Robotic versus open pancreatic surgery: a propensity score-matched cost-effectiveness analysis

Pancreatic Neuroendocrine Tumors: What Is the Best Surgical Option?

Background: Pancreatic neuroendocrine tumors (pNETs) represent a rare subset of pancreatic cancer. Functional tumors cause hormonal changes and clinical syndromes, while non-functional ones are often diagnosed late. Surgical management needs multidisciplinary planning, involving enucleation, distal pancreatectomy with or without spleen preservation, central pancreatectomy, pancreaticoduodenectomy or total pancreatectomy. Minimally invasive approaches have increased in the last decade compared to the open technique. The aim of this study was to analyze the current diagnostic and surgical trends for pNETs, to identify better interventions and their outcomes. Methods: The study adhered to the PRISMA guidelines, conducting a systematic review of the literature from May 2008 to March 2022 across multiple databases. Several combinations of keywords were used ("NET", "pancreatic", "surgery", "laparoscopic", "minimally invasive", "robotic", "enucleation", "parenchyma sparing") and relevant article references were manually checked. The manuscript quality was evaluated. Results: The study screened 3867 manuscripts and twelve studies were selected, primarily from Italy, the United States, and China. A total of 7767 surgically treated patients were collected from 160 included centers. The mean age was 56.3 y.o. Enucleation (EN) and distal pancreatectomy (DP) were the most commonly performed surgeries and represented 43.4% and 38.6% of the total interventions, respectively. Pancreatic fistulae, postoperative bleeding, re-operation, and follow-up were recorded and analyzed. Conclusions: Enucleation shows better postoperative outcomes and lower mortality rates compared to pancreaticoduodenectomy (PD) or distal pancreatectomy (DP), despite the similar risks of postoperative pancreatic fistulae (POPF). DP is preferred over enucleation for the pancreas body-tail, while laparoscopic enucleation is better for head pNETs.

Postoperative outcomes and costs of laparoscopic versus robotic distal pancreatectomy: a propensity-matched analysis

BACKGROUND

Minimally invasive distal pancreatectomy (MIDP) has established advantages over the open approach. The costs associated with robotic DP (RDP) versus laparoscopic DP (LDP) make the robotic approach controversial. We sought to compare outcomes and cost of LDP and RDP using propensity matching analysis at our institution.

METHODS

Patients undergoing LDP or RDP between 2000 and 2021 were retrospectively identified. Patients were optimally matched using age, gender, American Society of Anesthesiologists status, body mass index, and tumor size. Between-group differences were analyzed using the Wilcoxon signed-rank test for continuous data, and the McNemar's test for categorical data. Outcomes included operative duration, conversion to open surgery, postoperative length of stay, pancreatic fistula rate, pseudocyst requiring intervention, and costs.

RESULTS

298 patients underwent MIDP, 180 (60%) were laparoscopic and 118 (40%) were robotic. All RDPs were matched 1:1 to a laparoscopic case with absolute standardized mean differences for all matching covariates below 0.10, except for tumor type (0.16). RDP had longer operative times (268 vs 178 min, p < 0.01), shorter length of stay (2 vs 4 days, p < 0.01), fewer biochemical pancreatic leaks (11.9% vs 34.7%, p < 0.01), and fewer interventional radiological drainage (0% vs 5.9%, p = 0.01). The number of pancreatic fistulas (11.9% vs 5.1%, p = 0.12), collections requiring antibiotics or intervention (11.9% vs 5.1%, p = 0.12), and conversion rates (3.4% vs 5.1%, p = 0.72) were comparable between the two groups. The total direct index admission costs for RDP were 1.01 times higher than for LDP for FY16-19 (p = 0.372), and 1.33 times higher for FY20-22 (p = 0.031).

CONCLUSIONS

Although RDP required longer operative times than LDP, postoperative stays were shorter. The procedure cost of RDP was modestly more expensive than LDP, though this was partially offset by reduced hospital stay and reintervention rate.

