De-imFAR phase II project: a study protocol for a cluster randomised implementation trial to evaluate the effectiveness of de-implementation strategies to reduce low-value statin prescribing in the primary prevention of cardiovascular disease.

INTRODUCTION: This study aims to reduce potentially inappropriate prescribing (PIP) of statins and foster healthy lifestyle promotion in cardiovascular disease (CVD) primary prevention in low-risk patients. To this end, we will compare the effectiveness and feasibility of several de-implementation strategies developed following the structured design process of the Behaviour Change Wheel targeting key determinants of the clinical decision-making process in CVD prevention. METHODS AND ANALYSIS: A cluster randomised implementation trial, with an additional control group, will be launched, involving family physicians (FPs) from 13 Integrated Healthcare Organisations (IHOs) of Osakidetza-Basque Health Service with non-zero incidence rates of PIP of statins in 2021. All FPs will be exposed to a non-reflective decision assistance strategy based on reminders and decision support tools. Additionally, FPs from two of the IHOs will be randomly assigned to one of two increasingly intensive de-implementation strategies: adding a decision information strategy based on knowledge dissemination and a reflective decision structure strategy through audit/feedback. The target population comprises women aged 45-74 years and men aged 40-74 years with moderately elevated cholesterol levels but no diagnosed CVD and low cardiovascular risk (REGICOR<7.5%), who attend at least one appointment with any of the participating FPs (May 2022-May 2023), and will be followed until May 2024. We use the Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) framework to evaluate outcomes. The main outcome will be the change in the incidence rate of PIP of statins and healthy lifestyle counselling in the study population 12 and 24 months after FPs' exposure to the strategies. Moreover, FPs' perception of their feasibility and acceptability, and patient experience regarding the quality of care received will be evaluated. ETHICS AND DISSEMINATION: The study was approved by the Basque Country Clinical Research Ethics Committee and was registered in ClinicalTrials.gov (NCT04022850). Results will be disseminated in scientific peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT04022850.

Development and Validation of a Prediction Model and Score for Transthyretin Cardiac Amyloidosis Diagnosis: T-Amylo.

BACKGROUND: Although transthyretin cardiac amyloidosis (ATTR-CA) is often underdiagnosed, clinical suspicion is essential for early diagnosis. OBJECTIVES: The aim of this study was to develop and validate a feasible prediction model and score to facilitate the diagnosis of ATTR-CA. METHODS: This retrospective multicenter study enrolled consecutive patients who underwent 99mTc-DPD scintigraphy for suspected ATTR-CA. ATTR-CA was diagnosed if Grade 2 or 3 cardiac uptake was evidenced on 99mTc-DPD scintigraphy in the absence of a detectable monoclonal component or by demonstration of amyloid by biopsy. A prediction model for ATTR-CA diagnosis was developed in a derivation sample of 227 patients from 2 centers using multivariable logistic regression with clinical, electrocardiography, analytical, and transthoracic echocardiography variables. A simplified score was also created. Both of them were validated in an external cohort (n = 895) from 11 centers. RESULTS: The obtained prediction model combined age, gender, carpal tunnel syndrome, interventricular septum in diastole thickness, and low QRS interval voltages, with an area under the curve (AUC) of 0.92. The score had an AUC of 0.86. Both the T-Amylo prediction model and the score showed a good performance in the validation sample (ie, AUC: 0.84 and 0.82, respectively). They were tested in 3 clinical scenarios of the validation cohort: 1) hypertensive cardiomyopathy (n = 327); 2) severe aortic stenosis (n = 105); and 3) heart failure with preserved ejection fraction (n = 604), all with good diagnostic accuracy. CONCLUSIONS: The T-Amylo is a simple prediction model that improves the prediction of ATTR-CA diagnosis in patients with suspected ATTR-CA.