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1.
Journal of Clinical and Translational Science ; 6(s1):43, 2022.
Article in English | ProQuest Central | ID: covidwho-1795912

ABSTRACT

OBJECTIVES/GOALS: Using the covariate-rich Veteran Health Administration data, estimate the association between Proton Pump Inhibitor (PPI) use and severe COVID-19, rigorously adjusting for confounding using propensity score (PS)-weighting. METHODS/STUDY POPULATION: We assembled a national retrospective cohort of United States veterans who tested positive for SARS-CoV-2, with information on 33 covariates including comorbidity diagnoses, lab values, and medications. Current outpatient PPI use was compared to non-use (two or more fills and pills on hand at admission vs no PPI prescription fill in prior year). The primary composite outcome was mechanical ventilation use or death within 60 days;the secondary composite outcome included ICU admission. PS-weighting mimicked a 1:1 matching cohort, allowing inclusion of all patients while achieving good covariate balance. The weighted cohort was analyzed using logistic regression. RESULTS/ANTICIPATED RESULTS: Our analytic cohort included 97,674 veterans with SARS-CoV-2 testing, of whom 14,958 (15.3%) tested positive (6,262 [41.9%] current PPI-users, 8,696 [58.1%] non-users). After weighting, all covariates were well-balanced with standardized mean differences less than a threshold of 0.1. Prior to PS-weighting (no covariate adjustment), we observed higher odds of the primary (9.3% vs 7.5%;OR 1.27, 95% CI 1.13-1.43) and secondary (25.8% vs 21.4%;OR 1.27, 95% CI 1.18-1.37) outcomes among PPI users vs non-users. After PS-weighting, PPI use vs non-use was not associated with the primary (8.2% vs 8.0%;OR 1.03, 95% CI 0.91-1.16) or secondary (23.4% vs 22.9%;OR 1.03, 95% CI 0.95-1.12) outcomes. DISCUSSION/SIGNIFICANCE: The associations between PPI use and severe COVID-19 outcomes that have been previously reported may be due to limitations in the covariates available for adjustment. With respect to COVID-19, our robust PS-weighted analysis provides patients and providers with further evidence for PPI safety.

2.
Clin Epidemiol ; 14: 369-384, 2022.
Article in English | MEDLINE | ID: covidwho-1760056

ABSTRACT

Purpose: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD. Patients and Methods: We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services. Results: We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed. Conclusion: We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.

3.
BMC Med Res Methodol ; 22(1): 35, 2022 01 30.
Article in English | MEDLINE | ID: covidwho-1699687

ABSTRACT

BACKGROUND: We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient's risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. METHODS: We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. RESULTS: Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.72-0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. CONCLUSIONS: This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use.


Subject(s)
COVID-19 , Influenza, Human , Pneumonia , COVID-19 Testing , Humans , Influenza, Human/epidemiology , SARS-CoV-2 , United States
5.
JAMA Intern Med ; 182(4): 386-395, 2022 Apr 01.
Article in English | MEDLINE | ID: covidwho-1653126

ABSTRACT

Importance: Coronavirus disease 2019 (COVID-19) confers significant risk of acute kidney injury (AKI). Patients with COVID-19 with AKI have high mortality rates. Objective: Individuals with African ancestry with 2 copies of apolipoprotein L1 (APOL1) variants G1 or G2 (high-risk group) have significantly increased rates of kidney disease. We tested the hypothesis that the APOL1 high-risk group is associated with a higher-risk of COVID-19-associated AKI and death. Design, Setting, and Participants: This retrospective cohort study included 990 participants with African ancestry enrolled in the Million Veteran Program who were hospitalized with COVID-19 between March 2020 and January 2021 with available genetic information. Exposures: The primary exposure was having 2 APOL1 risk variants (RV) (APOL1 high-risk group), compared with having 1 or 0 risk variants (APOL1 low-risk group). Main Outcomes and Measures: The primary outcome was AKI. The secondary outcomes were stages of AKI severity and death. Multivariable logistic regression analyses adjusted for preexisting comorbidities, medications, and inpatient AKI risk factors; 10 principal components of ancestry were performed to study these associations. We performed a subgroup analysis in individuals with normal kidney function prior to hospitalization (estimated glomerular filtration rate ≥60 mL/min/1.73 m2). Results: Of the 990 participants with African ancestry, 905 (91.4%) were male with a median (IQR) age of 68 (60-73) years. Overall, 392 (39.6%) patients developed AKI, 141 (14%) developed stages 2 or 3 AKI, 28 (3%) required dialysis, and 122 (12.3%) died. One hundred twenty-five (12.6%) of the participants were in the APOL1 high-risk group. Patients categorized as APOL1 high-risk group had significantly higher odds of AKI (adjusted odds ratio [OR], 1.95; 95% CI, 1.27-3.02; P = .002), higher AKI severity stages (OR, 2.03; 95% CI, 1.37-2.99; P < .001), and death (OR, 2.15; 95% CI, 1.22-3.72; P = .007). The association with AKI persisted in the subgroup with normal kidney function (OR, 1.93; 95% CI, 1.15-3.26; P = .01). Data analysis was conducted between February 2021 and April 2021. Conclusions and Relevance: In this cohort study of veterans with African ancestry hospitalized with COVID-19 infection, APOL1 kidney risk variants were associated with higher odds of AKI, AKI severity, and death, even among individuals with prior normal kidney function.


