Translating cholesterol guidelines into primary care practice: a multimodal cluster randomized trial

Shared Decision-Making in Cardiovascular Risk Factor Management: A Systematic Review and Meta-Analysis

Importance

The effect of shared decision-making (SDM) and the extent of its use in interventions to improve cardiovascular risk remain unclear.

Objective

To assess the extent to which SDM is used in interventions aimed to enhance the management of cardiovascular risk factors and to explore the association of SDM with decisional outcomes, cardiovascular risk factors, and health behaviors.

Data Sources

For this systematic review and meta-analysis, a literature search was conducted in the Medline, CINAHL, Embase, Cochrane, Web of Science, Scopus, and ClinicalTrials.gov databases for articles published from inception to June 24, 2022, without language restrictions.

Study Selection

Randomized clinical trials (RCTs) comparing SDM-based interventions with standard of care for cardiovascular risk factor management were included.

Data Extraction and Synthesis

The systematic search resulted in 9365 references. Duplicates were removed, and 2 independent reviewers screened the trials (title, abstract, and full text) and extracted data. Data were pooled using a random-effects model. The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guideline.

Main Outcomes and Measures

Decisional outcomes, cardiovascular risk factor outcomes, and health behavioral outcomes.

Results

This review included 57 RCTs with 88 578 patients and 1341 clinicians. A total of 59 articles were included, as 2 RCTs were reported twice. Nearly half of the studies (29 [49.2%]) tested interventions that targeted both patients and clinicians, and an equal number (29 [49.2%]) exclusively focused on patients. More than half (32 [54.2%]) focused on diabetes management, and one-quarter focused on multiple cardiovascular risk factors (14 [23.7%]). Most studies (35 [59.3%]) assessed cardiovascular risk factors and health behaviors as well as decisional outcomes. The quality of studies reviewed was low to fair. The SDM intervention was associated with a decrease of 4.21 points (95% CI, -8.21 to -0.21) in Decisional Conflict Scale scores (9 trials; I2 = 85.6%) and a decrease of 0.20% (95% CI, -0.39% to -0.01%) in hemoglobin A1c (HbA1c) levels (18 trials; I2 = 84.2%).

Conclusions and Relevance

In this systematic review and meta-analysis of the current state of research on SDM interventions for cardiovascular risk management, there was a slight reduction in decisional conflict and an improvement in HbA1c levels with substantial heterogeneity. High-quality studies are needed to inform the use of SDM to improve cardiovascular risk management.

Patient-centered education in dyslipidemia management: a systematic review

Background

Dyslipidemia management is crucial to reduce mortality and morbidity from cardiovascular diseases (CVDs). Patients must be educated and empowered to enable them to manage their own diseases. Various methods of patient education, such as patient-centered education (PCE) or non-PCE (such as didactic education or any traditional form of education), have been implemented.

Objective

To review and determine the effectiveness of PCE for dyslipidemia management compared with usual care. The primary outcome chosen was cholesterol level. Other measures, such as psychosocial or cognitive, behavioral, and other relevant outcomes, were also extracted. Additionally, underlying theories and other contributing factors that may have led to the success of the intervention were also reviewed and discussed.

Methods

We conducted searches in PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, and Google Scholar from inception until April 2021. All studies involving randomized controlled trials were included. Study quality was assessed using the Critical Appraisal Skills Program (CASP) checklist specifically for randomized controlled trials.

Results

The search identified 8,847 records. Of these, 20 studies were eligible for inclusion. Interventions using a PCE approach were largely successful. Contributing factors extracted from the included studies were underlying theories, instant reward system, dietary education, collaborative care, duration of intervention with systematic follow-ups, social support, adherence assessment method, and usage of e-health.

Conclusions

PCE is successful in achieving the desired outcomes in dyslipidemia management. Future studies may incorporate the elements of PCE to improve the management of dyslipidemia in hospital or community settings where appropriate.

