A comprehensive evaluation of 80 consecutive robotic low anterior resections: impact of not mobilizing the splenic flexure alongside low-tie vascular ligation as a standardized technique.

Rectal cancer surgery represents challenges due to its location. To overcome them and minimize the risk of anastomosis-related complications, some technical maneuvers or even a diverting ileostomy may be required. One of these technical steps is the mobilization of the splenic flexure (SFM), especially in medium/low rectal cancer. High-tie vascular ligation may be another one. However, the need of these maneuvers may be controversial, as especially SFM may be time-consuming and increase the risk of iatrogenic. The objective is to present the short- and long-term outcomes of a low-tie ligation combined with no SFM in robotic low anterior resection (LAR) for mid- and low rectal cancer as a standardized technique. A retrospective observational single-cohort study was carried out at Reina Sofia University Hospital, Cordoba, Spain. 221 robotic rectal resections between Jul-18th-2018 and Jan-12th-2023 were initially considered. After case selection, 80 consecutive robotic LAR performed by a single surgeon were included. STROBE checklist assessed the methodological quality. Histopathological, morbidity and oncological outcomes were assessed. Anastomotic stricture occurrence and distance to anal verge were evaluated after LAR by rectosigmoidoscopy. Variables related to the ileostomy closure such as time to closure, post-operative complications or hospital stay were also considered. The majority of patients (81.2%) presented a mid-rectal cancer and the rest, lower location (18.8%). All patients had adequate perfusion of the anastomotic stump assessed by indocyanine green. Complete total mesorectal excision was performed in 98.8% of the patients with a lymph node ratio < 0.2 in 91.3%. The anastomotic leakage rate was 5%. One patient (1.5%) presented local recurrence. Anastomosis stricture occurred in 7.5% of the patients. The limitations were small cohort and retrospective design. The non-mobilization of the splenic flexure with a low-tie ligation in robotic LAR is a feasible and safe procedure that does not affect oncological outcomes.

Machine learning in liver surgery: Benefits and pitfalls.

The application of machine learning (ML) algorithms in various fields of hepatology is an issue of interest. However, we must be cautious with the results. In this letter, based on a published ML prediction model for acute kidney injury after liver surgery, we discuss some limitations of ML models and how they may be addressed in the future. Although the future faces significant challenges, it also holds a great potential.

Transition from laparoscopic to robotic approach in rectal cancer: a single-center short-term analysis based on the learning curve.

As a novel procedure becomes more and more used, knowledge about its learning curve and its impact on outcomes is useful for future implementations. Our aim is (i) to identify the phases of the robotic rectal surgery learning process and assess the safety and oncological outcomes during that period, (ii) to compare the robotic rectal surgery learning phases outcomes with laparoscopic rectal resections performed before the implementation of the robotic surgery program. We performed a retrospective study, based on a prospectively maintained database, with methodological quality assessment by STROBE checklist. All the procedures were performed by the same two surgeons. A total of 157 robotic rectal resections from June 2018 to January 2022 and 97 laparoscopic rectal resections from January 2018 to July 2019 were included. The learning phase was completed at case 26 for surgeon A, 36 for surgeon B, and 60 for the center (both A & B). There were no differences in histopathological results or postoperative complications between phases, achieving the same ratio of mesorectal quality, circumferential and distal resection margins as the laparoscopic approach. A transitory increase of major complications and anastomotic leakage could occur once overcoming the learning phase, secondary to the progressive complexity of cases. Robotic rectal cancer surgery learning curve phases in experienced laparoscopic surgeons was completed after 25-35 cases. Implementation of a robotic rectal surgery program is safe in oncologic terms, morbidity, mortality and length of stay.

Machine perfusion and the prevention of ischemic type biliary lesions following liver transplant: What is the evidence?

The widespread uptake of different machine perfusion (MP) strategies for liver transplant has been driven by an effort to minimize graft injury. Damage to the cholangiocytes during the liver donation, preservation, or early posttransplant period may result in stricturing of the biliary tree and inadequate biliary drainage. This problem continues to trouble clinicians, and may have catastrophic consequences for the graft and patient. Ischemic injury, as a result of compromised hepatic artery flow, is a well-known cause of biliary strictures, sepsis, and graft failure. However, very similar lesions can appear with a patent hepatic artery and these are known as ischemic type biliary lesions (ITBL) that are attributed to microcirculatory dysfunction rather than main hepatic arterial compromise. Both the warm and cold ischemic period duration appear to influence the onset of ITBL. All of the commonly used MP techniques deliver oxygen to the graft cells, and therefore may minimize the cholangiocyte injury and subsequently reduce the incidence of ITBL. As clinical experience and published evidence grows for these modalities, the impact they have on ITBL rates is important to consider. In this review, the evidence for the three commonly used MP strategies (abdominal normothermic regional perfusion [A-NRP], hypothermic oxygenated perfusion [HOPE], and normothermic machine perfusion [NMP] for ITBL prevention has been critically reviewed. Inconsistencies with ITBL definitions used in trials, coupled with variations in techniques of MP, make interpretation challenging. Overall, the evidence suggests that both HOPE and A-NRP prevent ITBL in donated after circulatory death grafts compared to cold storage. The evidence for ITBL prevention in donor after brain death grafts with any MP technique is weak.

Artificial intelligence and liver transplantation: Looking for the best donor-recipient pairing.

Decision-making based on artificial intelligence (AI) methodology is increasingly present in all areas of modern medicine. In recent years, models based on deep-learning have begun to be used in organ transplantation. Taking into account the huge number of factors and variables involved in donor-recipient (D-R) matching, AI models may be well suited to improve organ allocation. AI-based models should provide two solutions: complement decision-making with current metrics based on logistic regression and improve their predictability. Hundreds of classifiers could be used to address this problem. However, not all of them are really useful for D-R pairing. Basically, in the decision to assign a given donor to a candidate in waiting list, a multitude of variables are handled, including donor, recipient, logistic and perioperative variables. Of these last two, some of them can be inferred indirectly from the team's previous experience. Two groups of AI models have been used in the D-R matching: artificial neural networks (ANN) and random forest (RF). The former mimics the functional architecture of neurons, with input layers and output layers. The algorithms can be uni- or multi-objective. In general, ANNs can be used with large databases, where their generalizability is improved. However, they are models that are very sensitive to the quality of the databases and, in essence, they are black-box models in which all variables are important. Unfortunately, these models do not allow to know safely the weight of each variable. On the other hand, RF builds decision trees and works well with small cohorts. In addition, they can select top variables as with logistic regression. However, they are not useful with large databases, due to the extreme number of decision trees that they would generate, making them impractical. Both ANN and RF allow a successful donor allocation in over 80% of D-R pairing, a number much higher than that obtained with the best statistical metrics such as model for end-stage liver disease, balance of risk score, and survival outcomes following liver transplantation scores. Many barriers need to be overcome before these deep-learning-based models can be included for D-R matching. The main one of them is the resistance of the clinicians to leave their own decision to autonomous computational models.

Gastric and intestinal pneumatosis / Neumatosis gástrica e intestinal

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