The efficacy of disease management programs in improving the outcome of heart failure patients remains uncertain and may vary across health systems. This study explores whether a countrywide disease management program is superior to usual care in reducing adverse health outcomes and improving well-being among community-dwelling adult patients with moderate-to-severe chronic heart failure who have universal access to advanced health-care services and technologies. In this multicenter open-label trial, 1,360 patients recruited after hospitalization for heart failure exacerbation (38%) or from the community (62%) were randomly assigned to either disease management or usual care. Disease management, delivered by multi-disciplinary teams, included coordination of care, patient education, monitoring disease symptoms and patient adherence to medication regimen, titration of drug therapy, and home tele-monitoring of body weight, blood pressure and heart rate. Patients assigned to usual care were treated by primary care practitioners and consultant cardiologists. The primary composite endpoint was the time elapsed till first hospital admission for heart failure exacerbation or death from any cause. Secondary endpoints included the number of all hospital admissions, health-related quality of life and depression during follow-up. Intention-to-treat comparisons between treatments were adjusted for baseline patient data and study center. During the follow-up, 388 (56.9%) patients assigned to disease management and 387 (57.1%) assigned to usual care had a primary endpoint event. The median (range) time elapsed until the primary endpoint event or end of study was 2.0 (0–5.0) years among patients assigned to disease management, and 1.8 (0–5.0) years among patients assigned to usual care (adjusted hazard ratio, 0.908; 95% confidence interval, 0.788 to 1.047). Hospital admissions were mostly (70%) unrelated to heart failure. Patients assigned to disease management had a better health-related quality of life and a lower depression score during follow-up. This comprehensive disease management intervention was not superior to usual care with respect to the primary composite endpoint, but it improved health-related quality of life and depression. A disease-centered approach may not suffice to make a significant impact on hospital admissions and mortality in patients with chronic heart failure who have universal access to health care. Clinicaltrials.gov identifier: NCT00533013 . Trial registration date: 9 August 2007. Initial protocol release date: 20 September 2007.

Background: Ethnic minority populations exhibit disproportionately high rates of type 2 diabetes mellitus (T2DM). Electronic health tools have the potential to facilitate the cultural adaptation and tailoring of T2DM education to improve the knowledge and management of diabetes mellitus (DM). Objective: This study aimed (1) to develop an adaptable Interactive Lifestyle Assessment, Counseling, and Education (I-ACE) software to support dietitian-delivered lifestyle counseling among low-socioeconomic status (SES) ethnic minority patients with T2DM and (2) to evaluate its effect on DM-related dietary knowledge and management compared with standard lifestyle advice (SLA) in a randomized controlled trial (RCT). Methods: The I-ACE software, developed in consultation with clinical dieticians, incorporates evidence-based dietary and physical activity (PA) recommendations and educational materials. The features and behavioral change techniques include quantitative lifestyle (dietary intake and PA) assessment and simulation, individually tailored education and recommendations, motivational interviewing, and goal setting. For the unblinded pilot RCT, 50 overweight or obese Arab adults (aged 40-62 years) with poorly controlled T2DM were recruited from primary care clinics and randomly assigned to receive 4 in-person, dietician-delivered counseling sessions over 6 months using either (1) the I-ACE tool (experimental arm) or (2) the SLA methods (comparison arm). All outcome assessments were face-to-face. DM-related dietary knowledge (primary outcome) was measured at baseline, 3, 6, and 12 months. Lifestyle and other parameters were measured before, during, and after the intervention. Multiple linear regression and repeated measures linear mixed models were used to compare the changes in study outcomes and explore time trends in between-group and within-group changes. Results: A total of 25 participants were enrolled in each arm, of whom 24 and 21 completed the final assessment of the primary outcome in the I-ACE and SLA arms, respectively. DM-related lifestyle knowledge increased more rapidly in the I-ACE arm than in the SLA arm (P value for study arm×time interaction=.02). Within the I-ACE arm, the mean (SE) differences in added sugar and dietary fiber intakes from baseline to 12 months were −2.6% (SE 1.0%) of total energy (P=.03) and 2.7 (SE 0.0) g/1000 kcal (P=.003), respectively. The odds of engaging in any leisure PA at 12 months tended to be higher in the I-ACE arm versus SLA arm, but did not reach statistical significance (odds ratio 2.8; 95% CI 0.7-11.6; P=.16). Both arms exhibited significant reductions in HbA1c (P value for change over time <.001). Conclusions: The use of the I-ACE software in a 6-month, 4-session dietician-delivered lifestyle counseling intervention improved the efficiency of lifestyle education, compared with SLA, among low-SES, ethnic minority patients with T2DM. This pilot trial provides justification for conducting a large-scale trial to evaluate its effectiveness and applicability in routine clinical care among ethnically diverse populations. Trial Registration: ClinicalTrials.gov NCT01858506; https://clinicaltrials.gov/ct2/show/NCT01858506.