Decision-support tools via mobile devices to improve quality of care in primary healthcare settings

BACKGROUND

The ubiquity of mobile devices has made it possible for clinical decision-support systems (CDSS) to become available to healthcare providers on handheld devices at the point-of-care, including in low- and middle-income countries. The use of CDSS by providers can potentially improve adherence to treatment protocols and patient outcomes. However, the evidence on the effect of the use of CDSS on mobile devices needs to be synthesized. This review was carried out to support a World Health Organization (WHO) guideline that aimed to inform investments on the use of decision-support tools on digital devices to strengthen primary healthcare.

OBJECTIVES

To assess the effects of digital clinical decision-support systems (CDSS) accessible via mobile devices by primary healthcare providers in the context of primary care settings.

SEARCH METHODS

We searched CENTRAL, MEDLINE, Embase, Global Index Medicus, POPLINE, and two trial registries from 1 January 2000 to 9 October 2020. We conducted a grey literature search using mHealthevidence.org and issued a call for papers through popular digital health communities of practice. Finally, we conducted citation searches of included studies.

SELECTION CRITERIA

Study design: we included randomized trials, including full-text studies, conference abstracts, and unpublished data irrespective of publication status or language of publication.  Types of participants: we included studies of all cadres of healthcare providers, including lay health workers and other individuals (administrative, managerial, and supervisory staff) involved in the delivery of primary healthcare services using clinical decision-support tools; and studies of clients or patients receiving care from primary healthcare providers using digital decision-support tools. Types of interventions: we included studies comparing digital CDSS accessible via mobile devices with non-digital CDSS or no intervention, in the context of primary care. CDSS could include clinical protocols, checklists, and other job-aids which supported risk prioritization of patients. Mobile devices included mobile phones of any type (but not analogue landline telephones), as well as tablets, personal digital assistants, and smartphones. We excluded studies where digital CDSS were used on laptops or integrated with electronic medical records or other types of longitudinal tracking of clients.

DATA COLLECTION AND ANALYSIS

A machine learning classifier that gave each record a probability score of being a randomized trial screened all search results. Two review authors screened titles and abstracts of studies with more than 10% probability of being a randomized trial, and one review author screened those with less than 10% probability of being a randomized trial. We followed standard methodological procedures expected by Cochrane and the Effective Practice and Organisation of Care group. We used the GRADE approach to assess the certainty of the evidence for the most important outcomes.

MAIN RESULTS

Eight randomized trials across varying healthcare contexts in the USA,. India, China, Guatemala, Ghana, and Kenya, met our inclusion criteria. A range of healthcare providers (facility and community-based, formally trained, and lay workers) used digital CDSS. Care was provided for the management of specific conditions such as cardiovascular disease, gastrointestinal risk assessment, and maternal and child health. The certainty of evidence ranged from very low to moderate, and we often downgraded evidence for risk of bias and imprecision. We are uncertain of the effect of this intervention on providers' adherence to recommended practice due to the very low certainty evidence (2 studies, 185 participants). The effect of the intervention on patients' and clients' health behaviours such as smoking and treatment adherence is mixed, with substantial variation across outcomes for similar types of behaviour (2 studies, 2262 participants). The intervention probably makes little or no difference to smoking rates among people at risk of cardiovascular disease but probably increases other types of desired behaviour among patients, such as adherence to treatment. The effect of the intervention on patients'/clients' health status and well-being  is also mixed (5 studies, 69,767 participants). It probably makes little or no difference to some types of health outcomes, but we are uncertain about other health outcomes, including maternal and neonatal deaths, due to very low-certainty evidence. The intervention may slightly improve patient or client acceptability and satisfaction (1 study, 187 participants). We found no studies that reported the time between the presentation of an illness and appropriate management, provider acceptability or satisfaction, resource use, or unintended consequences.

AUTHORS' CONCLUSIONS

We are uncertain about the effectiveness of mobile phone-based decision-support tools on several outcomes, including adherence to recommended practice. None of the studies had a quality of care framework and focused only on specific health areas.   We need well-designed research that takes a systems lens to assess these issues.

