We have witnessed a remarkable evolution in the field of percutaneous coronary intervention (PCI) over the past half a century, transitioning from the first cases of balloon angioplasty to bare metal stents and, most notably, to the widespread use of drug-eluting stents (DES). The advent of DES substantially reduced restenosis rates by providing a mechanical scaffold combined with sustained release of an antiproliferative drug, eg, taxanes and then rapamycin derivatives. Considering their permanent and static nature, such metallic implants are not without limitations, including the potential for delayed healing, chronic inflammation, inhibition of positive vessel remodeling, and the need for prolonged antithrombotic therapy.1,2 Following this, the concept of bioresorbable vascular scaffolds emerged, promising a temporary scaffold that would “leave nothing behind”. Nonetheless, their initial promise was hampered by late scaffold thrombosis and a high rate of target lesion failure.3 At the same time, drug-coated balloons (DCB) emerged as a “metal-free” alternative delivering an antiproliferative drug to the vessel wall without leaving a permanent implant, thus preserving vessel anatomy, function, and allowing for adaptive remodeling. Currently, DCB are established in percutaneous coronary intervention (PCI) for in-stent restenosis (ISR) and, subsequently, for small-vessel native disease. Their role in larger native coronary arteries, however, remains debated, given the limited evidence from small randomized controlled trials (RCTs) with relatively short follow-up.4

In this context, in a recent paper published in REC: Interventional Cardiology, Sorolla Romero et al. report a timely and rigorous meta-analysis of RCT comparing DCB with DES in patients with native large coronary artery disease (PROSPERO CRD42024602012).5 A total of 2961 patients (n = 1476 for DCB and n = 1485 for DES) from 7 RCT published from 2016 through 2024 were included, and, compared with DES, DCB were associated with a similar risk of the primary endpoint of target lesion revascularization, and all-cause and cardiovascular mortality, myocardial infarction, and major adverse cardiovascular events, but a > 2-fold risk of target vessel revascularization. For angiographic outcomes, although DCB caused less late lumen loss, they were associated with a smaller minimal lumen diameter at follow-up. In light of these results, we hereby hope to provide current and future perspectives on the role of DCB for treatment of native large coronary artery disease.

LESION CHARACTERISTICS

The type of lesions included in the analyzed RCT is a key determinant of the external validity of the study findings, and we outline key considerations below.

• – Across the 7 RCT, patients with high clinical and anatomical complexity were consistently excluded (table 1).6-12 Notably, patients with extensive coronary artery disease (eg, long or multiple lesions, 3-vessel disease, or those requiring multiple devices), severe calcification, left main involvement, or chronic total occlusions were not evaluated. Additional characteristics that appeared among exclusion criteria, and could instead arguably represent favorable scenarios for DCB angioplasty, are requirement for hemodialysis, bifurcations lesions requiring treatment of both branches, and severe coronary artery tortuosity. This selective enrollment underscores the contrast with recent observational studies of DCB use in native large coronary artery disease, which have examined more complex scenarios in which DES may be less effective, technically challenging to deliver, or best avoided to limit long stent segments or multiple overlapping implants (figure 1).13-15

• – The degree of inter-study variability is also of note, particularly given the disproportionate contribution of individual RCT. As appropriately highlighted by the authors, REC-CAGEFREE I⁷ alone accounts for approximately 75% of the total patient population, and leave-one-out analyses yield different results. Moreover, enrollment periods span 8 years (2014–2022), introducing potential variability in procedural techniques, device technology, and adjunctive pharmacotherapy. The observed prediction intervals and measures of heterogeneity further support this consideration.
• – We acknowledge the clinical variability in defining “large” coronary artery disease. This meta-analysis applied a ≥ 2.5 mm-cutoff to define large vessels, which is at the lower end of what many would consider large. In several of the included studies, patients were eligible for enrollment regardless of treated vessel diameter, with some RCT allowing lesions within reference vessel diameters as small as 2.0 mm (table 1). Subgroup analyses within individual studies provide more specific insights into patients treated with larger devices. Given the significant interaction P value in the vessel size subgroup analysis of the largest included RCT,7 it is reasonable to question whether the overall results would have been superimposable had the analysis been limited to larger vessels. These observations should be interpreted in the context of the earlier discussion on the type of lesions included. Finally, this aspect may have sex-specific relevance: although women generally have smaller coronary vessels, a vessel of a given diameter may be more proximal and supply a larger myocardial territory in women than in men, potentially amplifying its clinical significance.16