Subject(s)
Acute Kidney Injury , COVID-19 , Veterans , Acute Kidney Injury/genetics , African Americans/genetics , Aged , Apolipoprotein L1/genetics , Cohort Studies , Female , Hospitalization , Humans , Male , Middle Aged , Retrospective Studies , Risk Factors
6.
BMJ Open ; 11(12): e057632, 2021 12 22.
Article in English | MEDLINE | ID: covidwho-1583090

ABSTRACT

OBJECTIVE: To characterise patients with and without prevalent hypertension and COVID-19 and to assess adverse outcomes in both inpatients and outpatients. DESIGN AND SETTING: This is a retrospective cohort study using 15 healthcare databases (primary and secondary electronic healthcare records, insurance and national claims data) from the USA, Europe and South Korea, standardised to the Observational Medical Outcomes Partnership common data model. Data were gathered from 1 March to 31 October 2020. PARTICIPANTS: Two non-mutually exclusive cohorts were defined: (1) individuals diagnosed with COVID-19 (diagnosed cohort) and (2) individuals hospitalised with COVID-19 (hospitalised cohort), and stratified by hypertension status. Follow-up was from COVID-19 diagnosis/hospitalisation to death, end of the study period or 30 days. OUTCOMES: Demographics, comorbidities and 30-day outcomes (hospitalisation and death for the 'diagnosed' cohort and adverse events and death for the 'hospitalised' cohort) were reported. RESULTS: We identified 2 851 035 diagnosed and 563 708 hospitalised patients with COVID-19. Hypertension was more prevalent in the latter (ranging across databases from 17.4% (95% CI 17.2 to 17.6) to 61.4% (95% CI 61.0 to 61.8) and from 25.6% (95% CI 24.6 to 26.6) to 85.9% (95% CI 85.2 to 86.6)). Patients in both cohorts with hypertension were predominantly >50 years old and female. Patients with hypertension were frequently diagnosed with obesity, heart disease, dyslipidaemia and diabetes. Compared with patients without hypertension, patients with hypertension in the COVID-19 diagnosed cohort had more hospitalisations (ranging from 1.3% (95% CI 0.4 to 2.2) to 41.1% (95% CI 39.5 to 42.7) vs from 1.4% (95% CI 0.9 to 1.9) to 15.9% (95% CI 14.9 to 16.9)) and increased mortality (ranging from 0.3% (95% CI 0.1 to 0.5) to 18.5% (95% CI 15.7 to 21.3) vs from 0.2% (95% CI 0.2 to 0.2) to 11.8% (95% CI 10.8 to 12.8)). Patients in the COVID-19 hospitalised cohort with hypertension were more likely to have acute respiratory distress syndrome (ranging from 0.1% (95% CI 0.0 to 0.2) to 65.6% (95% CI 62.5 to 68.7) vs from 0.1% (95% CI 0.0 to 0.2) to 54.7% (95% CI 50.5 to 58.9)), arrhythmia (ranging from 0.5% (95% CI 0.3 to 0.7) to 45.8% (95% CI 42.6 to 49.0) vs from 0.4% (95% CI 0.3 to 0.5) to 36.8% (95% CI 32.7 to 40.9)) and increased mortality (ranging from 1.8% (95% CI 0.4 to 3.2) to 25.1% (95% CI 23.0 to 27.2) vs from 0.7% (95% CI 0.5 to 0.9) to 10.9% (95% CI 10.4 to 11.4)) than patients without hypertension. CONCLUSIONS: COVID-19 patients with hypertension were more likely to suffer severe outcomes, hospitalisations and deaths compared with those without hypertension.