The Role of Health Kiosks: A Scoping Review (Preprint)

Background

Health kiosks are publicly accessible computing devices that provide access to services, including health information provision, clinical measurement collection, patient self–check-in, telemonitoring, and teleconsultation. Although the increase in internet access and ownership of smart personal devices could make kiosks redundant, recent reports have predicted that the market will continue to grow.

Objective

We seek to clarify the current and future roles of health kiosks by investigating the settings, roles, and clinical domains in which kiosks are used; whether usability evaluations of health kiosks are being reported, and if so, what methods are being used; and what the barriers and facilitators are for the deployment of kiosks.

Methods

We conducted a scoping review using a bibliographic search of Google Scholar, PubMed, and Web of Science databases for studies and other publications between January 2009 and June 2020. Eligible papers described the implementation as primary studies, systematic reviews, or news and feature articles. Additional reports were obtained by manual searching and querying the key informants. For each article, we abstracted settings, purposes, health domains, whether the kiosk was opportunistic or integrated with a clinical pathway, and whether the kiosk included usability testing. We then summarized the data in frequency tables.

Results

A total of 141 articles were included, of which 134 (95%) were primary studies, and 7 (5%) were reviews. Approximately 47% (63/134) of the primary studies described kiosks in secondary care settings. Other settings included community (32/134, 23.9%), primary care (24/134, 17.9%), and pharmacies (8/134, 6%). The most common roles of the health kiosks were providing health information (47/134, 35.1%), taking clinical measurements (28/134, 20.9%), screening (17/134, 12.7%), telehealth (11/134, 8.2%), and patient registration (8/134, 6.0%). The 5 most frequent health domains were multiple conditions (33/134, 24.6%), HIV (10/134, 7.5%), hypertension (10/134, 7.5%), pediatric injuries (7/134, 5.2%), health and well-being (6/134, 4.5%), and drug monitoring (6/134, 4.5%). Kiosks were integrated into the clinical pathway in 70.1% (94/134) of studies, opportunistic kiosks accounted for 23.9% (32/134) of studies, and in 6% (8/134) of studies, kiosks were used in both. Usability evaluations of kiosks were reported in 20.1% (27/134) of papers. Barriers (e.g., use of expensive proprietary software) and enablers (e.g., handling of on-demand consultations) of deploying health kiosks were identified.

Conclusions

Health kiosks still play a vital role in the health care system, including collecting clinical measurements and providing access to web-based health services and information to those with little or no digital literacy skills and others without personal internet access. We identified research gaps, such as training needs for teleconsultations and scant reporting on usability evaluation methods.

The effect of computerized decision support systems on cardiovascular risk factors: a systematic review and meta-analysis

Background

Cardiovascular risk management (CVRM) is notoriously difficult because of multi-morbidity and the different phenotypes and severities of cardiovascular disease. Computerized decision support systems (CDSS) enable the clinician to integrate the latest scientific evidence and patient information into tailored strategies. The effect on cardiovascular risk factor management is yet to be confirmed.

Methods

We performed a systematic review and meta-analysis evaluating the effects of CDSS on CVRM, defined as the change in absolute values and attainment of treatment goals of systolic blood pressure (SBP), low density lipoprotein cholesterol (LDL-c) and HbA1c. Also, CDSS characteristics related to more effective CVRM were identified. Eligible articles were methodologically appraised using the Cochrane risk of bias tool. We calculated mean differences, relative risks, and if appropriate (I² < 70%), pooled the results using a random-effects model.

Results

Of the 14,335 studies identified, 22 were included. Four studies reported on SBP, 3 on LDL-c, 10 on CVRM in patients with type II diabetes and 5 on guideline adherence. The CDSSs varied considerably in technical performance and content. Heterogeneity of results was such that quantitative pooling was often not appropriate. Among CVRM patients, the results tended towards a beneficial effect of CDSS, but only LDL-c target attainment in diabetes patients reached statistical significance. Prompting, integration into the electronical health record, patient empowerment, and medication support were related to more effective CVRM.

