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Comparative analysis of metabolic risk factors for progression of non-alcoholic fatty liver disease. Clin Exp Hepatol 2021; 7:241-247. [PMID: 34295993 PMCID: PMC8284171 DOI: 10.5114/ceh.2021.107567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 04/06/2021] [Indexed: 01/08/2023] Open
Abstract
Aim of the study Non-alcoholic fatty liver disease (NAFLD), a globally prevailing chronic liver condition, refers to a spectrum of disease ranging from bland steatosis to steatohepatitis causing fibrosis without significant alcohol intake. Prominent risk factors (RFs) include obesity, type 2 diabetes mellitus, and dyslipidemia. Currently, no established hierarchy exists for the influence of metabolic RFs on NAFLD progression. This retrospective cohort study investigated and ranked the independent and combined effects of three major RFs on NAFLD progression. Material and methods 652 NAFLD patients with ≥ 1 RF were categorized by RF combination to examine yearly changes in RF severity with liver stiffness measurement (LSM) over five years. Body mass index (BMI), hemoglo- bin A1c (HbA1c), total cholesterol (TC), and LSM were reviewed. Results In patients with any single improving RF, decreases in BMI were associated with a yearly LSM change of –1.26 kPa, while decreases in HbA1c and TC were associated with a change of –0.51 kPa and –0.56 kPa, respectively. In patients with any single worsening RF, increases in BMI were correlated with an LSM change of +0.74 kPa and increases in HbA1c and TC were correlated with a change of +0.43 kPa and +0.16 kPa, respectively. Patients with three RFs had the greatest LSM changes for both improving (–3.68 kPa) and worsening (+3.19 kPa) groups. The strongest predictors for LSM change were BMI and HbA1c, with standardized β coefficients of 0.236 and 0.226 (p < 0.001), while TC had the least influence [0.112 (p < 0.01), F(3,647) = 11.458, p < 0.001, R2 = 0.155]. Conclusions Obesity was the most prominent RF. Treatment of all three RFs over a five-year period presented a high likelihood of fibrosis stage regression for NAFLD patients.
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Jones LK, Tilberry S, Gregor C, Yaeger LH, Hu Y, Sturm AC, Seaton TL, Waltz TJ, Rahm AK, Goldberg A, Brownson RC, Gidding SS, Williams MS, Gionfriddo MR. Implementation strategies to improve statin utilization in individuals with hypercholesterolemia: a systematic review and meta-analysis. Implement Sci 2021; 16:40. [PMID: 33849601 PMCID: PMC8045284 DOI: 10.1186/s13012-021-01108-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 03/29/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Numerous implementation strategies to improve utilization of statins in patients with hypercholesterolemia have been utilized, with varying degrees of success. The aim of this systematic review is to determine the state of evidence of implementation strategies on the uptake of statins. METHODS AND RESULTS This systematic review identified and categorized implementation strategies, according to the Expert Recommendations for Implementing Change (ERIC) compilation, used in studies to improve statin use. We searched Ovid MEDLINE, Embase, Scopus, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and Clinicaltrials.gov from inception to October 2018. All included studies were reported in English and had at least one strategy to promote statin uptake that could be categorized using the ERIC compilation. Data extraction was completed independently, in duplicate, and disagreements were resolved by consensus. We extracted LDL-C (concentration and target achievement), statin prescribing, and statin adherence (percentage and target achievement). A total of 258 strategies were used across 86 trials. The median number of strategies used was 3 (SD 2.2, range 1-13). Implementation strategy descriptions often did not include key defining characteristics: temporality was reported in 59%, dose in 52%, affected outcome in 9%, and justification in 6%. Thirty-one trials reported at least 1 of the 3 outcomes of interest: significantly reduced LDL-C (standardized mean difference [SMD] - 0.17, 95% CI - 0.27 to - 0.07, p = 0.0006; odds ratio [OR] 1.33, 95% CI 1.13 to 1.58, p = 0.0008), increased rates of statin prescribing (OR 2.21, 95% CI 1.60 to 3.06, p < 0.0001), and improved statin adherence (SMD 0.13, 95% CI 0.06 to 0.19; p = 0.0002; OR 1.30, 95% CI 1.04 to 1.63, p = 0.023). The number of implementation strategies used per study positively influenced the efficacy outcomes. CONCLUSION Although studies demonstrated improved statin prescribing, statin adherence, and reduced LDL-C, no single strategy or group of strategies consistently improved outcomes. TRIAL REGISTRATION PROSPERO CRD42018114952 .
