Electronic health records to facilitate clinical research

OUP accepted manuscript

Objective

Materials and Methods

Results

Discussion and Conclusion

Routine childhood vaccination rates in an academic family health team before and during the first wave of the COVID-19 pandemic: a pre-post analysis of a retrospective chart review

BACKGROUND

There has been concern about declining routine vaccination rates during the COVID-19 pandemic. We evaluated the impact of the COVID-19 pandemic on early childhood vaccination rates at 2 sites of an academic family health team in the Greater Toronto Area, Ontario, serving both an urban and suburban patient population.

METHODS

We conducted a pre-post analysis of vaccination records from Jan. 1, 2018, to Nov. 30, 2020, for a cohort of children born between Jan. 1, 2018, and Aug. 31, 2020, from the electronic medical record (EMR) of the Mount Sinai Academic Family Health Team (including an urban academic site in Toronto and a suburban community site in Vaughan, Ontario). We estimated the proportion of children receiving timely, delayed or no vaccination for 10 publicly funded vaccines in the Ontario immunization schedule for the pre-COVID-19 (Jan. 1, 2018, to Mar. 16, 2020) and COVID-19 (Mar. 17 to Nov. 30, 2020) pandemic periods. We determined timeliness in accordance with the recommended age of administration, with a 28-day window; we considered vaccines administered after this window to be delayed. We estimated the median time to vaccination for each vaccine and present cumulative incidence curves.

RESULTS

The patient population was balanced between boys (52.4%) and girls (47.6%), with an average age of 18.5 months and representation across low-, middle- and high-income groups. Of the 506 children in our cohort, 422 were up to date with vaccinations (83.4%) by the end of the study period. Comparatively, 308 (83.2%) of the 370 eligible patients were up to date for all required vaccinations by the end of the pre-COVID-19 period. Among children younger than 12 months, vaccination rates were similar in the pre-COVID-19 and COVID-19 pandemic periods. Lower rates of timely vaccination for children between 12 and 18 months of age were amplified during the pandemic. Cumulative incidence curves were suggestive of a decrease in the timeliness of vaccinations in the COVID-19 period for the vaccines administered at 12, 15 and 18 months, compared with the pre-COVID-19 period.

INTERPRETATION

Our local findings suggest a deterioration in the uptake of routine childhood vaccines in children aged 12 to 18 months in the first year of the COVID-19 pandemic. Further study is needed to determine the extent of the vaccination gap in children across Canada, including the impact of subsequent waves of the COVID-19 pandemic.

Managing multiple myeloma in a resource-limited region: Diagnosis and treatment in Armenia

Multiple myeloma (MM) is the second most common blood cancer in adults leading to 117,000 deaths every year. Major breakthroughs in clinical research of the past decades transformed the diagnosis and treatment of MM improving the survival rates and overall quality of life of patients. Unfortunately, scientific advancements are not distributed equally around the globe leading to disparities in the treatment outcomes between different regions of the world. Management of MM in low- and middle-income countries represents a big challenge for healthcare providers considering the economic, technological, and infrastructural restraints in comparison to developed countries. Many standards of practice, including diagnostic tools and therapeutic regimens, are not available in developing regions of the world. As an example of an upper-middle-income country, Armenia has been witnessing considerable progress in the diagnosis and treatment of MM, including but not limited to the establishment of autologous stem cell transplant (ASCT), accessibility to modern anti-myeloma medications, and improved diagnostic and monitoring workup. Despite significant improvements, there is still a need for refinement in the management of MM. The aim of this review article is to discuss the latest developments and the current diagnosis and treatment of MM in Armenia as an example of a resource-limited region.

Supporting research, protecting data: one institution's approach to clinical data warehouse governance

Institutions must decide how to manage the use of clinical data to support research while ensuring appropriate protections are in place. Questions about data use and sharing often go beyond what the Health Insurance Portability and Accountability Act of 1996 (HIPAA) considers. In this article, we describe our institution’s governance model and approach. Common questions we consider include (1) Is a request limited to the minimum data necessary to carry the research forward? (2) What plans are there for sharing data externally?, and (3) What impact will the proposed use of data have on patients and the institution? In 2020, 302 of the 319 requests reviewed were approved. The majority of requests were approved in less than 2 weeks, with few or no stipulations. For the remaining requests, the governance committee works with researchers to find solutions to meet their needs while also addressing our collective goal of protecting patients.

Real world evidence of calcifediol or vitamin D prescription and mortality rate of COVID-19 in a retrospective cohort of hospitalized Andalusian patients

COVID-19 is a major worldwide health problem because of acute respiratory distress syndrome, and mortality. Several lines of evidence have suggested a relationship between the vitamin D endocrine system and severity of COVID-19. We present a survival study on a retrospective cohort of 15,968 patients, comprising all COVID-19 patients hospitalized in Andalusia between January and November 2020. Based on a central registry of electronic health records (the Andalusian Population Health Database, BPS), prescription of vitamin D or its metabolites within 15-30 days before hospitalization were recorded. The effect of prescription of vitamin D (metabolites) for other indication previous to the hospitalization was studied with respect to patient survival. Kaplan-Meier survival curves and hazard ratios support an association between prescription of these metabolites and patient survival. Such association was stronger for calcifediol (Hazard Ratio, HR = 0.67, with 95% confidence interval, CI, of [0.50-0.91]) than for cholecalciferol (HR = 0.75, with 95% CI of [0.61-0.91]), when prescribed 15 days prior hospitalization. Although the relation is maintained, there is a general decrease of this effect when a longer period of 30 days prior hospitalization is considered (calcifediol HR = 0.73, with 95% CI [0.57-0.95] and cholecalciferol HR = 0.88, with 95% CI [0.75, 1.03]), suggesting that association was stronger when the prescription was closer to the hospitalization.

Rapid adaptation of a local healthcare digital system to COVID-19: The experience in Birmingham (UK)

BACKGROUND

The COVID-19 pandemic created unprecedented pressure on hospitals globally. Digital tools developed before the crisis provided novel aspects of management, and new digital tools were rapidly developed as the crisis progressed. In our institution, a digitally mature NHS Trust in England which builds software systems, development during the early months of the crisis allowed increased patient safety and care, efficient management of the hospital and publication of data. The aim of this paper is to present this experience as a case study, describing development and lessons learned applicable to wider electronic healthcare record development.

METHODS

Request, triage, build and test processes for the digital systems were altered in response to the pandemic. Senior Responsible Officers appointed for the emergency triaged all changes and were supported by expert opinion and research active clinicians. Build and test cycles were compressed. New tools were built or existing ones modified in the central Electronic Healthcare Record, PICS (Prescribing, Information and Communication System), Clinical Dashboards and video platforms for remote consultation were developed.

FINDINGS

2236 patients were admitted to UHB with suspected COVID-19 between March and May 2020. Dashboards and visualisation tools enabled by efficient real-time data collection for all new patients, contributed to strategic, operational and clinical decision making.Over 70 urgent changes were made to digital systems, including a screening proforma, improved infection control functions, help and order panels, data dashboards, and updated prescribing features. Novel uses were found for existing functions.

INTERPRETATION

Digital tools contributed to a co-ordinated response to COVID-19 in an area with a high disease burden. Change management processes were modified during the pandemic and successfully delivered rapid software modifications and new tools. Principal benefits came from the ability to adapt systems to rapidly changing clinical situations. Lessons learned from this intense development period are widely applicable to EHR development.

LAY SUMMARY

Digital tools, which are well designed, can help clinicians and safeguard patients. Health crises such as the COVID pandemic drove rapid development of digital tools. This case study outlines accelerated development within a governance framework that successfully reused existing tools and built new ones. The lessons from this development are generalizable to digital developments in healthcare.

A Review of the Evidence and Recommendations on Communication Skills and the Patient-Provider Relationship: A Rome Foundation Working Team Report

BACKGROUND & AIMS

Over several decades, changes in health care have negatively impacted meaningful communication between the patient and provider and adversely affected their relationship. Under increasing time pressure, physicians rely more on technology than face-to-face time gathering data to make clinical decisions. As a result, they find it more challenging to understand the illness context and fully address patient needs. Patients experience dissatisfaction and a diminution of their role in the care process. For patients with disorders of gut-brain interaction, stigma leads to greater care dissatisfaction, as there is no apparent structural basis to legitimize the symptoms. Recent evidence suggests that practical communication skills can improve the patient-provider relationship (PPR) and clinical outcomes, but these data are limited.

METHODS

The Rome Foundation convened a multidisciplinary working team to review the scientific evidence with the following aims: a) to study the effect of communication skills on patient satisfaction and outcomes by performing an evidence-based review; b) to characterize the influence of sociocultural factors, health care system constraints, patient perspective, and telehealth on the PPR; c) to review the measurement and impact of communication skills training on these outcomes; and d) to make recommendations to improve communication skills training and the PPR.

RESULTS

Evidence supports the fact that interventions targeting patient-provider interactions improve population health, patient and provider experience, and costs. Communication skills training leads to improved patient satisfaction and outcomes. The following are relevant factors to consider in establishing an effective PPR: addressing health care system constraints; incorporating sociocultural factors and the role of gender, age, and chronic illness; and considering the changing role of telehealth on the PPR.

CONCLUSIONS

We concluded that effective communication skills can improve the PPR and health outcomes. This is an achievable goal through training and system change. More research is needed to confirm these findings.