Conclusion

We did not find a clear clinical benefit from CDSS in cardiovascular risk factor levels and target attainment. Some features of CDSS seem more promising than others. However, the variability in CDSS characteristics and heterogeneity of the results – emphasizing the immaturity of this research area - limit stronger conclusions. Clinical relevance of CDSS in CVRM might additionally be sought in the improvement of shared decision making and patient empowerment.

Electronic supplementary material

The online version of this article (10.1186/s12911-019-0824-x) contains supplementary material, which is available to authorized users.

Risk scoring for the primary prevention of cardiovascular disease

BACKGROUND

The current paradigm for cardiovascular disease (CVD) emphasises absolute risk assessment to guide treatment decisions in primary prevention. Although the derivation and validation of multivariable risk assessment tools, or CVD risk scores, have attracted considerable attention, their effect on clinical outcomes is uncertain.

OBJECTIVES

To assess the effects of evaluating and providing CVD risk scores in adults without prevalent CVD on cardiovascular outcomes, risk factor levels, preventive medication prescribing, and health behaviours.

SEARCH METHODS

We searched the Cochrane Central Register of Controlled Trials (CENTRAL) in the Cochrane Library (2016, Issue 2), MEDLINE Ovid (1946 to March week 1 2016), Embase (embase.com) (1974 to 15 March 2016), and Conference Proceedings Citation Index-Science (CPCI-S) (1990 to 15 March 2016). We imposed no language restrictions. We searched clinical trial registers in March 2016 and handsearched reference lists of primary studies to identify additional reports.

SELECTION CRITERIA

We included randomised and quasi-randomised trials comparing the systematic provision of CVD risk scores by a clinician, healthcare professional, or healthcare system compared with usual care (i.e. no systematic provision of CVD risk scores) in adults without CVD.

DATA COLLECTION AND ANALYSIS

Three review authors independently selected studies, extracted data, and evaluated study quality. We used the Cochrane 'Risk of bias' tool to assess study limitations. The primary outcomes were: CVD events, change in CVD risk factor levels (total cholesterol, systolic blood pressure, and multivariable CVD risk), and adverse events. Secondary outcomes included: lipid-lowering and antihypertensive medication prescribing in higher-risk people. We calculated risk ratios (RR) for dichotomous data and mean differences (MD) or standardised mean differences (SMD) for continuous data using 95% confidence intervals. We used a fixed-effects model when heterogeneity (I²) was at least 50% and a random-effects model for substantial heterogeneity (I² > 50%). We evaluated the quality of evidence using the GRADE framework.

MAIN RESULTS

We identified 41 randomised controlled trials (RCTs) involving 194,035 participants from 6422 reports. We assessed studies as having high or unclear risk of bias across multiple domains. Low-quality evidence evidence suggests that providing CVD risk scores may have little or no effect on CVD events compared with usual care (5.4% versus 5.3%; RR 1.01, 95% confidence interval (CI) 0.95 to 1.08; I² = 25%; 3 trials, N = 99,070). Providing CVD risk scores may reduce CVD risk factor levels by a small amount compared with usual care. Providing CVD risk scores reduced total cholesterol (MD -0.10 mmol/L, 95% CI -0.20 to 0.00; I² = 94%; 12 trials, N = 20,437, low-quality evidence), systolic blood pressure (MD -2.77 mmHg, 95% CI -4.16 to -1.38; I² = 93%; 16 trials, N = 32,954, low-quality evidence), and multivariable CVD risk (SMD -0.21, 95% CI -0.39 to -0.02; I² = 94%; 9 trials, N = 9549, low-quality evidence). Providing CVD risk scores may reduce adverse events compared with usual care, but results were imprecise (1.9% versus 2.7%; RR 0.72, 95% CI 0.49 to 1.04; I² = 0%; 4 trials, N = 4630, low-quality evidence). Compared with usual care, providing CVD risk scores may increase new or intensified lipid-lowering medications (15.7% versus 10.7%; RR 1.47, 95% CI 1.15 to 1.87; I² = 40%; 11 trials, N = 14,175, low-quality evidence) and increase new or increased antihypertensive medications (17.2% versus 11.4%; RR 1.51, 95% CI 1.08 to 2.11; I² = 53%; 8 trials, N = 13,255, low-quality evidence).

