Diagnosis and Classification of Diabetes Mellitus

Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.

/pdf/missing-data-our-view-of-the-state-of-the-art-3xnwc88wmd.pdf

Missing data: Our view of the state of the art.

XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

WRITING GROUP MEMBERS Emelia J. Benjamin, MD, SCM, FAHA Michael J. Blaha, MD, MPH Stephanie E. Chiuve, ScD Mary Cushman, MD, MSc, FAHA Sandeep R. Das, MD, MPH, FAHA Rajat Deo, MD, MTR Sarah D. de Ferranti, MD, MPH James Floyd, MD, MS Myriam Fornage, PhD, FAHA Cathleen Gillespie, MS Carmen R. Isasi, MD, PhD, FAHA Monik C. Jiménez, ScD, SM Lori Chaffin Jordan, MD, PhD Suzanne E. Judd, PhD Daniel Lackland, DrPH, FAHA Judith H. Lichtman, PhD, MPH, FAHA Lynda Lisabeth, PhD, MPH, FAHA Simin Liu, MD, ScD, FAHA Chris T. Longenecker, MD Rachel H. Mackey, PhD, MPH, FAHA Kunihiro Matsushita, MD, PhD, FAHA Dariush Mozaffarian, MD, DrPH, FAHA Michael E. Mussolino, PhD, FAHA Khurram Nasir, MD, MPH, FAHA Robert W. Neumar, MD, PhD, FAHA Latha Palaniappan, MD, MS, FAHA Dilip K. Pandey, MBBS, MS, PhD, FAHA Ravi R. Thiagarajan, MD, MPH Mathew J. Reeves, PhD Matthew Ritchey, PT, DPT, OCS, MPH Carlos J. Rodriguez, MD, MPH, FAHA Gregory A. Roth, MD, MPH Wayne D. Rosamond, PhD, FAHA Comilla Sasson, MD, PhD, FAHA Amytis Towfighi, MD Connie W. Tsao, MD, MPH Melanie B. Turner, MPH Salim S. Virani, MD, PhD, FAHA Jenifer H. Voeks, PhD Joshua Z. Willey, MD, MS John T. Wilkins, MD Jason HY. Wu, MSc, PhD, FAHA Heather M. Alger, PhD Sally S. Wong, PhD, RD, CDN, FAHA Paul Muntner, PhD, MHSc On behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee Heart Disease and Stroke Statistics—2017 Update

Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association

Author(s): Writing Group Members; Mozaffarian, Dariush; Benjamin, Emelia J; Go, Alan S; Arnett, Donna K; Blaha, Michael J; Cushman, Mary; Das, Sandeep R; de Ferranti, Sarah; Despres, Jean-Pierre; Fullerton, Heather J; Howard, Virginia J; Huffman, Mark D; Isasi, Carmen R; Jimenez, Monik C; Judd, Suzanne E; Kissela, Brett M; Lichtman, Judith H; Lisabeth, Lynda D; Liu, Simin; Mackey, Rachel H; Magid, David J; McGuire, Darren K; Mohler, Emile R; Moy, Claudia S; Muntner, Paul; Mussolino, Michael E; Nasir, Khurram; Neumar, Robert W; Nichol, Graham; Palaniappan, Latha; Pandey, Dilip K; Reeves, Mathew J; Rodriguez, Carlos J; Rosamond, Wayne; Sorlie, Paul D; Stein, Joel; Towfighi, Amytis; Turan, Tanya N; Virani, Salim S; Woo, Daniel; Yeh, Robert W; Turner, Melanie B; American Heart Association Statistics Committee; Stroke Statistics Subcommittee

Heart Disease and Stroke Statistics—2016 Update: A Report From the American Heart Association

Analyses of multivariate data are frequently hampered by missing values. Until recently, the only missing-data methods available to most data analysts have been relatively ad1 hoc practices such as listwise deletion. Recent dramatic advances in theoretical and computational statistics, however, have produced anew generation of flexible procedures with a sound statistical basis. These procedures involve multiple imputation (Rubin, 1987), a simulation technique that replaces each missing datum with a set of m > 1 plausible values. The rn versions of the complete data are analyzed by standard complete-data methods, and the results are combined using simple rules to yield estimates, standard errors, and p-values that formally incorporate missing-data uncertainty. New computational algorithms and software described in a recent book (Schafer, 1997a) allow us to create proper multiple imputations in complex multivariate settings. This article reviews the key ideas of multiple imputation, discusses the software programs currently available, and demonstrates their use on data from the Adolescent Alcohol Prevention Trial (Hansen & Graham, 199 I).

