Citations 2
Clinical Recommendations for Remote Robotic Assisted Surgery From the CRSA 2025 International Consensus Conference
Raptis, Dimitri Aristotle , Kavoussi, Louis, Wren, Sherry M. , Fowler, Dennis, Anvari, Mehran, Bianco, Francesco, Liang, Xiao, Li, Zheyong, Yin, Xiaoyu, Piccoli, Micaela, Marks, John, Shen, Baiyong, Ko, Clifford, Seeliger, Barbara, Hagendoorn, Jeroen, Essani, Rahila, Walsh, R. Matthew, Oleynikov, Dmitry, Jones, Stephanie B., Bindal, Vivek , Kim, Hyun Koo, Hyung, Woo Jin, Hashimoto, Daniel, Chen, Liaohai Leo, Burt, Bryan M., Lafaro, Kelly, Elli, Fernando, Wilson, Erik, Bianchi, Paolo Pietro, Morgan, Annette, Bartlett, Stephen T., Rashidi, Laila, Yu, Liu, Gawkins, Bill, Hinoul, Piet, Peine, Bill, Woo, Yanghee, Raoof, Mustafa, Jones, Daniel, Deshpande, Supriya, Bernard, Jordana , Wang, Yulun, Broering, Dieter C., Giulianotti, Pier Cristoforo, Fong, Yuman , Raptis, Dimitri , Wren, Sherry M , Aldousari, Saad , Ruurda, Jelle P. H. , Bindal, Vivek , Melstrom, Laleh , Celotto, Francesco , Kudsi, Yusef , Ratti, Francesca , Bernard, Jordana , Kavoussi, Louis R., Fowler, Dennis L. , Anvari, Mehran , Bianco, Francesco Maria , Piccoli, Micaela , Marks, John, Ko, Clifford, Seeliger, Barbara , Hagendoorn, Jeroen , Essani, Rahila, Walsh, R. Matthew , Oleynikov, Dmitry , Hyung, Woo Jin , Hashimoto, Daniel A. , Burt, Bryan M. , Lafaro, Kelly J. , Elli, Fernando, Bianchi, Paolo Pietro, Rashidi, Laila, Hinoul, Piet , Peine, Bill, Woo, Yanghee , Raoof, Mustafa , Jones, Daniel, Deshpande, Supriya , Bröering, Dieter C. , Giulianotti, Pier Cristoforo
World Journal of Surgery | July 14, 2026 | Volume: 50 | Issue: 8 | Pages: 2274-2288
ABSTRACT Background Implementation of remote robotic‐assisted surgery (rRAS) has the potential to significantly improve access to high‐quality surgical care for patients worldwide. Advances in robotic systems and telecommunications now enable highly reliable, low latency, and cybersecure connectivity. These technological developments, together with the broader adoption of telemedicine, have driven an increasing interest in the implementation of rRAS. Methods The Clinical Robotic Surgery Association (CRSA) convened a conference to develop international clinical recommendations for rRAS using the Danish Model of Consensus. The society identified five key issue categories and invited a panel of experts to address specific questions for each. Panel members and a jury of 10 unbiased, non‐expert in rRAS professionals participated in a day‐long conference on November 19, 2025 (Los Angeles, USA). During the conference, participants presented, discussed, and reached consensus on specific guidelines relevant to each category. Immediately after the conference, the jury reviewed and approved the recommended guidelines. Results Multiple recommendations were approved for each of the following categories: Operating Models, Roles and Responsibilities (2 recommendations), Clinical Pathway (2 recommendations), Emergency Management (8 recommendations), Technical Considerations (5 recommendations), and Medico‐legal and Ethical Considerations (4 recommendations). Conclusions There was consensus that with recent advances in telecommunication and robotic technologies, rRAS can be offered safely and effectively if the identified safeguards are realized. Adoption of rRAS is expected to continue advancing rapidly.