Table 1. Clinical, angiographic and procedural characteristics excluding patients from each study included in the meta-analysis

Characteristics	Nishiyama et al.6 (CCS) N = 60	REC-CAGEFREE I7 (45%, CCS; 55%, ACS) N = 2271	Yu et al.8 (11%, CCS; 89%, ACS) N = 170	REVELATION9 (STEMI) N = 120	Wang et al.10 (STEMI) N = 184	Gobi´c et al.11 (STEMI) N = 75	Hao et al.12 (STEMI) N = 80
Age, years					> 70		> 80
Hemodialysis	X
Previous MI					X
Previous PCI/CABG						Within 6 months	Within 6 months
Vessel size, mm			< 2.25 or > 4.0		< 2.0 or > 4.0		< 2.5 or > 4.0
Lesion length, mm	≥ 25		> 30
No. of DES or DCB/total DES or DCB length, mm		≥ 3/> 60
Extensive CAD		≥ 3 lesions/vessels				X
Severe calcification or atherectomy	X	X		X			X
Left main coronary artery	X	X
CTO	X	X
Bifurcation requiring treatment in both branches		X
Grafts		X
Severe coronary artery tortuosity						X
ACS, acute coronary syndrome; CABG, coronary artery bypass grafting; CAD, coronary artery disease; CCS, chronic coronary syndrome; CTO, chronic total coronary occlusion; DCB, drug-coated balloon; DES, drug-eluting stent; MI, myocardial infarction; PCI, percutaneous coronary intervention; STEMI, ST-segment elevation myocardial infarction.

Figure 1. Patient and lesion factors to be taken into consideration when evaluating native large coronary artery disease for percutaneous coronary intervention. Presence of any one of the factors highlighted beneath DCB should lead the operator to contemplate an approach to limit the number of permanent coronary artery implants. ACS, acute coronary syndrome; CKD, chronic kidney disease; CTO, chronic total coronary occlusion; DCB, drug-coated balloon; DES, drug-eluting stent; DM, diabetes mellitus; HBR, high bleeding risk.

LESION PREPARATION

Lesion preparation is a point of significant heterogeneity among the RCT included in the meta-analysis. For example, the REVELATION trial,9 conducted on patients with ST-segment elevation myocardial infarction, permitted proceeding with DCB angioplasty with a 50% residual percent diameter stenosis after predilation, and thrombectomy if visible thrombus was present, which contrasts with the more commonly embraced ≤ 30% threshold.5 Further complicating the procedural comparison is the timing of patient randomization, as 2 studies randomized patients before assessing the outcome of lesion preparation.10,11 Moreover, the specific methods of lesion preparation varied, with 1 study supporting the use of semicompliant balloon angioplasty before DCB inflation.12 The success of DCB angioplasty depends on a dedicated procedural strategy that hinges on meticulous lesion preparation and careful postoperative assessment, a nuance often lost when comparing outcomes across various methodologies.15,17,18

DCB CHARACTERISTICS

The field is characterized by a diversity of DCB platforms, antiproliferative agents and coatings. While the included RCT largely focus on paclitaxel-coated balloons, a growing body of evidence highlights differences in vascular response, downstream effects, and pharmacokinetics across different DCB, indicating that the choice of drug and coating technology could arguably influence clinical outcomes. Sirolimus-coated balloons have recently shown promising results in various clinical settings. Moving forward, future efforts should continue to differentiate between different technologies, as their clinical performance may not be uniform.19,20 Of note, the balloon coating and mechanism of drug release are also key aspects that should be taken into consideration. The DCB technologies assessed in this meta-analysis all used paclitaxel coating but different in platform; only 3 trials evaluated the same device (DCB; SeQuent Please, B. Braun, Germany) whereas the remaining studies used distinct systems, including an ultrasound-controlled paclitaxel delivery platform.10 Finally, inflation time is important for drug delievery and this was not uniform in the studies included in the meta-analysis, with recommended DCB inflation times as low as 30 seconds.6 Recommendations among studies currently enrolling (MAGICAL SV [NCT06271590] and Prevail Global [NCT06535854]) are also slightly different, and whether this might have clinical implications is still to be elucidated.