AUTHORS' CONCLUSIONS

There is uncertainty whether current strategies for providing CVD risk scores affect CVD events. Providing CVD risk scores may slightly reduce CVD risk factor levels and may increase preventive medication prescribing in higher-risk people without evidence of harm. There were multiple study limitations in the identified studies and substantial heterogeneity in the interventions, outcomes, and analyses, so readers should interpret results with caution. New models for implementing and evaluating CVD risk scores in adequately powered studies are needed to define the role of applying CVD risk scores in primary CVD prevention.

Clinical Decision Support Systems and Prevention: A Community Guide Cardiovascular Disease Systematic Review

CONTEXT

Clinical decision support systems (CDSSs) can help clinicians assess cardiovascular disease (CVD) risk and manage CVD risk factors by providing tailored assessments and treatment recommendations based on individual patient data. The goal of this systematic review was to examine the effectiveness of CDSSs in improving screening for CVD risk factors, practices for CVD-related preventive care services such as clinical tests and prescribed treatments, and management of CVD risk factors.

EVIDENCE ACQUISITION

An existing systematic review (search period, January 1975-January 2011) of CDSSs for any condition was initially identified. Studies of CDSSs that focused on CVD prevention in that review were combined with studies identified through an updated search (January 2011-October 2012). Data analysis was conducted in 2013.

EVIDENCE SYNTHESIS

A total of 45 studies qualified for inclusion in the review. Improvements were seen for recommended screening and other preventive care services completed by clinicians, recommended clinical tests completed by clinicians, and recommended treatments prescribed by clinicians (median increases of 3.8, 4.0, and 2.0 percentage points, respectively). Results were inconsistent for changes in CVD risk factors such as systolic and diastolic blood pressure, total and low-density lipoprotein cholesterol, and hemoglobin A1C levels.

CONCLUSIONS

CDSSs are effective in improving clinician practices related to screening and other preventive care services, clinical tests, and treatments. However, more evidence is needed from implementation of CDSSs within the broad context of comprehensive service delivery aimed at reducing CVD risk and CVD-related morbidity and mortality.

Interventions to improve adherence to cardiovascular disease guidelines: a systematic review

BACKGROUND

Successful management of cardiovascular disease (CVD) is impaired by poor adherence to clinical practice guidelines. The objective of our review was to synthesize evidence about the effectiveness of interventions that target healthcare providers to improve adherence to CVD guidelines and patient outcomes.

METHODS

We searched PubMed, EMBASE, Cochrane Library, PsycINFO, Web of Science and CINAHL databases from inception to June 2014, using search terms related to adherence and clinical practice guidelines. Studies were limited to randomized controlled trials testing an intervention to improve adherence to guidelines that measured both a patient and adherence outcome. Descriptive summary tables were created from data extractions. Meta-analyses were conducted on clinically homogeneous comparisons, and sensitivity analyses and subgroup analyses were carried out where possible. GRADE summary of findings tables were created for each comparison and outcome.

RESULTS AND DISCUSSION

We included 38 RCTs in our review. Interventions included guideline dissemination, education, audit and feedback, and academic detailing. Meta-analyses were conducted for several outcomes by intervention type. Many comparisons favoured the intervention, though only the adherence outcome for the education intervention showed statistically significant improvement compared to usual care (standardized mean difference = 0.58 [95 % confidence interval 0.35 to 0.8]).

CONCLUSIONS

Many interventions show promise to improve practitioner adherence to CVD guidelines. The quality of evidence and number of trials limited our ability to draw conclusions.

Clinical decision support improves physician guideline adherence for laboratory monitoring of chronic kidney disease: a matched cohort study

Background

Guidelines exist for chronic kidney disease (CKD) but are not well implemented in clinical practice. We evaluated the impact of a guideline-based clinical decision support system (CDSS) on laboratory monitoring and achievement of laboratory targets in stage 3–4 CKD patients.

