Evidence map›Paper›PMID 38127120›Full record

ArticleSurgical endoscopy2024

Hospital learning curves for robot-assisted surgeries: a population-based analysis.

Richard J B Walker, Thérèse A Stukel, Charles de Mestral, Avery Nathens, Rodney H Breau, Waël C Hanna, Laura Hopkins, Christopher M Schlachta, Timothy D Jackson, Bobby Shayegan and 2 more

Abstract read
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In one paragraph

Article in Surgical endoscopy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.3field-weighted citation impact, top 10% 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

8 citing papers in PubMed, 9 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

12 authors at 8 institutions in 1 country.

Richard J B WalkerDivision of General Surgery, Department of Surgery, University of Toronto, Toronto, Canada.ORCID 0000-0002-4917-1527
Thérèse A StukelInstitute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
Charles de MestralInstitute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
Avery NathensDivision of General Surgery, Department of Surgery, University of Toronto, Toronto, Canada.
Rodney H BreauDivision of Urology, Department of Surgery, University of Ottawa, Ottawa, Canada.
Waël C HannaDivision of Thoracic Surgery, Department of Surgery, McMaster University, Hamilton, Canada.
Laura HopkinsDivision of Oncology, Saskatchewan Cancer Agency, Saskatoon, Canada.
Christopher M SchlachtaDivision of General Surgery, Department of Surgery, Western University, London, Canada.
Timothy D JacksonDivision of General Surgery, Department of Surgery, University of Toronto, Toronto, Canada.
Bobby ShayeganJuravinski Cancer Centre, McMaster University, Hamilton, Canada.
Stephen E PautlerDivisions of Urology and Surgical Oncology, Departments of Surgery and Oncology, Western University, London, Canada.
Paul J KaranicolasDivision of General Surgery, Department of Surgery, University of Toronto, Toronto, Canada. paul.karanicolas@sunnybrook.ca.
University of Toronto · CASunnybrook Health Science Centre · CAWestern University · CAJuravinski Cancer Centre · CAMcMaster University · CASaskatchewan Cancer Agency · CASt. Michael's Hospital · CAUniversity of Ottawa · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRobot-assisted surgery has been rapidly adopted. It is important to define the learning curve to inform credentialling requirements, training programs, identify fast and slow learners, and protect patients. This study aimed to characterize the hospital learning curve for common robot-assisted procedures. STUDY

designThis cohort study, using administrative health data for Ontario, Canada, included adult patients who underwent a robot-assisted radical prostatectomy (RARP), total robotic hysterectomy (TRH), robot-assisted partial nephrectomy (RAPN), or robotic portal lobectomy using four arms (RPL-4) between 2010 and 2021. The association between cumulative hospital volume of a robot-assisted procedure and major complications was evaluated using multivariable logistic models adjusted for patient characteristics and clustering at the hospital level.

resultsA total of 6814 patients were included, with 5230, 543, 465, and 576 patients in the RARP, TRH, RAPN, and RPL-4 cohorts, respectively. There was no association between cumulative hospital volume and major complications. Visual inspection of learning curves demonstrated a transient worsening of outcomes followed by subsequent improvements with experience. Operative time decreased for all procedures with increasing volume and reached plateaus after approximately 300 RARPs, 75 TRHs, and 150 RPL-4s. The odds of a prolonged length of stay decreased with increasing volume for patients undergoing a RARP (OR 0.87; 95% CI 0.82-0.92) or RPL-4 (OR 0.77; 95% CI 0.68-0.87).

conclusionHospitals may adopt robot-assisted surgery without significantly increasing the risk of major complications for patients early in the learning curve and with an expectation of increasing efficiency.

Indexed as

Robotic Surgical ProceduresAdultCohort StudiesFemaleHospitalsHumansLearning CurveMaleOntarioProstatectomyTreatment OutcomeLearning curveLength of stayOperative timePostoperative complicationsRobotic surgical procedures

Identifiers

PMID38127120
OpenAlexW4390052968

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.