A Nationwide Assessment of the “July Effect” and Predictors of Post-Endoscopic Retrograde Cholangiopancreatography Sepsis at Urban Teaching Hospitals in the United States

BACKGROUND/AIMS: To analyze the incidence of post-endoscopic retrograde cholangiopancreatography (ERCP) sepsis in the early (July to September) and later (October to June) academic months to assess the “July effect”. METHODS: The National Inpatient Sample (2010–2014) was used to identify ERCP-related adult hospitalizations at urban teaching hospitals by applying relevant procedure codes from the International Classification of Diseases, 9th revision, Clinical Modification. Post-ERCP outcomes were compared between the early and later academic months. A multivariate analysis was performed to evaluate the odds of post-ERCP sepsis and its predictors. RESULTS: Of 481,193 ERCP procedures carried out at urban teaching hospitals, 124,934 were performed during the early academic months. The demographics were comparable for ERCP procedures performed during the early and later academic months. A higher incidence (9.4% vs. 8.8%, p<0.001) and odds (odds ratio [OR], 1.07) of post-ERCP sepsis were observed in ERCP performed during the early academic months. The in-hospital mortality rate (7% vs. 7.5%, p=0.072), length of stay, and total hospital charges in patients with post-ERCP sepsis were also equivalent between the 2 time points. Pre-ERCP cholangitis (OR, 3.20) and post-ERCP complications such as cholangitis (OR, 6.27), perforation (OR, 3.93), and hemorrhage (OR, 1.42) were significant predictors of higher post-ERCP sepsis in procedures performed during the early academic months. CONCLUSIONS: The July effect was present in the incidence of post-ERCP sepsis, and academic programs should take into consideration the predictors of post-ERCP sepsis to lower health-care burden.

Intelligent Handheld Expert System (HES) For Diagnosis Of Autism Spectrum Disorder And Its Severity Level

@#Objective: Autism Spectrum Disorder (ASD) is a complex neurological developmental disorder that could be diagnosed early usually before the age of 3 years and the diagnosis is the most important determining factor for the treatment of ASD. The aim of present work is to design and implement a Handheld Expert System (HES) based on Diagnostic and Statistical Manual of Mental Disorder, fifth edition (DSM-V) for the diagnosis and severity assessment of ASD. The hand-held device was trained by artificial neural network to correctly diagnosis ASD and identifies its severity level. Methods: The learning of HES for ASD diagnosis was performed by a back propagation neural network algorithm with data set created based on DSM-V. The ability of Artificial Intelligence (AI) based HES was measured in terms of epochs, training/testing data, and statistical stability on the basis of accuracy, losses, mean squared error, and execution time to validate the performance of the system. The HES was designed to consume less training/testing time with more efficient and accurate AI approach. The stability of HES was validated for the data set of 40 ASD and Typically Developed (TD) subjects (20 ASD and 20 TD). Results: The implementation of HES for diagnosis of 40 subjects (20 ASD and 20 TD) based on the proposed expert system has provided 100% accuracy in reference with DSM-V. The results were also validated by statistical analysis. Conclusion: Since AI based HES for diagnosis of ASD and determination of its severity provided accurate results in reference to DSM-V criteria, the possibility of the use of proposed HES for diagnosis of ASD is very high.