BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data.

Nowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge from the datasets collected from 930 COVID-19 patients hospitalized in Italy during the first wave of COVID-19 (March-June 2020). The dataset consists of twenty-five biomarkers from electronic health record and Chest X-ray (CXR) images. It is found that the system can diagnose low- or high-risk patients with an accuracy, sensitivity, and F1-score of 89.03%, 90.44%, and 89.03%, respectively. The system exhibits 6% higher accuracy than the systems that employ either CXR images or biomarker data. In addition, the system can calculate the mortality risk of high-risk patients using multivariate logistic regression-based nomogram scoring technique. Interested physicians can use the presented system to predict the early mortality risks of COVID-19 patients using the web-link: Covid-severity-grading-AI. In this case, a physician needs to input the following information: CXR image file, Lactate Dehydrogenase (LDH), Oxygen Saturation (O2%), White Blood Cells Count, C-reactive protein, and Age. This way, this study contributes to the management of COVID-19 patients by predicting early mortality risk. Supplementary Information: The online version contains supplementary material available at 10.1007/s00521-023-08606-w.

Impact of COVID-19 status on patients receiving neuraxial analgesia during labor: A national retrospective-controlled study

Background: Pregnancy affects a woman's susceptibility to and severity of certain infectious diseases. Central neuraxial block for analgesia during labor is superior to nonneuraxial methods in efficacy, safety, and maternal satisfaction. Although Coronavirus disease (COVID-19) can be vertically transmitted from mother to fetus, little is known about the effects of COVID-19 on pregnant women or about anesthesia management and the risk of adverse effects related to neuraxial techniques in women with untreated COVID-19 during gestation. Aim: This investigation assesses the effects of neuraxial analgesia during labor of COVID-19-positive parturients on their hemodynamic stability. Results: The study was conducted on 64 patients and involved 32 parturients positive for SARS-CoV-2 by polymerase chain reaction (PCR) and a similar number of control “negative” patients. The affected group had an uneventful course during gestation. Seven were positive for ground-glass opacities on chest X-rays, and none underwent computed tomography (CT) scans. Two neonates were PCR-positive for SARS-CoV-2, and all 32 neonates were released from the hospital. No clinical differences were observed between the neonates in the COVID-19 and control groups. Although parturients in both groups were hemodynamically stable, hemodynamic stability was subnormal in the COVID-19 group regarding blood pressure, oxygen saturation, heart rate, and body temperature. None of the women in either group required a vasopressor or oxygen supplementation during delivery. No other clinical differences were observed between the COVID-19 and control groups. Conclusion: This is the first case-controlled study testing the anesthetic implications of neuraxial labor analgesia in pregnant, COVID-19-positive women. Although management of neuraxial labor analgesia did not differ in pregnant women positive and negative for COVID-19, their hemodynamic characteristics differed significantly. Therefore, care is required to prevent adverse outcomes in pregnant women positive for COVID-19.