Implementation of evidence-based practices into routine clinical care within a health system remains a challenge. Rigorous evaluation of clinical implementation efforts with data collection guided by an implementation science framework can provide significant insight into variation in outcomes across the health system as well as learnings related to contextual factors that may contribute to both clinical and implementation outcomes. We describe the protocols for two implementation projects, each of which aims to implement into routine practice previously published research findings of a care delivery innovation addressing management of a chronic disease within primary care settings. The two implementation projects include Implementation of Intensive Lifestyle Treatment for Weight Loss in Primary Care Settings and Implementation of Effective Hypertension Management Approaches, each funded through the Patient-Centered Outcomes Research Institute Health Systems Implementation Initiative (PCORI HSII) program. Both care delivery innovations will be implemented in 56 primary care practices (suburban, urban, rural) in one health system in Northeast Ohio and will involve alignment of clinical, operational, and evaluation teams. For the weight loss project, we will build on our health system model of shared medical appointments led by multidisciplinary teams to deliver a group visit intervention for patients with obesity (body mass index >/=30 kg/m2). The primary outcome will be change in body weight. For the hypertension project, we will implement an intensive home blood pressure management program for patients with uncontrolled blood pressure (>150/95 mmHg) with follow-up every 2-4 weeks with a pharmacist or advanced practice provider. The primary outcome will be change in systolic blood pressure. Each care model will be implemented in primary care practices utilizing a randomized stepped-wedge design. Specific implementation strategies will be utilized, and implementation outcomes collected utilizing an implementation framework. Adaptations at the practice group level will be documented. The two implementation projects described have the potential to significantly improve treatment for both obesity and uncontrolled hypertension in primary care practices in one health system. Clinical effectiveness and implementation outcomes will be collected and will inform scale-up of the programs as well as need for tailoring of future health system implementation efforts. NCT07268417(Initial Release Date 11/13/2025): Group Medical Appointments for Intensive Lifestyle Treatment for Obesity in Cleveland Clinic Primary Care Practices (ACTIVATE OC) NCT07232017(Initial Release Date 11/14/2025): Implementation of Intensive Hypertension Management Approaches: Cleveland Clinic (IN-HOME BP).

BACKGROUND/OBJECTIVES:Rilonacept (RI), an interleukin (IL)-1α/β cytokine trap, is a biological treatment to prevent recurrence in recurrent pericarditis (RP). Discontinuation of RI in patients after 18 months with RP has been associated with a higher rate of RP. We aimed to propose a preliminary strategy for stopping Rl in select patients and evaluate their long-term outcomes. METHODS:We conducted a retrospective cohort study at the pericardial disease centre at Cleveland Clinic, Ohio. Adults included had RP refractory to first-line therapies, receiving Rl for at least 12 months (median duration 24 months) with improvement in symptoms and late gadolinium enhancement on cardiac MRI. The primary outcome was recurrence of RP post abrupt Rl discontinuation versus slow taper (gradual decrease in Rl frequency from weekly, to bimonthly, to monthly and then cessation). We also evaluated the effects of colchicine prophylaxis at the end of Rl therapy, time to recurrence and the overall safety profile of long-term Rl therapy. RESULTS:A total of 53 patients were included in the study (61.8% female; mean age 54.6±14.17 y), of which 34 (62%) discontinued RI abruptly/without tapering and 19 (35%) underwent a RI taper after shared decision making with their clinician. A higher percentage of recurrent cases was observed in the abrupt cessation arm (82%) versus the taper arm (37%) (p=0.042). Treatment with colchicine post-completion of Rl therapy was associated with delaying the average time to flare by 60.7 weeks. Most patients (80%) resumed Rl after a recurrence. Long-term Rl therapy was well-tolerated during the follow-up period. CONCLUSIONS:Long-term Rl therapy beyond 18-24 months leads to sustained disease remission in RP, and therapy withdrawal is associated with a recurrence in two-thirds of patients. A strategy that involves a gradual taper of RI therapy appears to be associated with a relatively lower risk of disease recurrence compared with discontinuation without tapering. Larger-scale, multicentre studies with extended follow-up periods are warranted to elucidate optimal treatment durations and to establish standardised cessation protocols.