ANGIOGRAPHIC AND CLINICAL OUTCOMES

DES implantation typically provides a larger acute gain in lumen diameter than balloon angioplasty, a concept highlighted also within the REVELATION trial,9 where the residual percent diameter stenosis to define a successful procedure was different after DCB angioplasty (< 30%) and DES implantation (< 20%). While the meta-analysis reports the endpoint late lumen loss, we recognize that this metric may not fully capture the relative efficacy of these 2 technologies. The use of endpoints, such as net lumen gain, providing a more comprehensive and meaningful comparison between these 2 fundamentally different strategies by focusing on the overall therapeutic effect on the vessel lumen, rather than just the restenotic response following the intervention, should be implemented in upcoming studies. In addition, we acknowledge the limitation in comparing the incidence rate of composite endpoints such as major adverse cardiovascular events when these include different single components across the studies. Finally, we highlight the importance for future studies to concentrate on the reporting of any target vessel thrombosis, a key safety endpoint which remained underreported in the meta-analysis. Still, a significant concern in clinical practice and a key factor impacting the wider implementation of a DCB-based strategy (COPERNICAN [NCT06353594]).

CONCLUSIONS

The meta-analysis by Sorolla Romero et al. provides a timely summary of the current evidence on the use of DCB in large native coronary arteries, and its findings provide hypothesis-generating evidence that challenges the long-standing paradigm of DES as the default choice for any lesion. This work underscores that the evolution of PCI is ongoing and invites reconsideration of therapeutic algorithms toward a more personalized approach, in which the choice between DCB and DES is guided by patient- and lesion-specific factors (figure 1). Moving forward, the focus must shift towards refining patient selection, optimizing procedural techniques, and conducting further RCT with long-term follow-up to clarify the role of DCB in this new therapeutic paradigm.

FUNDING

None declared.

CONFLICTS OF INTEREST

None declared.

REFERENCES

To the Editor,

Transcatheter aortic valve implantation (TAVI) has revolutionized the management of severe aortic stenosis (AS).1 Despite increasing TAVI experience and procedural improvement, outcomes remain hard to foresee.1 Several clinical and anatomical risk factors have been well established as independent predictors of adverse events.2 Nonetheless, the macro-level interactions between them are complex and challenging to quantify with traditional models, particularly given the dynamic clinical trajectory of AS.

Although standardized risk scores, such as the Society of Thoracic Surgeons (STS) score and the EuroSCORE II offer estimates of procedural risk3 they miss the broader clinical profile and interactions. Advanced statistical techniques, such as multivariate cluster analysis, can identify subgroups, potentially uncovering patterns overlooked by conventional risk stratification. This study aimed to stratify TAVI patients using a 2-step cluster analysis based on clinical and risk factor variables and evaluate the association between these clusters and procedural timing and clinical outcomes.

We conducted a retrospective, single-center study with 300 patients undergoing TAVI from 2020 through 2023, without immediate cardiac surgery back-up. Data were retrospectively analyzed. Procedural and outcome definitions followed the Valve Academic Research Consortium-3 criteria.4 A 2-step cluster analysis was performed, incorporating variables such as age, sex, New York Heart Association (NYHA) functional class, significant mitral regurgitation, pulmonary hypertension, and relevant comorbidities, including chronic kidney disease and atrial fibrillation.

Clusters were compared regarding baseline characteristics, procedural variables, and outcomes. The primary composite endpoint was 30-day mortality, stroke, and 1-year hospital readmission. Secondary endpoints included 1-year mortality, stroke, hospital readmission, permanent pacemaker implantation, and vascular complications. Statistical analyses were performed using IBM SPSS Statistics, Version 30.0 (IBM Corp., Armonk, NY, USA).