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Affiliation(s)
- Laney K Jones
- Genomic Medicine Institute, Geisinger, 100 N Academy Ave., Danville, PA, 17822, USA.
| | - Stephanie Tilberry
- Genomic Medicine Institute, Geisinger, 100 N Academy Ave., Danville, PA, 17822, USA
| | - Christina Gregor
- Center for Pharmacy Innovation and Outcomes, Geisinger, Danville, PA, USA
| | - Lauren H Yaeger
- Bernard Becker Medical Library, Washington University in St. Louis, St. Louis, MO, USA
| | - Yirui Hu
- Population Health Sciences, Geisinger, Danville, PA, USA
| | - Amy C Sturm
- Genomic Medicine Institute, Geisinger, 100 N Academy Ave., Danville, PA, 17822, USA
| | - Terry L Seaton
- University of Health Sciences and Pharmacy in St. Louis, St. Louis, MO, USA
- Population Health, Mercy Clinic-East Communities, St. Louis, MO, USA
| | | | - Alanna K Rahm
- Genomic Medicine Institute, Geisinger, 100 N Academy Ave., Danville, PA, 17822, USA
| | - Anne Goldberg
- Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Ross C Brownson
- Prevention Research Center in St. Louis, Brown School, Washington University in St. Louis, St. Louis, MO, USA
- Department of Surgery (Division of Public Health Sciences) and Alvin J. Siteman Cancer Center, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Samuel S Gidding
- Genomic Medicine Institute, Geisinger, 100 N Academy Ave., Danville, PA, 17822, USA
| | - Marc S Williams
- Genomic Medicine Institute, Geisinger, 100 N Academy Ave., Danville, PA, 17822, USA
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Palmer MJ, Barnard S, Perel P, Free C. Mobile phone-based interventions for improving adherence to medication prescribed for the primary prevention of cardiovascular disease in adults. Cochrane Database Syst Rev 2018; 6:CD012675. [PMID: 29932455 PMCID: PMC6513181 DOI: 10.1002/14651858.cd012675.pub2] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
BACKGROUND Cardiovascular disease (CVD) is a major cause of disability and mortality globally. Premature fatal and non-fatal CVD is considered to be largely preventable through the control of risk factors via lifestyle modifications and preventive medication. Lipid-lowering and antihypertensive drug therapies for primary prevention are cost-effective in reducing CVD morbidity and mortality among high-risk people and are recommended by international guidelines. However, adherence to medication prescribed for the prevention of CVD can be poor. Approximately 9% of CVD cases in the EU are attributed to poor adherence to vascular medications. Low-cost, scalable interventions to improve adherence to medications for the primary prevention of CVD have potential to reduce morbidity, mortality and healthcare costs associated with CVD. OBJECTIVES To establish the effectiveness of interventions delivered by mobile phone to improve adherence to medication prescribed for the primary prevention of CVD in adults. SEARCH METHODS We searched CENTRAL, MEDLINE, Embase, and two other databases on 21 June 2017 and two clinical trial registries on 14 July 2017. We searched reference lists of relevant papers. We applied no language or date restrictions. SELECTION CRITERIA We included randomised controlled trials investigating interventions delivered wholly or partly by mobile phones to improve adherence to cardiovascular medications prescribed for the primary prevention of CVD. We only included trials with a minimum of one-year follow-up in order that the outcome measures related to longer-term, sustained medication adherence behaviours and outcomes. Eligible comparators were usual care or control groups receiving no mobile phone-delivered component of the intervention. DATA COLLECTION AND ANALYSIS We used standard methodological procedures recommended by Cochrane. We contacted study authors for disaggregated data when trials included a subset of eligible participants. MAIN RESULTS We included four trials with 2429 randomised participants. Participants were recruited from community-based primary care or outpatient clinics in high-income (Canada, Spain) and upper- to middle-income countries (South Africa, China). The interventions received varied widely; one trial evaluated an intervention focused on blood