Comparison of major definitions of sarcopenia based on the skeletal muscle index in patients with urothelial carcinoma

For the past decade, sarcopenia has been actively investigated in various cancers, including urothelial carcinoma (UC). Although skeletal muscle index (SMI) is the main parameter used to evaluate sarcopenia in oncology, the optimal definition of SMI-based sarcopenia is not entirely standardized. We recently highlighted the potential limitations of current definitions of SMI-based sarcopenia in another journal. In this study, we reviewed studies that assessed sarcopenia in UC patients. We then performed a comparative validation of three major SMI-based definitions of sarcopenia, including Prado's, the international and Martin's definitions in metastatic UC patients. We believe that the standardization of the sarcopenia definition is an urgent issue in oncology, and this paper discusses a possible new direction to address this issue.

Artificial neural networks improve LDCT lung cancer screening: a comparative validation study

Background This study proposes a prediction model for the automatic assessment of lung cancer risk based on an artificial neural network (ANN) with a data-driven approach to the low-dose computed tomography (LDCT) standardized structure report. Methods This comparative validation study analysed a prospective cohort from Chiayi Chang Gung Memorial Hospital, Taiwan. In total, 836 asymptomatic patients who had undergone LDCT scans between February 2017 and August 2018 were included, comprising 27 lung cancer cases and 809 controls. A derivation cohort of 602 participants (19 lung cancer cases and 583 controls) was collected to construct the ANN prediction model. A comparative validation of the ANN and Lung-RADS was conducted with a prospective cohort of 234 participants (8 lung cancer cases and 226 controls). The areas under the curves (AUCs) of the receiver operating characteristic (ROC) curves were used to compare the prediction models. Results At the cut-off of category 3, the Lung-RADS had a sensitivity of 12.5%, specificity of 96.0%, positive predictive value of 10.0%, and negative predictive value of 96.9%. At its optimal cut-off value, the ANN had a sensitivity of 75.0%, specificity of 85.0%, positive predictive value of 15.0%, and negative predictive value of 99.0%. The area under the ROC curve was 0.764 for the Lung-RADS and 0.873 for the ANN (P = 0.01). The two most important predictors used by the ANN for predicting lung cancer were the documented sizes of partially solid nodules and ground-glass nodules. Conclusions Compared to the Lung-RADS, the ANN provided better sensitivity for the detection of lung cancer in an Asian population. In addition, the ANN provided a more refined discriminative ability than the Lung-RADS for lung cancer risk stratification with population-specific demographic characteristics. When lung nodules are detected and documented in a standardized structured report, ANNs may better provide important insights for lung cancer prediction than conventional rule-based criteria.

Independent side-by-side validation and comparison of four serological platforms for SARS-CoV-2 antibody testing

Highly sensitive and specific platforms for the detection of anti-SARS-CoV-2 antibodies are becoming increasingly important for (1) evaluating potential SARS-CoV-2 convalescent plasma donors, (2) studying the spread of SARS-CoV-2 infections and (3) identifying individuals with seroconversion. This study provides a comparative validation of four anti-SARS-CoV-2 platforms. Unique feature of this study is the use of a representative cohort of COVID-19-convalescent patients with mild-to-moderate disease course. All platforms showed significant correlations with a SARS-CoV-2 plaque-reduction-neutralization test, with highest sensitivities for the Euroimmun and the Roche platforms, suggesting their preferential use for screening of persons at increased risk of SARS-CoV-2 infections.

Prediction of fall events during admission using eXtreme gradient boosting: a comparative validation study

As the performance of current fall risk assessment tools is limited, clinicians face significant challenges in identifying patients at risk of falling. This study proposes an automatic fall risk prediction model based on eXtreme gradient boosting (XGB), using a data-driven approach to the standardized medical records. This study analyzed a cohort of 639 participants (297 fall patients and 342 controls) from Chang Gung Memorial Hospital, Chiayi Branch, Taiwan. A derivation cohort of 507 participants (257 fall patients and 250 controls) was collected for constructing the prediction model using the XGB algorithm. A comparative validation of XGB and the Morse Fall Scale (MFS) was conducted with a prospective cohort of 132 participants (40 fall patients and 92 controls). The areas under the curves (AUCs) of the receiver operating characteristic (ROC) curves were used to compare the prediction models. This machine learning method provided a higher sensitivity than the standard method for fall risk stratification. In addition, the most important predictors found (Department of Neuro-Rehabilitation, Department of Surgery, cardiovascular medication use, admission from the Emergency Department, and bed rest) provided new information on in-hospital fall event prediction and the identification of patients with a high fall risk.