Skip to content
DraconiticResearch
Search Sources API
197,768 studies31 sourcesUpdated 37 min ago
Peer-reviewedCC BY 4.0Journal of Clinical Medicine

The Learning Curve in Robotic HPB Surgery: Myth, Metrics, and Misinterpretations

  • Silvio Caringi1
  • Antonella Delvecchio1,2
  • Annachiara Casella1
  • Valentina Ferraro1
  • Matteo Stasi1
  • Nunzio Tralli1
  • Tommaso Maria Manzia3
  • Riccardo Inchingolo2,4
  • Michele Tedeschi1,2
  • Riccardo Memeo1,2
4 affiliations
  1. 1Unit of Hepato-Biliary and Pancreatic Surgery, “F. Miulli” General Hospital, 70021 Acquaviva delle Fonti, Italy
  2. 2Department of Medicine and Surgery, LUM University, 70010 Casamassima, Italy
  3. 3Transplant and HPB Unit, Department of Surgery Sciences, University of Rome Tor Vergata, 00133 Rome, Italy
  4. 4Unit of Interventional Radiology, “F. Miulli” General Hospital, 70021 Acquaviva delle Fonti, Italy
Read full textPDFDOICite

Abstract

Background: The evolution of robotic surgery has widened the indications for minimally invasive hepatopancreatobiliary (HPB) surgery, making possible the execution of more complicated cases. Within this context, the learning curve concept is now an essential factor when assessing surgical training and expertise, as well as patient safety within a robotic HPB program. Nevertheless, existing methodologies often make use of procedure volume and surrogate intraoperative measures, which may be an insufficient way to define surgical competence. Methods: This narrative review examines the learning curve concept in robotic HPB surgery, focusing on existing performance metrics, methodological limitations, procedural complexity, risk adjustment, and the influence of team-related and institutional factors. Results: The time of operation, the estimated blood loss, the rate of conversion, and postoperative morbidity are frequently applied to assess learning curves. Nevertheless, these metrics are affected by the criteria of patient selection, the level of complexity of the procedure, and some other factors like institutional characteristics and experience of the team performing the procedure. As a result, one single value or an inflection point might fail to adequately capture the concept of surgical competence. The concepts of competency, proficiency, and mastery are distinct stages of learning, whereas the speed of learning for such skills as technical proficiency, clinical outcomes, oncological sufficiency, and intraoperative decision-making might be different. Conclusions: The learning curve in robotic HPB surgery should be viewed as a multi-dimensional and complex process and not a straightforward correlation between the number of cases and the results. The future assessment of the learning curve needs to consider different domains, including technical, clinical, oncological, cognitive, and team-related aspects, taking into account the level of complexity and institutional context of each case.

Related

  1. Learning curve and clinical outcomes of robotic-assisted heart bypass surgery: Real-world evidence from India
    M. M. Yusuf FRCS (Glasgow), FRCS (JCIE), Ganapathy Arumugam Chandrasekharan, Varun Bansal DNB, FACS +2
    Therapeutic Advances in Cardiovascular DiseasePeer-reviewedCC BYFull textSurgery & anaesthesiaDOAJ
  2. Initial surgical results of subcostal uniportal robotic lung resection: a study of the perioperative outcomes and a CUSUM analysis of the learning curve
    Teruhisa Kawaguchi, Fumiaki Watanabe, Shinji Kaneda +2
    Surgery TodayPeer-reviewedCC BY 4.0Full textGeneral medicinePMC
  3. Learning Curve and Early Outcomes of Thoracoscopic Esophageal Atresia Repair in an Emerging Pediatric Minimally Invasive Surgery (MIS) Program: A Cumulative Sum Analysis
    Andreea Moga, Patricia Cîmpeanu, Laura Bălănescu +1
    Children (Basel, Switzerland)Peer-reviewedCC BYFull textSurgery & anaesthesiaEurope PMCDOAJ
  4. Learning curve of da vinci robotic lobectomy: a single-surgeon CUSUM analysis of operative time and perioperative outcomes
    Chien-Ming Lo, Meng-Yun Tsai, David Kwan Ru Huang +3
    Journal of Robotic SurgeryPeer-reviewedCC BY-NC-ND 4.0Full textSurgery & anaesthesiaPMC
  5. Modified dual-portal robotic-assisted thoracic surgery using a Japanese-manufactured robotic platform
    JTCVS techniquesPeer-reviewedCC BY-NC-NDFull textGeneral medicineEurope PMC

Cite

BibTeXRIS
BibTeX
@article{caringi2026learning,
  title = {{The Learning Curve in Robotic HPB Surgery: Myth, Metrics, and Misinterpretations}},
  author = {Silvio Caringi and Antonella Delvecchio and Annachiara Casella and Valentina Ferraro and Matteo Stasi and Nunzio Tralli and Tommaso Maria Manzia and Riccardo Inchingolo and Michele Tedeschi and Riccardo Memeo},
  journal = {Journal of Clinical Medicine},
  year = {2026},
  month = oct,
  doi = {10.3390/jcm15208043},
  url = {https://www.mdpi.com/2077-0383/15/20/8043},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - The Learning Curve in Robotic HPB Surgery: Myth, Metrics, and Misinterpretations
AU  - Silvio Caringi
AU  - Antonella Delvecchio
AU  - Annachiara Casella
AU  - Valentina Ferraro
AU  - Matteo Stasi
AU  - Nunzio Tralli
AU  - Tommaso Maria Manzia
AU  - Riccardo Inchingolo
AU  - Michele Tedeschi
AU  - Riccardo Memeo
PY  - 2026
DA  - 2026/10/01
JO  - Journal of Clinical Medicine
DO  - 10.3390/jcm15208043
UR  - https://www.mdpi.com/2077-0383/15/20/8043
AB  - Background: The evolution of robotic surgery has widened the indications for minimally invasive hepatopancreatobiliary (HPB) surgery, making possible the execution of more complicated cases. Within this context, the learning curve concept is now an essential factor when assessing surgical training and expertise, as well as patient safety within a robotic HPB program. Nevertheless, existing methodologies often make use of procedure volume and surrogate intraoperative measures, which may be an insufficient way to define surgical competence. Methods: This narrative review examines the learning curve concept in robotic HPB surgery, focusing on existing performance metrics, methodological limitations, procedural complexity, risk adjustment, and the influence of team-related and institutional factors. Results: The time of operation, the estimated blood loss, the rate of conversion, and postoperative morbidity are frequently applied to assess learning curves. Nevertheless, these metrics are affected by the criteria of patient selection, the level of complexity of the procedure, and some other factors like institutional characteristics and experience of the team performing the procedure. As a result, one single value or an inflection point might fail to adequately capture the concept of surgical competence. The concepts of competency, proficiency, and mastery are distinct stages of learning, whereas the speed of learning for such skills as technical proficiency, clinical outcomes, oncological sufficiency, and intraoperative decision-making might be different. Conclusions: The learning curve in robotic HPB surgery should be viewed as a multi-dimensional and complex process and not a straightforward correlation between the number of cases and the results. The future assessment of the learning curve needs to consider different domains, including technical, clinical, oncological, cognitive, and team-related aspects, taking into account the level of complexity and institutional context of each case.
SN  - 2077-0383
ER  -