Objective: Pacing artifacts in electrocardiogram (ECG) signals can interfere with waveform analysis and downstream quantitative interpretation. This study presents an algorithm for detecting and removing pacing artifacts from routine ECG recordings. Methods: The algorithm applies high pass filtering and Shannon energy computation to suppress cardiac components and enhance pacing artifacts. Principal component analysis (PCA) is then applied to the 12-lead ECGs to identify artifact start and end points from the first principal component. At each candidate location, slopes are calculated across leads, and artifacts are removed by linear interpolation only when the slope magnitude exceeds a predefined threshold. The algorithm was developed using 493 12-lead ECGs from the Chronic Renal Insufficiency Cohort and validated using 203 Cleveland Clinic 12-lead ECGs, 80 Cardiac Memory with ICD 12 lead ECGs, and 200 10-second two-lead Holter ECG epochs from the MIT-BIH Arrhythmia Database. A graphical user interface was developed for review and refinement. Results: The algorithm automatically detected and removed pacing artifacts in 84% of validation ECG files; the remaining files required user-guided refinement. Overall sensitivity and specificity for pacing artifact detection were 98.4% and 76.2%, respectively. Conclusion: The proposed algorithm detects and removes pacing artifacts with substantial automation while allowing guided refinement in challenging recordings. Significance: The framework combines multilead PCA-based localization, lead-specific slope confirmation, and adaptive artifact-width removal directly on routine ECG, enabling morphology-preserving preprocessing for quantitative and AI-enabled ECG analysis

BackgroundStatin Choice is a shared decision-making encounter tool embedded in the electronic health record.ObjectiveTo describe the association between the use of Statin Choice, statin prescriptions by clinicians, prescription fills (primary adherence), and statin adherence at 12 mo (secondary adherence).DesignObservational cohort study at the Cleveland Clinic Health System.SubjectsStatin-naïve adults aged 40 to 75 y with a 10-y atherosclerotic cardiovascular disease (ASCVD) risk of ≥5% and a primary care appointment between January 2020 and July 2021.Main MeasuresThe primary exposure was the use of Statin Choice during a clinical encounter. We measured whether the use of Statin Choice was associated with statin prescriptions. We measured statin adherence based on pharmacy fill data at 60 d (primary adherence) and 12 mo (secondary adherence). We used mixed-effects logistic regression to estimate the adjusted odds of statin prescriptions and adherence at the 3 time points by the use of Statin Choice.Key ResultsAmong 17,001 statin-naïve patients, 13% viewed Statin Choice and 7% were prescribed a statin. The median ASCVD risk was 10%. Patients who were shown Statin Choice had 9.04 higher odds of being prescribed a statin compared with patients not shown Statin Choice (95% confidence interval [CI]: 7.86-10.4). Among patients prescribed a statin, the use of Statin Choice was associated with 5.75 higher odds of primary adherence compared with usual care (95% CI: 4.22-7.83). At 12 mo, Statin Choice use was significantly associated with adherence in the unadjusted analysis (OR: 1.58; 95% CI: 1.05-2.08) but was not significant after adjustment for patient factors. Patients shown Statin Choice had an average of 12 mg/dL reduction in low-density lipoprotein cholesterol at 12 mo (95% CI: -16 mg/dL, -10) compared with those not shown Statin Choice.ConclusionIn this observational study, Statin Choice use was strongly associated with statin prescription and fills and weakly associated with adherence to statins for up to 1 y. A randomized trial is needed to confirm causality.HighlightsStatin Choice is an electronic health record-embedded shared decision-making encounter tool available for free in many health care systems.Small randomized controlled trials have found modest associations between the use of Statin Choice and statin adherence using patient-reported data.In our large study using pharmacy fill data, clinician use of Statin Choice during a medical encounter was associated with significantly greater patient adherence with statins up to 1 y later.Exposure to Statin Choice was associated with a significant reduction in low-density lipoprotein cholesterol over 1 y.

