Global overview of the management of acute cholecystitis during the COVID-19 pandemic (CHOLECOVID study)

Background: This study provides a global overview of the management of patients with acute cholecystitis during the initial phase of the COVID-19 pandemic. Methods: CHOLECOVID is an international, multicentre, observational comparative study of patients admitted to hospital with acute cholecystitis during the COVID-19 pandemic. Data on management were collected for a 2-month study interval coincident with the WHO declaration of the SARS-CoV-2 pandemic and compared with an equivalent pre-pandemic time interval. Mediation analysis examined the influence of SARS-COV-2 infection on 30-day mortality. Results: This study collected data on 9783 patients with acute cholecystitis admitted to 247 hospitals across the world. The pandemic was associated with reduced availability of surgical workforce and operating facilities globally, a significant shift to worse severity of disease, and increased use of conservative management. There was a reduction (both absolute and proportionate) in the number of patients undergoing cholecystectomy from 3095 patients (56.2 per cent) pre-pandemic to 1998 patients (46.2 per cent) during the pandemic but there was no difference in 30-day all-cause mortality after cholecystectomy comparing the pre-pandemic interval with the pandemic (13 patients (0.4 per cent) pre-pandemic to 13 patients (0.6 per cent) pandemic; P = 0.355). In mediation analysis, an admission with acute cholecystitis during the pandemic was associated with a non-significant increased risk of death (OR 1.29, 95 per cent c.i. 0.93 to 1.79, P = 0.121). Conclusion: CHOLECOVID provides a unique overview of the treatment of patients with cholecystitis across the globe during the first months of the SARS-CoV-2 pandemic. The study highlights the need for system resilience in retention of elective surgical activity. Cholecystectomy was associated with a low risk of mortality and deferral of treatment results in an increase in avoidable morbidity that represents the non-COVID cost of this pandemic

Predicting the Stone-Free Status of Percutaneous Nephrolithotomy with the Machine Learning System

Rami AlAzab,1 Owais Ghammaz,2 Nabil Ardah,2 Ayah Al-Bzour,2 Layan Zeidat,2 Zahraa Mawali,2 Yaman B Ahmed,2 Tha’er Abdulkareem Alguzo,1 Azhar Mohanad Al-Alwani,1 Mahmoud Samara1 1Department of General Surgery and Urology, King Abdullah University Hospital, Irbid, Jordan; 2Faculty of Medicine, Jordan University of Science and Technology, Irbid, JordanCorrespondence: Owais Ghammaz, Faculty of Medicine, Jordan University of Science and Technology, P.O Box 3030, Irbid, 22110, Jordan, Tel +962775741299, Email [email protected]: The study aimed to create a machine learning model (MLM) to predict the stone-free status (SFS) of patients undergoing percutaneous nephrolithotomy (PCNL) and compare its performance to the S.T.O.N.E. and Guy’s stone scores.Patients and Methods: This is a retrospective study that included 320 PCNL patients. Pre-operative and post-operative variables were extracted and entered into three MLMs: RFC, SVM, and XGBoost. The methods used to assess the performance of each were mean bootstrap estimate, 10-fold cross-validation, classification report, and AUC. Each model was externally validated and evaluated by mean bootstrap estimate with CI, classification report, and AUC.Results: Out of the 320 patients who underwent PCNL, the SFS was found to be 69.4%. The RFC mean bootstrap estimate was 0.75 and 95% CI: [0.65– 0.85], 10-fold cross-validation of 0.744, an accuracy of 0.74, and AUC of 0.761. The XGBoost results were 0.74 [0.63– 0.85], 0.759, 0.72, and 0.769, respectively. The SVM results were 0.70 [0.60– 0.79], 0.725, 0.74, and 0.751, respectively. The AUC of Guy’s stone score and the S.T.O.N.E. score were 0.666 and 0.71, respectively. The RFC external validation set had a mean bootstrap estimate of 0.87 and 95% CI: [0.81– 0.92], an accuracy of 0.70, and an AUC of 0.795, While the XGBoost results were 0.84 [0.78– 0.91], 0.74, and 0.84, respectively. The SVM results were 0.86 [0.80– 0.91], 0.79, and 0.858, respectively.Conclusion: MLMs can be used with high accuracy in predicting SFS for patients undergoing PCNL. MLMs we utilized predicted the SFS with AUCs superior to those of GSS and S.T.O.N.E scores.Keywords: Guy’s stone score, machine learning, percutaneous nephrolithotomy, renal stones, S.T.O.N.E scor