Evidence map›Paper›PMID 39909889›Full record

ArticleClinical research in cardiology : official journal of the German Cardiac Society2026

Manual support during robotic-assisted percutaneous coronary intervention.

Benjamin Bay, Alina Goßling, Jonathan Rilinger, Constantin von Zur Mühlen, Felix Hofmann, Holger Nef, Helge Möllmann, Caroline Kellner, Moritz Seiffert, Fabian J Brunner

Abstract readMulticenter Study
In one paragraph

Article in Clinical research in cardiology : official journal of the German Cardiac Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Benjamin BayDepartment of Cardiology, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany.
Alina GoßlingDepartment of Cardiology, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany.
Jonathan RilingerDepartment of Cardiology and Angiology, Faculty of Medicine, University Heart Center Freiburg - Bad Krozingen, University of Freiburg, Freiburg, Germany.
Constantin von Zur MühlenDepartment of Cardiology and Angiology, Faculty of Medicine, University Heart Center Freiburg - Bad Krozingen, University of Freiburg, Freiburg, Germany.
Felix HofmannDepartment of Cardiology and Angiology, University Hospital of Giessen and Marburg, Giessen, Germany.
Holger NefDepartment of Cardiology and Angiology, University Hospital of Giessen and Marburg, Giessen, Germany.
Helge MöllmannDepartment of Cardiology, St. Johannes Hospital, Dortmund, Germany.
Caroline KellnerDepartment of Cardiology, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany.
Moritz SeiffertDepartment of Cardiology and Angiology, BG University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany.
Fabian J BrunnerDepartment of Cardiology, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany. fa.brunner@uke.de.ORCID http://orcid.org/0000-0003-0335-8625

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRobotic-assisted percutaneous coronary intervention (R-PCI) is an efficacious and safe treatment option for coronary artery disease. However, predictors of manual support during R-PCI are unknown, which we aimed to investigate in a multi-center study.

methodsWe utilized patient-level data from R-PCIs carried out from 2020 to 2022 at four sites in Germany. Manual support was defined as the combination of partial manual assistance, where the procedure is ultimately completed using robotic techniques, and manual conversion. A two-step selection process based on akaike information criteria was used to identify the ideal multivariable model predicting manual support.

resultsIn 210 patients (median age 69.0 years; 25.7% female), a total of 231 coronary lesions were treated by R-PCI. Manual support was needed in 46 lesions (19.9%). Procedures requiring manual support were associated with significantly longer procedural times, greater total contrast fluid volumes, longer fluoroscopy times, and higher dose-area products. Amongst the predictors of manual support were lesions in the left anterior descending artery [OR: 1.09 (95%-CI: 0.99-1.20)], aorto-ostial lesions [OR: 1.35 (95%-CI: 1.11-1.64)], chronic total occlusions [OR: 1.78 (95%-CI: 1.38-2.31)], true bifurcations [OR: 1.37 (95%-CI: 1.17-1.59)], and severe calcification [OR: 1.13 (95%-CI: 1.00-1.27)].

conclusionOur findings reveal that nearly one out five of patients undergoing R-PCI required manual support, which was linked to longer procedure durations. Predictors of manual support reflected characteristics of more complex coronary lesions. These results highlight the limitations of current R-PCI platforms and underscore the need for technical advancements to address different clinical scenarios.

Indexed as

Coronary Artery DiseasePercutaneous Coronary InterventionRobotic Surgical ProceduresAgedFemaleGermanyHumansMaleMiddle AgedRetrospective StudiesTreatment OutcomeManual supportPercutaneous coronary interventionPredictorsRobotics

Identifiers

PMID39909889
PMCPMC12783154

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.