The Effect of Computer and Internet Attitudes and Anxiety on e-Health Search Behaviors

While the need for health information is a seemingly universal concept, comfort using computers and the Internet is not. Yet studies have shown that users within a large range of years of computer experience search the Internet for health information (“e-health information”) at ever-increasing rates. The purpose of the current study is to discover how a searcher’s attitudes toward and self-perception of their computer and Internet competence affect his or her e-health information-seeking behaviors. An online survey was distributed with questions that served to measure participants’ computer and Internet anxiety, as well as questions pertaining to their e-health attitudes and search behaviors. Participants’ anxiety levels had a statistically significant effect on participants’ (1) feeling that their e-health searches are generally successful (or unsuccessful), (2) satisfaction with the information obtained, and (3) tendency to share e-health information with a health care provider

Clinical Data: Sources and Types, Regulatory Constraints, Applications.

Access to clinical data is critical for the advancement of translational research. However, the numerous regulations and policies that surround the use of clinical data, although critical to ensure patient privacy and protect against misuse, often present challenges to data access and sharing. In this article, we provide an overview of clinical data types and associated regulatory constraints and inferential limitations. We highlight several novel approaches that our team has developed for openly exposing clinical data

Alloimmunization is associated with older age of transfused red blood cells in sickle cell disease

Red blood cell (RBC) alloimmunization is a significant clinical complication of sickle cell disease (SCD). It can lead to difficulty with cross-matching for future transfusions and may sometimes trigger life-threatening delayed hemolytic transfusion reactions. We conducted a retrospective study to explore the association of clinical complications and age of RBC with alloimmunization in patients with SCD followed at a single institution from 2005 to 2012. One hundred and sixty six patients with a total of 488 RBC transfusions were evaluated. Nineteen patients (11%) developed new alloantibodies following blood transfusions during the period of review. The median age of RBC units was 20 days (interquartile range: 14-27 days). RBC antibody formation was significantly associated with the age of RBC units (P = 0.002), with a hazard ratio of 3.5 (95% CI: 1.71-7.11) for a RBC unit that was 7 days old and 9.8 (95% CI: 2.66-35.97) for a unit that was 35 days old, 28 days after the blood transfusion. No association was observed between RBC alloimmunization and acute vaso-occlusive complications. Although increased echocardiography-derived tricuspid regurgitant jet velocity (TRV) was associated with the presence of RBC alloantibodies (P = 0.02), TRV was not significantly associated with alloimmunization when adjusted for patient age and number of transfused RBC units. Our study suggests that RBC antibody formation is significantly associated with older age of RBCs at the time of transfusion. Prospective studies in patients with SCD are required to confirm this finding

Pre-existing autoimmunity is associated with increased severity of COVID-19: A retrospective cohort study using data from the National COVID Cohort Collaborative (N3C)

Identifying individuals with a higher risk of developing severe COVID-19 outcomes will inform targeted or more intensive clinical monitoring and management. To date, there is mixed evidence regarding the impact of pre-existing autoimmune disease (AID) diagnosis and/or immunosuppressant (IS) exposure on developing severe COVID-19 outcomes.A retrospective cohort of adults diagnosed with COVID-19 was created in the National COVID Cohort Collaborative enclave. Two outcomes, life-threatening disease, and hospitalization were evaluated by using logistic regression models with and without adjustment for demographics and comorbidities.Of the 2,453,799 adults diagnosed with COVID-19, 191,520 (7.81%) had a pre-existing AID diagnosis and 278,095 (11.33%) had a pre-existing IS exposure. Logistic regression models adjusted for demographics and comorbidities demonstrated that individuals with a pre-existing AID (OR = 1.13, 95% CI 1.09 - 1.17; P< 0.001), IS (OR= 1.27, 95% CI 1.24 - 1.30; P< 0.001), or both (OR = 1.35, 95% CI 1.29 - 1.40; P< 0.001) were more likely to have a life-threatening COVID-19 disease. These results were consistent when evaluating hospitalization. A sensitivity analysis evaluating specific IS revealed that TNF inhibitors were protective against life-threatening disease (OR = 0.80, 95% CI 0.66- 0.96; P=0.017) and hospitalization (OR = 0.80, 95% CI 0.73 - 0.89; P< 0.001).Patients with pre-existing AID, exposure to IS, or both are more likely to have a life-threatening disease or hospitalization. These patients may thus require tailored monitoring and preventative measures to minimize negative consequences of COVID-19

An efficient approach for surveillance of childhood diabetes by type derived from electronic health record data: the SEARCH for Diabetes in Youth Study

Objective To develop an efficient surveillance approach for childhood diabetes by type across 2 large US health care systems, using phenotyping algorithms derived from electronic health record (EHR) data

NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study.

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. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. RESULTS: Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. CONCLUSIONS: Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database

Long COVID risk and pre-COVID vaccination in an EHR-based cohort study from the RECOVER program

Long COVID, or complications arising from COVID-19 weeks after infection, has become a central concern for public health experts. The United States National Institutes of Health founded the RECOVER initiative to better understand long COVID. We used electronic health records available through the National COVID Cohort Collaborative to characterize the association between SARS-CoV-2 vaccination and long COVID diagnosis. Among patients with a COVID-19 infection between August 1, 2021 and January 31, 2022, we defined two cohorts using distinct definitions of long COVID—a clinical diagnosis (n = 47,404) or a previously described computational phenotype (n = 198,514)—to compare unvaccinated individuals to those with a complete vaccine series prior to infection. Evidence of long COVID was monitored through June or July of 2022, depending on patients’ data availability. We found that vaccination was consistently associated with lower odds and rates of long COVID clinical diagnosis and high-confidence computationally derived diagnosis after adjusting for sex, demographics, and medical history., The extent to which COVID-19 vaccination protects against long COVID is not well understood. Here, the authors use electronic health record data from the United States and find that, for people who received their vaccination prior to infection, vaccination was associated with lower incidence of long COVID

Use of administrative and electronic health record data for development of automated algorithms for childhood diabetes case ascertainment and type classification: the SEARCH for Diabetes in Youth Study

The performance of automated algorithms for childhood diabetes case ascertainment and type classification may differ by demographic characteristics

Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery.

Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies

De-black-boxing health AI: demonstrating reproducible machine learning computable phenotypes using the N3C-RECOVER Long COVID model in the All of Us data repository

Machine learning (ML)-driven computable phenotypes are among the most challenging to share and reproduce. Despite this difficulty, the urgent public health considerations around Long COVID make it especially important to ensure the rigor and reproducibility of Long COVID phenotyping algorithms such that they can be made available to a broad audience of researchers. As part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, researchers with the National COVID Cohort Collaborative (N3C) devised and trained an ML-based phenotype to identify patients highly probable to have Long COVID. Supported by RECOVER, N3C and NIH’s All of Us study partnered to reproduce the output of N3C’s trained model in the All of Us data enclave, demonstrating model extensibility in multiple environments. This case study in ML-based phenotype reuse illustrates how open-source software best practices and cross-site collaboration can de-black-box phenotyping algorithms, prevent unnecessary rework, and promote open science in informatics