Evidence map›Paper›PMID 34422354›Full record

ArticleJournal of thoracic disease2021

Safe implementation of robotic-assisted minimally invasive direct coronary artery bypass: application of learning curves and cumulative sum analysis.

Jef Van den Eynde, Hannah Vaesen Bentein, Tom Decaluwé, Herbert De Praetere, MaryAnn C Wertan, Francis P Sutter, Husam H Balkhy, Wouter Oosterlinck

Open access · diamondAbstract read
In one paragraph

Article in Journal of thoracic disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
4.1field-weighted citation impact, top 5% of its field
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

15 citing papers in PubMed, 39 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Robotic Assisted Minimally Invasive Coronary Revascularisation: Midterm Results.The international journal of medical robotics + computer assisted surgery : MRCAS · 2025
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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

8 authors at 4 institutions in 2 countries.

Jef Van den EyndeDepartment of Cardiovascular Diseases, Research Unit of Cardiac Surgery, University Hospitals Leuven, Leuven, Belgium.
Hannah Vaesen BenteinDepartment of Cardiovascular Diseases, Research Unit of Cardiac Surgery, University Hospitals Leuven, Leuven, Belgium.
Tom DecaluwéDepartment of Cardiovascular Diseases, Research Unit of Cardiac Surgery, University Hospitals Leuven, Leuven, Belgium.
Herbert De PraetereDepartment of Cardiovascular Diseases, Research Unit of Cardiac Surgery, University Hospitals Leuven, Leuven, Belgium.
MaryAnn C WertanDivision of Cardiac Surgery, Lankenau Medical Center, Wynnewood, PA, USA.
Francis P SutterDivision of Cardiac Surgery, Lankenau Medical Center, Wynnewood, PA, USA.
Husam H BalkhyDivision of Cardiac Surgery, Department of Surgery, University of Chicago Medicine, Chicago, IL, USA.
Wouter OosterlinckDepartment of Cardiovascular Diseases, Research Unit of Cardiac Surgery, University Hospitals Leuven, Leuven, Belgium.
KU Leuven · BELankenau Medical Center · USJohns Hopkins Hospital · USUniversity of Chicago · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLearning curves are inevitably encountered when first implementing an innovative and complex surgical technique. Nevertheless, a cluster of failures or complications should be detected early, but not deter learning, to ensure safe implementation. Here, we aimed to examine the presence and impact of learning curves on outcome after robotic-assisted minimally invasive direct coronary artery bypass (RA-MIDCAB).

methodsA retrospective analysis of the first 300 RA-MIDCAB surgeries between July 2015 and December 2020 was performed. Learning curves were obtained via logarithmic regression for surgical time. Cumulative sum (CUSUM) analysis was performed for (I) major complications including MI, stroke, repeat revascularization, and mortality, and (II) other complications, including prolonged ventilation, pneumonia, pleura puncture, lung herniation, pericarditis, pleuritis, arrhythmia, wound complications, and delirium. Expected and unacceptable rates were set at 12% and 20%, respectively, for major complications, and at 40% and 60% for other complications, based on historical data in conventional coronary artery bypass grafting (CABG).

resultsDemographic characteristics did not differ between terciles, except for more smokers in the first tercile, and less hypercholesterolemia and more complex procedures in the third tercile. The mean surgical time for all operations was 258±81 minutes, ranging from 127 to 821 minutes. A learning curve was only observed in the first tercile. Subgroup analysis revealed that this learning curve was only observed for procedures consisting of single internal mammary artery (SIMA) with 1 or 2 distal anastomoses but not with bilateral internal mammary arteries (BIMA) or more than 2 distal anastomoses. CUSUM analysis showed that the cumulative rate of major and other complications never crossed the lines for unacceptable rates. Rather, the lower 95% confidence boundary was crossed after 50 cases, indicating improvement in safety.

conclusionsThese results suggest that integration of RA-MIDCAB in the surgical landscape can be safely achieved and complication rates can quickly be reduced below those expected in traditional CABG. Collective experience plays a key role in overcoming the learning curve when more complex procedures and cases are introduced.

Indexed as

Cardiac surgerycoronary artery bypass grafting (CABG)learning curveminimally invasive surgeryteaching

Identifiers

PMID34422354
PMCPMC8339757
OpenAlexW3174682489

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.