Two clusters were identified: Cluster 1 (n = 182) and Cluster 2 (n = 32) (silhouette coefficient, 0.69). The remaining patients had incomplete data for clustering variables. Baseline demographic and comorbidity profiles were similar between clusters. Mean age (82 ± 5 vs 83 ± 5 years; P = .6), female sex (54% vs 50%; P = .7), and comorbidities did not differ significantly (table 1). Additionally, echocardiographic and computed tomography parameters were similar between the 2 clusters (table 1).

Table 1. Baseline characteristics, procedural data, and clinical outcomes according to cluster analysis in patients undergoing TAVI

Variable	Total (n = 300)	Cluster 1 (n = 182)	Cluster 2 (n = 32)	P-value
Baseline
Age
Mean, SD	82 ± 5	82 ± 6	83 ± 5	.6
Median, IQR	82 [78-86]	82 [78-86]	84 [79-87]
Female, (%)	54%	54%	50%	.7
Katz score > 4 (%)	96%	97%	94%	.6
STS score
Mean, SD	5.2 ± 4.5	4.9 ± 4.2	5.8 ± 4.3	.3
Median, IQR	3.8 [2.8-6.9]	3.7 [2.7-6.6]	4.0 [2.8-7.8]
STS score high risk (> 8)	17%	13%	22%	.2
EuroSCORE	2.32-2.4	2.2-2	2.6-2	.5
Hospital admission due to AS	22%	28%	3%	.03
NYHA > 2	51%	52%	25%	.005
Comorbidities
HTN	86%	85%	88%	.7
DM	35%	36%	41%	.6
CAD	21%	16%	25%	.2
COPD/OSA	11%	10%	16%	.3
GFR < 30 mL/kg/m2	11%	11%	16%	.5
Atrial fibrillation	22%	24%	19%	.5
MI	9%	9%	13%	.5
PCI	14%	12%	22%	.1
Stroke	8%	8%	18%	.07
ECG
1st AV block	12%	11%	13%	.8
LBBB	9%	8%	7%	.8
RBBB	7%	6%	16%	.05
TTE
Mean gradient (mmHg)	48 ± 14	49 ± 13	46 ± 15	.2
AVA (cm2)	0.7 ± 0.2	0.7 ± 0.2	0.8 ± 0.2	.08
LVEF (%)	56 ± 11	55 ± 10	57 ± 10	.7
LVEF < 40%	13%	10%	10%	.9
SPAP > 40mmHg	54%	64%	43%	.03
Significant MR	30%	30%	12%	.05
CT
Aortic calcium score	721 ± 88			.3
Min femoral diameter (mm)	7.3-1.8	7.0-1.9	7.3-1.6	.3
Laboratory findings
Hemoglobin	12.2 ± 1.9	12.3 ± 1.8	12.1 ± 2.2	.8
Serum creatinine	1.2, 0.6	1.0, 0.6	1.0, 0.8	.8
NT-proBNP	526 ± 284	510 ± 269	657 ± 291	.09
TAVI waiting time (days)	60-101	48-98	93-92	.03
Outcomes
Death, stroke and hospital readmission	25%	12%	100%	< .001
30-day mortality rate	3.7%	1%	6%	.05
1-year mortality rate	12%	7%	29%	< .001
Stroke	2.8%	0.5%	16%	< .001
Hospital admission	17%	13%	88%	< .01
Pacemaker implantation	20%	21%	23%	.9
Vascular complication	7.8%	5.5%	9.4%	.4
AS, aortic stenosis; AV, atrioventricular; AVA, aortic valve area; CAD, coronary artery disease; CT, computed tomography; COPD, chronic obstructive pulmonary disease; DM, diabetes mellitus; ECG, electrocardiogram; GFR, glomerular filtration rate; HTN, hypertension; IQR, interquartile range; LBBB, left bundle branch block; LVEF, left ventricular ejection fraction; MI, myocardial infarction; MR, mitral regurgitation; NT-proBNP, N-terminal pro-Btype natriuretic peptide; NYHA, New York Heart Association; OSA, obstructive sleep apnea; PCI, percutaneous coronary intervention; RBBB, right bundle branch block; SD, standard deviation; SPAP, systolic pulmonary artery pressure; STS, Society of Thoracic Surgeons; TAVI, transcatheter aortic valve implantation; TTE, transthoracic echocardiogram. Data are expressed as no. (%), mean ± standard deviation or median [interquartile range].

