Pilot study to validate a computer-based clinical decision support system for dyslipidemia treatment (HTE-DLP)

Zamora, Alberto; Bobadilla, F Fernández de; Carrión, Carme; Vázquez, Guillermo J.; Paluzié, G.; Elosúa, Roberto; Vilaseca, M. A.; Martín-Urda, Anabel; Rivera, Ángela; Plana, Núria

doi:10.1016/j.atherosclerosis.2013.09.029

Cited by 40 publications

(31 citation statements)

References 19 publications

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“…So, This study demonstrated that in the "real-clinic-world" expert physicians in the cardiovascular field can improve cholesterol management by using a specific CDSS. The cost of reducing 1 mg/dL of LDL-C was less in the intervention group than in control group (0.89 EUR/day vs 1.10 EUR/day [14]. Physicians expressed good agreement with the 1st HTE-DLP recommendation in 86.1% of cases and use was described as ease in 85% of cases.…”

Section: International Journal Of Cardiovascular Practicementioning

confidence: 63%

“…As far as we know, this is the first study in which a CDSS for prescribing lipid-lowering therapy has been analyzed with a standardized tool for mHealth applications. We believe that global positive evaluation of HTE-DLPR is due to the significant participation of vascular risk experts in the design of its hardware and software [14]. Therefore, clinicians have ensured that HTE-DLPR is faithful to top scientific evidence and the recommendations of the European Society of Arteriosclerosis [15].…”

Section: Discussionmentioning

confidence: 99%

“…A multi-center observational prospective study has revealed that the number of patients at very high cardiovascular risk, reaching the treatment goal defined by the European Guidelines for the Management of Dyslipidemia 2011 [15] (LDL-C < 70 mg/dL. ), increases 4.4 times when expert clinicians in the lipidology field use HTE-DL-PR [14]. So, This study demonstrated that in the "real-clinic-world" expert physicians in the cardiovascular field can improve cholesterol management by using a specific CDSS.…”

Section: International Journal Of Cardiovascular Practicementioning

confidence: 99%

“…All statistical tests were two-sided and a value of <0.05 was considered significant. The study was designed to have 95% power to detect at least a 40% difference between the intervention and the control group [14]. Results are expressed in mean and standard deviation (SD).…”

Section: International Journal Of Cardiovascular Practicementioning

confidence: 99%

“…Patient data management systems (PDMS) integrated with a clinical decision support system (CDSS) can improve outcomes in chronicles diseases as hypercholesterolemia [13]. HTE-DLPR is the first CDSS and PDMS system developed to monitor dyslipidemia treatment in Spain and the first to be validated in clinical practice in Europe [14]. HTE-DLPR performs a sequence of clinical decisions as expert lipidologyst including all statins and ezetimibe and creates specific recommendations for each patient using efficiency, safety and cost criteria.…”

Section: International Journal Of Cardiovascular Practicementioning

confidence: 99%

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Evaluation of a Clinical Decision Support System for Dyslipidemia Treatment (HTE-DLPR) by QoE questionnaire

Zamora¹,

Carrión²,

Aymerich³

et al. 2017

ijcp

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Introduction: Clinical decision support systems (CDSS) are computer systems designed to assist clinicians with patient-related decision making, such as diagnosis and treatment. CDSS have shown to improve both patient outcomes and cost of care. Methods: A multi-center observational prospective study was conducted. Ten physicians agreed to participate. Seventy-seven patients with high or very high cardiovascular risk were included. After using CDSS for dyslipidemia (HTE-DLPR) for a 3 months period, participants were asked to evaluate their experience with HTE-DLPR using a quality of experience questionnaire (QoE) tool for mHealth applications. Results: Total score on the QoE was 3.89 out of 5. The highest scores were received for precision, ease of use and content quality. The lowest scores were given to security, appearance and performance. Physicians were in strong agreement with the 1st HTE-DLPR recommendation in 86.1% and the system's use was described as comfortable in 85% of cases. Users positively evaluated the development of a new version of HTE-DLPR in the future receiving a total score of 4.25 out of 5. Conclusions: A CDSS for dyslipidemia (HTE-DLP) has been positively evaluated by physicians using QoE questionnaire.

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Section: International Journal Of Cardiovascular Practicementioning

confidence: 63%

Section: Discussionmentioning

confidence: 99%

Section: International Journal Of Cardiovascular Practicementioning

confidence: 99%

Section: International Journal Of Cardiovascular Practicementioning

confidence: 99%

Section: International Journal Of Cardiovascular Practicementioning

confidence: 99%

See 3 more Smart Citations

Evaluation of a Clinical Decision Support System for Dyslipidemia Treatment (HTE-DLPR) by QoE questionnaire

Zamora¹,

Carrión²,

Aymerich³

et al. 2017

ijcp

View full text Add to dashboard Cite

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Risk scoring for the primary prevention of cardiovascular disease

Karmali

Persell

Perel

et al. 2017

Cochrane Database of Systematic Reviews

109

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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.

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Network Medicine Approach in Prevention and Personalized Treatment of Dyslipidemias

Benincasa

Candia

Costa

et al. 2020

Lipids

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Dyslipidemias can affect molecular networks underlying the metabolic homeostasis and vascular function leading to atherogenesis at early stages of development. Since disease‐related proteins often interact with each other in functional modules, many advanced network‐oriented algorithms were applied to patient‐derived big data to identify the complex gene–environment interactions underlying the early pathophysiology of dyslipidemias and atherosclerosis. Both the proprotein convertase subtilisin/kexin type 7 (PCSK7) and collagen type 1 alpha 1 chain (COL1A1) genes arose from the application of TFfit and WGCNA algorithms, respectively, as potential useful therapeutic targets in prevention of dyslipidemias. Moreover, the Seed Connector algorithm (SCA) algorithm suggested a putative role of the neuropilin‐1 (NRP1) protein as drug target, whereas a regression network analysis reported that niacin may provide benefits in mixed dyslipidemias. Dyslipidemias are highly heterogeneous at the clinical level; thus, it would be helpful to overcome traditional evidence‐based paradigm toward a personalized risk assessment and therapy. Network Medicine uses omics data, artificial intelligence (AI), imaging tools, and clinical information to design personalized therapy of dyslipidemias and atherosclerosis. Recently, a novel non‐invasive AI‐derived biomarker, named Fat Attenuation Index (FAI™) has been established to early detect clinical signs of atherosclerosis. Moreover, an integrated AI‐radiomics approach can detect fibrosis and microvascular remodeling improving the customized risk assessment. Here, we offer a network‐based roadmap ranging from novel molecular pathways to digital therapeutics which can improve personalized therapy of dyslipidemias.

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Pilot study to validate a computer-based clinical decision support system for dyslipidemia treatment (HTE-DLP)

Cited by 40 publications

References 19 publications

Evaluation of a Clinical Decision Support System for Dyslipidemia Treatment (HTE-DLPR) by QoE questionnaire

Evaluation of a Clinical Decision Support System for Dyslipidemia Treatment (HTE-DLPR) by QoE questionnaire

Risk scoring for the primary prevention of cardiovascular disease

Network Medicine Approach in Prevention and Personalized Treatment of Dyslipidemias

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