Puskarich, Michael A.

Abstract The burden of senescent cells (SnCs), which do not divide but are metabolically active and resistant to death by apoptosis, is increased in older adults and those with chronic diseases. These individuals are also at the greatest risk for morbidity and mortality from SARS‐CoV‐2 infection. SARS‐CoV‐2 complications include cytokine storm and multiorgan failure mediated by the same factors as often produced by SnCs through their senescence‐associated secretory phenotype (SASP). The SASP can be amplified by infection‐related pathogen‐associated molecular profile factors. Senolytic agents, such as Fisetin, selectively eliminate SnCs and delay, prevent, or alleviate multiple disorders in aged experimental animals and animal models of human chronic diseases, including obesity, diabetes, and respiratory diseases. Senolytics are now in clinical trials for multiple conditions linked to SnCs, including frailty; obesity/diabetes; osteoporosis; and cardiovascular, kidney, and lung diseases, which are also risk factors for SARS‐CoV‐2 morbidity and mortality. A clinical trial is underway to test if senolytics decrease SARS‐CoV‐2 progression and morbidity in hospitalized older adults. We describe here a National Institutes of Health‐funded, multicenter, placebo‐controlled clinical trial of Fisetin for older adult skilled nursing facility (SNF) residents who have been, or become, SARS‐CoV‐2 rtPCR‐positive, including the rationale for targeting fundamental aging mechanisms in such patients. We consider logistic challenges of conducting trials in long‐term care settings in the SARS‐CoV‐2 era, including restricted access, consent procedures, methods for obtaining biospecimens and clinical data, staffing, investigational product administration issues, and potential solutions for these challenges. We propose developing a national network of SNFs engaged in interventional clinical trials.

AbstractObjectiveWith COVID-19, there was a need for a rapidly scalable annotation system that facilitated real-time integration with clinical decision support systems (CDS). Current annotation systems suffer from a high-resource utilization and poor scalability limiting real-world integration with CDS. A potential solution to mitigate these issues is to use the rule-based gazetteer developed at our institution.Materials and MethodsPerformance, resource utilization, and runtime of the rule-based gazetteer were compared with five annotation systems: BioMedICUS, cTAKES, MetaMap, CLAMP, and MedTagger.ResultsThis rule-based gazetteer was the fastest, had a low resource footprint, and similar performance for weighted microaverage and macroaverage measures of precision, recall, and f1-score compared to other annotation systems.DiscussionOpportunities to increase its performance include fine-tuning lexical rules for symptom identification. Additionally, it could run on multiple compute nodes for faster runtime.ConclusionThis rule-based gazetteer overcame key technical limitations facilitating real-time symptomatology identification for COVID-19 and integration of unstructured data elements into our CDS. It is ideal for large-scale deployment across a wide variety of healthcare settings for surveillance of acute COVID-19 symptoms for integration into prognostic modeling. Such a system is currently being leveraged for monitoring of postacute sequelae of COVID-19 (PASC) progression in COVID-19 survivors. This study conducted the first in-depth analysis and developed a rule-based gazetteer for COVID-19 symptom extraction with the following key features: low processor and memory utilization, faster runtime, and similar weighted microaverage and macroaverage measures for precision, recall, and f1-score compared to industry-standard annotation systems.