Predicting delirium and the effects of medications in hospitalized COVID-19 patients using machine learning: A retrospective study within the Korean Multidisciplinary Cohort for Delirium Prevention (KoMCoDe).

Objective: Delirium is commonly reported from the inpatients with Coronavirus disease 2019 (COVID-19) infection. As delirium is closely associated with adverse clinical outcomes, prediction and prevention of delirium is critical. We developed a machine learning (ML) model to predict delirium in hospitalized patients with COVID-19 and to identify modifiable factors to prevent delirium. Methods: The data set (n = 878) from four medical centers was constructed. Total of 78 predictors were included such as demographic characteristics, vital signs, laboratory results and medication, and the primary outcome was delirium occurrence during hospitalization. For analysis, the extreme gradient boosting (XGBoost) algorithm was applied, and the most influential factors were selected by recursive feature elimination. Among the indicators of performance for ML model, the area under the curve of the receiver operating characteristic (AUROC) curve was selected as the evaluation metric. Results: Regarding the performance of developed delirium prediction model, the accuracy, precision, recall, F1 score, and the AUROC were calculated (0.944, 0.581, 0.421, 0.485, 0.873, respectively). The influential factors of delirium in this model included were mechanical ventilation, medication (antipsychotics, sedatives, ambroxol, piperacillin/tazobactam, acetaminophen, ceftriaxone, and propacetamol), and sodium ion concentration (all p < 0.05). Conclusions: We developed and internally validated an ML model to predict delirium in COVID-19 inpatients. The model identified modifiable factors associated with the development of delirium and could be clinically useful for the prediction and prevention of delirium in COVID-19 inpatients.

Automated Detection of Surgical Implants on Plain Knee Radiographs Using a Deep Learning Algorithm.

Background and Objectives: The number of patients who undergo multiple operations on a knee is increasing. The objective of this study was to develop a deep learning algorithm that could detect 17 different surgical implants on plain knee radiographs. Materials and Methods: An internal dataset consisted of 5206 plain knee antero-posterior X-rays from a single, tertiary institute for model development. An external set contained 238 X-rays from another tertiary institute. A total of 17 different types of implants including total knee arthroplasty, unicompartmental knee arthroplasty, plate, and screw were labeled. The internal dataset was approximately split into a train set, a validation set, and an internal test set at a ratio of 7:1:2. You Only look Once (YOLO) was selected as the detection network. Model performances with the validation set, internal test set, and external test set were compared. Results: Total accuracy, total sensitivity, total specificity value of the validation set, internal test set, and external test set were (0.978, 0.768, 0.999), (0.953, 0.810, 0.990), and (0.956, 0.493, 0.975), respectively. Means ± standard deviations (SDs) of diagonal components of confusion matrix for these three subsets were 0.858 ± 0.242, 0.852 ± 0.182, and 0.576 ± 0.312, respectively. True positive rate of total knee arthroplasty, the most dominant class of the dataset, was higher than 0.99 with internal subsets and 0.96 with an external test set. Conclusion: Implant identification on plain knee radiographs could be automated using a deep learning technique. The detection algorithm dealt with overlapping cases while maintaining high accuracy on total knee arthroplasty. This could be applied in future research that analyzes X-ray images with deep learning, which would help prompt decision-making in clinics.

Interaction between platelets and endothelial progenitor cells via LPA-Edg-2 axis is augmented by PPAR-δ activation.

BACKGROUND: Peroxisome proliferator-activated receptor (PPAR)-δ is a nuclear receptor regulating cell metabolism. The role of PPAR-δ in late endothelial progenitor cells (EPCs) has not been fully elucidated. We aim to understand the effects of PPAR-δ activation on late EPC and to reveal the underlying mechanism. METHODS AND RESULTS: Treatment with a highly selective PPAR-δ agonist (GW501516) induced proliferation of late EPCs and enhanced their vasculogenic potential. Search for the target molecule of PPAR-δ activation revealed endothelial differentiation gene (Edg)-2. Chromatin immunoprecipitation and promoter assays demonstrated that Edg-2 gene was specifically induced by PPAR-δ through direct transcriptional activation. Lysophosphatidic acid (LPA), an Edg ligand, mimicked the pro-vasculogenic effects of GW501516 in late EPCs whereas Edg antagonist (Ki16425) blocked these effects. Edg-2 is a membrane receptor for LPA which is a major growth factor from activated platelets. Thus, the interaction between platelets and late EPCs via the LPA-Edg-2 axis was assessed. Platelet supernatant boosted the pro-vasculogenic effects of GW501516, which was reversed by antagonist to PPAR-δ (GSK0660) or Edg (Ki16425). Both of in vivo Matrigel plug model and mouse skin punch-wound model demonstrated that the combination of platelets and PPAR-δ-activated late EPCs synergistically enhanced vascular regeneration. CONCLUSIONS: There exists a synergistic interaction between human platelets and late EPCs leading to vascular regeneration. This interaction consists of LPA from platelets and its receptor Edg-2 on the surface of EPCs and can be potentiated by PPAR-δ activation in EPCs. A PPAR-δ agonist is a good candidate to achieve vasculogenesis for ischemic vascular disease.

Bi-functional ion exchangers for enhanced performance of dye-sensitized solar cells.

Ion exchange using aerosol OT (AOT) offers dye adsorption twice as fast as known methods. Moreover, it suppresses the dye-agglomeration that may cause insufficient dye-coverage on the photoelectrode surface. Consequently, its dual function of fast dye-loading and higher dye-coverage significantly improves the power conversion efficiency of dye-sensitized solar cells.