Differences emerged in clinical presentation and procedural timing. Cluster 1 had a higher proportion of NYHA III/IV patients (52% vs 25%; P = .005), previous hospitalization for AS (28% vs 3%; P = .03), significant mitral regurgitation (30% vs 12%; P = .05), and pulmonary hypertension (64% vs 43%; P = .03) at baseline initial assessment. Notably, these patients had a significantly shorter median TAVI waiting time (48 [24-72] vs 93 [47-139] days; P = .03), suggesting a prioritization based on symptomatic burden and perceived procedural urgency.

Despite patients from Cluster 1 being more symptomatic, their outcomes were better vs those from Cluster 2. The primary composite endpoint of death, stroke, and hospital readmission occurred in 12% of Cluster 1 patients vs 100% of Cluster 2 patients (risk ratio [RR], 8.3; 95% confidence interval [95%CI], 5.2-13.3; P < .001). The 30-day all-cause mortality rate was 1% in Cluster 1 vs 6% in Cluster 2 (RR ,5.7; 95%CI, 0.8-38.9; P = .05). The 1-year mortality rate remained significantly lower in Cluster 1 at 7% vs 29% in Cluster 2 (RR, 4.1; 95%CI, 1.9-8.6; P < .001). Similarly, stroke occurred in only 0.5% of patients from Cluster 1 while 16% of the patients from Cluster 2 experienced this complication (RR, 33.3; 95%CI, 4.5-247.7; P < .001). The 1-year rate of hospital readmissions was also less common in Cluster 1, occurring in 13% of patients vs 88% in Cluster 2 (RR, 6.77; 95%CI, 3.7-12.5; P < .01). Rates of vascular complications and permanent pacemaker implantation were similar between the clusters (5.5% vs 9.4%, RR, 1.7; 95%CI, 0.5-5.7; P = .4 and 21% vs 23%, RR, 1.10; 95%CI, 0.6-2.2; P = .9, respectively).

This study demonstrates that multivariate clustering can identify distinct clinical profiles within a TAVI cohort, revealing paradoxical but clinically meaningful outcome patterns. Patients with advanced symptoms (NYHA III/IV) and prior AS-related hospitalizations, typically considered higher risk, achieved better survival and lower complication rates vs less symptomatic patients.

Procedural timing and patient surveillance intensity might contribute to the different outcomes reported. More symptomatic patients tend to undergo closer clinical follow-up and prioritized TAVI scheduling, as reflected by the significantly shorter waiting times observed in Cluster 1. Conversely, patients with less severe symptoms are often deprioritized, experiencing procedural delays during which subclinical deterioration or decline in functional status can be significant. AS is a progressive condition, with substantial mortality on the waiting list. Moreover, a history of unplanned hospital admission for AS should be considered a significant warning sign to anticipate intervention, given its association with increased risk of subsequent events. Former studies have shown that delayed intervention is associated with higher rates of adverse outcomes,5 thus supporting the notion that waiting time is a critical modifiable risk factor. Moreover, current risk prediction models inadequately account for dynamic clinical evolution and complex factor interactions. STS and EuroSCORE II values were comparable between clusters, yet outcomes differed substantially. The higher outcome rate from Cluster 2 raises concerns about unrecognized vulnerability and cumulative procedural risk aggravated by disease progression during the waiting period. These findings suggest that, beyond baseline comorbidities, procedural timing and dynamic clinical follow-up should be part of risk stratification and procedural prioritization strategies in TAVI programs.