Integration of Biobanks in National eHealth Ecosystems Facilitating Long-Term Longitudinal Clinical-Omics Studies and Citizens' Engagement in Research Through eHealthBioR

Biobanks have long existed to support research activities with BBMRI-ERIC formed as a European research infrastructure supporting the coordination for biobanking with 20 country members and one international organization. Although the benefits of biobanks to the research community are well-established, the direct benefit to citizens is limited to the generic benefit of promoting future research. Furthermore, the advent of General Data Protection Regulation (GDPR) legislation raised a series of challenges for scientific research especially related to biobanking associate activities and longitudinal research studies. Electronic health record (EHR) registries have long existed in healthcare providers. In some countries, even at the national level, these record the state of the health of citizens through time for the purposes of healthcare and data portability between different providers. The potential of EHRs in research is great and has been demonstrated in many projects that have transformed EHR data into retrospective medical history information on participating subjects directly from their physician's collected records; many key challenges, however, remain. In this paper, we present a citizen-centric framework called eHealthBioR, which would enable biobanks to link to EHR systems, thus enabling not just retrospective but also lifelong prospective longitudinal studies of participating citizens. It will also ensure strict adherence to legal and ethical requirements, enabling greater control that encourages participation. Citizens would benefit from the real and direct control of their data and samples, utilizing technology, to empower them to make informed decisions about providing consent and practicing their rights related to the use of their data, as well as by having access to knowledge and data generated from samples they provided to biobanks. This is expected to motivate patient engagement in future research and even leads to participatory design methodologies with citizen/patient-centric designed studies. The development of platforms based on the eHealthBioR framework would need to overcome significant challenges. However, it would shift the burden of addressing these to experts in the field while providing solutions enabling in the long term the lower monetary and time cost of longitudinal studies coupled with the option of lifelong monitoring through EHRs.

A narrative review on the validity of electronic health record-based research in epidemiology

Electronic health records (EHRs) are widely used in epidemiological research, but the validity of the results is dependent upon the assumptions made about the healthcare system, the patient, and the provider. In this review, we identify four overarching challenges in using EHR-based data for epidemiological analysis, with a particular emphasis on threats to validity. These challenges include representativeness of the EHR to a target population, the availability and interpretability of clinical and non-clinical data, and missing data at both the variable and observation levels. Each challenge reveals layers of assumptions that the epidemiologist is required to make, from the point of patient entry into the healthcare system, to the provider documenting the results of the clinical exam and follow-up of the patient longitudinally; all with the potential to bias the results of analysis of these data. Understanding the extent of as well as remediating potential biases requires a variety of methodological approaches, from traditional sensitivity analyses and validation studies, to newer techniques such as natural language processing. Beyond methods to address these challenges, it will remain crucial for epidemiologists to engage with clinicians and informaticians at their institutions to ensure data quality and accessibility by forming multidisciplinary teams around specific research projects.

Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data

The increasing availability of electronic health record (EHR) systems has created enormous potential for translational research. However, it is difficult to know all the relevant codes related to a phenotype due to the large number of codes available. Traditional data mining approaches often require the use of patient-level data, which hinders the ability to share data across institutions. In this project, we demonstrate that multi-center large-scale code embeddings can be used to efficiently identify relevant features related to a disease of interest. We constructed large-scale code embeddings for a wide range of codified concepts from EHRs from two large medical centers. We developed knowledge extraction via sparse embedding regression (KESER) for feature selection and integrative network analysis. We evaluated the quality of the code embeddings and assessed the performance of KESER in feature selection for eight diseases. Besides, we developed an integrated clinical knowledge map combining embedding data from both institutions. The features selected by KESER were comprehensive compared to lists of codified data generated by domain experts. Features identified via KESER resulted in comparable performance to those built upon features selected manually or with patient-level data. The knowledge map created using an integrative analysis identified disease-disease and disease-drug pairs more accurately compared to those identified using single institution data. Analysis of code embeddings via KESER can effectively reveal clinical knowledge and infer relatedness among codified concepts. KESER bypasses the need for patient-level data in individual analyses providing a significant advance in enabling multi-center studies using EHR data.

Real-world Cardiovascular Outcomes Associated With Degarelix vs Leuprolide for Prostate Cancer Treatment

IMPORTANCE

With a growing interest in the use of real-world evidence for regulatory decision-making, it is important to understand whether real-world data can be used to emulate the results of randomized clinical trials.

OBJECTIVE

To use electronic health record and administrative claims data to emulate the ongoing PRONOUNCE trial (A Trial Comparing Cardiovascular Safety of Degarelix Versus Leuprolide in Patients With Advanced Prostate Cancer and Cardiovascular Disease).

DESIGN, SETTING, AND PARTICIPANTS

This retrospective, propensity-matched cohort study included adult men with a diagnosis of prostate cancer and cardiovascular disease who initiated either degarelix or leuprolide between December 24, 2008, and June 30, 2019. Participants were commercially insured individuals and Medicare Advantage beneficiaries included in a large US administrative claims database.

EXPOSURES

Degarelix or leuprolide.

MAIN OUTCOMES AND MEASURES

The primary end point was time to first occurrence of a major adverse cardiovascular event (MACE), defined as death due to any cause, myocardial infarction, or stroke, analogous to the PRONOUNCE trial. Secondary end points were time to death due to any cause, myocardial infarction, stroke, and angina. Cox proportional hazards regression was used to evaluate primary and secondary end points.

RESULTS

A total of 32 172 men initiated degarelix or leuprolide for prostate cancer; of them, 9490 (29.5%) had cardiovascular disease, and 7800 (24.2%) met the PRONOUNCE trial eligibility criteria and were included in this study. Overall, 165 participants (2.1%) were Asian, 1390 (17.8%) were Black, 663 (8.5%) were Hispanic, and 5258 (67.4%) were White. The mean (SD) age was 74.4 (7.4) years. Among 2226 propensity score-matched patients, no significant difference was observed in the risk of MACE for patients taking degarelix vs those taking leuprolide (10.18 vs 8.60 events per 100 person-years; hazard ratio [HR], 1.18; 95% CI, 0.86-1.61). Degarelix was associated with a higher risk of death from any cause (HR, 1.48; 95% CI, 1.01-2.18) but not of myocardial infarction (HR, 1.16; 95% CI, 0.60-2.25), stroke (HR, 0.92; 95% CI, 0.45-1.85), or angina (HR, 1.36; 95% CI, 0.43-4.27).

CONCLUSIONS AND RELEVANCE

In this emulation of a clinical trial of men with cardiovascular disease undergoing treatment for prostate cancer, degarelix was not associated with a lower risk of cardiovascular events than leuprolide. Comparison of these data with PRONOUNCE trial results, when published, will help enhance our understanding of the appropriate role of using real-world data to emulate clinical trials.

The Role of Age on Beta-Amyloid1-42 Plasma Levels in Healthy Subjects

Beta-amyloid (Aβ) plaques have been observed in the brain of healthy elderlies with frequencies strongly influenced by age. The aim of the study is to evaluate the role of age and other biochemical and hematological parameters on Aβ_1–42 plasma levels in cognitively and neurologically normal individuals. Two-hundred and seventy-five normal subjects stratified by age groups (<35 years, 35–65 years, and >65 years) were included in the study. Aβ_1–42 plasma levels significantly correlated with age (r_s = 0.27; p < 0.0001) in the whole sample, inversely correlated with age in the first age group (r_s = −0.25, p = 0.01), positively correlated in the second group (r_s = 0.22, p = 0.03), while there was no significant correlation in the older group (r_s = 0.02, p = 0.86). Both age (β-estimate = 0.08; p < 0.001) and cholesterol (β-estimate = 0.03; p = 0.009) were significantly associated with Aβ_1–42 plasma level in multivariable analysis. However, only the association with age survived post hoc adjustment for multiple comparisons. The different effects of age on the Aβ level across age groups should be explored in further studies to better understand the age-dependent variability. This could better define the value of plasma Aβ as a biomarker of the Alzheimer neuropathology.

Health system challenges affecting falls prevention in persons living with HIV: perspectives from physiotherapists in four sub-Saharan regions

Aim:

The aim of this study was to explore the perspectives of physiotherapists in four selected regions of sub-Saharan Africa regarding health system challenges impacting the integration of physiotherapy-led falls prevention services in the primary care of persons living with HIV (PLWH).

Background:

Falls may pose a significant problem among younger PLWH in low- and middle-income countries. Physiotherapists’ role in optimising function and quality of life can do much in the prevention of falls in PLWH and reducing the harm that results. However, falls prevention strategies have not been implemented effectively especially in primary health care settings in sub-Saharan Africa. Physiotherapists’ account of the health system challenges they encounter may provide insights into potential strategies that may be considered in optimising fall prevention for PLWH in poorly resourced settings.

Methods:

A descriptive qualitative study was conducted in selected urban districts in the capital cities of four sub-Saharan African countries. In-depth interviews were conducted with 21 purposively selected physiotherapists involved in the primary care of PLWH. Audio recordings of interviews were transcribed verbatim and analysed using deductive thematic content analysis.

Findings:

The main results are presented in the theme ‘Health care system challenges’ and in nine categories informed by the WHO health system framework: lack of policies and clinical practice guidelines, shortage/Inaccessible falls prevention services, inadequate human resource, physiotherapists not adequately equipped in falls prevention, inaccessible/No facilities for BMD measurement, inefficient data capturing systems, lack of evidence regarding falls among PLWH, unclear physiotherapy role descriptions, inefficient referral system. Physiotherapists highlighted the need for more information and research regarding fall prevention for PLWH, promote their role in the primary care of PLWH and adopt a patient-centred approach to fall prevention.