Multiple Imputation for Multivariate Missing-Data Problems: A Data Analyst's Perspective

Background: Low-carbohydrate diets remain popular despite a paucity of scientific evidence on their effectiveness. Objective: To compare the effects of a low-carbohydrate, ketogenic diet program with those of a low-fat, low-cholesterol, reduced-calorie diet. Design: Randomized, controlled trial. Setting: Outpatient research clinic. Participants: 120 overweight, hyperlipidemic volunteers from the community. Intervention: Low-carbohydrate diet (initially, <20 g of carbohydrate daily) plus nutritional supplementation, exercise recommendation, and group meetings, or low-fat diet (<30% energy from fat, <300 mg of cholesterol daily, and deficit of 500 to 1000 kcal/d) plus exercise recommendation and group meetings. Measurements: Body weight, body composition, fasting serum lipid levels, and tolerability. Results: A greater proportion of the low-carbohydrate diet group than the low-fat diet group completed the study (76% vs. 57%; P= 0.02). At 24 weeks, weight loss was greater in the low-carbohydrate diet group than in the low-fat diet group (mean change, -12.9% vs. -6.7%; P < 0.001). Patients in both groups lost substantially more fat mass (change, -9.4 kg with the low-carbohydrate diet vs. -4.8 kg with the low-fat diet) than fat-free mass (change, -3.3 kg vs. -2.4 kg, respectively). Compared with recipients of the low-fat diet, recipients of the low-carbohydrate diet had greater decreases in serum triglyceride levels (change, -0.84 mmol/L vs. -0.31 mmol/L [-74.2 mg/dL vs. -27.9 mg/ dL]; P = 0.004) and greater increases in high-density lipoprotein cholesterol levels (0.14 mmol/L vs. -0.04 mmol/L [5.5 mg/dL vs. -1.6 mg/dL]; P< 0.001). Changes in low-density lipoprotein cholesterol level did not differ statistically (0.04 mmol/L [1.6 mg/dL] with the low-carbohydrate diet and -0.19 mmol/L [-7.4 mg/dL] with the low-fat diet; P = 0.2). Minor adverse effects were more frequent in the low-carbohydrate diet group. Limitations: We could not definitively distinguish effects of the low-carbohydrate diet and those of the nutritional supplements provided only to that group. In addition, participants were healthy and were followed for only 24 weeks. These factors limit the generalizability of the study results. Conclusions: Compared with a low-fat diet, a low-carbohydrate diet program had better participant retention and greater weight loss. During active weight loss, serum triglyceride levels decreased more and high-density lipoprotein cholesterol level increased more with the low-carbohydrate diet than with the low-fat diet.

A low-carbohydrate, ketogenic diet versus a low-fat diet to treat obesity and hyperlipidemia: a randomized, controlled trial.

Background Complications of Staphylococcus aureus bacteremia (SAB) are often difficult to identify. The ability to accurately predict the likelihood of these complications would impact patient management. This investigation sought to define readily available clinical characteristics that could help identify patients at risk for complicated SAB. Methods A prospective, observational cohort study was conducted from September 1994 through December 1999. Patients were followed up for 12 weeks after the initial positive blood culture result. The primary end point was complicated SAB (attributable mortality, complicated infection, embolic stroke, or recurrent S aureus infection during the 12-week follow-up period). The predictive model was validated using bootstrap resampling. Results Complicated SAB was present in 43% of 724 consecutive adult hospitalized patients identified during the study period. The full predictive model had a high discriminative ability (bootstrap-corrected c index, 0.78). The strongest predictor of complicated SAB was a positive follow-up blood culture result at 48 to 96 hours. A scoring system based on the presence or absence of 4 risk factors (community acquisition, skin examination findings suggesting acute systemic infection, persistent fever at 72 hours, and positive follow-up blood culture results at 48-96 hours) accurately identified complicated SAB (bootstrap-corrected c index, 0.76). Conclusion Readily available clinical variables can help identify patients at risk for complicated SAB.

https://jamanetwork.com/journals/jamainternalmedicine/articlepdf/216060/ioi20655.pdf

Clinical identifiers of complicated Staphylococcus aureus bacteremia.

