Dataset Information

Interpretation of CVD risk predictions in clinical practice: Mission impossible?

ABSTRACT:

Background

Cardiovascular disease (CVD) risk prediction models are often used to identify individuals at high risk of CVD events. Providing preventive treatment to these individuals may then reduce the CVD burden at population level. However, different prediction models may predict different (sets of) CVD outcomes which may lead to variation in selection of high risk individuals. Here, it is investigated if the use of different prediction models may actually lead to different treatment recommendations in clinical practice.

Method

The exact definition of and the event types included in the predicted outcomes of four widely used CVD risk prediction models (ATP-III, Framingham (FRS), Pooled Cohort Equations (PCE) and SCORE) was determined according to ICD-10 codes. The models were applied to a Dutch population cohort (n = 18,137) to predict the 10-year CVD risks. Finally, treatment recommendations, based on predicted risks and the treatment threshold associated with each model, were investigated and compared across models.

Results

Due to the different definitions of predicted outcomes, the predicted risks varied widely, with an average 10-year CVD risk of 1.2% (ATP), 5.2% (FRS), 1.9% (PCE), and 0.7% (SCORE). Given the variation in predicted risks and recommended treatment thresholds, preventive drugs would be prescribed for 0.2%, 14.9%, 4.4%, and 2.0% of all individuals when using ATP, FRS, PCE and SCORE, respectively.

Conclusion

Widely used CVD prediction models vary substantially regarding their outcomes and associated absolute risk estimates. Consequently, absolute predicted 10-year risks from different prediction models cannot be compared directly. Furthermore, treatment decisions often depend on which prediction model is applied and its recommended risk threshold, introducing unwanted practice variation into risk-based preventive strategies for CVD.

SUBMITTER: Lagerweij GR

PROVIDER: S-EPMC6326414 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Interpretation of CVD risk predictions in clinical practice: Mission impossible?

Lagerweij G R GR Moons K G M KGM de Wit G A GA Koffijberg H H

PloS one 20190109 1

<h4>Background</h4>Cardiovascular disease (CVD) risk prediction models are often used to identify individuals at high risk of CVD events. Providing preventive treatment to these individuals may then reduce the CVD burden at population level. However, different prediction models may predict different (sets of) CVD outcomes which may lead to variation in selection of high risk individuals. Here, it is investigated if the use of different prediction models may actually lead to different treatment r ...[more]

PMID: 30625177

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Project description:Objective:In children many antiepileptic drugs (AEDs) are prescribed off-label due to a lack of well-designed randomized controlled trials (RCTs). We conducted a multicenter RCT in the Netherlands to compare levetiracetam and valproic acid as monotherapy in children with newly diagnosed epilepsy. After 2 years, we had to stop this investigator-initiated trial prematurely because the inclusion rate was too low. We analyzed the reasons for this failure, assessed the various issues involved in performing RCTs in children, and now give recommendations for future studies. Methods:A questionnaire was completed by all investigators involved in the study. It included questions about the motivation to participate and the perceived reasons for recruitment failure. We also studied literature about financial, logistic, legal, and ethical aspects of RCTs in children. Results:Main reasons for recruitment failure were overestimation of the number of eligible AED-naive children referred by general pediatricians; personal preferences of investigators for specific antiepileptic drugs; and the extensive administrative load due to extra regulations and guidelines for children. Fundraising for investigator-initiated trials is difficult and the majority of RCTs concerning AEDs are sponsored by pharmaceutical companies. Involving children requires balancing between protection and participation; the randomization procedure and obtaining informed consent are complex for both children and parents. Significance:Performing RCTs with AEDs in children is important but complicated by logistic, regulatory, legal, and ethical restrictions. Based on our recent experience, our advice to colleagues who are planning a similar trial would be to perform a feasibility pilot study; to set up intensive collaboration with referring pediatricians; to arrange support of a clinical trials unit and a local research nurse during the complete trial period; and to incorporate the possibility of extending the recruitment period. Major investments, both financially from governmental organizations and in time, are imperative for independent RCTs in children.

Project description:BackgroundAlthough risk prediction has become an integral part of clinical practice guidelines for cardiovascular disease (CVD) prevention, multiple studies have shown that patients' risk still plays almost no role in clinical decision-making. Because little is known about why this is so, we sought to understand providers' views on the opportunities, barriers, and facilitators of incorporating risk prediction to guide their use of cardiovascular preventive medicines.MethodsWe conducted semi-structured interviews with primary care providers (n = 33) at VA facilities in the Midwest. Facilities were chosen using a maximum variation approach according to their geography, size, proportion of MD to non-MD providers, and percentage of full-time providers. Providers included MD/DO physicians, physician assistants, nurse practitioners, and clinical pharmacists. Providers were asked about their reaction to a hypothetical situation in which the VA would introduce a risk prediction-based approach to CVD treatment. We conducted matrix and content analysis to identify providers' reactions to risk prediction, reasons for their reaction, and exemplar quotes.ResultsMost providers were classified as Enthusiastic (n = 14) or Cautious Adopters (n = 15), with only a few Non-Adopters (n = 4). Providers described four key concerns toward adopting risk prediction. Their primary concern was that risk prediction is not always compatible with a "whole patient" approach to patient care. Other concerns included questions about the validity of the proposed risk prediction model, potential workflow burdens, and whether risk prediction adds value to existing clinical practice. Enthusiastic, Cautious, and Non-Adopters all expressed both doubts about and support for risk prediction categorizable in the above four key areas of concern.ConclusionsProviders were generally supportive of adopting risk prediction into CVD prevention, but many had misgivings, which included concerns about impact on workflow, validity of predictive models, the value of making this change, and possible negative effects on providers' ability to address the whole patient. These concerns have likely contributed to the slow introduction of risk prediction into clinical practice. These concerns will need to be addressed for risk prediction, and other approaches relying on "big data" including machine learning and artificial intelligence, to have a meaningful role in clinical practice.

Dataset Information

Interpretation of CVD risk predictions in clinical practice: Mission impossible?

Background

Method

Results

Conclusion

Publications

Interpretation of CVD risk predictions in clinical practice: Mission impossible?

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