The Problem of Fairness in Synthetic Healthcare Data

Access to healthcare data such as electronic health records (EHR) is often restricted by laws established to protect patient privacy. These restrictions hinder the reproducibility of existing results based on private healthcare data and also limit new research. Synthetically-generated healthcare data solve this problem by preserving privacy and enabling researchers and policymakers to drive decisions and methods based on realistic data. Healthcare data can include information about multiple in- and out- patient visits of patients, making it a time-series dataset which is often influenced by protected attributes like age, gender, race etc. The COVID-19 pandemic has exacerbated health inequities, with certain subgroups experiencing poorer outcomes and less access to healthcare. To combat these inequities, synthetic data must "fairly" represent diverse minority subgroups such that the conclusions drawn on synthetic data are correct and the results can be generalized to real data. In this article, we develop two fairness metrics for synthetic data, and analyze all subgroups defined by protected attributes to analyze the bias in three published synthetic research datasets. These covariate-level disparity metrics revealed that synthetic data may not be representative at the univariate and multivariate subgroup-levels and thus, fairness should be addressed when developing data generation methods. We discuss the need for measuring fairness in synthetic healthcare data to enable the development of robust machine learning models to create more equitable synthetic healthcare datasets.

Provision of supportive spiritual care for hepatopancreatic cancer patients: an unmet need?

BACKGROUND

Among patients with a serious cancer diagnosis, like hepatopancreatic (HP) cancer, spiritual distress needs to be addressed, as these psychosocial-spiritual symptoms are often more burdensome than some physical symptoms. The objective of the current study was to characterize supportive spiritual care utilization among patients with HP cancers.

METHODS

Patients with HP cancer were identified from the electronic medical record at a large comprehensive cancer center; data on patients with breast/prostate cancer (non-HP) were collected for comparison. Associations between patient characteristics and receipt of supportive spiritual care were evaluated within the overall sample and end-of-life subsample.

RESULTS

Among 8,961 individuals (n_HP=1,419, n_non-HP =7,542), 51.7% of HP patients utilized supportive spiritual care versus 19.8% of non-HP patients (p<0.001). Younger age and religious identity were associated with receiving spiritual care (p<0.001). HP patients had higher odds of receiving spiritual care versus non-HP patients (OR 2.41, 95%CI: 2.10, 2.78). Within the end-of-life subsample, HP patients more frequently received spiritual care to "accept their illness" (39.5% vs. 22.5%, p<0.001), while non-HP patients needed support to "define their purpose in life" (13.1% vs. 4.5%, p=0.001).

DISCUSSION

Supportive spiritual care was important to a large subset of HP patients and should be integrated into their care.

Drug repurposing for opioid use disorders: integration of computational prediction, clinical corroboration, and mechanism of action analyses

Morbidity and mortality from opioid use disorders (OUD) and other substance use disorders (SUD) is a major public health crisis, yet there are few medications to treat them. There is an urgency to accelerate SUD medication development. We present an integrated drug repurposing strategy that combines computational prediction, clinical corroboration using electronic health records (EHRs) of over 72.9 million patients and mechanisms of action analysis. Among top-ranked repurposed candidate drugs, tramadol, olanzapine, mirtazapine, bupropion, and atomoxetine were associated with increased odds of OUD remission (adjusted odds ratio: 1.51 [1.38-1.66], 1.90 [1.66-2.18], 1.38 [1.31-1.46], 1.37 [1.29-1.46], 1.48 [1.25-1.76], p value < 0.001, respectively). Genetic and functional analyses showed these five candidate drugs directly target multiple OUD-associated genes including BDNF, CYP2D6, OPRD1, OPRK1, OPRM1, HTR1B, POMC, SLC6A4 and OUD-associated pathways, including opioid signaling, G-protein activation, serotonin receptors, and GPCR signaling. In summary, we developed an integrated drug repurposing approach and identified five repurposed candidate drugs that might be of value for treating OUD patients, including those suffering from comorbid conditions.

Using Electronic Medical Record Data for Research in a Healthcare Information and Management Systems Society (HIMSS) Analytics Electronic Medical Record Adoption Model (EMRAM) Stage 7 Hospital in Beijing: Cross-sectional Study

BACKGROUND

With the proliferation of electronic medical record (EMR) systems, there is an increasing interest in utilizing EMR data for medical research; yet, there is no quantitative research on EMR data utilization for medical research purposes in China.

OBJECTIVE

This study aimed to understand how and to what extent EMR data are utilized for medical research purposes in a Healthcare Information and Management Systems Society (HIMSS) Analytics Electronic Medical Record Adoption Model (EMRAM) Stage 7 hospital in Beijing, China. Obstacles and issues in the utilization of EMR data were also explored to provide a foundation for the improved utilization of such data.

METHODS

For this descriptive cross-sectional study, cluster sampling from Xuanwu Hospital, one of two Stage 7 hospitals in Beijing, was conducted from 2016 to 2019. The utilization of EMR data was described as the number of requests, the proportion of requesters, and the frequency of requests per capita. Comparisons by year, professional title, and age were conducted by double-sided chi-square tests.

RESULTS

From 2016 to 2019, EMR data utilization was poor, as the proportion of requesters was 5.8% and the frequency was 0.1 times per person per year. The frequency per capita gradually slowed and older senior-level staff more frequently used EMR data compared with younger staff.

CONCLUSIONS

The value of using EMR data for research purposes is not well studied in China. More research is needed to quantify to what extent EMR data are utilized across all hospitals in Beijing and how these systems can enhance future studies. The results of this study also suggest that young doctors may be less exposed or have less reason to access such research methods.

COVID-19 and dementia: Analyses of risk, disparity, and outcomes from electronic health records in the US

INTRODUCTION

At present, there is limited data on the risks, disparity, and outcomes for COVID-19 in patients with dementia in the United States.

METHODS

This is a retrospective case-control analysis of patient electronic health records (EHRs) of 61.9 million adult and senior patients (age ≥ 18 years) in the United States up to August 21, 2020.

RESULTS

Patients with dementia were at increased risk for COVID-19 compared to patients without dementia (adjusted odds ratio [AOR]: 2.00 [95% confidence interval (CI), 1.94-2.06], P < .001), with the strongest effect for vascular dementia (AOR: 3.17 [95% CI, 2.97-3.37], P < .001), followed by presenile dementia (AOR: 2.62 [95% CI, 2.28-3.00], P < .001), Alzheimer's disease (AOR: 1.86 [95% CI, 1.77-1.96], P < .001), senile dementia (AOR: 1.99 [95% CI, 1.86-2.13], P < .001) and post-traumatic dementia (AOR: 1.67 [95% CI, 1.51-1.86] P < .001). Blacks with dementia had higher risk of COVID-19 than Whites (AOR: 2.86 [95% CI, 2.67-3.06], P < .001). The 6-month mortality and hospitalization risks in patients with dementia and COVID-19 were 20.99% and 59.26%, respectively.

DISCUSSION

These findings highlight the need to protect patients with dementia as part of the strategy to control the COVID-19 pandemic.

Real-world Evidence of Diagnostic Testing and Treatment Patterns in US Patients With Breast Cancer With Implications for Treatment Biomarkers From RNA Sequencing Data

OBJECTIVE/BACKGROUND

We performed a retrospective analysis of longitudinal real-world data (RWD) from patients with breast cancer to replicate results from clinical studies and demonstrate the feasibility of generating real-world evidence. We also assessed the value of transcriptome profiling as a complementary tool for determining molecular subtypes.

METHODS

De-identified, longitudinal data were analyzed after abstraction from records of patients with breast cancer in the United States (US) structured and stored in the Tempus database. Demographics, clinical characteristics, molecular subtype, treatment history, and survival outcomes were assessed according to strict qualitative criteria. RNA sequencing and clinical data were used to predict molecular subtypes and signaling pathway enrichment.

RESULTS

The clinical abstraction cohort (n = 4000) mirrored the demographics and clinical characteristics of patients with breast cancer in the US, indicating feasibility for RWE generation. Among patients who were human epidermal growth factor receptor 2-positive (HER2+), 74.2% received anti-HER2 therapy, with ∼70% starting within 3 months of a positive test result. Most non-treated patients were early stage. In this RWD set, 31.7% of patients with HER2+ immunohistochemistry (IHC) had discordant fluorescence in situ hybridization results recorded. Among patients with multiple HER2 IHC results at diagnosis, 18.6% exhibited intra-test discordance. Through development of a whole-transcriptome model to predict IHC receptor status in the molecular sequenced cohort (n = 400), molecular subtypes were resolved for all patients (n = 36) with equivocal HER2 statuses from abstracted test results. Receptor-related signaling pathways were differentially enriched between clinical molecular subtypes.

CONCLUSIONS

RWD in the Tempus database mirrors the overall population of patients with breast cancer in the US. These results suggest that real-time, RWD analyses are feasible in a large, highly heterogeneous database. Furthermore, molecular data may aid deficiencies and discrepancies observed from breast cancer RWD.