Methods

We performed a matched cohort study of 12,353 stage 3–4 CKD patients whose physicians opted to receive an automated guideline-based CDSS with CKD-related lab results, and 42,996 matched controls whose physicians did not receive the CDSS. Physicians were from US community-based physician practices utilizing a large, commercial laboratory (LabCorp®).

We compared the percentage of laboratory tests obtained within guideline-recommended intervals and the percentage of results within guideline target ranges between CDSS and non-CDSS patients. Laboratory tests analyzed included estimated glomerular filtration rate, plasma parathyroid hormone, serum calcium, phosphorus, 25-hydroxy vitamin D (25-D), total carbon dioxide, transferrin saturation (TSAT), LDL cholesterol (LDL-C), blood hemoglobin, and urine protein measurements.

Results

Physicians who used the CDSS ordered all CKD-relevant testing more in accord with guidelines than those who did not use the system. Odds ratios favoring CDSS ranged from 1.29 (TSAT) to 1.88 (serum phosphorus) [CI, 1.20 to 2.01], p < 0.001 for all tests. The CDSS impact was greater for primary care physicians versus nephrologists. CDSS physicians met guideline targets for LDL-C and 25-D more often, but hemoglobin targets less often, than non-CDSS physicians. Use of CDSS did not impact guideline target achievement for the remaining tests.

Conclusions

Use of an automated laboratory-based CDSS may improve physician adherence to guidelines with respect to timely monitoring of CKD.

Electronic supplementary material

The online version of this article (doi:10.1186/s12882-015-0159-5) contains supplementary material, which is available to authorized users.

Low-density lipoprotein-cholesterol (LDL-C) greater than 100 mg/dL as a quality indicator: locating risk in person, place and time

RATIONALE, AIMS AND OBJECTIVES

Achieving control over elevated lipid parameters, particularly low-density lipoprotein (LDL)-cholesterol, is an acknowledged quality indicator in primary care. The Centers for Disease Control and Prevention (CDC)'s model for investigation of outbreaks (person-place-time) can be applied to the analysis of quality indicators.

METHODS

A sample of 322 family medicine patients for whom lipid levels were ordered was extracted. LDL > 100 mg/dL was cross-tabulated by personal characteristics [age group, gender, body mass index (BMI), diagnoses], month (time) and ordering department (place).

RESULTS

Age (except one age category), gender, time and location were not related to LDL > 100 mg/dL after adjustment for covariates. All levels of BMI above normal elevated the risk of LDL > 100 mg/dL [BMI 25-29.9: odds ratio (OR) = 3.41, confidence interval (CI) = 1.61-7.23, P = 0.0014; BMI 30-34.9: OR = 2.93, CI = 1.28-6.70, P = 0.0109; BMI ≥ 35: OR = 2.75, CI = 1.19-6.37, P = 0.0181]. Patients with coronary artery disease (CAD) and diabetes mellitus (DM) were at reduced risk for LDL > 100 mg/dL (CAD: OR = 0.47, CI = 0.24-0.91, P = 0.0254; DM: OR = 0.28, CI = 0.14-0.55, P = 0.0002).

CONCLUSION

An outbreak investigation model is useful for analysing variations in this quality indicator. Patients with higher BMI and those not diagnosed with CAD or DM type I/II may be considered for intensified lipid lowering using quality improvement efforts. These might include counselling for lifestyle changes or medication therapy depending upon their calculated cardiac risk.

Physicians' behavior following changes in LDL cholesterol target goals

Background

In 01/2011 Clalit Health Services (CHS), changed the LDL-Cholesterol target definitions in its quality indicators program, from a universal target to values stratified by risk assessment based on ATP III criteria. The objective of this study is to evaluate the effect of this change on achievement of LDL-C targets and on physicians’ prescriptions of statins.

Study Design: A descriptive study based on administrative dataset 06/2010-06/2012.

Methods

Setting: CHS, The largest health maintenance organization in Israel that insures above 4,000,000 beneficiaries.

Patients: Patients who had been in the same risk group throughout the study period.

Measurements: Attainment of targets for LDL-C and purchases of statins prior to, and following, implementation of the guidelines in the CHS quality indicators program.

