Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. MethodsWe did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung's disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. FindingsWe included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung's disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58•0%) were male. Median gestational age at birth was 38 weeks (IQR 36-39) and median bodyweight at presentation was 2•8 kg (2•3-3•3). Mortality among all patients was 37 (39•8%) of 93 in low-income countries, 583 (20•4%) of 2860 in middle-income countries, and 50 (5•6%) of 896 in high-income countries (p<0•0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90•0%] of ten in lowincome countries, 97 [31•9%] of 304 in middle-income countries, and two [1•4%] of 139 in high-income countries; p≤0•0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2•78 [95% CI 1•88-4•11], p<0•0001; middle-income vs high-income countries, 2•11 [1•59-2•79], p<0•0001), sepsis at presentation (1•20 [1•04-1•40], p=0•016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4-5 vs ASA 1-2, 1•82 [1•40-2•35], p<0•0001; ASA 3 vs ASA 1-2, 1•58, [1•30-1•92], p<0•0001]), surgical safety checklist not used (1•39 [1•02-1•90], p=0•035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1•96, [1•4...

BackgroundThere is a tendency toward nonoperative management of appendicitis resulting in an increasing need for preoperative diagnosis and classification. For medical purposes, simple conceptual decision-making models that can learn are widely used. Decision trees are reliable and effective techniques which provide high classification accuracy. We tested if we could detect appendicitis and differentiate uncomplicated from complicated cases using machine learning algorithms. MethodsWe analyzed all cases admitted between 2010 and 2016 that fell into the following categories: healthy controls (Group 1); sham controls (Group 2); sham disease (Group 3), and acute abdomen (Group 4). The latter group was further divided into four groups: false laparotomy; uncomplicated appendicitis; complicated appendicitis without abscess, and complicated appendicitis with abscess. Patients with comorbidities and whose complete blood count and/or pathology results were lacking were excluded. Data were collected for demographics, preoperative blood analysis, and postoperative diagnosis. Various machine learning algorithms were applied to detect appendicitis patients. ResultsThere were 7244 patients with a mean age of 6.84±5.31 years, of whom 82.3% (5960/7244) were male. Most algorithms tested, especially linear methods, provided similar performance measures. We preferred the decision tree model due to its easier interpretability. With this algorithm, we detected appendicitis patients with 93.97 % area under the curve (AUC), 94.69% accuracy, 93.55% sensitivity, and 96.55% specificity, and uncomplicated appendicitis with 79.47% AUC, 70.83% accuracy, 66.81% sensitivity, and 81.88% specificity. ConclusionsMachine learning is a novel approach to prevent unnecessary operations and decrease the burden of appendicitis both for patients and health systems.

OBJECTIVES: The purpose of the study was that monitoring, which is used in diagnosis of acute appendicitis, and laboratory values, were evaluated for verifying diagnosis of complicated appendicitis and these parameters revealed cut-off values in complicated acute/non-complicated appendicitis. METHODS: 195 patients, who had had an operation for acute appendicitis between January 2012 and March 2015 and who were proved to have acute complicated/non-complicated appendicitis from the results of histopathology consideration, were included in this study. Patients' age, preoperative serum, WBC, CRP, NLR and BT with USG results were evaluated. RESULTS: Among the groups, there were no meaningful differences in the sense of age. CONCLUSION: It is important that treatment options are evaluated to be able to discriminate complicated appendicitis fast and with a high accuracy. In the case that serum WBC is higher than 13800. CRP is higher than 5.98, NLR is higher than 4.87 and appendicitis diameter is longer than 11mm, infl ammation of appendicitis is complex with gangrene, perforation and abscess and it emphasizes the suggestion of surgical treatment option to patients (Tab. 4, Fig. 1, Ref. 28). Text in PDF www.elis.sk.