Learning Curves and Proficiency Milestones in Robotic Ventral Hernia Repair: A Systematic Review by Surgical Technique.

Lyons G, Neves VDS, Morais MC, Almeida DPA, Nogueira R, Cavazzola LT, Malcher F, Lima DL.

Systematic ReviewAbstract only — full text under license

AI summary — for doctors, generated from the abstract only

Learning curve data for robotic ventral hernia repair are sparse and mostly from single surgeons, so proficiency thresholds remain uncertain.

What was studied

PRISMA systematic review through August 2025 of seven retrospective cohorts on robotic ventral hernia repair techniques including rIPUM, rTAR, rTAPP and r-eTEP.

Key findings

Six studies were single-surgeon series and one was a population analysis of 12,609 cases.
Patients in the single-surgeon series were mostly class I obesity.
Specific case-number milestones are not shown in the cut-off abstract.

Clinical relevance

Helps programmes plan training and expectations when adopting robotic hernia techniques.

Who should care

Pediatric Surgery

Limitations

Small, retrospective, heterogeneous evidence from single surgeons with differing definitions of proficiency.

Plan proctored, structured training and track outcomes rather than relying on a fixed case-number threshold.

This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.

Abstract

Aim

Robotic ventral hernia repair (VHR) has enabled minimally invasive management of complex abdominal wall hernias. As robotic procedures involve distinct technical demands and require progressive skill acquisition, understanding learning curves (LCs) is essential to define proficiency thresholds, optimize surgical training, and ensure safe implementation of these techniques. This systematic review compared LCs in robotic VHR across surgical techniques.

Methods

A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A comprehensive search of major medical databases (PubMed/MEDLINE, EMBASE, Web of Science, Lilacs, and the Cochrane Library) was performed through August 2025, including cohort studies on adult patients undergoing elective robotic VHR.

Results

Seven retrospective cohort studies were included, comprising six single-surgeon series: one robotic intraperitoneal underlay mesh (rIPUM), one robotic transversus abdominis release (rTAR), one robotic transabdominal preperitoneal (rTAPP), three robotic enhanced-view totally extraperitoneal (r-eTEP), and one population-based analysis including 12,609 cases. The single-surgeon series predominantly involved patients with class I obesity (mean body mass index range: 31.0-33.0 kg/m2). LC assessment was heterogeneous, employing cumulative sum (CUSUM), risk-adjusted CUSUM (RA-CUSUM), chronological case grouping, and multivariable regression modeling. Across single-surgeon studies, operative efficiency improved earlier than complication-adjusted or technical quality outcomes. Operative-time (OT) proficiency thresholds were reported at 26 cases for rIPUM, 29-38 for r-eTEP, 46 for rTAPP, and 49 cases for rTAR. However, stabilization of complication-adjusted performance required higher volumes, ranging from 51 to 64 cases. Prior experience substantially shortened the LC, reducing OT proficiency for r-eTEP to just 8 cases. In contrast, population-level analysis suggested that 16-19 robotic cases were required to achieve recurrence-related reoperation rates comparable to open or laparoscopic repair.

Conclusion

LCs in robotic VHR follow a two-phase, technique-dependent pattern. While operative efficiency is achieved earlier, optimal patient outcomes require higher case volumes.

DOI

10.1177/10926429261449956

PMID

42091572

Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.

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