Laparoscopic right hemicolectomy: the SICE (Societ\ue0 Italiana di Chirurgia Endoscopica e Nuove Tecnologie) network prospective trial on 1225 cases comparing intra corporeal versus extra corporeal ileo-colic side-to-side anastomosis

Background: While laparoscopic approach for right hemicolectomy (LRH) is considered appropriate for the surgical treatment of both malignant and benign diseases of right colon, there is still debate about how to perform the ileo-colic anastomosis. The ColonDxItalianGroup (CoDIG) was designed as a cohort, observational, prospective, multi-center national study with the aims of evaluating the surgeons\u2019 attitude regarding the intracorporeal (ICA) or extra-corporeal (ECA) anastomotic technique and the related surgical outcomes. Methods: One hundred and twenty-five Surgical Units experienced in colorectal and advanced laparoscopic surgery were invited and 85 of them joined the study. Each center was asked not to change its surgical habits. Data about demographic characteristics, surgical technique and postoperative outcomes were collected through the official SICE website database. One thousand two hundred and twenty-five patients were enrolled between March 2018 and September 2018. Results: ICA was performed in 70.4% of cases, ECA in 29.6%. Isoperistaltic anastomosis was completed in 85.6%, stapled in 87.9%. Hand-sewn enterotomy closure was adopted in 86%. Postoperative complications were reported in 35.4% for ICA and 50.7% for ECA; no significant difference was found according to patients\u2019 characteristics and technologies used. Median hospital stay was significantly shorter for ICA (7.3 vs. 9 POD). Postoperative pain in patients not prescribed opioids was significantly lower in ICA group. Conclusions: In our survey, a side-to-side isoperistaltic stapled ICA with hand-sewn enterotomy closure is the most frequently adopted technique to perform ileo-colic anastomosis after any indications for elective LRH. According to literature, our study confirmed better short-term outcomes for ICA, with reduction of hospital stay and postoperative pain. Trial registration: Clinical trial (Identifier: NCT03934151)

Detecting sepsis from photoplethysmography:strategies for dataset preparation

Abstract Sepsis is one of the most frequent causes of death in Intensive Care Units, and its prognosis greatly depend on timeliness of diagnosis. MIMIC-III database is a frequent source of data for developing method for automatic sepsis detection. However, the heterogeneity of data jeopardize the feasibility of the task. In this work we propose a selection strategy for generating high quality data suitable for training a sepsis detection system based on the utilization of only plethysmographic data. Clinical relevance A system for detecting sepsis based only on PPG may be potentially at virtually no cost in any case clinicians suspect the possibility of developing sepsis

Classifying sepsis from photoplethysmography

Abstract Purpose: Sepsis is a life-threatening organ dysfunction. It is caused by a dysregulated immune response to an infection and is one of the leading causes of death in the intensive care unit (ICU). Early detection and treatment of sepsis can increase the survival rate of patients. The use of devices such as the photoplethysmograph could allow the early evaluation in addition to continuous monitoring of septic patients. The aim of this study was to verify the possibility of detecting sepsis in patients from whom the photoplethysmographic signal was acquired via a pulse oximeter. In this work, we developed a deep learning-based model for sepsis identification. The model takes a single input, the photoplethysmographic signal acquired by pulse oximeter, and performs a binary classification between septic and nonseptic samples. To develop the method, we used MIMIC-III database, which contains data from ICU patients. Specifically, the selected dataset includes 85 septic subjects and 101 control subjects. The PPG signals acquired from these patients were segmented, processed and used as input for the developed model with the aim of identifying sepsis. The proposed method achieved an accuracy of 76.37% with a sensitivity of 70.95% and a specificity of 81.04% on the test set. As regards the ROC curve, the Area Under Curve reached a value of 0.842. The results of this study indicate how the plethysmographic signal can be used as a warning sign for the early detection of sepsis with the aim of reducing the time for diagnosis and therapeutic intervention. Furthermore, the proposed method is suitable for integration in continuous patient monitoring