Showing papers by "David M. Hwang published in 2022"

EGFR Targeting TKI-Related Skin Toxicities in a Patient with Darker Skin: A Case Report

Abstract: Epidermal growth factor receptor (EGFR) targeting tyrosine kinase inhibitors (TKIs) can result in significant skin toxicities that may impact patients’ quality of life. While these skin reactions are well documented in patients with lighter skin, there is a paucity of literature and images to guide clinicians in their assessment in patients with darker skin tones. Given that dermatological reactions in patients with darker skin are not well represented, this can result in the undertreatment or mistreatment of these otherwise common toxicities. Herein, we present a case of a female patient with a darker skin tone with metastatic non-small cell lung carcinoma (NSCLC) with EGFR-TKI-related skin toxicity and her clinical course.

Predictive modeling of antibiotic eradication therapy success for new-onset Pseudomonas aeruginosa pulmonary infections in children with cystic fibrosis

Abstract: Chronic Pseudomonas aeruginosa (Pa) lung infections are the leading cause of mortality among cystic fibrosis (CF) patients; therefore, the eradication of new-onset Pa lung infections is an important therapeutic goal that can have long-term health benefits. The use of early antibiotic eradication therapy (AET) has been shown to eradicate the majority of new-onset Pa infections, and it is hoped that identifying the underlying basis for AET failure will further improve treatment outcomes. Here we generated random forest machine learning models to predict AET outcomes based on pathogen genomic data. We used a nested cross validation design, population structure control, and recursive feature selection to improve model performance and showed that incorporating population structure control was crucial for improving model interpretation and generalizability. Our best model, controlling for population structure and using only 30 recursively selected features, had an area under the curve of 0.87 for a holdout test dataset. The top-ranked features were generally associated with motility, adhesion, and biofilm formation. AUTHOR SUMMARY Cystic fibrosis (CF) patients are susceptible to lung infections by the opportunistic bacterial pathogen Pseudomonas aeruginosa (Pa) leading to increased morbidity and earlier mortality. Consequently, doctors use antibiotic eradication therapy (AET) to clear these new-onset Pa infections, which is successful in 60%-90% of cases. The hope is that by identifying the factors that lead to AET failure, we will improve treatment outcomes and improve the lives of CF patients. In this study, we attempted to predict AET success or failure based on the genomic sequences of the infecting Pa strains. We used machine learning models to determine the role of Pa genetics and to identify genes associated with AET failure. We found that our best model could predict treatment outcome with an accuracy of 0.87, and that genes associated with chronic infection (e.g., bacterial motility, biofilm formation, antimicrobial resistance) were also associated with AET failure.

Lung allograft standardized histological analysis (LASHA) template: A research consensus proposal.

Abstract: Routine monitoring of lung-transplanted patients is crucial for the identification of immunological and non-immunological complications. Determining the etiology of acute allograft dysfunction, particularly in alloimmune-mediated disorders, relies heavily on the lung biopsy with histopathologic analysis. Standardization of the pathologic diagnosis of rejection (e.g., cellular and antibody-mediated) is based on consensus statements and guidelines, indicating the importance of a multidisciplinary approach to achieve a definitive etiological diagnosis. In addition to these statements and guidelines, refinements and standardizations are feasible through systematic analysis morphological, immunophenotypic and molecular alterations observed in transbronchial biopsies. This study is to identify key morphologic features to be assessed, select consistent and reproducible terminology for each histological feature, and provide standardized definitions for pathological assessment and grading.A template was created by experts in lung transplantation including pathologists, pulmonologists, immunologists. An initial draft was circulated, followed by discussions and multiple revisions by email and conference calls.The "lung allograft standardized histological analysis - LASHA" template was created and structured as multiple-choice questions with number of fields to be filled in to allow for standardization of results and easy transfer into a future electronic spreadsheet.This template will help facilitate multicenter studies through a uniform protocol and correlations with new diagnostic modalities. After validation in large-scale studies, an optimized template could be included in routine clinical practice to enhance graft assessment and medical decision-making.