The prediction of therapy-benefit for individual cardiovascular disease prevention: rationale, implications, and implementation

Jaspers, N E M; Ridker, Paul M.; Dorresteijn, Jannick A.N.; Visseren, Frank L.J.

doi:10.1097/mol.0000000000000554

Cited by 21 publications

(14 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, in oncology clinicians use Adjuvant Online to calculate the risk of recurrence with and without adjuvant therapy for breast cancer patients [20]. Recently U-Prevent was introduced for clinicians to calculate cardiovascular risk and therapy benefit [33,34]. The major challenge for ADappt is its large-scale implementation.…”

Section: Discussionmentioning

confidence: 99%

Development and Usability of ADappt: Web-Based Tool to Support Clinicians, Patients, and Caregivers in the Diagnosis of Mild Cognitive Impairment and Alzheimer Disease

Maurik¹,

Visser²,

Pel‐Littel³

et al. 2019

JMIR Form Res

View full text Add to dashboard Cite

Background As a result of advances in diagnostic testing in the field of Alzheimer disease (AD), patients are diagnosed in earlier stages of the disease, for example, in the stage of mild cognitive impairment (MCI). This poses novel challenges for a clinician during the diagnostic workup with regard to diagnostic testing itself, namely, which tests are to be performed, but also on how to engage patients in this decision and how to communicate test results. As a result, tools to support decision making and improve risk communication could be valuable for clinicians and patients. Objective The aim of this study was to present the design, development, and testing of a Web-based tool for clinicians in a memory clinic setting and to ascertain whether this tool can (1) facilitate the interpretation of biomarker results in individual patients with MCI regarding their risk of progression to dementia, (2) support clinicians in communicating biomarker test results and risks to MCI patients and their caregivers, and (3) support clinicians in a process of shared decision making regarding the diagnostic workup of AD. Methods A multiphase mixed-methods approach was used. Phase 1 consisted of a qualitative needs assessment among professionals, patients, and caregivers; phase 2, consisted of an iterative process of development and the design of the tool (ADappt); and phase 3 consisted of a quantitative and qualitative assessment of usability and acceptability of ADappt. Across these phases, co-creation was realized via a user-centered qualitative approach with clinicians, patients, and caregivers. Results In phase 1, clinicians indicated the need for risk calculation tools and visual aids to communicate test results to patients. Patients and caregivers expressed their needs for more specific information on their risk for developing AD and related consequences. In phase 2, we developed the content and graphical design of ADappt encompassing 3 modules: a risk calculation tool, a risk communication tool including a summary sheet for patients and caregivers, and a conversation starter to support shared decision making regarding the diagnostic workup. In phase 3, ADappt was considered to be clear and user-friendly. Conclusions Clinicians in a memory clinic setting can use ADappt, a Web-based tool, developed using multiphase design and co-creation, for support that includes an individually tailored interpretation of biomarker test results, communication of test results and risks to patients and their caregivers, and shared decision making on diagnostic testing.

show abstract

Section: Discussionmentioning

confidence: 99%

Development and Usability of ADappt: Web-Based Tool to Support Clinicians, Patients, and Caregivers in the Diagnosis of Mild Cognitive Impairment and Alzheimer Disease

Maurik¹,

Visser²,

Pel‐Littel³

et al. 2019

JMIR Form Res

View full text Add to dashboard Cite

show abstract

“…There are decision support tools to demonstrate the effects of cardiovascular risk factors, which have created attractive interfaces to illustrate both the baseline risk and treatment effects, but they only focus on single or composite outcomes, and do not consider the adverse events. 10,22–24 One of the stated reasons for this is that the weight of adverse events may be subjective. 10 Our proposed interface has overcome this challenge by formalizing the combination of objective measures of risk with subjective views of severity.…”

Section: Discussionmentioning

confidence: 99%

“…Risk models have been developed to calculate patients’ individual risks in a variety of medical fields, but most models are focused on a specific outcome 9,10 or a composite of several outcomes, 7,11,12 which are often not reflective of the multiple separate effects of many interventions. Many risk prediction models do not account for the trade-offs between potential benefits and harms (adverse events), 10 and the few that do take harms into account have used a single negative treatment consequence or an overall estimation of negative effects. 4,6,13,14 Furthermore, patient engagement and preferences are largely not actively incorporated into this weighing of benefits and harms.…”

