Prospective Evaluation of the Learning Curve and Diagnostic Accuracy for Pre-TAVI Cardiac Computed Tomography Analysis by Cardiologists in Training: The LEARN-CT Study

First insights into multidisciplinary and multispecialty long COVID networks-a SWOT analysis from the perspective of ambulatory health care professionals

Introduction

Multidisciplinary and multispecialty approaches with central integration of primary care, individualized long-term rehabilitative care, and multidisciplinary care pathways are recommended by international consortia to face the challenges of care of long COVID. Two regional long COVID networks-Rhein-Neckar (RN) and Ludwigsburg (LU) have emerged as ad hoc examples of best practice in Southern Germany. The aim of the community case study is to provide first insights into the experiences of the networks.

Methods

The exploratory observational study was conducted between April and June 2023, focusing on an observation period of just under 24 months and using a document analysis supported by MAXQDA and SWOT analysis with ambulatory health care professionals in two online group discussions.

Results

The document analysis revealed that both networks have defined network participants who have agreed on common goals and patient pathways and have established ways of communicating, organizing, and collaborating. Both networks agreed on a primary care-based, multidisciplinary and multispecialty approach. The main differences in realization emerged in LU as a focus on the ambulatory setting and very concrete application to individual patients, while RN showed a focus on an intersectoral character with participation of the specialized university hospital sector, knowledge transfer and a supra-regional approach with the involvement of the meso and macro level. The SWOT analysis (n = 14 participants, n = 6 male, 7 physicians (4 disciplines), 7 therapists (5 professions)) showed strengths such as resulting collaboration, contribution to knowledge transfer, and improvement of care for individual patients. As barriers, e.g., lack of reimbursement, high efforts of care, and persistent motivation gaps became apparent. Potentials mentioned were, e.g., transferability to other diseases such as Myalgic Encephalomyelitis/Chronic Fatigue Syndrome, promotion of addressing a "difficult topic" and promotion of intersectoral care concepts; risks mentioned were, e.g., limited network resources and negative effects on the development of other structures.

Conclusion

Resulting implications for practice and research address a call to policy makers and funders to support further research to find out what generalizable results regarding usefulness, effectiveness, and efficiency including transferability to other post-infectious diseases can be derived.

Multi-modality imaging in aortic stenosis: an EACVI clinical consensus document

In this EACVI clinical scientific update, we will explore the current use of multi-modality imaging in the diagnosis, risk stratification, and follow-up of patients with aortic stenosis, with a particular focus on recent developments and future directions. Echocardiography is and will likely remain the key method of diagnosis and surveillance of aortic stenosis providing detailed assessments of valve haemodynamics and the cardiac remodelling response. Computed tomography (CT) is already widely used in the planning of transcutaneous aortic valve implantation. We anticipate its increased use as an anatomical adjudicator to clarify disease severity in patients with discordant echocardiographic measurements. CT calcium scoring is currently used for this purpose; however, contrast CT techniques are emerging that allow identification of both calcific and fibrotic valve thickening. Additionally, improved assessments of myocardial decompensation with echocardiography, cardiac magnetic resonance, and CT will become more commonplace in our routine assessment of aortic stenosis. Underpinning all of this will be widespread application of artificial intelligence. In combination, we believe this new era of multi-modality imaging in aortic stenosis will improve the diagnosis, follow-up, and timing of intervention in aortic stenosis as well as potentially accelerate the development of the novel pharmacological treatments required for this disease.

TAVI-PREP: A Deep Learning-Based Tool for Automated Measurements Extraction in TAVI Planning

BACKGROUND

Transcatheter aortic valve implantation (TAVI) is a less invasive alternative to open-heart surgery for treating severe aortic stenosis. Despite its benefits, the risk of procedural complications necessitates careful preoperative planning.

METHODS

This study proposes a fully automated deep learning-based method, TAVI-PREP, for pre-TAVI planning, focusing on measurements extracted from computed tomography (CT) scans. The algorithm was trained on the public MM-WHS dataset and a small subset of private data. It uses MeshDeformNet for 3D surface mesh generation and a 3D Residual U-Net for landmark detection. TAVI-PREP is designed to extract 22 different measurements from the aortic valvular complex. A total of 200 CT-scans were analyzed, and automatic measurements were compared to the ones made manually by an expert cardiologist. A second cardiologist analyzed 115 scans to evaluate inter-operator variability.

RESULTS

High Pearson correlation coefficients between the expert and the algorithm were obtained for most parameters (0.90-0.97), except for left and right coronary height (0.8 and 0.72, respectively). Similarly, the mean absolute relative error was within 5% for most measurements, except for left and right coronary height (11.6% and 16.5%, respectively). A greater consensus was observed among experts than when compared to the automatic approach, with TAVI-PREP showing no discernable bias towards either the lower or higher ends of the measurement spectrum.

CONCLUSIONS

TAVI-PREP provides reliable and time-efficient measurements of the aortic valvular complex that could aid clinicians in the preprocedural planning of TAVI procedures.

Outcomes in patients with moderate and asymptomatic severe aortic stenosis followed up in heart valve clinics

BACKGROUND

Heart valve clinics (HVC) have been introduced to manage patients with valvular heart disease within a multidisciplinary team.

OBJECTIVE

To determine the outcome benefit of HVC approach compared with standard of care (SOC) for patients with moderate and asymptomatic severe aortic stenosis (mAS and asAS).

METHODS

Single-centre, observational registry of patients with mAS and asAS with at least one cardiac ambulatory consultation at our Cardiovascular Centre. Based on the outpatient strategy, patients were divided into HVC group, if receiving at least one visit at HVC, and SOC group, if followed by routine cardiac consultations.

RESULTS

2129 patients with mAS and asAS were divided into those followed in HVC (n=251) versus SOC group (n=1878). The mean age was 76.5±12.4 years; 919 (43.2%) had asAS. During a follow-up of 4.8±1.8 years, 822 patients (38.6%) died, 307 (14.4%) were hospitalised for heart failure and 596 (28%) underwent aortic valve replacement (AVR). After propensity score matching, the number of consultations per year, exercise stress tests, brain natriuretic peptide (BNP) determinations and CTs were higher in the HVC cohort (p<0.05 for all). A shorter time between indication of AVR and less advanced New York Heart Association class was reported in the HVC cohort (p<0.001 and p=0.032). Compared with SOC, the HVC approach was associated with reduced all-cause mortality (HR=0.63, 95% CI 0.40 to 0.98, p=0.038) and cardiovascular death (p=0.030). At multivariable analysis, the HVC remained an independent predictor of all-cause mortality (HR=0.54, 95% CI 0.34 to 0.85, p=0.007).

CONCLUSIONS

In patients with mAS and asAS, the HVC approach was associated with more efficient management and outcome benefit compared with SOC.