Textbook outcome in urgent early cholecystectomy for acute calculous cholecystitis: results post hoc of the S.P.Ri.M.A.C.C study

Introduction: A textbook outcome patient is one in which the operative course passes uneventful, without complications, readmission or mortality. There is a lack of publications in terms of TO on acute cholecystitis. Objetive: The objective of this study is to analyze the achievement of TO in patients with urgent early cholecystectomy (UEC) for Acute Cholecystitis. and to identify which factors are related to achieving TO. Materials and methods: This is a post hoc study of the SPRiMACC study. It ́s a prospective multicenter observational study run by WSES. The criteria to define TO in urgent early cholecystectomy (TOUEC) were no 30-day mortality, no 30-day postoperative complications, no readmission within 30 days, and hospital stay ≤ 7 days (75th percentile), and full laparoscopic surgery. Patients who met all these conditions were taken as presenting a TOUEC. Outcomes: 1246 urgent early cholecystectomies for ACC were included. In all, 789 patients (63.3%) achieved all TOUEC parameters, while 457 (36.6%) failed to achieve one or more parameters and were considered non-TOUEC. The patients who achieved TOUEC were younger had significantly lower scores on all the risk scales analyzed. In the serological tests, TOUEC patients had lower values for in a lot of variables than non-TOUEC patients. The TOUEC group had lower rates of complicated cholecystitis. Considering operative time, a shorter duration was also associated with a higher probability of reaching TOUEC. Conclusion: Knowledge of the factors that influence the TOUEC can allow us to improve our results in terms of textbook outcome

Clinical correlates of grey matter pathology in multiple sclerosis

Traditionally, multiple sclerosis has been viewed as a disease predominantly affecting white matter. However, this view has lately been subject to numerous changes, as new evidence of anatomical and histological changes as well as of molecular targets within the grey matter has arisen. This advance was driven mainly by novel imaging techniques, however, these have not yet been implemented in routine clinical practice. The changes in the grey matter are related to physical and cognitive disability seen in individuals with multiple sclerosis. Furthermore, damage to several grey matter structures can be associated with impairment of specific functions. Therefore, we conclude that grey matter damage - global and regional - has the potential to become a marker of disease activity, complementary to the currently used magnetic resonance markers (global brain atrophy and T2 hyperintense lesions). Furthermore, it may improve the prediction of the future disease course and response to therapy in individual patients and may also become a reliable additional surrogate marker of treatment effect

Colorectal Cancer Stage at Diagnosis Before vs During the COVID-19 Pandemic in Italy

IMPORTANCE Delays in screening programs and the reluctance of patients to seek medical attention because of the outbreak of SARS-CoV-2 could be associated with the risk of more advanced colorectal cancers at diagnosis. OBJECTIVE To evaluate whether the SARS-CoV-2 pandemic was associated with more advanced oncologic stage and change in clinical presentation for patients with colorectal cancer. DESIGN, SETTING, AND PARTICIPANTS This retrospective, multicenter cohort study included all 17 938 adult patients who underwent surgery for colorectal cancer from March 1, 2020, to December 31, 2021 (pandemic period), and from January 1, 2018, to February 29, 2020 (prepandemic period), in 81 participating centers in Italy, including tertiary centers and community hospitals. Follow-up was 30 days from surgery. EXPOSURES Any type of surgical procedure for colorectal cancer, including explorative surgery, palliative procedures, and atypical or segmental resections. MAIN OUTCOMES AND MEASURES The primary outcome was advanced stage of colorectal cancer at diagnosis. Secondary outcomes were distant metastasis, T4 stage, aggressive biology (defined as cancer with at least 1 of the following characteristics: signet ring cells, mucinous tumor, budding, lymphovascular invasion, perineural invasion, and lymphangitis), stenotic lesion, emergency surgery, and palliative surgery. The independent association between the pandemic period and the outcomes was assessed using multivariate random-effects logistic regression, with hospital as the cluster variable. RESULTS A total of 17 938 patients (10 007 men [55.8%]; mean [SD] age, 70.6 [12.2] years) underwent surgery for colorectal cancer: 7796 (43.5%) during the pandemic period and 10 142 (56.5%) during the prepandemic period. Logistic regression indicated that the pandemic period was significantly associated with an increased rate of advanced-stage colorectal cancer (odds ratio [OR], 1.07; 95%CI, 1.01-1.13; P = .03), aggressive biology (OR, 1.32; 95%CI, 1.15-1.53; P < .001), and stenotic lesions (OR, 1.15; 95%CI, 1.01-1.31; P = .03). CONCLUSIONS AND RELEVANCE This cohort study suggests a significant association between the SARS-CoV-2 pandemic and the risk of a more advanced oncologic stage at diagnosis among patients undergoing surgery for colorectal cancer and might indicate a potential reduction of survival for these patients

Fast learning from distributed datasets without entity matching

Consider the following scenario: two datasets/peers contain the same real-world entities described using partially shared features, e.g. banking and insurance company records of the same customer base. Our goal is to learn a classifier in the cross product space of the two domains, in the hard case in which no shared ID is available -e.g. due to anonymization. Traditionally, the problem is approached by first addressing entity matching and subsequently learning the classifier in a standard manner. We present an end-to-end solution which bypasses matching entities, based on the recently introduced concept of Rademacher observations (rados). Informally, we replace the minimisation of a loss over examples, which requires entity resolution, by the equivalent minimisation of a (different) loss over rados. We show that (i) a potentially exponential-size subset of these rados does not require entity matching, and (ii) the algorithm that provably minimizes the loss over rados has time and space complexities smaller than the algorithm minimizing the equivalent example loss. Last, we relax a key assumption, that the data is vertically partitioned among peers-in this case, we would not even know the existence of a solution to entity resolution. In this more general setting, experiments validate the possibility of beating even the optimal peer in hindsigh