Publications by authors named "Andrew E Williams"
Article Synopsis
- Percutaneous image-guided needle biopsies are a safe and minimally invasive procedure used to obtain tissue from bone lesions, with radiologists playing a crucial role in patient care and coordination with clinical teams for accurate diagnoses.
- The review emphasizes the importance of patient selection, imaging workup, and managing risks like bleeding and thrombosis before conducting biopsies, often performed under moderate sedation for patient comfort.
- While computed tomography is the primary method for guidance, advancements in powered drill technology are improving the safety and efficiency of sampling from tough bone lesions, along with discussions on special techniques for challenging anatomical regions.
Background: A wealth of clinically relevant information is only obtainable within unstructured clinical narratives, leading to great interest in clinical natural language processing (NLP). While a multitude of approaches to NLP exist, current algorithm development approaches have limitations that can slow the development process. These limitations are exacerbated when the task is emergent, as is the case currently for NLP extraction of signs and symptoms of COVID-19 and postacute sequelae of SARS-CoV-2 infection (PASC).
View Article and Find Full Text PDFBackground: The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) that is developed and maintained by the Observational Health Data Sciences and Informatics (OHDSI) community supports large scale cancer research by enabling distributed network analysis. As the number of studies using the OMOP CDM for cancer research increases, there is a growing need for an overview of the scope of cancer research that relies on the OMOP CDM ecosystem.
Objectives: In this study, we present a comprehensive review of the adoption of the OMOP CDM for cancer research and offer some insights on opportunities in leveraging the OMOP CDM ecosystem for advancing cancer research.
Article Synopsis
- A study investigated the prevalence of vestibular disorders in patients with COVID-19 compared to those without the virus using data from the National COVID Cohort Collaborative database.
- Results showed that individuals with COVID-19 were significantly more likely to experience vestibular disorders, with the highest risk associated with the omicron 23A variant (OR of 8.80).
- The findings underscore the need for further research on the long-term effects of vestibular disorders in COVID-19 patients and implications for patient counseling.
Background: Social determinants of health (SDOH) have been linked to neurocritical care outcomes. We sought to examine the extent to which SDOH explain differences in decisions regarding life-sustaining therapy, a key outcome determinant. We specifically investigated the association of a patient's home geography, individual-level SDOH, and neighborhood-level SDOH with subsequent early limitation of life-sustaining therapy (eLLST) and early withdrawal of life-sustaining therapy (eWLST), adjusting for admission severity.
View Article and Find Full Text PDFBackground: Management of hypertension, dyslipidemia, diabetes and other modifiable factors may mitigate the cardiovascular disease (CVD) risk in people with human immunodeficiency virus (HIV, PWH) compared with people without HIV (PWoH).
Methods: This was a retrospective cohort study of 8285 PWH and 170 517 PWoH from an integrated health system. Risk factor control was measured using a novel disease management index (DMI) accounting for amount/duration above treatment goals (0% to 100% [perfect control]), including 2 DMIs for hypertension (diastolic and systolic blood pressure), 3 for dyslipidemia (low-density lipoprotein, total cholesterol, triglycerides), and 1 for diabetes (HbA1c).
The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English.
View Article and Find Full Text PDFBackground: The cause and symptoms of long COVID are poorly understood. It is challenging to predict whether a given COVID-19 patient will develop long COVID in the future.
Methods: We used electronic health record (EHR) data from the National COVID Cohort Collaborative to predict the incidence of long COVID.
Article Synopsis
- Conservative management for prostate cancer (PCa) aims to delay or avoid curative therapy, with the PIONEER initiative using big data to enhance PCa care in Europe.
- The study analyzed over 527,000 diagnosed PCa cases, focusing on 123,146 patients who did not receive treatment within six months of diagnosis to assess long-term outcomes.
- Findings showed common comorbidities like hypertension and diabetes among patients, with notable rates of hospitalization and symptomatic progression, though limitations included insufficient data on treatment intent and patient characteristics.
Article Synopsis
- Common data models standardize electronic health record (EHR) data but struggle to fully integrate the necessary resources for deep phenotyping.
- The OMOP2OBO algorithm automates the mapping of Observational Medical Outcomes Partnership (OMOP) vocabularies to Open Biological and Biomedical Ontology (OBO) ontologies, significantly reducing the need for manual curation.
- With OMOP2OBO, mappings for a large number of conditions, drugs, and measurements were created, facilitating the identification of undiagnosed patients in rare diseases and enhancing opportunities for EHR-based deep phenotyping.
Article Synopsis
- * The OHDSI consortium's NLP Working Group created methods and tools to improve the use of textual data in observational studies, detailing a framework for integrating this information into the OMOP Common Data Model (CDM).
- * The authors also highlight the workflow for extracting and transforming data from clinical notes, share current applications of the NLP solution, and discuss challenges and lessons learned to aid other researchers in implementing NLP in their studies.
Background: Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use.
Methods: A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative.
Article Synopsis
- Linear mixed models are useful in healthcare for analyzing data from multiple sites, but sharing sensitive individual patient data is often restricted due to privacy regulations.