Using a health information technology survey to explore the availability of addiction treatment data in the electronic health records: A National Drug Abuse Treatment Clinical Trials Network study

BACKGROUND

Healthcare data from electronic health records (EHRs) and related health information technology (IT) tools are critical data sources for pragmatic clinical trials and observational studies aimed at producing real-world evidence. To unlock the full potential of such data to advance science, the data must be complete and in structured formats to facilitate research use.

METHODS

A Health IT survey was conducted within the National Drug Abuse Treatment Clinical Trials Network (CTN) to explore information related to data completeness and presence of unstructured data (e.g., clinical notes, free text) for conducting the EHR-based research for substance use disorders (SUDs). The analysis was based on 36 participants from 36 facilities located in 14 states and affiliated with the CTN.

RESULTS

The mean age of the participants (n = 34) was 48.0 years (SD = 9.8). Of the participants enrolled, 50.0% were female and 82.4% were white. Participants' facilities were from four census-defined regions (South 35.3%, Northeast 29.4%, West 20.6%, Midwest 11.8%, Missing 2.9%) and represented diverse settings. The EHR was used by all surveyed facilities including 17 different kinds of EHR platforms or vendors, and 17.6% (n = 6) of surveyed facilities also used a separate EHR for behavioral health care (e.g., SUD care). Paper records were also used by 76.5% of surveyed facilities for clinical care (e.g., for health risk appraisal questionnaires, substance use screening or assessment, check-in screening, substance use specific intervention/treatment or referral, or labs/testing). The prevalence of using a patient portal, practice management system, and mHealth for patient care was 76.5%, 50.0%, and 29.4%, respectively.

CONCLUSION

While results are descriptive in nature, they reveal the heterogeneity in the existing EHRs and frequent use of paper records to document patient care tasks, especially for SUD care. The use of a separate EHR for behavioral healthcare also suggests the challenge of obtaining complete EHR data to support research for SUDs. Much EHR development, integration, and standardization needs to be done especially in regard to SUD treatment to facilitate research across disparate healthcare systems.

Nutritional supplementation for nonalcohol-related fatty liver disease: a network meta-analysis

BACKGROUND

The prevalence of non-alcohol-related fatty liver disease (NAFLD) varies between 19% and 33% in different populations. NAFLD decreases life expectancy and increases risks of liver cirrhosis, hepatocellular carcinoma, and the requirement for liver transplantation. Uncertainty surrounds relative benefits and harms of various nutritional supplements in NAFLD. Currently no nutritional supplement is recommended for people with NAFLD.

OBJECTIVES

• To assess the benefits and harms of different nutritional supplements for treatment of NAFLD through a network meta-analysis • To generate rankings of different nutritional supplements according to their safety and efficacy SEARCH METHODS: We searched the Cochrane Central Register of Controlled Trials, MEDLINE, Embase, Science Citation Index Expanded, Conference Proceedings Citation Index-Science, the World Health Organization International Clinical Trials Registry Platform, and trials registers until February 2021 to identify randomised clinical trials in people with NAFLD.

SELECTION CRITERIA

We included only randomised clinical trials (irrespective of language, blinding, or status) for people with NAFLD, irrespective of method of diagnosis, age and diabetic status of participants, or presence of non-alcoholic steatohepatitis (NASH). We excluded randomised clinical trials in which participants had previously undergone liver transplantation.

DATA COLLECTION AND ANALYSIS

We performed a network meta-analysis with OpenBUGS using Bayesian methods whenever possible and calculated differences in treatments using hazard ratios (HRs), odds ratios (ORs), and rate ratios with 95% credible intervals (CrIs) based on an available-case analysis, according to National Institute of Health and Care Excellence Decision Support Unit guidance.

MAIN RESULTS

We included in the review a total of 202 randomised clinical trials (14,200 participants). Nineteen trials were at low risk of bias. A total of 32 different interventions were compared in these trials. A total of 115 trials (7732 participants) were included in one or more comparisons. The remaining trials did not report any of the outcomes of interest for this review. Follow-up ranged from 1 month to 28 months. The follow-up period in trials that reported clinical outcomes was 2 months to 28 months. During this follow-up period, clinical events related to NAFLD such as mortality, liver cirrhosis, liver decompensation, liver transplantation, hepatocellular carcinoma, and liver-related mortality were sparse. We did not calculate effect estimates for mortality because of sparse data (zero events for at least one of the groups in the trial). None of the trials reported that they measured overall health-related quality of life using a validated scale. The evidence is very uncertain about effects of interventions on serious adverse events (number of people or number of events). We are very uncertain about effects on adverse events of most of the supplements that we investigated, as the evidence is of very low certainty. However, people taking PUFA (polyunsaturated fatty acid) may be more likely to experience an adverse event than those not receiving an active intervention (network meta-analysis results: OR 4.44, 95% CrI 2.40 to 8.48; low-certainty evidence; 4 trials, 203 participants; direct evidence: OR 4.43, 95% CrI 2.43 to 8.42). People who take other supplements (a category that includes nutritional supplements other than vitamins, fatty acids, phospholipids, and antioxidants) had higher numbers of adverse events than those not receiving an active intervention (network meta-analysis: rate ratio 1.73, 95% CrI 1.26 to 2.41; 6 trials, 291 participants; direct evidence: rate ratio 1.72, 95% CrI 1.25 to 2.40; low-certainty evidence). Data were sparse (zero events in all groups in the trial) for liver transplantation, liver decompensation, and hepatocellular carcinoma. So, we did not perform formal analysis for these outcomes. The evidence is very uncertain about effects of other antioxidants (antioxidants other than vitamins) compared to no active intervention on liver cirrhosis (HR 1.68, 95% CrI 0.23 to 15.10; 1 trial, 99 participants; very low-certainty evidence). The evidence is very uncertain about effects of interventions in any of the remaining comparisons, or data were sparse (with zero events in at least one of the groups), precluding formal calculations of effect estimates. Data were probably because of the very short follow-up period (2 months to 28 months). It takes follow-up of 8 to 28 years to detect differences in mortality between people with NAFLD and the general population. Therefore, it is unlikely that differences in clinical outcomes are noted in trials providing less than 5 to 10 years of follow-up.

AUTHORS' CONCLUSIONS

The evidence indicates considerable uncertainty about effects of nutritional supplementation compared to no additional intervention on all clinical outcomes for people with non-alcohol-related fatty liver disease. Accordingly, high-quality randomised comparative clinical trials with adequate follow-up are needed. We propose registry-based randomised clinical trials or cohort multiple randomised clinical trials (study design in which multiple interventions are trialed within large longitudinal cohorts of patients to gain efficiencies and align trials more closely to standard clinical practice) comparing interventions such as vitamin E, prebiotics/probiotics/synbiotics, PUFAs, and no nutritional supplementation. The reason for the choice of interventions is the impact of these interventions on indirect outcomes, which may translate to clinical benefit. Outcomes in such trials should be mortality, health-related quality of life, decompensated liver cirrhosis, liver transplantation, and resource utilisation measures including costs of intervention and decreased healthcare utilisation after minimum follow-up of 8 years (to find meaningful differences in clinically important outcomes).

Reframing Recruitment: Evaluating Framing in Authorization for Research Contact Programs

BACKGROUND

The changing clinical research recruitment landscape involves practical challenges but introduces opportunities. Researchers can now identify large numbers of eligible patients through electronic health record review and can directly contact those who have authorized contact. Applying behavioral science-driven strategies to design and frame communication could affect patients' willingness to authorize contact and their understanding of these programs. The ethical and practical implications of various strategies warrant empirical evaluation.

METHODS

We conducted an online survey (n = 1070) using a nationally-representative sample. Participants were asked to imagine being asked for authorization for research contact in clinic. They were randomly assigned to view one of three flyers: #1-neutral text flyer; #2-a positive text flyer; or #3-positive graphics-based flyer. Primary outcomes included likelihood of enrollment and comprehension of the program. Chi-Square tests and regression analyses were used to examine whether those who saw the positive flyers were more likely to enroll and had increased comprehension.

RESULTS

Compared to the neutral flyer, individuals who received the positive text flyer were numerically more likely to enroll, but this was not statistically significant (24.2% v. 19.0%, p = 0.11). Individuals who received the positive graphics flyer were more likely to enroll (28.7% v. 19.0%, p = 0.002). After adjustment, individuals assigned to both novel flyers had increased odds of being likely to enroll (OR = 1.55 95%CI [1.04, 2.31] and OR = 1.95 95%CI [1.31, 2.91]). Flyer type did not affect overall comprehension (p = 0.21), and greater likelihood of enrollment was observed only in individuals with better comprehension.

CONCLUSIONS

This study demonstrated that employing behavioral science-driven communication strategies for authorization for research contact had an effect on likelihood of hypothetical enrollment but did not significantly affect comprehension. Strategies using simple, positive language and visual tools may be effective and ethically appropriate. Further studies should explore how these and other approaches can help to optimize research recruitment.

Medical Documentation in Low- and Middle-income Countries: Lessons Learned from Implementing Specialized Charting Software

Background:

The implementation of electronic health record (EHR) software at healthcare facilities in low- and middle-income countries (LMICs) is limited by financial and technological constraints. Smile Train, the world’s largest cleft charity, developed a cleft treatment EHR system, Smile Train Express (STX), and distributed it to their partnered institutions. The purpose of this study was to investigate trends in medical documentation practices amongst Smile Train-partner institutions to characterize the impact that specialized EHR software has on medical documentation practices at healthcare facilities in LMICs.