Results

433,662 patients remained in the same risk groups throughout the study period; 55.8% were women; the average age was 53.0 ± 10.3 years; 63.9%, 13.4%, and 22.7% were at low, medium, and high risk respectively. After implementation, the proportion of patients reaching LDL-C targets increased in all risk groups: from 58.6% to 61.6%, from 55.1% to 61.1%, and from 44.5% to 49.0%, in low, medium, and high risk groups respectively (p < 0.001). The proportion of patients treated with potent statins increased in all risk groups; from 3.4% to 5.6%, from 6.7% to 10.3%, and from 14.5% to 20.3% respectively (p < 0.001).

Conclusion

The risk stratification approach as a basis for the quality indicators program was implemented and better achievement of target LDL-C levels ensued. We suggest that implementation of quality indicators that are consistent with the current literature can lead to improvements that exceeds temporal trends.

Variations in lipid screening frequency in family medicine patients with cardiovascular risk factors

RATIONALE, AIMS AND OBJECTIVES

This study was undertaken to assess the frequency of lipid screening in comparison with the United States Preventive Services Task Force guideline in a sample of family medicine patients. In addition, we sought to determine the association between testing frequency and achievement of lipid targets.

METHODS

A random sample was extracted from 271 patients from among all patients cared for in our Department of Family Medicine for whom lipid screening was ordered from March to September 2012 and who had ≥2 well-defined cardiovascular risk factors. Lipid testing frequency was classified in three ways: semi-annual or less often (0-12 tests over 6 years), annual or less often (0-6 tests), or biennial (0-3 tests).

RESULTS

Multiple logistic regression analysis revealed that the predictors of lipid screening more often than semi-annually were age ≥60 years [odds ratio (OR) = 3.7] and diabetes mellitus (DM) (OR = 30.6). Predictors of screening more often than annually were DM (OR = 4.3), hypertension (OR = 2.1), family history of premature coronary artery disease (OR = 5.6) and statin treatment (OR = 3.5). Lipid goal attainment was not associated with testing frequency except with regard to low-density lipoprotein levels (P = 0.043, P < 0.001, P = 0.005, by semi-annual, annual and biennial, respectively) and total cholesterol levels (P = 0.015, P = 0.025 by semi-annual and annual, respectively).

CONCLUSIONS

Questionable high frequency of lipid testing was detected even when the more conservative approach of annual monitoring was assumed. Frequency of testing was not associated with goal attainment for most parameters. Physicians should request the lipid testing based on overall risk assessment and person variability in accordance with published guidelines.

Statin treatment patterns and clinical profile of patients with risk factors for coronary heart disease defined by National Cholesterol Education Program Adult Treatment Panel III

OBJECTIVE

To compare clinical characteristics, statin treatment patterns and adherence among patients at different risk for coronary heart disease (CHD) as defined by National Cholesterol Education Program (NCEP) Adult Treatment Panel (ATP) III guidelines.

METHODS

Patients ≥ 18 years old with ≥ 1 claim for dyslipidemia, ≥ 1 statin claim, or ≥ 1 LDL-C value ≥ 100 mg/dL were identified from 1 January 2007 to 31 July 2012. Patients were classified as low risk (LR) (0-1 risk factor: hypertension, age ≥ 45 years [men] or ≥ 55 years [women], or low HDL-C), moderate/moderately high risk (MR) (≥ 2 risk factors), high risk (HR) (CHD or CHD risk equivalent), or very high risk (VHR) (acute coronary syndrome, or established cardiovascular disease plus diabetes or metabolic syndrome). Medication use and lipid levels during the 12 months before and statin use during the 6 months after index were compared across risk groups.

RESULTS

There were 1,524,351 LR, 242,357 MR, 188,222 HR, and 57,469 VHR patients identified. Statin use was observed in 15% of all patients, but was higher in the VHR group (45%) versus LR (12%), MR (18%), and HR (29%) groups. Simvastatin accounted for 50%-52% of all statin use, and average statin dose was higher among VHR patients compared with all other groups. Adherence was low overall (mean proportion of days covered [PDC]: 0.57), but higher among VHR (0.69) versus others (mean PDC: 0.55, 0.59, and 0.59 in LR, MR, and HR groups, respectively).