- The proposed algorithm allows for fitting distributed linear mixed models (DLMMs) without needing to share individual patient data, achieving the same results as if pooled data were used.
- The study demonstrates this algorithm's effectiveness by analyzing factors related to hospital stays in over 120,000 COVID-19 patients from various global sources while only requiring minimal aggregated data from each site.
Article Synopsis
- The study aimed to develop COVID-19 prediction models using influenza data to quickly and accurately assess risks of hospital admission and death in patients diagnosed with COVID-19.
- The researchers created three COVID-19 Estimated Risk (COVER) scores that quantify risks related to pneumonia and mortality based on historical data and validated them using a large dataset of COVID-19 patients across multiple countries.
- They found that seven key health predictors, along with age and sex, effectively distinguished which patients were likely to face severe outcomes, achieving strong performance in model validation.
Neutrophil migration into the airways is an important process to fight infection and is mediated by cell adhesion molecules. The intercellular adhesion molecules, ICAM-1 (CD54) and ICAM-2 (CD102) are known ligands for the neutrophil integrins, lymphocyte function associated antigen (LFA)-1 (αβ; CD11a/CD18), and macrophage-1 antigen (Mac-1;αβ;CD11b/CD18) and are implicated in leukocyte migration into the lung. However, it is ill-defined how neutrophils exit the lung and the role for ICAMs in trans-epithelial migration (TEpM) across the bronchial or alveolar epithelium.
View Article and Find Full Text PDFArticle Synopsis
- - The National COVID Cohort Collaborative (N3C) is a massive electronic health record database that provides valuable insights into COVID-19, supporting the development of better diagnostic tools and clinical practices.
- - This study analyzed data from nearly 2 million adults across 34 medical centers to evaluate the severity of COVID-19 and its risk factors over time, using advanced machine learning techniques to predict severe outcomes.
- - Among the 174,568 adults infected with SARS-CoV-2, a significant portion experienced severe illness, highlighting the need for continuous monitoring and adjustment of treatment approaches based on demographic characteristics and disease severity.
Objectives: To characterize the demographics, comorbidities, symptoms, in-hospital treatments, and health outcomes among children and adolescents diagnosed or hospitalized with coronavirus disease 2019 (COVID-19) and to compare them in secondary analyses with patients diagnosed with previous seasonal influenza in 2017-2018.
Methods: International network cohort using real-world data from European primary care records (France, Germany, and Spain), South Korean claims and US claims, and hospital databases. We included children and adolescents diagnosed and/or hospitalized with COVID-19 at age <18 between January and June 2020.
Background: Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use.
Methods: A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative.
Article Synopsis
- The National COVID Cohort Collaborative (N3C) is the largest U.S. COVID-19 patient database, created to provide a comprehensive analysis of clinical characteristics, disease progression, and treatment outcomes across multiple health centers, enhancing predictive and diagnostic tools for COVID-19.
- A study involving over 1.9 million patients from 34 medical centers found significant clinical data, showing that certain factors like age, sex, and underlying conditions affect disease severity, with a notable decrease in mortality rates among hospitalized patients over time.
- The N3C dataset was utilized in machine learning models to successfully predict severe outcomes in COVID-19 patients, achieving high accuracy rates and demonstrating the potential of using electronic health
Background: Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) have been postulated to affect susceptibility to COVID-19. Observational studies so far have lacked rigorous ascertainment adjustment and international generalisability. We aimed to determine whether use of ACEIs or ARBs is associated with an increased susceptibility to COVID-19 in patients with hypertension.
View Article and Find Full Text PDFDigital medical records have enabled us to employ clinical data in many new and innovative ways. However, these advances have brought with them a complex set of demands for healthcare institutions regarding data sharing with topics such as data ownership, the loss of privacy, and the protection of the intellectual property. The lack of clear guidance from government entities often creates conflicting messages about data policy, leaving institutions to develop guidelines themselves.
View Article and Find Full Text PDFArticle Synopsis
- The study aimed to analyze the demographics, comorbidities, symptoms, treatments, and outcomes of children and adolescents diagnosed or hospitalized with COVID-19, comparing these to those diagnosed with seasonal influenza.
- Utilizing real-world data from multiple countries including France, Germany, Spain, South Korea, and the US, the research included over 55,000 children with COVID-19 and nearly 2 million with influenza between specified periods.
- Key findings indicate that comorbidities were more prevalent in hospitalized COVID-19 cases, fever was the most common symptom, and while hospitalization rates were low, complications like pneumonia and multi-system inflammatory syndrome were significantly more common in COVID-19 cases compared to influenza.
Background: Unhealthy alcohol use among persons living with HIV (PLWH) is linked to significant morbidity, and use of alcohol services may differ by HIV status. Our objective was to compare unhealthy alcohol use screening and treatment by HIV status in primary care.
Methods: Cohort study of adult (≥18 years) PLWH and HIV-uninfected participants frequency matched 20:1 to PLWH by age, sex, and race/ethnicity who were enrolled in a large integrated healthcare system in the United States, with information ascertained from an electronic health record.