Methods:

Surveys were administered electronically to 843 Smile Train-partnered institutions across 68 LMICs. The survey inquired about institutions’ internet connection, documentation methods used during patient encounters, rationale for using said methods, and documentation methods for cloud-based storage of healthcare data. Institutions were grouped by economic and geographic subgroups for analysis.

Results:

A total of 162 institutions (19.2%) responded to the survey. Most institutions employed paper charting (64.2%) or institutional EHR software (25.9%) for data entry during a patient encounter with the latter’s use varying significantly across geographical subgroups (P = 0.01). STX was used by 18 institutions (11.1%) during a patient encounter. Workflow was the most frequently cited reason for institutions to employ their entry method during a patient encounter (51.4%).

Conclusions:

The provision of STX to partnered institutions influenced medical documentation practices at several institutions; however, regulations and guidelines have likely limited its complete integration into clinical workflows. Further studies are needed to characterize trends in medical documentation in LMICs at a more granular level.

Lifestyle modifications for nonalcohol-related fatty liver disease: a network meta-analysis

BACKGROUND

The prevalence of nonalcohol-related fatty liver disease (NAFLD) varies between 19% and 33% in different populations. NAFLD decreases life expectancy and increases the risks of liver cirrhosis, hepatocellular carcinoma, and requirement for liver transplantation. There is uncertainty surrounding the relative benefits and harms of various lifestyle interventions for people with NAFLD.

OBJECTIVES

To assess the comparative benefits and harms of different lifestyle interventions in the treatment of NAFLD through a network meta-analysis, and to generate rankings of the different lifestyle interventions according to their safety and efficacy.

SEARCH METHODS

We searched CENTRAL, MEDLINE, Embase, Science Citation Index Expanded, Conference Proceedings Citation Index - Science, World Health Organization International Clinical Trials Registry Platform, and trials registers until February 2021 to identify randomised clinical trials in people with NAFLD.

SELECTION CRITERIA

We included only randomised clinical trials (irrespective of language, blinding, or status) in people with NAFLD, whatever the method of diagnosis, age, and diabetic status of participants, or presence of non-alcoholic steatohepatitis (NASH). We excluded randomised clinical trials in which participants had previously undergone liver transplantation.

DATA COLLECTION AND ANALYSIS

We planned to perform a network meta-analysis with OpenBUGS using Bayesian methods and to calculate the differences in treatments using hazard ratios (HRs), odds ratios (ORs), and rate ratios (RaRs) with 95% credible intervals (CrIs) based on an available-participant analysis, according to National Institute of Health and Care Excellence Decision Support Unit guidance. However, the data were too sparse for the clinical outcomes. We therefore performed only direct comparisons (head-to-head comparisons) with OpenBUGS using Bayesian methods.

MAIN RESULTS

We included a total of 59 randomised clinical trials (3631 participants) in the review. All but two trials were at high risk of bias. A total of 33 different interventions, ranging from advice to supervised exercise and special diets, or a combination of these and no additional intervention were compared in these trials. The reference treatment was no active intervention. Twenty-eight trials (1942 participants) were included in one or more comparisons. The follow-up ranged from 1 month to 24 months. The remaining trials did not report any of the outcomes of interest for this review. The follow-up period in the trials that reported clinical outcomes was 2 months to 24 months. During this short follow-up period, clinical events related to NAFLD such as mortality, liver cirrhosis, liver decompensation, liver transplantation, hepatocellular carcinoma, and liver-related mortality were sparse. This is probably because of the very short follow-up periods. It takes a follow-up of 8 years to 28 years to detect differences in mortality between people with NAFLD and the general population. It is therefore unlikely that differences by clinical outcomes will be noted in trials with less than 5 years to 10 years of follow-up. In one trial, one participant developed an adverse event. There were no adverse events in any of the remaining participants in this trial, or in any of the remaining trials, which seemed to be directly related to the intervention.

AUTHORS' CONCLUSIONS

The evidence indicates considerable uncertainty about the effects of the lifestyle interventions compared with no additional intervention (to general public health advice) on any of the clinical outcomes after a short follow-up period of 2 months to 24 months in people with nonalcohol-related fatty liver disease. Accordingly, high-quality randomised clinical trials with adequate follow-up are needed. We propose registry-based randomised clinical trials or cohort multiple randomised clinical trials (a study design in which multiple interventions are trialed within large longitudinal cohorts of participants to gain efficiencies and align trials more closely to standard clinical practice), comparing aerobic exercise and dietary advice versus standard of care (exercise and dietary advice received as part of national health promotion). The reason for the choice of aerobic exercise and dietary advice is the impact of these interventions on indirect outcomes which may translate to clinical benefit. The outcomes in such trials should be mortality, health-related quality of life, decompensated liver cirrhosis, liver transplantation, and resource use measures including costs of intervention and decreased healthcare use after a minimum follow-up of eight years, to find meaningful differences in the clinically important outcomes.

Enhanced access to healthcare utilization data through medical record review: Lessons learned from a community-based, multi-site project

Collecting accurate healthcare utilization (HCU) data on community-based interventions is essential to establishing their clinical effectiveness and cost-related impact. Strategies used to enhance receiving medical records for HCU data extraction in a multi-site longitudinal randomized control trial with urban adolescents are presented. Successful strategies included timely assessment of procedures and practice preferences for access to electronic health records and hardcopy medical charts. Repeated outreach to clinical practice sites to identify and accommodate their preferred procedure for medical record release and flexibility in obtaining chart information helped achieve a 75% success rate in this study. Maintaining participant contact, updating provider information, and continuously evaluating site-specific personnel needs are recommended.

Developing and evaluating a pediatric asthma severity computable phenotype derived from electronic health records

BACKGROUND

Extensive data available in electronic health records (EHRs) have the potential to improve asthma care and understanding of factors influencing asthma outcomes. However, this work can be accomplished only when the EHR data allow for accurate measures of severity, which at present are complex and inconsistent.

OBJECTIVE

Our aims were to create and evaluate a standardized pediatric asthma severity phenotype based in clinical asthma guidelines for use in EHR-based health initiatives and studies and also to examine the presence and absence of these data in relation to patient characteristics.

METHODS

We developed an asthma severity computable phenotype and compared the concordance of different severity components contributing to the phenotype to trends in the literature. We used multivariable logistic regression to assess the presence of EHR data relevant to asthma severity.

RESULTS

The asthma severity computable phenotype performs as expected in comparison with national statistics and the literature. Severity classification for a child is maximized when based on the long-term medication regimen component and minimized when based only on the symptom data component. Use of the severity phenotype results in better, clinically grounded classification. Children for whom severity could be ascertained from these EHR data were more likely to be seen for asthma in the outpatient setting and less likely to be older or Hispanic. Black children were less likely to have lung function testing data present.

CONCLUSION

We developed a pragmatic computable phenotype for pediatric asthma severity that is transportable to other EHRs.

Patient Recruitment Into a Multicenter Clinical Cohort Linking Electronic Health Records From 5 Health Systems: Cross-sectional Analysis

Background

There is growing interest in identifying and recruiting research participants from health systems using electronic health records (EHRs). However, few studies have described the practical aspects of the recruitment process or compared electronic recruitment methods to in-person recruitment, particularly across health systems.

Objective

The objective of this study was to describe the steps and efficiency of the recruitment process and participant characteristics by recruitment strategy.

Methods

EHR-based eligibility criteria included being an adult patient engaged in outpatient primary or bariatric surgery care at one of 5 health systems in the PaTH Clinical Research Network and having ≥2 weight measurements and 1 height measurement recorded in their EHR within the last 5 years. Recruitment strategies varied by site and included one or more of the following methods: (1) in-person recruitment by study staff from clinical sites, (2) US postal mail recruitment letters, (3) secure email, and (4) direct EHR recruitment through secure patient web portals. We used descriptive statistics to evaluate participant characteristics and proportion of patients recruited (ie, efficiency) by modality.

Results

The total number of eligible patients from the 5 health systems was 5,051,187. Of these, 40,048 (0.8%) were invited to enter an EHR-based cohort study and 1085 were enrolled. Recruitment efficiency was highest for in-person recruitment (33.5%), followed by electronic messaging (2.9%), including email (2.9%) and EHR patient portal messages (2.9%). Overall, 779 (65.7%) patients were enrolled through electronic messaging, which also showed greater rates of recruitment of Black patients compared with the other strategies.

Conclusions

We recruited a total of 1085 patients from primary care and bariatric surgery settings using 4 recruitment strategies. The recruitment efficiency was 2.9% for email and EHR patient portals, with the majority of participants recruited electronically. This study can inform the design of future research studies using EHR-based recruitment.