Early graft failure after adult living donor liver transplantation: A multicenter risk analysis and development of the early allograft failure in living donor liver transplantation (EAGLE-LDLT) model
Li, Zhihao, Centonze, Leonardo , Raptis, Dimitri , Marquez, Kris Ann Hervera , Rammohan, Ashwin, Gunasekaran, Vasanthakumar , Hong, Suyoung , Chen, I Lun, KIM, Jongman , Hsu, Shih-Chao , Akamatsu, Nobuhisa, Shaked, Oren, Genedy, Lara, Dutkowski, Philipp , Nadalin, Silvio, Boehnert, Markus U, Mathur, Abhishek, Emond, J., Testa, Giuliano, Olthoff, Kim, Taner, Timucin, Wong, Tiffany CL, Hasegawa, Kiyoshi, Balci, D., Cattral, Mark, Sapisochin, G., Selzner, Nazia, Jeng, Long-Bin, Chen, Chao-Long, Suh, Kyung-Suk, Rela, Mohamed, Broering, Dieter, Clavien, Pierre-Alain, Kim, Jong Man , Raptis, Dimitri Aristotle , Li, Zhihao , Hong, Su young , Chen, I. , Centonze, Leonardo , Li, Zhihao , Rammohan, Ashwin , Chen, Itsuko Chih-Yi , Kirimker, Elvan Onur , Akamatsu, Nobuhisa , Shaked, Oren , Finotti, Michele , Yeow, Marcus , Nadalin, Silvio , Boehnert, Markus U , Polak, Wojciech G , Bonney, Glenn K , Samstein, Benjamin , Emond, Jean C , Testa, Giuliano , Olthoff, Kim M , Heimbach, Julie K , Taner, Timucin , Wong, Tiffany C L , Balci, Deniz , Cattral, Mark , Sapisochin, Gonzalo , Selzner, Nazia , Jeng, Long-Bin , Joh, Jae-Won , Chen, Chao-Long , Suh, Kyung-Suk , Di Sandro, Stefano , Rela, Mohamed , Broering, Dieter , Clavien, Pierre-Alain , Akamatsu, Nobuhisa , Shaked, Oren , Boehnert, Markus , Polak, Wojciech G. , Mathur, Abhishek , Samstein, Benjamin , Emond, Jean C. , Testa, Giuliano , Heimbach, Julie K. , Taner, Timuçin , Wong, Tiffany , Hasegawa, Kiyoshi , Cattral, Mark S. , Sapisochín, Gonzalo , Selzner, Nazia , Joh, Jae‐Won , Rela, Mohamed , Bröering, Dieter C. , Clavien, Pierre‐Alain
Liver Transplantation | January 21, 2026 | Volume: 32 | Issue: 7 | Pages: 1027-1039
Early allograft failure (EAF) after living donor liver transplantation (LDLT) remains a clinical challenge. Existing prediction models developed for deceased donor transplantation poorly apply to LDLT due to distinct surgical and physiological factors. This study identifies clinical determinants of EAF and develops an LDLT-specific prediction model. We conducted a multicenter retrospective cohort study from 17 high-volume LDLT centers (January 2016–December 2020) with external validation at a tertiary center in Saudi Arabia (January 2015–December 2022). The primary outcome was EAF (graft loss or patient death ≤90 d). Multivariable mixed-effects logistic regression identified preoperative/intraoperative risk factors. The EAGLE-LDLT model was constructed using postoperative laboratory values. Performance was compared against established models (EAD, MEAF, A2ALL) using ROC analysis and decision curve analysis. The development cohort included 2944 adult LDLT recipients (67.7% male; median age 55 y; median MELD 14) with a 5.5% EAF rate. External validation included 1020 recipients (median MELD 21, 6.7% EAF). Independent risk factors for EAF were MELD (OR 1.06, 95% CI 1.04–1.08), donor BMI (OR 1.05, 95% CI 1.00–1.10), portal vein thrombosis (OR 1.73, 95% CI 1.13–2.63), and hepaticojejunostomy (OR 1.58, 95% CI 1.06–2.36). The EAGLE-LDLT model incorporating peak ALT (>468 U/L), peak INR (>1.9), and bilirubin (>3.5 mg/dL) and INR (>1.3) at POD7, demonstrated superior discrimination (AUC=0.81) compared with MEAF (AUC=0.77, p =0.004), EAD (AUC=0.67, p <0.001), and A2ALL (AUC=0.65, p <0.001). EAGLE-LDLT achieved balanced sensitivity (75.0%) and specificity (73.7%), effectively stratifying patients into high-risk (15% of patients; 40.4% EAF incidence) and low-risk groups. Preoperative and intraoperative clinical factors predict EAF in LDLT. The EAGLE-LDLT model accurately identifies LDLT recipients at the highest risk for EAF postoperatively.