This study has several limitations. Its retrospective single-center design may limit external validity. Small sample size, especially in Cluster 2, limits power. Unmeasured factors, such as frailty may have influenced outcomes. The 2-step cluster model, while robust, is sensitive to the included variables and missing data, potentially affecting cluster assignment and interpretation. Additionally, our conclusions may not be applicable to centers with short waiting lists.

This clustering method allows a macroscopic view and the identification of potential interactions between multiple clinical variables by organizing patients into groups. However, further studies with larger sample sizes are needed to validate this risk assessment approach. These findings highlight the importance of minimizing waiting times and ensuring close follow-up in managing AS. Multidimensional clinical profiling and dynamic procedural scheduling should be considered when optimizing TAVI care pathways to improve patient outcomes.

DATA AVAILABILITY

The data that support the findings of this study are available from the corresponding author upon reasonable request

FUNDING

None declared.

ETHICAL CONSIDERATIONS

This study was conducted in full compliance with the Declaration of Helsinki and the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines on clinical research. As a retrospective analysis of anonymized data, formal ethical approval and informed consent were waived. This study was conducted in full compliance with the SAGER (Sex and Gender Equity in Research) guidelines. Sex and gender considerations were addressed appropriately, and any potential sex- or gender-related differences were assessed and reported where relevant.

STATEMENT ON THE USE OF ARTIFICIAL INTELLIGENCE

No artificial intelligence tools were used in the preparation of this manuscript.

AUTHORS’ CONTRIBUTIONS

A. Rocha de Almeida: conceptualization, methodology, data curation, formal analysis, investigation, writing – original draft, writing – review and editing. R. Viana: writing – original draft, writing – review and editing. R. Fernandes: writing – review and editing. Â. Bento: writing – review and editing. L. Patrício: conceptualization, supervision, writing – review and editing, validation.

CONFLICTS OF INTEREST

None declared.

REFERENCES

To the Editor,

The complex and highly variable 3D anatomy of the left atrial appendage (LAA) makes it challenging for planning and device sizing for left atrial appendage closure (LAAC).1 Echocardiography and multi-slice computed tomography (CT) are widely used imaging modalities for this purpose. 3Mensio Structural Heart (Pie Medical Imaging BV, The Netherlands) is the most widely used software for CT evaluation of LAA providing automatic segmentation of the heart. TribusConnect (TribusMed Beheer BV, The Netherlands) is a novel cloud-based Digital Imaging and Communications in Medicine (DICOM) viewer that can also be used to securely access, review, interpret, manipulate, measure and visualize images with automatic cardiac segmentation for LAA evaluation. Furthermore, TribusConnect allows for manual correction or adjustment of the automatically generated measurements or segmentation which could be crucial for centers with varying image quality, or challenging anatomies. This study aimed to investigate the feasibility, accuracy and reproducibility of evaluating the LAA in TribusConnect compared with the 3Mensio for preprocedural planning of LAAC.

Seventeen patients who underwent LAAC at Hospital Clínico Universitario de Valladolid (Valladolid, Spain) were included in our study. A total of 52.9% (9 patients) of these patients underwent LAAC by Amplatzer Amulet (Abbott, United States) while 17.6% (3 patients) and 29.4% (5 patients) received the Watchman (Boston Scientific, United States) and Omega (Vascular Innovations, Thailand) left atrial appendage occlude devices, respectively. The device size used varied from 18 mm to 35 mm. Only 1 of the 17 patients had mild peridevice leak (< 3 mm) due to device malapposition while 0 patients had device embolization or need for changing the device size or device type during the procedure. All patients underwent preoperative contrast-enhanced, electrocardiogram-gated high-pitch spiral acquisition mode CT. Images were obtained at 30%-60% of the R-R interval with a delayed scan after contrast injection in full compliance with LAA-specific expert recommendations on CT acquisition.2 All datasets were saved as DICOM files and processed with dedicated software (3mensio Structural Heart) and novel TribusConnect. In the presence of inadequate delineation of the endocardial border due to incomplete contrast opacification of the LAA, the images were considered insufficient and excluded from the study. All datasets were evaluated, and measurements were performed by 2 independent cardiologists. Conventional measurements of LAA sizes (ostium, landing zone, depth, and working depth) were compared. The landing zone (LZ) was defined at a location 10 mm from the ostium into the LAA after adjusting the angle. The working depth was measured as a perpendicular line drawn from ostium to the LAA roof.