Onset and recurrence of psychiatric disorders associated with anti-hypertensive drug classes

The major anti-hypertensive (AHT) drug classes have been associated with differential risks of psychiatric disorders. However, existing data are limited largely to depression, and confounding variables have not always been controlled for. We sought to fill the evidence gap, using TriNetX Analytics, an electronic health records network. Amongst 58.6 million patients aged 18-90 years, patients prescribed a calcium channel blocker (CCB) were compared with those taking a diuretic, angiotensin-converting enzyme inhibitor (ACEI), angiotensin receptor blocker (ARB), or β-blocker. Cohorts were propensity score-matched for age, sex, race, and blood pressure. Over a 2-year exposure period, we measured the incidence and risk ratio of a first diagnosis (ICD-10 codes), or a recurrence, of psychotic, affective, and anxiety disorders, as well as substance use disorders and sleep disorders. Cohort sizes ranged from 33,734 to 322,814. CCBs were associated with a lower incidence of psychotic, affective, and anxiety disorders than β-blockers (risk ratios 0.69-0.99) and a higher incidence than ARBs (risk ratios 1.04-2.23) for both first and recurrent diagnoses. Comparisons of CCBs with ACEIs or diuretics showed smaller risk ratios that varied between disorders, and between first episode and recurrence. AHT classes were also associated with the incidence of substance use and sleep disorders. Results remained largely unchanged after more extensive cohort matching for additional potential confounders. In a secondary analysis, a comparison between ARBs and ACEIs showed lower rates of psychotic, affective, and substance use disorders with ARBs, but higher risks of anxiety and sleep disorders. In conclusion, AHT classes are differentially associated with the incidence of psychiatric disorders. ARBs show the most advantageous profile and β-blockers the least. The apparent beneficial effects of ARBs merit further study.

Leveraging Genetic Reports and Electronic Health Records for the Prediction of Primary Cancers: Algorithm Development and Validation Study

BACKGROUND

Precision oncology has the potential to leverage clinical and genomic data in advancing disease prevention, diagnosis, and treatment. A key research area focuses on the early detection of primary cancers and potential prediction of cancers of unknown primary in order to facilitate optimal treatment decisions.

OBJECTIVE

This study presents a methodology to harmonize phenotypic and genetic data features to classify primary cancer types and predict cancers of unknown primaries.

METHODS

We extracted genetic data elements from oncology genetic reports of 1011 patients with cancer and their corresponding phenotypical data from Mayo Clinic's electronic health records. We modeled both genetic and electronic health record data with HL7 Fast Healthcare Interoperability Resources. The semantic web Resource Description Framework was employed to generate the network-based data representation (ie, patient-phenotypic-genetic network). Based on the Resource Description Framework data graph, Node2vec graph-embedding algorithm was applied to generate features. Multiple machine learning and deep learning backbone models were compared for cancer prediction performance.

RESULTS

With 6 machine learning tasks designed in the experiment, we demonstrated the proposed method achieved favorable results in classifying primary cancer types (area under the receiver operating characteristic curve [AUROC] 96.56% for all 9 cancer predictions on average based on the cross-validation) and predicting unknown primaries (AUROC 80.77% for all 8 cancer predictions on average for real-patient validation). To demonstrate the interpretability, 17 phenotypic and genetic features that contributed the most to the prediction of each cancer were identified and validated based on a literature review.

CONCLUSIONS

Accurate prediction of cancer types can be achieved with existing electronic health record data with satisfactory precision. The integration of genetic reports improves prediction, illustrating the translational values of incorporating genetic tests early at the diagnosis stage for patients with cancer.

When hematologic malignancies meet COVID-19 in the United States: Infections, death and disparities

Scientific data is limited on the risks, adverse outcomes and racial disparities for COVID-19 illness in individuals with hematologic malignancies in the United States. To fill this void, we screened and analyzed a nation-wide database of patient electronic health records (EHRs) of 73 million patients in the US (up to September 1st) for COVID-19 and eight major types of hematologic malignancies. Patients with hematologic malignancies had increased odds of COVID-19 infection compared with patients without hematologic malignancies for both all-time diagnosis (malignancy diagnosed in the past year or prior) (adjusted Odds ratio or AOR: 2.27 [2.17-2.36], p < 0.001) and recent diagnosis (malignancy diagnosed in the past year) (AOR:11.91 [11.31-12.53], p < 0.001), with strongest effect for recently diagnosed acute lymphoid leukemia (AOR: 31.03 [25.87-37.27], p < 0.001), essential thrombocythemia (AOR: 20.65 [19.10-22.32], p < 0.001), acute myeloid leukemia (AOR: 18.94 [15.79-22.73], p < 0.001), multiple myeloma (AOR: 14.21 [12.72-15.89], p < 0.001). Among patients with hematologic malignancies, African Americans had higher odds of COVID-19 infection than Caucasians with largest racial disparity for multiple myeloma (AOR: 4.23 [3.21-5.56], p < 0.001). Patients with recently diagnosed hematologic malignancies had worse outcomes (hospitalization: 51.9%, death: 14.8%) than COVID-19 patients without hematologic malignancies (hospitalization: 23.5%, death: 5.1%) (p < 0.001) and hematologic malignancy patients without COVID-19 (hospitalization: 15.0%, death: 4.1%) (p < 0.001).

Leveraging unstructured data to identify hereditary angioedema patients in electronic medical records

Background

The epidemiologic impact of hereditary angioedema (HAE) is difficult to quantify, due to misclassification in retrospective studies resulting from non-specific diagnostic coding. The aim of this study was to identify cohorts of patients with HAE-1/2 by evaluating structured and unstructured data in a US ambulatory electronic medical record (EMR) database.

Methods

A retrospective feasibility study was performed using the GE Centricity EMR Database (2006–2017). Patients with ≥ 1 diagnosis code for HAE-1/2 (International Classification of Diseases, Ninth Revision, Clinical Modification 277.6 or International Classification of Diseases, Tenth Revision, Clinical Modification D84.1) and/or ≥ 1 physician note regarding HAE-1/2 and ≥ 6 months’ data before and after the earliest code or note (index date) were included. Two mutually exclusive cohorts were created: probable HAE (≥ 2 codes or ≥ 2 notes on separate days) and suspected HAE (only 1 code or note). The impact of manually reviewing physician notes on cohort formation was assessed, and demographic and clinical characteristics of the 2 final cohorts were described.

Results

Initially, 1691 patients were identified: 190 and 1501 in the probable and suspected HAE cohorts, respectively. After physician note review, the confirmed HAE cohort comprised 254 patients and the suspected HAE cohort decreased to 1299 patients; 138 patients were determined not to have HAE and were excluded. The overall false-positive rate for the initial algorithms was 8.2%. Across final cohorts, the median age was 50 years and > 60% of patients were female. HAE-specific prescriptions were identified for 31% and 2% of the confirmed and suspected HAE cohorts, respectively.

Conclusions

Unstructured EMR data can provide valuable information for identifying patients with HAE-1/2. Further research is needed to develop algorithms for more representative HAE cohorts in retrospective studies.

Application of openEHR archetypes to automate data quality rules for electronic health records: a case study

Background

Ensuring data is of appropriate quality is essential for the secondary use of electronic health records (EHRs) in research and clinical decision support. An effective method of data quality assessment (DQA) is automating data quality rules (DQRs) to replace the time-consuming, labor-intensive manual process of creating DQRs, which is difficult to guarantee standard and comparable DQA results. This paper presents a case study of automatically creating DQRs based on openEHR archetypes in a Chinese hospital to investigate the feasibility and challenges of automating DQA for EHR data.

Methods

The clinical data repository (CDR) of the Shanxi Dayi Hospital is an archetype-based relational database. Four steps are undertaken to automatically create DQRs in this CDR database. First, the keywords and features relevant to DQA of archetypes were identified via mapping them to a well-established DQA framework, Kahn’s DQA framework. Second, the templates of DQRs in correspondence with these identified keywords and features were created in the structured query language (SQL). Third, the quality constraints were retrieved from archetypes. Fourth, these quality constraints were automatically converted to DQRs according to the pre-designed templates and mapping relationships of archetypes and data tables. We utilized the archetypes of the CDR to automatically create DQRs to meet quality requirements of the Chinese Application-Level Ranking Standard for EHR Systems (CARSES) and evaluated their coverage by comparing with expert-created DQRs.

Results

We used 27 archetypes to automatically create 359 DQRs. 319 of them are in agreement with the expert-created DQRs, covering 84.97% (311/366) requirements of the CARSES. The auto-created DQRs had varying levels of coverage of the four quality domains mandated by the CARSES: 100% (45/45) of consistency, 98.11% (208/212) of completeness, 54.02% (57/87) of conformity, and 50% (11/22) of timeliness.

Conclusion

It’s feasible to create DQRs automatically based on openEHR archetypes. This study evaluated the coverage of the auto-created DQRs to a typical DQA task of Chinese hospitals, the CARSES. The challenges of automating DQR creation were identified, such as quality requirements based on semantic, and complex constraints of multiple elements. This research can enlighten the exploration of DQR auto-creation and contribute to the automatic DQA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01481-2.

Application of Bayesian networks to generate synthetic health data

OBJECTIVE

This study seeks to develop a fully automated method of generating synthetic data from a real dataset that could be employed by medical organizations to distribute health data to researchers, reducing the need for access to real data. We hypothesize the application of Bayesian networks will improve upon the predominant existing method, medBGAN, in handling the complexity and dimensionality of healthcare data.

MATERIALS AND METHODS

We employed Bayesian networks to learn probabilistic graphical structures and simulated synthetic patient records from the learned structure. We used the University of California Irvine (UCI) heart disease and diabetes datasets as well as the MIMIC-III diagnoses database. We evaluated our method through statistical tests, machine learning tasks, preservation of rare events, disclosure risk, and the ability of a machine learning classifier to discriminate between the real and synthetic data.

RESULTS

Our Bayesian network model outperformed or equaled medBGAN in all key metrics. Notable improvement was achieved in capturing rare variables and preserving association rules.