Background/Objectives: Millions of individuals worldwide continue to experience symptoms following SARS-CoV-2 infection. This study aimed to assess the prevalence and phenotype of multi-system symptoms attributed to Long COVID—including fatigue, pain, cognitive-emotional disturbances, headache, cardiopulmonary issues, and alterations in taste and smell—that have persisted for at least two years after acute infection, which we define as “persistent Long COVID”. Additionally, the study aimed to identify clinical features and blood biomarkers associated with persistent Long COVID symptoms. Methods: We sent a detailed long COVID symptoms questionnaire to an existing cohort of 1258 vaccinated adults (age 18–79 years) who had mild infection (e.g., non-hospitalized) SARS-CoV-2 Delta variant 2 years earlier. These individuals had comprehensive datasets, including blood samples, available for further analysis. We estimated prevalence of persistent long COVID two years post-infection using weighted adjustment (Horvitz–Thompson estimator) to overcome reporting bias. Multivariable logistic regression models were used to determine association of clinical features and blood biomarkers (pre-infection SARS-CoV-2 RBD-IgG, SARS-CoV-2 neutralizing antibodies, and pre-infection and post-infection neurofilament light) with prevalence of persistent long COVID. Results: N = 323 participants responded to the survey, of whom N = 74 (23%) reported at least one long COVID symptom that had persisted for two years after the acute infection. Weighted prevalence of persistent long COVID symptoms was 21.5% (95% CI = 16.7–26.3%). Female gender, smoking, and severity of acute COVID-19 infection were significantly associated with persistent Long COVID. The blood biomarkers assessed were not significantly associated with persistent Long COVID. Conclusions: Among vaccinated adults two years after mild infection with Delta variant SARS-CoV-2, persistent symptoms attributed to Long COVID are extremely common, certain subgroups are at higher risk, and further research into biological mechanisms and potential treatment targets is needed.

We describe a procedure for imputing missing values of time-dependent covariates in a discrete time Cox model using the chained equations method. The procedure multiply imputes the missing values for each time-period in a time-sequential manner, using covariates from the current and previous time-periods as well as the survival outcome. The form of the outcome variable used in the imputation model depends on the functional form of the time-dependent covariate(s) and differs from the case of Cox regression with only baseline covariates. This time-sequential approach provides an approximation to a fully conditional approach. We illustrate the procedure with data on diabetics, evaluating the association of their glucose control with the risk of selected cancers. Using simulations we show that the suggested estimator performed well (in terms of bias and coverage) for completely missing at random, missing at random and moderate non-missing-at-random patterns. However, for very strong non-missing-at-random patterns, the estimator was seriously biased and the coverage was too low. The procedure can be implemented using multiple imputation with the Fully conditional Specification (FCS) method (MI procedure in SAS with FCS statement or similar packages in other software, e.g. MICE in R). For use with event times on a continuous scale, the events would need to be grouped into time-intervals.