A semicontinuous variable has a portion of responses equal to a single value (typically 0) and a continuous, often skewed, distribution among the remaining values. In cross-sectional analyses, variables of this type may be described by a pair of regression models; for example, a logistic model for the probability of nonzero response and a conditional linear model for the mean response given that it is nonzero. We extend this two-part regression approach to longitudinal settings by introducing random coefficients into both the logistic and the linear stages. Fitting a two-part random-effects model poses computational challenges similar to those found with generalized linear mixed models. We obtain maximum likelihood estimates for the fixed coefficients and variance components by an approximate Fisher scoring procedure based on high-order Laplace approximations. To illustrate, we apply the technique to data from the Adolescent Alcohol Prevention Trial, examining reported recent alcohol use for students in g...

A Two-Part Random-Effects Model for Semicontinuous Longitudinal Data

Importance Bariatric surgery induces significant weight loss for severely obese patients, but there is limited evidence of the durability of weight loss compared with nonsurgical matches and across bariatric procedures. Objectives To examine 10-year weight change in a large, multisite, clinical cohort of veterans who underwent Roux-en-Y gastric bypass (RYGB) compared with nonsurgical matches and the 4-year weight change in veterans who underwent RYGB, adjustable gastric banding (AGB), or sleeve gastrectomy (SG). Design, Setting, and Participants In this cohort study, differences in weight change up to 10 years after surgery were estimated in retrospective cohorts of 1787 veterans who underwent RYGB from January 1, 2000, through September 30, 2011 (573 of 700 eligible [81.9%] with 10-year follow-up), and 5305 nonsurgical matches (1274 of 1889 eligible [67.4%] with 10-year follow-up) in mixed-effects models. Differences in weight change up to 4 years were compared among veterans undergoing RYGB (n = 1785), SG (n = 379), and AGB (n = 246). Data analysis was performed from September 9, 2014, to February 12, 2016. Exposures Bariatric surgical procedures and usual care. Main Outcomes and Measures Weight change up to 10 years after surgery through December 31, 2014. Results The 1787 patients undergoing RYGB had a mean (SD) age of 52.1 (8.5) years and 5305 nonsurgical matches had a mean (SD) age of 52.2 (8.4) years. Patients undergoing RYGB and nonsurgical matches had a mean body mass index of 47.7 and 47.1, respectively, and were predominantly male (1306 [73.1%] and 3911 [73.7%], respectively). Patients undergoing RYGB lost 21% (95% CI, 11%-31%) more of their baseline weight at 10 years than nonsurgical matches. A total of 405 of 564 patients undergoing RYGB (71.8%) had more than 20% estimated weight loss, and 224 of 564 (39.7%) had more than 30% estimated weight loss at 10 years compared with 134 of 1247 (10.8%) and 48 of 1247 (3.9%), respectively, of nonsurgical matches. Only 19 of 564 patients undergoing RYGB (3.4%) regained weight back to within an estimated 5% of their baseline weight by 10 years. At 4 years, patients undergoing RYGB lost 27.5% (95% CI, 23.8%-31.2%) of their baseline weight, patients undergoing AGB lost 10.6% (95% CI, 0.6%-20.6%), and patients undergoing SG lost 17.8% (95% CI, 9.7%-25.9%). Patients undergoing RYGB lost 16.9% (95% CI, 6.2%-27.6%) more of their baseline weight than patients undergoing AGB and 9.7% (95% CI, 0.8%-18.6%) more than patients undergoing SG. Conclusions and Relevance Patients in the Veterans Administration health care system lost substantially more weight than nonsurgical matches and sustained most of this weight loss in the long term. Roux-en-Y gastric bypass induced significantly greater weight loss among veterans than SG or AGB at 4 years. These results provide further evidence of the beneficial association between surgery and long-term weight loss that has been demonstrated in shorter-term studies of younger, predominantly female populations.

/pdf/bariatric-surgery-and-long-term-durability-of-weight-loss-4f2tv4tobf.pdf

Maren K. Olsen

Papers

Multiple Imputation for Multivariate Missing-Data Problems: A Data Analyst's Perspective

A low-carbohydrate, ketogenic diet versus a low-fat diet to treat obesity and hyperlipidemia: a randomized, controlled trial.

Clinical identifiers of complicated Staphylococcus aureus bacteremia.

A Two-Part Random-Effects Model for Semicontinuous Longitudinal Data

Bariatric Surgery and Long-term Durability of Weight Loss