Article

The impact of a disease management program (COACH) on the attainment of better cardiovascular risk control in dyslipidaemic patients at primary care centres (The DISSEMINATE Study): a randomised controlled trial

BMC Family Practice (Impact Factor: 1.74). 10/2012; 13(1):97. DOI: 10.1186/1471-2296-13-97
Source: PubMed

ABSTRACT BACKGROUND: To evaluate the efficacy of Counselling and Advisory Care for Health (COACH) programme in managing dyslipidaemia among primary care practices in Malaysia. This open-label, parallel, randomised controlled trial compared the COACH programme delivered by primary care physicians alone (PCP arm) and primary care physicians assisted by nurse educators (PCP-NE arm). METHODS: This was a multi-centre, open label, randomised trial of a disease management programme (COACH) among dyslipidaemic patients in 21 Malaysia primary care practices. The participating centres enrolled 297 treatment naive subjects who had the primary diagnosis of dyslipidaemia; 149 were randomised to the COACH programme delivered by primary care physicians assisted by nurse educators (PCP-NE) and 148 to care provided by primary care physicians (PCP) alone. The primary efficacy endpoint was the mean percentage change from baseline LDL-C at week 24 between the 2 study arms. Secondary endpoints included mean percentage change from baseline of lipid profile (TC, LDL-C, HDL-C, TG, TC: HDL ratio), Framingham Cardiovascular Health Risk Score and absolute risk change from baseline in blood pressure parameters at week 24. The study also assessed the sustainability of programme efficacy at week 36. RESULTS: Both study arms demonstrated improvement in LDL-C from baseline. The least squares (LS) mean change from baseline LDL-C were -30.09% and -27.54% for PCP-NE and PCP respectively. The difference in mean change between groups was 2.55% (p=0.288), with a greater change seen in the PCP-NE arm. Similar observations were made between the study groups in relation to total cholesterol change at week 24. Significant difference in percentage change from baseline of HDL-C were observed between the PCP-NE and PCP groups, 3.01%, 95% CI 0.12-5.90, p=0.041, at week 24. There was no significant difference in lipid outcomes between 2 study groups at week 36 (12 weeks after the programme had ended). CONCLUSION: Patients who received coaching and advice from primary care physicians (with or without the assistance by nurse educators) showed improvement in LDL-cholesterol. Disease management services delivered by PCP-NE demonstrated a trend towards add-on improvements in cholesterol control compared to care delivered by physicians alone; however, the improvements were not maintained when the services were withdrawn.Trial registrationNational Medical Research Registration (NMRR) Number: NMRR-08-287-1442Trial Registration Number (ClinicalTrials.gov Identifier): NCT00708370.

2 Followers
 · 
207 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: To evaluate evidence from published randomised controlled trials (RCTs) for the use of task-shifting strategies for cardiovascular disease (CVD) risk reduction in low-income and middle-income countries (LMICs).
    BMJ Open 10/2014; 4(10):e005983. DOI:10.1136/bmjopen-2014-005983 · 2.06 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Objective The aim of this systematic review was to describe the effects of health coaching on adult patients with chronic diseases. Methods The reviewers searched electronic databases and performed a manual search for studies published from 2009-2013. The inclusion criteria covered health coaching for adults with chronic diseases by health care professionals. The studies were original, randomized controlled trials or quasi-experimental designs. Results Thirteen studies were selected using the inclusion criteria. The results indicate that health coaching produces positive effects on patients’ physiological, behavioral and psychological conditions and on their social life. In particular, statistically significant results revealed better weight management, increased physical activity and improved physical and mental health status. Conclusion Health coaching improves the management of chronic diseases. Further research into the cost-effectiveness of health coaching and its long-term effectiveness for chronic diseases is needed. Practice implications Health care professionals play key roles in promoting healthy behavior and motivating good care for adults with chronic diseases. Health coaching is an effective patient education method that can be used to motivate and take advantage of a patient's willingness to change their life style and to support the patient's home-based self-care.
    Patient Education and Counseling 11/2014; 97(2). DOI:10.1016/j.pec.2014.07.026 · 2.60 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Probability estimation for binary and multicategory outcome using logistic and multinomial logistic regression has a long-standing tradition in biostatistics. However, biases may occur if the model is misspecified. In contrast, outcome probabilities for individuals can be estimated consistently with machine learning approaches, including k-nearest neighbors (k-NN), bagged nearest neighbors (b-NN), random forests (RF), and support vector machines (SVM). Because machine learning methods are rarely used by applied biostatisticians, the primary goal of this paper is to explain the concept of probability estimation with these methods and to summarize recent theoretical findings. Probability estimation in k-NN, b-NN, and RF can be embedded into the class of nonparametric regression learning machines; therefore, we start with the construction of nonparametric regression estimates and review results on consistency and rates of convergence. In SVMs, outcome probabilities for individuals are estimated consistently by repeatedly solving classification problems. For SVMs we review classification problem and then dichotomous probability estimation. Next we extend the algorithms for estimating probabilities using k-NN, b-NN, and RF to multicategory outcomes and discuss approaches for the multicategory probability estimation problem using SVM. In simulation studies for dichotomous and multicategory dependent variables we demonstrate the general validity of the machine learning methods and compare it with logistic regression. However, each method fails in at least one simulation scenario. We conclude with a discussion of the failures and give recommendations for selecting and tuning the methods. Applications to real data and example code are provided in a companion article (doi: 10.1002/bimj.201300077).
    Biometrical Journal 07/2014; 56(4). DOI:10.1002/bimj.201300068 · 1.24 Impact Factor

Preview (2 Sources)

Download
3 Downloads
Available from