Search

• Based on the new ARDS definition, approximately 22.6 % of hospitalized SOTRs infected with SARS-CoV-2 developed ARDS. • Cerebrovascular disease, leukocyte counts, albumin levels, and IL-6 levels were independent risk factors for the development of ARDS following COVID-19 infection in SOTRs. • A nomogram prediction model was created utilizing the aforementioned factors to facilitate early prediction of ARDS, exhibiting an AUC of 0.81. Solid organ transplant recipients (SOTRs) are more likely to suffer complications after being infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We aimed to describe the clinical features of SOTRs infected with SARS-CoV-2 and to assess independent risk factors associated with the development of acute respiratory distress syndrome (ARDS) following COVID-19 infection in SOTRs based on the new ARDS definition. 358 SOTRs infected with SARS-CoV-2 were recruited and divided into two groups, patients with ARDS (n = 81) and patients without ARDS (n = 277). Demographic data, initial laboratory findings, therapeutic measures, and outcome indicators were compared between the two groups. The association between the onset of ARDS and related factors was analyzed using a logistic regression model. A nomogram was created to estimate the probability of developing ARDS. Approximately 22.6 % (81/358) of hospitalized SOTRs infected with SARS-CoV-2 developed ARDS. In comparison to patients without ARDS, those with ARDS presented with more underlying conditions, decreased lymphocyte counts and serum albumin levels, but increased levels of leukocytes, serum creatinine, nitrogen urea, uric acid, and inflammatory markers. Cerebrovascular disease, leukocyte counts, albumin levels, and IL-6 levels were independent risk factors for the development of ARDS in this population. Furthermore, a nomogram prediction model was created utilizing the aforementioned factors to facilitate early prediction of ARDS, exhibiting an AUC (area under curve) of 0.81. Cerebrovascular disease, leukocyte counts, albumin levels, and IL-6 levels were independent risk factors for the development of ARDS following COVID-19 infection in SOTRs. [ABSTRACT FROM AUTHOR]

To construct and validate a deep learning radiomics (DLR) model based on X-ray images for predicting and distinguishing acute and chronic osteoporotic vertebral fractures (OVFs). A total of 942 cases (1076 vertebral bodies) with both vertebral X-ray examination and MRI scans were included in this study from three hospitals. They were divided into a training cohort (n = 712), an internal validation cohort (n = 178), an external validation cohort (n = 111), and a prospective validation cohort (n = 75). The ResNet-50 model architecture was used for deep transfer learning (DTL), with pre-training performed on RadImageNet and ImageNet datasets. DTL features and radiomics features were extracted from lateral X-ray images of OVFs patients and fused together. A logistic regression model with the least absolute shrinkage and selection operator was established, with MRI showing bone marrow edema as the gold standard for acute OVFs. The performance of the model was evaluated using receiver operating characteristic curves. Eight machine learning classification models were evaluated for their ability to distinguish between acute and chronic OVFs. The Nomogram was constructed by combining clinical baseline data to achieve visualized classification assessment. The predictive performance of the best RadImageNet model and ImageNet model was compared using the Delong test. The clinical value of the Nomogram was evaluated using decision curve analysis (DCA). Pre-training resulted in 34 and 39 fused features after feature selection and fusion. The most effective machine learning algorithm in both DLR models was Light Gradient Boosting Machine. Using the Delong test, the area under the curve (AUC) for distinguishing between acute and chronic OVFs in the training cohort was 0.979 and 0.972 for the RadImageNet and ImageNet models, respectively, with no statistically significant difference between them (P = 0.235). In the internal validation cohort, external validation cohort, and prospective validation cohort, the AUCs for the two models were 0.967 vs 0.629, 0.886 vs 0.817, and 0.933 vs 0.661, respectively, with statistically significant differences in all comparisons (P < 0.05). The deep learning radiomics nomogram (DLRN) was constructed by combining the predictive model of RadImageNet with clinical baseline features, resulting in AUCs of 0.981, 0.974, 0.895, and 0.902 in the training cohort, internal validation cohort, external validation cohort, and prospective validation cohort, respectively. Using the Delong test, the AUCs for the fused feature model and the DLRN in the training cohort were 0.979 and 0.981, respectively, with no statistically significant difference between them (P = 0.169). In the internal validation cohort, external validation cohort, and prospective validation cohort, the AUCs for the two models were 0.967 vs 0.974, 0.886 vs 0.895, and 0.933 vs 0.902, respectively, with statistically significant differences in all comparisons (P < 0.05). The Nomogram showed a slight improvement in predictive performance in the internal and external validation cohort, but a slight decrease in the prospective validation cohort (0.933 vs 0.902). DCA showed that the Nomogram provided more benefits to patients compared to the DLR models. Compared to the ImageNet model, the RadImageNet model has higher diagnostic value in distinguishing between acute and chronic OVFs. Furthermore, the diagnostic performance of the model is further improved when combined with clinical baseline features to construct the Nomogram. [ABSTRACT FROM AUTHOR]