CONCLUSIONS

Statin treatment was low across all risk groups, and VHR patients had higher doses and better adherence compared with other risk groups. However, adherence was not optimal, indicating a potential limited benefit from statin treatment.

Patient and public attitudes to and awareness of clinical practice guidelines: a systematic review with thematic and narrative syntheses

Background

Clinical practice guidelines are typically written for healthcare providers but there is increasing interest in producing versions for the public, patients and carers. The main objective of this review is to identify and synthesise evidence of the public’s attitudes towards clinical practice guidelines and evidence-based recommendations written for providers or the public, together with their awareness of guidelines.

Methods

We included quantitative and qualitative studies of any design reporting on public, patient (and their carers) attitudes and awareness of guidelines written for providers or patients/public. We searched electronic databases including MEDLINE, PSYCHINFO, ERIC, ASSIA and the Cochrane Library from 2000 to 2012. We also searched relevant websites, reviewed citations and contacted experts in the field. At least two authors independently screened, abstracted data and assessed the quality of studies. We conducted a thematic analysis of first and second order themes and performed a separate narrative synthesis of patient and public awareness of guidelines.

Results

We reviewed 5415 records and included 26 studies (10 qualitative studies, 13 cross sectional and 3 randomised controlled trials) involving 24 887 individuals. Studies were mostly good to fair quality. The thematic analysis resulted in four overarching themes: Applicability of guidelines; Purpose of guidelines for patient; Purpose of guidelines for health care system and physician; and Properties of guidelines. Overall, participants had mixed attitudes towards guidelines; some participants found them empowering but many saw them as a way of rationing care. Patients were also concerned that the information may not apply to their own health care situations. Awareness of guidelines ranged from 0-79%, with greater awareness in participants surveyed on national guideline websites.

Conclusion

There are many factors, not only formatting, that may affect the uptake and use of guideline-derived material by the public. Producers need to make clear how the information is relevant to the reader and how it can be used to make healthcare improvements although there were problems with data quality. Awareness of guidelines is generally low and guideline producers cannot assume that the public has a more positive perception of their material than of alternative sources of health information.

Effect of health information technology interventions on lipid management in clinical practice: a systematic review of randomized controlled trials

BACKGROUND

Large gaps in lipid treatment and medication adherence persist in high-risk outpatients in the United States. Health information technology (HIT) is being applied to close quality gaps in chronic illness care, but its utility for lipid management has not been widely studied.

OBJECTIVE

To perform a qualitative review of the impact of HIT interventions on lipid management processes of care (screening or testing; drug initiation, titration or adherence; or referrals) or clinical outcomes (percent at low density lipoprotein cholesterol goal; absolute lipid levels; absolute risk scores; or cardiac hospitalizations) in outpatients with coronary heart disease or at increased risk.

METHODS

PubMed and Google Scholar databases were searched using Medical Subject Headings related to clinical informatics and cholesterol or lipid management. English language articles that described a randomized controlled design, tested at least one HIT tool in high risk outpatients, and reported at least 1 lipid management process measure or clinical outcome, were included.

RESULTS

Thirty-four studies that enrolled 87,874 persons were identified. Study ratings, outcomes, and magnitude of effects varied widely. Twenty-three trials reported a significant positive effect from a HIT tool on lipid management, but only 14 showed evidence that HIT interventions improve clinical outcomes. There was mixed evidence that provider-level computerized decision support improves outcomes. There was more evidence in support of patient-level tools that provide connectivity to the healthcare system, as well as system-level interventions that involve database monitoring and outreach by centralized care teams.

CONCLUSION

Randomized controlled trials show wide variability in the effects of HIT on lipid management outcomes. Evidence suggests that multilevel HIT approaches that target not only providers but include patients and systems approaches will be needed to improve lipid treatment, adherence and quality.