pressure medication adherence delivered solely through short messaging service (SMS), and one intervention involved blood pressure monitoring combined with feedback delivered via smartphone. Two trials involved interventions which targeted a combination of lifestyle modifications, alongside CVD medication adherence, one of which was delivered through text messages, written information pamphlets and self-completion cards for participants, and the other through a multi-component intervention comprising of text messages, a computerised CVD risk evaluation and face-to-face counselling. Due to heterogeneity in the nature and delivery of the interventions, we did not conduct a meta-analysis, and therefore reported results narratively.We judged the body of evidence for the effect of mobile phone-based interventions on objective outcomes (blood pressure and cholesterol) of low quality due to all included trials being at high risk of bias, and inconsistency in outcome effects. Of two trials targeting medication adherence alongside other lifestyle modifications, one reported a small beneficial intervention effect in reducing low-density lipoprotein cholesterol (mean difference (MD) -9.2 mg/dL, 95% confidence interval (CI) -17.70 to -0.70; 304 participants), and the other found no benefit (MD 0.77 mg/dL, 95% CI -4.64 to 6.18; 589 participants). One trial (1372 participants) of a text messaging-based intervention targeting adherence showed a small reduction in systolic blood pressure (SBP) for the intervention arm which delivered information-only text messages (MD -2.2 mmHg, 95% CI -4.4 to -0.04), but uncertain evidence of benefit for the second intervention arm that provided additional interactivity (MD -1.6 mmHg, 95% CI -3.7 to 0.5). One study examined the effect of blood pressure monitoring combined with smartphone messaging, and reported moderate intervention benefits on SBP and diastolic blood pressure (DBP) (SBP: MD -7.10 mmHg, 95% CI -11.61 to -2.59; DBP: -3.90 mmHg, 95% CI -6.45 to -1.35; 105 participants). There was mixed evidence from trials targeting medication adherence alongside lifestyle advice using multi-component interventions. One trial found large benefits for SBP and DBP (SBP: MD -12.45 mmHg, 95% CI -15.02 to -9.88; DBP: MD -12.23 mmHg, 95% CI -14.03 to -10.43; 589 participants), whereas the other trial demonstrated no beneficial effects on SBP or DBP (SBP: MD 0.83 mmHg, 95% CI -2.67 to 4.33; DBP: MD 1.64 mmHg, 95% CI -0.55 to 3.83; 304 participants).Two trials reported on adverse events and provided low-quality evidence that the interventions did not cause harm. One study provided low-quality evidence that there was no intervention effect on reported satisfaction with treatment.Two trials were conducted in high-income countries, and two in upper- to middle-income countries. The interventions evaluated employed between three and 16 behaviour change techniques according to coding using Michie's taxonomic method. Two trials evaluated interventions that involved potential users in their development. AUTHORS' CONCLUSIONS There is low-quality evidence relating to the effects of mobile phone-delivered interventions to increase adherence to medication prescribed for the primary prevention of CVD; some trials reported small benefits while others found no effect. There is low-quality evidence that these interventions do not result in harm. On the basis of this review, there is currently uncertainty around the effectiveness of these interventions. We identified six ongoing trials being conducted in a range of contexts including low-income settings with potential to generate more precise estimates of the effect of primary prevention medication adherence interventions delivered by mobile phone.
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Affiliation(s)
- Melissa J Palmer
- London School of Hygiene and Tropical MedicineDepartment of Population HealthLondonUK
| | | | - Pablo Perel
- London School of Hygiene and Tropical MedicineDepartment of Population HealthLondonUK
| | - Caroline Free
- London School of Hygiene & Tropical MedicineClinical Trials Unit, Department of Population HealthKeppel StreetLondonUKWC1E 7HT
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