Background: Central pancreatectomy (CP) can be performed as an alternative surgical approach to distal pancreatectomy (DP) in the treatment of benign or low-grade malignant lesions located in the neck and body of the pancreas, aiming to reduce loss of parenchyma and therefore organ failure. The objective of this study was to evaluate the short-and long-term outcome of CP in comparison to DP. Methods: Patients who received CP in a large tertiary care pancreatic surgery center between 2001 and 2020 were identified from a prospectively maintained database and compared via propensity score matching with patients receiving DP during the same time period. Perioperative rate of complications and long-term outcome of pancreatic endocrine and exocrine function were evaluated. Results: One hundred and seven patients undergoing open CP were compared to 107 patients with open DP. No significant difference in rates or severity of most surgical complications could be found including postoperative pancreatic fistula, intraabdominal fluid collection, delayed gastric emptying and wound infection.However, patients receiving CP had a significantly higher risk of grade C postpancreatectomy hemorrhage (PPH) (CP: 10 patients, 9.3% versus DP: 1 patient, 0.9%; p = .0019). Perioperative mortality was comparable. Long-term follow-up of 60 matched pairs revealed significantly less patients with new-onset diabetes after CP (eight patients, 13.3%) compared to DP (22 patients, 36.7%, p = .0056). Conclusion:CP offers an improved endocrine long-term outcome at the expense of a higher risk of PPH without increased perioperative mortality. As evidence on this parenchyma sparing surgical technique is sparse, more prospective data are needed.

The current grading of facial nerve function is based on subjective impression with the established assessment scale of House and Brackmann (HB). Especially for research a more objective method is needed to lower the interobserver variability to a minimum. We developed a semi-automated grading system based on (facial) surface EMG-data measuring the facial nerve function of 28 patients with vestibular schwannoma surgery. The sEMG was recorded preoperatively, postoperatively and after 3–12 months. In addition, the HB grade was determined. After manual selection and preprocessing, the data were subjected to machine learning classificators (Logistic regression, SVM and KNN). Lateralization indices were calculated and multivariant machine learning analysis was performed according to three scenarios [differentiation of normal (1) and slight (2) vs. impaired facial nerve function and classification of HB 1-3 (3)]. The calculated AUC for each scenario showed overall good differentiation capability with a median AUC of 0.72 for scenario 1, 0.91 for scenario 2 and multiclass AUC of 0.74 for scenario 3. This study approach using sEMG and machine learning shows feasibility regarding facial nerve grading in perioperative VS-surgery setting. sEMG may be a viable alternative to House Brackmann regarding objective evaluation of facial function especially for research purposes.

Purpose Facial nerve damage in vestibular schwannoma surgery is associated with A-train patterns in free-running EMG, correlating with the degree of postoperative facial palsy. However, anatomy, preoperative functional status, tumor size and occurrence of A-trains clusters, i.e., sudden A-trains in most channels may further contribute. In the presented study, we examine neural networks to estimate postoperative facial function based on such features. Methods Data from 200 consecutive patients were used to train neural feed-forward networks (NN). Estimated and clinical postoperative House and Brackmann (HB) grades were compared. Different input sets were evaluated. Results Networks based on traintime, preoperative HB grade and tumor size achieved good estimation of postoperative HB grades (chi2 = 54.8), compared to using tumor size or mean traintime alone (chi2 = 30.6 and 31.9). Separate intermediate nerve or detection of A-train clusters did not improve performance. Removal of A-train cluster traintime improved results (chi2 = 54.8 vs. 51.3) in patients without separate intermediate nerve. Conclusion NN based on preoperative HB, traintime and tumor size provide good estimations of postoperative HB. The method is amenable to real-time implementation and supports integration of information from different sources. NN could enable multimodal facial nerve monitoring and improve postoperative outcomes.