Trends in access to minimally invasive pancreaticoduodenectomy for pancreatic cancers

BACKGROUND

Minimally invasive pancreaticoduodenectomy (MIPD), including robotic (RPD) and laparoscopy (LPD), is becoming more frequently employed in the management of pancreatic ductal adenocarcinoma (PDAC), though the majority of operations are still performed via open approach (OPD). Access to technologic advances often neglect the underserved. Whether disparities in access to MIPD exist, remain unclear.

METHODS

The National Cancer Database (NCDB) was queried (2010-2020) for patients who underwent pancreatoduodenectomy for PDAC. Cochran-Armitage tests assessed for trends over time. Social determinants of health (SDH) were compared between approaches. Multinomial logistic models identified predictors of MIPD.

RESULTS

Of 16,468 patients, 80.03 % underwent OPD and 19.97 % underwent MIPD (22.60 % robotic; 77.40 % laparoscopic). Black race negatively predicted LPD (vs white (OR 0.822; 95 % CI 0.701-0.964)). Predictors of RPD included Medicare/other government insurance (vs uninsured or Medicaid (OR 1.660; 95 % CI 1.123-2.454)) and private insurance (vs uninsured or Medicaid (OR 1.597; 95 % CI 1.090-2.340)). Early (2010-2014) vs late (2015-2020) diagnosis, stratified by race, demonstrated an increase in Non-White patients undergoing OPD (13.15 % vs 14.63 %; p = 0.016), but not LPD (11.41 % vs 13.57 %;p = 0.125) or RPD (14.15 % vs 15.23 %; p = 0.774).

CONCLUSION

SDH predict surgical approach more than clinical stage, facility type, or comorbidity status. Disparities in race and insurance coverage are different between surgical approaches.

International consensus guidelines on robotic pancreatic surgery in 2023

Background

With the rapid development of robotic surgery, especially for the abdominal surgery, robotic pancreatic surgery (RPS) has been applied increasingly around the world. However, evidence-based guidelines regarding its application, safety, and efficacy are still lacking. To harvest robust evidence and comprehensive clinical practice, this study aims to develop international guidelines on the use of RPS.

Methods

World Health Organization (WHO) Handbook for Guideline Development, GRADE Grid method, Delphi vote, and the AGREE-II instrument were used to establish the Guideline Steering Group, Guideline Development Group, and Guideline Secretary Group, formulate 19 clinical questions, develop the recommendations, and draft the guidelines. Three online meetings were held on 04/12/2020, 30/11/2021, and 25/01/2022 to vote on the recommendations and get advice and suggestions from all involved experts. All the experts focusing on minimally invasive surgery from America, Europe and Oceania made great contributions to this consensus guideline.

Results

After a systematic literature review 176 studies were included, 19 questions were addressed and 14 recommendations were developed through the expert assessment and comprehensive judgment of the quality and credibility of the evidence.

Conclusions

The international RPS guidelines can guide current practice for surgeons, patients, medical societies, hospital administrators, and related social communities. Further randomized trials are required to determine the added value of RPS as compared to open and laparoscopic surgery.

Ethical, Legal, and Financial Considerations of Artificial Intelligence in Surgery

Machine learning systems have become integrated into some of the most vital decision-making aspects of humanity, including hiring decisions, loan applications, and automobile safety, to name just a few. As applications increase in both gravity and complexity, the data quality and algorithmic interpretability of the systems must rise to meet those challenges. This is especially vital for navigating the nuances of health care, particularly among the high stakes of surgical operations. In addition to inherent ethical challenges of enabling a "black box" system to influence decision-making in patient care, the creation of biased datasets leads to biased algorithms with the power to perpetuate discrimination and reinforce disparities. Transparency and responsibility are paramount to the implementation of artificial intelligence in surgical decision-making and autonomous robotic surgery. Machine learning has been permeating health care across diverse clinical and surgical contexts but continues to face sizable obstacles, including apprehension from patients and providers alike. To integrate the technology fully while upholding standard of care and patient-provider trust, one must acknowledge and address the ethical, financial, and legal implications of using artificial intelligence for patient care.