The clinical impact of pectoral nerve block in an 'enhanced recovery after surgery' program in breast surgery.

Background: Pectoral nerve block (PECS) is increasingly performed in breast surgery. Aim: The study evaluated the clinical impact of these blocks in the postoperative course. Patients & methods: In this case-control study, patients undergoing breast surgery with 'enhanced recovery after surgery' pathways were divided into group 1 (57 patients) in whom PECS was performed before general anesthesia, and group 2 (57 patients) in whom only general anesthesia was effected. Results: Postoperative opioid consumption (p < 0.002), pain at 32 h after surgery (p < 0.005) and the length of stay (p < 0.003) were significantly lower in group 1. Conclusion: Reducing opioid consumption and pain after surgery, PECS could favor a faster recovery with a reduction in length of stay, ensuring a higher turnover of patients undergoing breast surgery.

'Enhanced recovery after surgery' (ERAS) protocols have been recently applied in breast cancer patients in order to improve the postoperative course. However, the incidence of moderate to severe pain after breast surgery is frequent, and a multimodal approach is recommended. In this view, the interfascial plane blocks are advocated as a valid alternative to both paravertebral and epidural blockade. In this study, we evaluated the effects of these blocks on the postoperative course in patients undergoing breast surgery with ERAS protocols. We compared two patient groups: in the first, pectoral blocks were performed before general anesthesia, while in the second no block was carried out. We found that in the patient group receiving the blocks, postoperative opioid consumption (with essentially the same pain after surgery) and length of stay were significantly lower. Therefore, although more robust studies are needed to confirm our findings, these emerging locoregional techniques could favor a faster recovery in the context of ERAS in breast surgery. These results could have important clinical implications in terms of not only reducing healthcare costs but also ensuring a higher turnover of patients undergoing breast surgery.

The new SUMPOT to predict postoperative complications using an Artificial Neural Network.

An accurate assessment of preoperative risk may improve use of hospital resources and reduce morbidity and mortality in high-risk surgical patients. This study aims at implementing an automated surgical risk calculator based on Artificial Neural Network technology to identify patients at risk for postoperative complications. We developed the new SUMPOT based on risk factors previously used in other scoring systems and tested it in a cohort of 560 surgical patients undergoing elective or emergency procedures and subsequently admitted to intensive care units, high-dependency units or standard wards. The whole dataset was divided into a training set, to train the predictive model, and a testing set, to assess generalization performance. The effectiveness of the Artificial Neural Network is a measure of the accuracy in detecting those patients who will develop postoperative complications. A total of 560 surgical patients entered the analysis. Among them, 77 patients (13.7%) suffered from one or more postoperative complications (PoCs), while 483 patients (86.3%) did not. The trained Artificial Neural Network returned an average classification accuracy of 90% in the testing set. Specifically, classification accuracy was 90.2% in the control group (46 patients out of 51 were correctly classified) and 88.9% in the PoC group (8 patients out of 9 were correctly classified). The Artificial Neural Network showed good performance in predicting presence/absence of postoperative complications, suggesting its potential value for perioperative management of surgical patients. Further clinical studies are required to confirm its applicability in routine clinical practice.