Artificial neural network based prediction of the lung tissue involvement as an independent in-hospital mortality and mechanical ventilation risk factor in COVID-19.

INTRODUCTION: During COVID-19 pandemic, artificial neural network (ANN) systems have been providing aid for clinical decisions. However, to achieve optimal results, these models should link multiple clinical data points to simple models. This study aimed to model the in-hospital mortality and mechanical ventilation risk using a two step approach combining clinical variables and ANN-analyzed lung inflammation data. METHODS: A data set of 4317 COVID-19 hospitalized patients, including 266 patients requiring mechanical ventilation, was analyzed. Demographic and clinical data (including the length of hospital stay and mortality) and chest computed tomography (CT) data were collected. Lung involvement was analyzed using a trained ANN. The combined data were then analyzed using unadjusted and multivariate Cox proportional hazards models. RESULTS: Overall in-hospital mortality associated with ANN-assigned percentage of the lung involvement (hazard ratio [HR]: 5.72, 95% confidence interval [CI]: 4.4-7.43, p < 0.001 for the patients with >50% of lung tissue affected by COVID-19 pneumonia), age category (HR: 5.34, 95% CI: 3.32-8.59 for cases >80 years, p < 0.001), procalcitonin (HR: 2.1, 95% CI: 1.59-2.76, p < 0.001, C-reactive protein level (CRP) (HR: 2.11, 95% CI: 1.25-3.56, p = 0.004), glomerular filtration rate (eGFR) (HR: 1.82, 95% CI: 1.37-2.42, p < 0.001) and troponin (HR: 2.14, 95% CI: 1.69-2.72, p < 0.001). Furthermore, the risk of mechanical ventilation is also associated with ANN-based percentage of lung inflammation (HR: 13.2, 95% CI: 8.65-20.4, p < 0.001 for patients with >50% involvement), age, procalcitonin (HR: 1.91, 95% CI: 1.14-3.2, p = 0.14, eGFR (HR: 1.82, 95% CI: 1.2-2.74, p = 0.004) and clinical variables, including diabetes (HR: 2.5, 95% CI: 1.91-3.27, p < 0.001), cardiovascular and cerebrovascular disease (HR: 3.16, 95% CI: 2.38-4.2, p < 0.001) and chronic pulmonary disease (HR: 2.31, 95% CI: 1.44-3.7, p < 0.001). CONCLUSIONS: ANN-based lung tissue involvement is the strongest predictor of unfavorable outcomes in COVID-19 and represents a valuable support tool for clinical decisions.

[Assessment of the validity of Doppler sonography in the diagnosis of acute scrotum in boys]. / Ocena wartosci badania ultrasonograficznego metoda Dopplera w diagnostyce ostrej moszny u chlópców.

PURPOSE: Comparison of preoperative scrotal Doppler sonography and final diagnosis of subsequent exploration in patients with acute scrotum. MATERIAL AND METHODS: Retrospective analysis of the medical history of 61 patients operated on in The Department of Paediatric and Oncological Surgery in Szczecin in 2008-2014. RESULTS: Mean patient age (years) with testicular torsion was 12.7 ± 4.0, with torsion of a testicular appendage was 8.6 ± 4.5, and with orchitis or epididymitis was 7.2 ± 8.5. During operation we diagnosed testicular torsion in 28 (45%) patients, torsion of the testicular appendage in 26 (43%), and orchitis or epididymitis in 7 (12%). The specificity of Doppler sonography for testicular torsion was 46%, for torsion testicular appendage 20%, and for orchitis/epididymitis 57%. CONCLUSIONS: Doppler sonography of the scrotum shows limited specificity in the diagnosis of acute scrotum in boys. Surgical exploration is recommended even with apparently normal Doppler sonography examinations.