DISCUSSION

Bayesian networks generated data sufficiently similar to the original data with minimal risk of disclosure, while offering additional transparency, computational efficiency, and capacity to handle more data types in comparison to existing methods. We hope this method will allow healthcare organizations to efficiently disseminate synthetic health data to researchers, enabling them to generate hypotheses and develop analytical tools.

CONCLUSION

We conclude the application of Bayesian networks is a promising option for generating realistic synthetic health data that preserves the features of the original data without compromising data privacy.

An Interdisciplinary Approach to Reducing Errors in Extracted Electronic Health Record Data for Research

Erroneous electronic health record (EHR) data capture is a barrier to preserving data integrity. We assessed the impact of an interdisciplinary process in minimizing EHR data loss from prescription orders. We implemented a three-step approach to reduce data loss due to missing medication doses: Step 1-A data analyst updated the request code to optimize data capture; Step 2-A pharmacist and physician identified variations in EHR prescription workflows; and Step 3-The clinician team determined daily doses for patients with multiple prescriptions in the same encounter. The initial report contained 1421 prescriptions, with 377 (26.5 percent) missing dosages. Missing dosages reduced to 361 (26.3 percent) prescriptions following Step 1, and twenty-three (1.7 percent) records after Step 2. After Step 3, 1210 prescriptions remained, including 16 (1.3 percent) prescriptions missing doses. Prescription data is susceptible to missing values due to multiple data capture workflows. Our approach minimized data loss, improving its validity in retrospective research.

Perspectives of Pregnant and Breastfeeding Women on Participating in Longitudinal Mother-Baby Studies Involving Electronic Health Records: Qualitative Study

BACKGROUND

Electronic health records (EHRs) hold great potential for longitudinal mother-baby studies, ranging from assessing study feasibility to facilitating patient recruitment to streamlining study visits and data collection. Existing studies on the perspectives of pregnant and breastfeeding women on EHR use have been limited to the use of EHRs to engage in health care rather than to participate in research.

OBJECTIVE

The aim of this study is to explore the perspectives of pregnant and breastfeeding women on releasing their own and their infants' EHR data for longitudinal research to identify factors affecting their willingness to participate in research.

METHODS

We conducted semistructured interviews with pregnant or breastfeeding women from Alachua County, Florida. Participants were asked about their familiarity with EHRs and EHR patient portals, their comfort with releasing maternal and infant EHR data to researchers, the length of time of the data release, and whether individual research test results should be included in the EHR. The interviews were transcribed verbatim. Transcripts were organized and coded using the NVivo 12 software (QSR International), and coded data were thematically analyzed using constant comparison.

RESULTS

Participants included 29 pregnant or breastfeeding women aged between 22 and 39 years. More than half of the sample had at least an associate degree or higher. Nearly all participants (27/29, 93%) were familiar with EHRs and had experience accessing an EHR patient portal. Less than half of the participants (12/29, 41%) were willing to make EHR data available to researchers for the duration of a study or longer. Participants' concerns about sharing EHRs for research purposes emerged in 3 thematic domains: privacy and confidentiality, transparency by the research team, and surrogate decision-making on behalf of infants. The potential release of sensitive or stigmatizing information, such as mental or sexual health history, was considered in the decisions to release EHRs. Some participants viewed the simultaneous use of their EHRs for both health care and research as potentially beneficial, whereas others expressed concerns about mixing their health care with research.

CONCLUSIONS

This exploratory study indicates that pregnant and breastfeeding women may be willing to release EHR data to researchers if researchers adequately address their concerns regarding the study design, communication, and data management. Pregnant and breastfeeding women should be included in EHR-based research as long as researchers are prepared to address their concerns.

Barriers and Facilitators to the Collection and Aggregation of Electronic Health Record HIV Data: An Analysis of Study Recruitment Venues Within the Adolescent Medicine Trials Network for HIV/AIDS Interventions (ATN)

Electronic health record (EHR) data can be leveraged for prospective cohort studies and pragmatic clinical trials, targeting youth living with HIV (YLH). Using EHRs in this manner may minimize the need for costly research infrastructure in service to lowering disease burden. This study characterizes HIV prevention and care continua variables and identifies factors likely to impede or facilitate EHR use for research and interventions. We conducted telephone-based qualitative interviews with National Experts (n = 10) and Key Stakeholders (n = 19) from subject recruitment venues (SRVs), providing care services to YLH and youth at risk for HIV. We found 17 different EHR systems being used for various purposes (e.g., workflow management and billing). Thematic content analysis of interviews highlighted six broad categories of perspectives on barriers to and facilitators of EHR use: specific variable collection, general use barriers, and facilitators, general data collection barriers and facilitators, EHRs for surveillance and research, EHRs for personnel and resource management and capture of HIV specific variables. These findings may inform implementation strategies of future studies, in which we conduct routine monitoring of the youth HIV prevention and care continua using EHRs and test an eHealth intervention.

Acute Myocardial Infarction Cohorts Defined by International Classification of Diseases, Tenth Revision Versus Diagnosis-Related Groups: Analysis of Diagnostic Agreement and Quality Measures in an Integrated Health System

BACKGROUND

Among Medicare value-based payment programs for acute myocardial infarction (AMI), the Hospital Readmissions Reduction Program uses International Classification of Diseases, Tenth Revision (ICD-10) codes to identify the program denominator, while the Bundled Payments for Care Improvement Advanced program uses diagnosis-related groups (DRGs). The extent to which these programs target similar patients, whether they target the intended population (type 1 myocardial infarction), and whether outcomes are comparable between cohorts is not known.

METHODS

In a retrospective study of 2176 patients hospitalized in an integrated health system, a cohort of patients assigned a principal ICD-10 diagnosis of AMI and a cohort of patients assigned an AMI DRG were compared according to patient-level agreement and outcomes such as mortality and readmission.

RESULTS

One thousand nine hundred thirty-five patients were included in the ICD-10 cohort compared with 662 patients in the DRG cohort. Only 421 patients were included in both AMI cohorts (19.3% agreement). DRG cohort patients were older (70 versus 65 years, P<0.001), more often female (48% versus 30%, P<0.001), and had higher rates of heart failure (52% versus 33%, P<0.001) and kidney disease (42% versus 25%, P<0.001). Comparing outcomes, the DRG cohort had significantly higher unadjusted rates of 30-day mortality (6.6% versus 2.5%, P<0.001), 1-year mortality (21% versus 8%, P<0.001), and 90-day readmission (26% versus 19%, P=0.006) than the ICD-10 cohort. Two observations help explain these differences: 61% of ICD-10 cohort patients were assigned procedural DRGs for revascularization instead of an AMI DRG, and type 1 myocardial infarction patients made up a smaller proportion of the DRG cohort (34%) than the ICD-10 cohort (78%).

CONCLUSIONS

The method used to identify denominators for value-based payment programs has important implications for the patient characteristics and outcomes of the populations. As national and local quality initiatives mature, an emphasis on ICD-10 codes to define AMI cohorts would better represent type 1 myocardial infarction patients.

Challenges and opportunities using online portals to recruit diverse patients to behavioral trials

We describe the use of an online patient portal to recruit and enroll primary care patients in a randomized trial testing the effectiveness of a colorectal cancer (CRC) screening decision support program. We use multiple logistic regression to identify patient characteristics associated with trial recruitment, enrollment, and engagement. We found that compared to Whites, Blacks had lower odds of viewing the portal message (OR = 0.46, 95% CI = 0.37-0.57), opening the attached link containing the study material (OR = 0.75, 95% CI = 0.62-0.92), and consenting to participate in the trial (OR = 0.85, 95% CI = 0.67-0.93). We also found that compared to Whites, Asians had lower odds of viewing the portal message (OR = 0.53, 95% CI = 0.33-0.64), opening the attached link containing the study material (OR = 0.76, 95% CI = 0.54-0.97), consenting to participate in the trial (OR = 0.68, 95% CI = 0.53-0.95), and completing the trial's baseline questionnaire (OR = 0.59, 95% CI = 0.36-0.90). While portals offer an opportunity to mitigate human bias in trial invitations, because of racial disparities-not only in who has a portal account, but in how they interact with trial recruitment and enrollment material within the portal-using portals alone for trial recruitment may generate study samples that are not racially diverse.

Use of electronic health records to support a public health response to the COVID-19 pandemic in the United States: a perspective from 15 academic medical centers

Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencies.

Increased risk of COVID-19 infection and mortality in people with mental disorders: analysis from electronic health records in the United States

Concerns have been expressed that persons with a pre-existing mental disorder may represent a population at increased risk for COVID-19 infec-tion and with a higher likelihood of adverse outcomes of the infection, but there is no systematic research evidence in this respect. This study assessed the impact of a recent (within past year) diagnosis of a mental disorder - including attention-deficit/hyperactivity disorder (ADHD), bipolar disorder, depression and schizophrenia - on the risk for COVID-19 infection and related mortality and hospitalization rates. We analyzed a nation-wide database of electronic health records of 61 million adult patients from 360 hospitals and 317,000 providers, across 50 states in the US, up to July 29, 2020. Patients with a recent diagnosis of a mental disorder had a significantly increased risk for COVID-19 infection, an effect strongest for depression (adjusted odds ratio, AOR=7.64, 95% CI: 7.45-7.83, p<0.001) and schizophrenia (AOR=7.34, 95% CI: 6.65-8.10, p<0.001). Among patients with a recent diagnosis of a mental disorder, African Americans had higher odds of COVID-19 infection than Caucasians, with the strongest ethnic disparity for depression (AOR=3.78, 95% CI: 3.58-3.98, p<0.001). Women with mental disorders had higher odds of COVID-19 infection than males, with the strongest gender disparity for ADHD (AOR=2.03, 95% CI: 1.73-2.39, p<0.001). Patients with both a recent diagnosis of a mental disorder and COVID-19 infection had a death rate of 8.5% (vs. 4.7% among COVID-19 patients with no mental disorder, p<0.001) and a hospitalization rate of 27.4% (vs. 18.6% among COVID-19 patients with no mental disorder, p<0.001). These findings identify individuals with a recent diagnosis of a mental disorder as being at increased risk for COVID-19 infection, which is further exacerbated among African Americans and women, and as having a higher frequency of some adverse outcomes of the infection. This evidence highlights the need to identify and address modifiable vulnerability factors for COVID-19 infection and to prevent delays in health care provision in this population.