Section: Introductionmentioning

confidence: 99%

Translating clinical trial results into personalized recommendations by considering multiple outcomes and subjective views

et al. 2019

View full text Add to dashboard Cite

Currently, clinicians rely mostly on population-level treatment effects from RCTs, usually considering the treatment's benefits. This study proposes a process, focused on practical usability, for translating RCT data into personalized treatment recommendations that weighs benefits against harms and integrates subjective perceptions of relative severity. Intensive blood pressure treatment (IBPT) was selected as the test case to demonstrate the suggested process, which was divided into three phases: (1) Prediction models were developed using the Systolic Blood-Pressure Intervention Trial (SPRINT) data for benefits and adverse events of IBPT. The models were externally validated using retrospective Clalit Health Services (CHS) data; (2) Predicted risk reductions and increases from these models were used to create a yes/no IBPT recommendation by calculating a severity-weighted benefit-to-harm ratio; (3) Analysis outputs were summarized in a decision support tool. Based on the individual benefit-to-harm ratios, 62 and 84% of the SPRINT and CHS populations, respectively, would theoretically be recommended IBPT. The original SPRINT trial results of significant decrease in cardiovascular outcomes following IBPT persisted only in the group that received a “yes-treatment” recommendation by the suggested process, while the rate of serious adverse events was slightly higher in the "no-treatment" recommendation group. This process can be used to translate RCT data into individualized recommendations by identifying patients for whom the treatment’s benefits outweigh the harms, while considering subjective views of perceived severity of the different outcomes. The proposed approach emphasizes clinical practicality by mimicking physicians’ clinical decision-making process and integrating all recommendation outputs into a usable decision support tool.

show abstract

“…Differences in pre-treatment risk do not significantly influence the RR reduction established by lipid-lowering therapy,13 but pre-treatment cardiovascular risk does heavily influence the potential absolute risk reduction. For example, in two patients with the same estimated life expectancy, benefits of lipid-lowering therapy are greatest in the patient with the highest pre-treatment risk of CVD 14 15. However, it is important to realise that high pre-treatment risk of CVD does not necessarily mean a high lifetime risk of CVD 14 15.…”

Section: Heterogeneity In Cardiovascular Risk and Life Expectancymentioning

confidence: 99%

“…For example, in two patients with the same estimated life expectancy, benefits of lipid-lowering therapy are greatest in the patient with the highest pre-treatment risk of CVD 14 15. However, it is important to realise that high pre-treatment risk of CVD does not necessarily mean a high lifetime risk of CVD 14 15. This can be explained by competing risks16; older adults are likely to have one or more chronic diseases, which puts them at high risk of non-vascular death 17.…”

Section: Heterogeneity In Cardiovascular Risk and Life Expectancymentioning

confidence: 99%

Treatment of hypercholesterolaemia in older adults calls for a patient-centred approach

Kleipool

Dorresteijn

Smulders

et al. 2019

Heart

View full text Add to dashboard Cite

Due to an increasing number of older adults with (risk factors for) cardiovascular disease (CVD), the sum of older adults eligible for lipid-lowering drugs will increase. This has risen questions about benefits and harms of lipid-lowering therapy in older adults with a varying number of (cardiovascular) comorbidities and functional status. The heterogeneity in physical and functional health increases with age, leading to a much wider variety in cardiovascular risk and life expectancy than in younger adults. We suggest treatment decisions on hypercholesterolaemia in adults aged ≥75 years should shift from a strictly 10-year cardiovascular risk-driven approach to a patient-centred and lifetime benefit-based approach. With this, estimated 10-year risk of CVD should be placed into the perspective of life expectancy. Moreover, frailty and safety concerns must be taken into account for a risk–benefit discussion between clinician and patient. Based on the Dutch addendum ‘Cardiovascular Risk Management in (frail) older adults’, our approach offers more detailed information on when not to initiate or deprescribe therapy than standard guidelines. Instead of using traditional risk estimating tools which tend to overestimate risk of CVD in older adults, use a competing risk adjusted, older adults-specific risk score (available at https://u-prevent.com). By filling in a patient’s (cardiovascular) health profile (eg, cholesterol, renal function), the tool estimates risk of CVD and models the effect of medication in terms of absolute risk reduction for an individual patient. Using this tool can guide doctors and patients in making shared decisions on initiating, continuing or deprescribing lipid-lowering therapy.

show abstract

The prediction of therapy-benefit for individual cardiovascular disease prevention: rationale, implications, and implementation

Cited by 21 publications

References 70 publications

Development and Usability of ADappt: Web-Based Tool to Support Clinicians, Patients, and Caregivers in the Diagnosis of Mild Cognitive Impairment and Alzheimer Disease

Development and Usability of ADappt: Web-Based Tool to Support Clinicians, Patients, and Caregivers in the Diagnosis of Mild Cognitive Impairment and Alzheimer Disease

Translating clinical trial results into personalized recommendations by considering multiple outcomes and subjective views

Treatment of hypercholesterolaemia in older adults calls for a patient-centred approach

Contact Info

Product

Resources

About