnicity (P< 0.001 for each comparison). The model used these variations by age and race to distribute the 5-year risk for invasive breast cancer across the 4 breast density subgroups. We used the methods described by Gail and colleagues (7) to translate the hazard ratios and risk factor distributions into absolute risks. The age-, sex-, and ethnicity-specific competing risks for death for women were calcula

Prevention of Breast Cancer in Postmenopausal Women: Approaches to Estimating and Reducing Risk

BACKGROUND It is uncertain whether evidence supports routinely estimating a postmenopausal womans risk of breast cancer and intervening to reduce risk. METHODS We systematically reviewed prospective studies about models and sex hormone levels to assess breast cancer risk and used meta-analysis with random effects models to summarize the predictive accuracy of breast density. We also reviewed prospective studies of the effects of exercise, weight management, healthy diet, moderate alcohol consumption, and fruit and vegetable intake on breast cancer risk, and used random effects models for a meta-analyses of tamoxifen and raloxifene for primary prevention of breast cancer. All studies reviewed were published before June 2008, and all statistical tests were two-sided. RESULTS Risk models that are based on demographic characteristics and medical history had modest discriminatory accuracy for estimating breast cancer risk (c-statistics range = 0.58-0.63). Breast density was strongly associated with breast cancer (relative risk [RR] = 4.03, 95% confidence interval [CI] = 3.10 to 5.26, for Breast Imaging Reporting and Data System category IV vs category I; RR = 4.20, 95% CI = 3.61 to 4.89, for >75% vs <5% of dense area), and adding breast density to models improved discriminatory accuracy (c-statistics range = 0.63-0.66). Estradiol was also associated with breast cancer (RR range = 2.0-2.9, comparing the highest vs lowest quintile of estradiol, P < .01). Most studies found that exercise, weight reduction, low-fat diet, and reduced alcohol intake were associated with a decreased risk of breast cancer. Tamoxifen and raloxifene reduced the risk of estrogen receptor-positive invasive breast cancer and invasive breast cancer overall. CONCLUSIONS Evidence from this study supports screening for breast cancer risk in all postmenopausal women by use of risk factors and breast density and considering chemoprevention for those found to be at high risk. Several lifestyle changes with the potential to prevent breast cancer should be recommended regardless of risk.

Mammographic Breast Density and the Gail Model for Breast Cancer Risk Prediction in a Screening Population

SummaryBackground. Estimating an individual woman’s absolute risk for breast cancer is essential for decision making about screening and preventive recommendations. Although the current standard, the Gail model, is well calibrated in populations, it performs poorly for individuals. Mammographic breast density (BD) may improve the predictive accuracy of the Gail model.Methods. Prospective observational cohort of 81,777 women in the San Francisco Mammography Registry presenting for mammography during 1993 through 2002 who had no prior diagnosis of breast cancer. Breast density was rated by clinical radiologists using the Breast Imaging Reporting and Data System classification (almost entirely fat; scattered fibroglandular densities; heterogeneously dense; extremely dense). Breast cancer cases were identified through linkage to Northern California Surveillance Epidemiology End Results (SEER) program. We compared the predictive accuracy of models with Gail risk, breast density, and the combination. All models were adjusted for age and ethnicity.Results. During 5.1 years of follow-up, 955 women were diagnosed with invasive breast cancer. The Gail model had modest predictive accuracy (concordance index (c-index) 0.67; 95% CI 0.65–0.68). Adding breast density to the model increased the predictive accuracy to 0.68 (95% CI .66–.70, p < 0.01 compared with the Gail model alone). The model containing only breast density adjusted for age and ethnicity had predictive accuracy equivalent to the Gail model (c-index 0.67, 95% CI 0.65–0.68).Conclusion. The addition of breast density measured by BI-RADS categories minimally improved the predictive accuracy of the Gail model. A model based on breast density alone adjusted for age and ethnicity was as accurate as the Gail model.

Cost-effectiveness of PCSK9 Inhibitor Therapy in Patients With Heterozygous Familial Hypercholesterolemia or Atherosclerotic Cardiovascular Disease

IMPORTANCE Proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors were recently approved for lowering low-density lipoprotein cholesterol in heterozygous familial hypercholesterolemia (FH) or atherosclerotic cardiovascular disease (ASCVD) and have potential for broad ASCVD prevention. Their long-term cost-effectiveness and effect on total health care spending are uncertain. OBJECTIVE To estimate the cost-effectiveness of PCSK9 inhibitors and their potential effect on US health care spending. DESIGN, SETTING, AND PARTICIPANTS The Cardiovascular Disease Policy Model, a simulation model of US adults aged 35 to 94 years, was used to evaluate cost-effectiveness of PCSK9 inhibitors or ezetimibe in heterozygous FH or ASCVD. The model incorporated 2015 annual PCSK9 inhibitor costs of

Genetic Testing Before Anticoagulation? A Systematic Review of Pharmacogenetic Dosing of Warfarin

14,350 (based on mean wholesale acquisition costs of evolocumab and alirocumab); adopted a health-system perspective, lifetime horizon; and included probabilistic sensitivity analyses to explore uncertainty. EXPOSURES Statin therapy compared with addition of ezetimibe or PCSK9 inhibitors. MAIN OUTCOMES AND MEASURES Lifetime major adverse cardiovascular events (MACE: cardiovascular death, nonfatal myocardial infarction, or stroke), incremental cost per quality-adjusted life-year (QALY), and total effect on US health care spending over 5 years. RESULTS Adding PCSK9 inhibitors to statins in heterozygous FH was estimated to prevent 316,300 MACE at a cost of

Exercise tolerance testing to screen for coronary heart disease: a systematic review for the technical support for the U.S. Preventive Services Task Force.

503,000 per QALY gained compared with adding ezetimibe to statins (80% uncertainty interval [UI],

The relation of C-reactive protein levels to total and cardiovascular mortality in older U.S. women

493,000-

Relation of serum ascorbic acid to mortality among US adults.

1,737,000). In ASCVD, adding PCSK9 inhibitors to statins was estimated to prevent 4.3 million MACE compared with adding ezetimibe at

Effects of isoflavones on breast density in pre- and post-menopausal women: a systematic review and meta-analysis of randomized controlled trials

414,000 per QALY (80% UI,