Renal function and attributable risk of death and cardiovascular hospitalization in participants with diabetes from a registry-based cohort

AIMS

To estimate the attributable risk of renal function on all-cause mortality and cardiovascular hospitalization in patients with diabetes.

METHODS

A prospective cohort study in 19,469 adults with diabetes, free of cardiovascular disease, attending primary care in Spain (2008-2011). The estimated glomerular filtration rate (eGFR) and other variables were collected and patients were followed to the first hospitalization for coronary or stroke event, or death, until the end of 2012. The cumulative incidence of the study endpoints by eGFR categories was graphically displayed and adjusted population attributable risks (PARs) for low eGFR was calculated.

RESULTS

Mean follow-up was 3.2 years and 506 deaths and 1720 hospitalizations were recorded. The cumulative risk for the individual events increased as eGFR levels decreased. The PAR associated with having an eGFR of 60mL/min/1.73m² or less was 11.4% (95% CI 4.8-18.3) for all-cause mortality, 9.2% (95% CI 5.3-13.4) for coronary heart disease, and 2.6% (95% CI -1.8 to 7.4) for stroke.

CONCLUSIONS

Reduced eGFR levels were associated with a larger proportion of avoidable deaths and cardiovascular hospitalizations in people with diabetes compared to previously reported results in people with other cardiovascular risk factors.

Analyses of Risk, Racial Disparity, and Outcomes Among US Patients With Cancer and COVID-19 Infection

Importance

Patients with specific cancers may be at higher risk than those without cancer for coronavirus disease 2019 (COVID-19) and its severe outcomes. At present, limited data are available on the risk, racial disparity, and outcomes for COVID-19 illness in patients with cancer.

Objectives

To investigate how patients with specific types of cancer are at risk for COVID-19 infection and its adverse outcomes and whether there are cancer-specific race disparities for COVID-19 infection.

Design, Setting, and Participants

This retrospective case-control analysis of patient electronic health records included 73.4 million patients from 360 hospitals and 317 000 clinicians across 50 US states to August 14, 2020. The odds of COVID-19 infections for 13 common cancer types and adverse outcomes were assessed.

Exposures

The exposure groups were patients diagnosed with a specific cancer, whereas the unexposed groups were patients without the specific cancer.

Main Outcomes and Measures

The adjusted odds ratio (aOR) and 95% CI were estimated using the Cochran-Mantel-Haenszel test for the risk of COVID-19 infection.

Results

Among the 73.4 million patients included in the analysis (53.6% female), 2 523 920 had at least 1 of the 13 common cancers diagnosed (all cancer diagnosed within or before the last year), and 273 140 had recent cancer (cancer diagnosed within the last year). Among 16 570 patients diagnosed with COVID-19, 1200 had a cancer diagnosis and 690 had a recent cancer diagnosis of at least 1 of the 13 common cancers. Those with recent cancer diagnosis were at significantly increased risk for COVID-19 infection (aOR, 7.14 [95% CI, 6.91-7.39]; P < .001), with the strongest association for recently diagnosed leukemia (aOR, 12.16 [95% CI, 11.03-13.40]; P < .001), non-Hodgkin lymphoma (aOR, 8.54 [95% CI, 7.80-9.36]; P < .001), and lung cancer (aOR, 7.66 [95% CI, 7.07-8.29]; P < .001) and weakest for thyroid cancer (aOR, 3.10 [95% CI, 2.47-3.87]; P < .001). Among patients with recent cancer diagnosis, African Americans had a significantly higher risk for COVID-19 infection than White patients; this racial disparity was largest for breast cancer (aOR, 5.44 [95% CI, 4.69-6.31]; P < .001), followed by prostate cancer (aOR, 5.10 [95% CI, 4.34-5.98]; P < .001), colorectal cancer (aOR, 3.30 [95% CI, 2.55-4.26]; P < .001), and lung cancer (aOR, 2.53 [95% CI, 2.10-3.06]; P < .001). Patients with cancer and COVID-19 had significantly worse outcomes (hospitalization, 47.46%; death, 14.93%) than patients with COVID-19 without cancer (hospitalization, 24.26%; death, 5.26%) (P < .001) and patients with cancer without COVID-19 (hospitalization, 12.39%; death, 4.03%) (P < .001).

Conclusions and Relevance

In this case-control study, patients with cancer were at significantly increased risk for COVID-19 infection and worse outcomes, which was further exacerbated among African Americans. These findings highlight the need to protect and monitor patients with cancer as part of the strategy to control the pandemic.

COVID-19 risk, disparities and outcomes in patients with chronic liver disease in the United States

BACKGROUND

Scientific evidence is lacking regarding the risk of patients with chronic liver disease (CLD) for COVID-19, and how these risks are affected by age, gender and race.

METHODS

We performed a case-control study of electronic health records of 62.2 million patients (age >18 years) in the US up to October 1st, 2020, including 1,034,270 patients with CLD, 16,530 with COVID-19, and 820 with both COVID-19 and CLD. We assessed the risk, disparities, and outcomes of COVID-19 in patients with six major CLDs.

FINDINGS

Patients with a recent medical encounter for CLD were at significantly increased risk for COVID-19 compared with patients without CLD, with the strongest effect in patients with chronic non-alcoholic liver disease [adjusted odd ratio (AOR)=13.11, 95% CI: 12.49-13.76, p < 0.001] and non-alcoholic cirrhosis (AOR=11.53, 95% CI: 10.69-12.43, p < 0.001), followed by chronic hepatitis C (AOR=8.93, 95% CI:8.25-9.66, p < 0.001), alcoholic liver damage (AOR=7.05, 95% CI:6.30-7.88, p < 0.001), alcoholic liver cirrhosis (AOR=7.00, 95% CI:6.15-7.97, p < 0.001) and chronic hepatitis B (AOR=4.37, 95% CI:3.35-5.69, p < 0.001). African Americans with CLD were twice more likely to develop COVID-19 than Caucasians. Patients with COVID-19 and a recent encounter for CLD had a death rate of 10.3% (vs. 5.5% among COVID-19 patients without CLD, p < 0.001) and a hospitalization rate of 41.0% (vs. 23.9% among COVID-19 patients without CLD, p < 0.001).

INTERPRETATION

Patients with CLD, especially African Americans, were at increased risk for COVID-19, highlighting the need to protect these patients from exposure to virus infection.

FUNDING

National Institutes of Health (AG057557, AG061388, AG062272, 1UL1TR002548-01), American Cancer Society (RSG-16-049-01-MPC).

COVID-19 risk and outcomes in patients with substance use disorders: analyses from electronic health records in the United States

The global pandemic of COVID-19 is colliding with the epidemic of opioid use disorders (OUD) and other substance use disorders (SUD) in the United States (US). Currently, there is limited data on risks, disparity, and outcomes for COVID-19 in individuals suffering from SUD. This is a retrospective case-control study of electronic health records (EHRs) data of 73,099,850 unique patients, of whom 12,030 had a diagnosis of COVID-19. Patients with a recent diagnosis of SUD (within past year) were at significantly increased risk for COVID-19 (adjusted odds ratio or AOR = 8.699 [8.411-8.997], P < 10-30), an effect that was strongest for individuals with OUD (AOR = 10.244 [9.107-11.524], P < 10-30), followed by individuals with tobacco use disorder (TUD) (AOR = 8.222 ([7.925-8.530], P < 10-30). Compared to patients without SUD, patients with SUD had significantly higher prevalence of chronic kidney, liver, lung diseases, cardiovascular diseases, type 2 diabetes, obesity and cancer. Among patients with recent diagnosis of SUD, African Americans had significantly higher risk of COVID-19 than Caucasians (AOR = 2.173 [2.01-2.349], P < 10-30), with strongest effect for OUD (AOR = 4.162 [3.13-5.533], P < 10-25). COVID-19 patients with SUD had significantly worse outcomes (death: 9.6%, hospitalization: 41.0%) than general COVID-19 patients (death: 6.6%, hospitalization: 30.1%) and African Americans with COVID-19 and SUD had worse outcomes (death: 13.0%, hospitalization: 50.7%) than Caucasians (death: 8.6%, hospitalization: 35.2%). These findings identify individuals with SUD, especially individuals with OUD and African Americans, as having increased risk for COVID-19 and its adverse outcomes, highlighting the need to screen and treat individuals with SUD as part of the strategy to control the pandemic while ensuring no disparities in access to healthcare support.