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Abstract The coronavirus disease 2019 pandemic had a major impact on most medical services. Our aim was to assess the outcome of acute cholecystitis during the nationwide lockdown period. All patients admitted to our emergency department for AC were analysed. Patient characteristics, performance status, AC severity, treatment modality and outcome of AC were assessed during the lockdown period (Period II: 1 April 2020–30 November 2021) and compared to a historical control period (Period I: 1 May 2017–31 December 2018). AC admissions increased by 72.8% in Period II. Patients were younger (70 vs. 74 years, p = 0.017) and greater in number in the CCI 1 group (20.4% vs. 11.2%, p = 0.043) in Period II. The unplanned readmission rate (6.3 vs. 0%, p = 0.004) and the gallbladder perforation (GP) rate was higher (18.0 vs. 7.3%, p = 0.006) in Period II. Percutaneous transhepatic gallbladder drainage (PTGBD) was more frequent (24.1 vs. 12.8%, p = 0.012) in Period II. In addition to a drop in patient age and CCI, a significant rise in the prevalence of acute cholecystitis, GP and unplanned readmissions was observed during the nationwide lockdown due to the COVID-19 pandemic. PTGBD was more frequent during this period, whereas successful conservative treatment was less frequent.

Abstract Recognition of anomalous events is a challenging but critical task in many scientific and industrial fields, especially when the properties of anomalies are unknown. In this paper, we introduce a new anomaly concept called “unicorn” or unique event and present a new, model-free, unsupervised detection algorithm to detect unicorns. The key component of the new algorithm is the Temporal Outlier Factor (TOF) to measure the uniqueness of events in continuous data sets from dynamic systems. The concept of unique events differs significantly from traditional outliers in many aspects: while repetitive outliers are no longer unique events, a unique event is not necessarily an outlier; it does not necessarily fall out from the distribution of normal activity. The performance of our algorithm was examined in recognizing unique events on different types of simulated data sets with anomalies and it was compared with the Local Outlier Factor (LOF) and discord discovery algorithms. TOF had superior performance compared to LOF and discord detection algorithms even in recognizing traditional outliers and it also detected unique events that those did not. The benefits of the unicorn concept and the new detection method were illustrated by example data sets from very different scientific fields. Our algorithm successfully retrieved unique events in those cases where they were already known such as the gravitational waves of a binary black hole merger on LIGO detector data and the signs of respiratory failure on ECG data series. Furthermore, unique events were found on the LIBOR data set of the last 30 years.