INTRODUCTION:An electrocardiogram (ECG) is commonly used in clinical practice. Poor data quality, artifacts, and misplacement of electrodes have to be identified before the clinical interpretation of ECG. We aimed to develop an algorithm to automatically identify ECG artifacts and lead misplacement. METHODS AND RESULTS:We utilized 42,743 ECGs from UK Biobank (UKB; n = 42,743 participants; age 55±8 y; cardiovascular disease 1.2 %; diabetes 0.9 %; chronic kidney disease 0.5 %; ventricular pacing 0 %) for the algorithm development and 41,495 ECGs from the Chronic Renal Insufficiency Cohort (CRIC; n = 3912 participants; age 63 ± 11 y; cardiovascular disease 78 %; diabetes 56 %; chronic kidney disease 100 %; ventricular pacing 3.5 %) for external validation. We developed a fully automated algorithm to detect non-physiological ECG artifacts, such as high or low peak-to-peak amplitude, frequency-based outliers, and misplaced electrodes. In UKB, the algorithm demonstrated a sensitivity of 84.9 %, a specificity of 100 %, an ROC AUC of 0.924, and a Kappa statistic of 0.91. We observed 98.81 % agreement between ground truth and algorithm-identified non-physiological ECG artifacts, significantly (p < 0.00001) larger than the random agreement of 86.91 % expected at the observed 7.6 % prevalence. The misplacement of limb lead electrodes in UKB affected the Wilson Central Terminal. In CRIC, we observed an agreement of 94.90 %, which was significantly (p < 0.00001) better than by chance (93.27 % at the observed 5.3 % prevalence, including pacing artifacts), 16.8 % sensitivity, 99.3 % specificity, and an ROC AUC of 0.580. CONCLUSION:The fully automated algorithm can accurately detect ECG artifacts and potential lead misplacement, thus permitting automated quality control of ECG analysis. The code is provided at https://github.com/Tereshchenkolab/ECG-quality-control.

Background: Cardiac damage stage predicts clinical outcomes in severe aortic stenosis (AS), yet its validation in moderate AS and natural history preaortic valve replacement (AVR) and post-AVR is poorly understood. Objectives: This paper sought to: 1) determine whether index cardiac damage stage predicts cardiovascular outcomes in patients with moderate AS; and 2) describe the impact of AVR on cardiac damage stage over time. Methods: This retrospective single-center cohort study stratified patients with moderate AS according to index cardiac damage stage. We assessed whether cardiac damage stage predicted a primary composite outcome of all-cause death and heart failure (HF) hospitalization and a secondary outcome of AVR. Changes in stage over time were also assessed. Results: Among 688 patients (median follow-up of 3.6 years), baseline cardiac damage stages 0, 1, 2, and 3 to 4 were evident in 25.1%, 24.1%, 41.6%, and 9.2%, respectively. Escalating baseline stage conferred an increased risk of the composite primary endpoint (incidence per 1,000 person years, stage 0: 52 [37, 72]; stage 1: 100 [77, 129], stage 2: 94 [77, 116]; stages 3 to 4: 255 [185, 352]; HR (95% CI); P < 0.001], which was mirrored in survival analyses (all-cause death and HF hospitalization: P log-rank < 0.001). The net result of awaiting AVR (median time 3.1 years) was an accumulation of cardiac damage, which partially resolved at 1-year. Conclusions: Cardiac damage stage is associated with an increasing risk of all-cause mortality and HF hospitalization in patients with moderate AS. The degree of damage accumulates as patients await AVR, which partially recovers post-AVR.