The intraclass correlation coefficients (ICC) between TribusConnect and 3Mensio for minimum, maximum, and mean diameters were, respectively, 0.912 (95%CI, 0.780-0.967), 0.826 (95%CI, 0.592-0.933), and 0.944 (95%CI, 0.852-0.979) at the ostium, and 0.667 (95%CI, 0.058-0.887), 0.806 (95%CI, 0.548-0.925), and 0.835 (95%CI, 0.371-0.948) at the LZ. This showed a good intraclass correlation. The Bland Altman plot for the measurements of ostium and LZ using the 2 software applications is shown in figure 1. ICC were 0.666 (95%CI, 0.286-0.865) for LAA depth and 0.753 (95%CI, 0.451-0.902) for working depth.

Figure 1. Bland Altman Plot showing the difference in measurement of minimum, maximum and mean diameter at ostium (A,B,C) and landing zone (D,E,F) between TribusConnect and 3Mensio. SD, standard deviation.

The ICC for the interobserver analysis for TribusConnect at the ostium (minimum, maximum, mean diameters) was 0.941 (0.846-0.978), 0.978 (0.941-0.992) and 0.973 (0.928-0.990) vs 0.901 (0.753-0.963), 0.815 (0.526-0.931) and 0.861 (0.662-0.947) for 3Mensio. Similarly, at the LZ, the ICC for TribusConnect (minimum, maximum, mean diameters) was 0.887 (0.719-0.957), 0.873 (0.689-0.952) and 0.941 (0.849-0.978) vs 0.736 (0.404-0.896), 0.718 (0.390-0.887) and 0.831 (0.602-0.935) for 3Mensio reflecting a better reproducibility of results across different operators with TribusConnect.

ICC for depth and working depth measurements was high for both systems. For TribusConnect, ICCs were 0.813 (95%CI, 0.445-0.935) for depth and 0.828 (95%CI, 0.467-0.941) for working depth. For 3Mensio, ICCs were 0.761 (95%CI, 0.348-0.914) for depth and 0.845 (95%CI, 0.629-0.941) for working depth.

TribusConnect was deemed by the operators to have better accessibility (video 1 of the supplementary data). Since it is a cloud-based software with no need for any licensing or software installation into a device, CT images can be retrieved from any device and location across the globe. Secondly, TribusConnect has a workflow agnostic approach, bringing the user directly into the LAA without the need for restrictive steps in a workflow. Thirdly, TribusConnect ensures better data safety as no patient data is downloaded and the CT is anonymised by the software. Fourthly, all results are automatically saved, and the analysed results can be shared wherein multiple users can view, edit or improve the analysis. This was a retrospective study with a small number of patients with potential influence of unknown confounders. Further progressive studies might be needed to assess the impact of usability of this novel software on LAA device sizing and eventually clinical outcomes.

The study demonstrates a strong ICC between TribusConnect and 3Mensio in the CT assessment of the LAA for LAAC. TribusConnect exhibited lower interobserver variability and provided the added benefit of remote access to patient data.

FUNDING

None declared.

ETHICAL CONSIDERATIONS

This study did not involve patient participants or animals, which is why ethical clearance was deemed unnecessary. Although this study included male and female patients alike, sex disaggregated analyses were not performed due to lack of expected sex related differences.

STATEMENT ON THE USE OF ARTIFICIAL INTELLIGENCE

No artificial intelligence tools were used in the writing of this study.

AUTHORS’ CONTRIBUTIONS

A. Jain and I.J. Amat-Santos conducted the study and wrote the draft. The remaining authors helped to collect the data. All authors approved the final version.

CONFLICTS OF INTEREST

L. Verstraaeten and J. Vogelaar are shareholders and employees of TribusMed. The remaining authors declared no conflicts of interest whatsoever.

SUPPLEMENTARY DATA

Vídeo 1. Jain A. DOI: 10.24875/RECICE.M25000536

REFERENCES