Evidence map›Paper›PMID 33032921›Full record

ArticleUrologic oncology2021

Postoperative assessment of nosocomial transmission of COVID-19 after robotic surgical procedures during the pandemic.

Thomas Tabourin, Judith Sarfati, Ugo Pinar, Nicolas Beaud, Jerôme Parra, Christophe Vaessen, Florie Gomez, Daniel Benamran, Geoffroy Canlorbe, Jérémie Belghiti and 4 more

Open access · greenAbstract read
In one paragraph

Article in Urologic oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.5field-weighted citation impact, top 33% 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

7 citing papers in PubMed, 8 citations in OpenAlex.

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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

14 authors at 6 institutions in 2 countries.

Thomas TabourinSorbonne University, GRC n 5, Predictive Onco-Urology, APHP, Hôpital Pitié-Salpêtrière, Urology, Paris, France.
Judith SarfatiSorbonne Université, GRC n 5, Predictive Onco-Urology, APHP, Hôpital Tenon, Urology, Paris, France.
Ugo PinarSorbonne University, GRC n 5, Predictive Onco-Urology, APHP, Hôpital Pitié-Salpêtrière, Urology, Paris, France.
Nicolas BeaudSorbonne University, GRC n 5, Predictive Onco-Urology, APHP, Hôpital Pitié-Salpêtrière, Urology, Paris, France.
Jerôme ParraSorbonne University, APHP, Hôpital Pitié-Salpêtrière, Department of Urology, Paris, France.
Christophe VaessenSorbonne University, APHP, Hôpital Pitié-Salpêtrière, Department of Urology, Paris, France.
Florie GomezSorbonne Université, GRC n 5, Predictive Onco-Urology, APHP, Hôpital Tenon, Urology, Paris, France.
Daniel BenamranDivision of Urology, Geneva University Hospitals, Geneva, Switzerland.
Geoffroy CanlorbeSorbonne University, APHP, Hôpital Pitié-Salpêtrière, Department of Surgical Gynaecology, Paris, France.
Jérémie BelghitiDivision of Urology, Geneva University Hospitals, Geneva, Switzerland.
Emmanuel Chartier-KastlerSorbonne University, APHP, Hôpital Pitié-Salpêtrière, Department of Urology, Paris, France.
Olivier CussenotSorbonne Université, GRC n 5, Predictive Onco-Urology, APHP, Hôpital Tenon, Urology, Paris, France.
Thomas SeisenSorbonne University, GRC n 5, Predictive Onco-Urology, APHP, Hôpital Pitié-Salpêtrière, Urology, Paris, France.
Morgan RoupretSorbonne University, GRC n 5, Predictive Onco-Urology, APHP, Hôpital Pitié-Salpêtrière, Urology, Paris, France. Electronic address: morgan.roupret@aphp.fr.
Assistance Publique – Hôpitaux de Paris · FRSorbonne Université · FROnco-Urologie PrédictivePitié-Salpêtrière Hospital · FRUniversity Hospital of Geneva · CHHôpital Tenon · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo assess potential nosocomial coronavirus disease-2019 (COVID-19) transmission in patients who underwent robot-assisted laparoscopic procedures during the pandemic. MATERIAL AND

methodsProspective study in patients undergoing robot-assisted laparoscopy in urology or gynaecology within 2 academic hospitals. Patients underwent local preoperative COVID-19 screening using a symptoms questionnaire. Patients with suspicious screening underwent coronavirus real time-polymerase chain reaction (RT-PCR) and were excluded from robotic surgery if positive. Patients with symptoms postsurgery were systematically tested for coronavirus by RT-PCR. One-month postsurgery, all patients had a telephone consultation to evaluate COVID-19 symptoms.

resultsSixty-eight patients underwent robotic surgery during the study period (median age: 63-years [IQR: 53-70], 1.8 male: female ratio). Oncology was the main indication for robotic surgery (n = 62, 91.2%) and 26 patients (38.2%) received a chest CT-scan prior to surgery. Eleven patients (16.2%) were symptomatic after surgery of whom only 1 tested positive for coronavirus by RT-PCR (1.5%) and was transferred to COVID-19 unit with no life-threatening condition. No attending surgeon was diagnosed with COVID-19 during the study.

conclusionsRobot-assisted laparoscopic surgery seemed safe in the era of COVID-19 as long as all recommended precautions are followed. The rate of nosocomial COVID-19 transmission was extremely low despite the fact that we only used RT-PCR testing in symptomatic patients during the preoperative work-up. Larger cohort is needed to validate these results.

Indexed as

Academic Medical CentersAgedCOVID-19Cross InfectionFemaleGynecologic Surgical ProceduresHumansLaparoscopyMaleMiddle AgedPandemicsPostoperative PeriodProspective StudiesRobotic Surgical ProceduresSARS-CoV-2Surveys and QuestionnairesCommunicable diseaseCoronavirusCOVID-19GynaecologyNosocomial transmissionRoboticsSARS-CoV-2SurgeryUrology

Identifiers

PMID33032921
PMCPMC7505595
OpenAlexW3087627142

What OpenQuestion holds

Textmetadata
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.