Subject(s)
COVID-19 , Hypertension , COVID-19 Testing , Cohort Studies , Comorbidity , Female , Hospitalization , Humans , Hypertension/epidemiology , Middle Aged , Retrospective Studies , SARS-CoV-2
7.
Mil Med ; 2021 Nov 13.
Article in English | MEDLINE | ID: covidwho-1522250

ABSTRACT

Pragmatic clinical trials (PCTs) are well-suited to address unmet healthcare needs, such as those arising from the dual public health crises of chronic pain and opioid misuse, recently exacerbated by the COVID-19 pandemic. These overlapping epidemics have complex, multifactorial etiologies, and PCTs can be used to investigate the effectiveness of integrated therapies that are currently available but underused. Yet individual pragmatic studies can be limited in their reach because of existing structural and cultural barriers to dissemination and implementation. The National Institutes of Health, Department of Defense, and Department of Veterans Affairs formed an interagency research partnership, the Pain Management Collaboratory. The partnership combines pragmatic trial design with collaborative tools and relationship building within a large network to advance the science and impact of nonpharmacological approaches and integrated models of care for the management of pain and common co-occurring conditions. The Pain Management Collaboratory team supports 11 large-scale, multisite PCTs in veteran and military health systems with a focus on team science with the shared aim that the "whole is greater than the sum of the parts." Herein, we describe this integrated approach and lessons learned, including incentivizing all parties; proactively offering frequent opportunities for problem-solving; engaging stakeholders during all stages of research; and navigating competing research priorities. We also articulate several specific strategies and their practical implications for advancing pain management in active clinical, "real-world," settings.

8.
Cancer Epidemiol Biomarkers Prev ; 30(10): 1884-1894, 2021 10.
Article in English | MEDLINE | ID: covidwho-1450633

ABSTRACT

BACKGROUND: We described the demographics, cancer subtypes, comorbidities, and outcomes of patients with a history of cancer and coronavirus disease 2019 (COVID-19). Second, we compared patients hospitalized with COVID-19 to patients diagnosed with COVID-19 and patients hospitalized with influenza. METHODS: We conducted a cohort study using eight routinely collected health care databases from Spain and the United States, standardized to the Observational Medical Outcome Partnership common data model. Three cohorts of patients with a history of cancer were included: (i) diagnosed with COVID-19, (ii) hospitalized with COVID-19, and (iii) hospitalized with influenza in 2017 to 2018. Patients were followed from index date to 30 days or death. We reported demographics, cancer subtypes, comorbidities, and 30-day outcomes. RESULTS: We included 366,050 and 119,597 patients diagnosed and hospitalized with COVID-19, respectively. Prostate and breast cancers were the most frequent cancers (range: 5%-18% and 1%-14% in the diagnosed cohort, respectively). Hematologic malignancies were also frequent, with non-Hodgkin's lymphoma being among the five most common cancer subtypes in the diagnosed cohort. Overall, patients were aged above 65 years and had multiple comorbidities. Occurrence of death ranged from 2% to 14% and from 6% to 26% in the diagnosed and hospitalized COVID-19 cohorts, respectively. Patients hospitalized with influenza (n = 67,743) had a similar distribution of cancer subtypes, sex, age, and comorbidities but lower occurrence of adverse events. CONCLUSIONS: Patients with a history of cancer and COVID-19 had multiple comorbidities and a high occurrence of COVID-19-related events. Hematologic malignancies were frequent. IMPACT: This study provides epidemiologic characteristics that can inform clinical care and etiologic studies.


Subject(s)
COVID-19/mortality , Neoplasms/epidemiology , Outcome Assessment, Health Care/statistics & numerical data , Adolescent , Adult , Aged , Aged, 80 and over , Child , Cohort Studies , Comorbidity , Databases, Factual , Female , Hospitalization/statistics & numerical data , Humans , Influenza, Human/epidemiology , Male , Middle Aged , Pandemics , Prevalence , Risk Factors , SARS-CoV-2 , Spain/epidemiology , United States/epidemiology , Young Adult
9.
Cancer Epidemiol Biomarkers Prev ; 30(10): 1884-1894, 2021 10.
Article in English | MEDLINE | ID: covidwho-1317085

ABSTRACT

BACKGROUND: We described the demographics, cancer subtypes, comorbidities, and outcomes of patients with a history of cancer and coronavirus disease 2019 (COVID-19). Second, we compared patients hospitalized with COVID-19 to patients diagnosed with COVID-19 and patients hospitalized with influenza. METHODS: We conducted a cohort study using eight routinely collected health care databases from Spain and the United States, standardized to the Observational Medical Outcome Partnership common data model. Three cohorts of patients with a history of cancer were included: (i) diagnosed with COVID-19, (ii) hospitalized with COVID-19, and (iii) hospitalized with influenza in 2017 to 2018. Patients were followed from index date to 30 days or death. We reported demographics, cancer subtypes, comorbidities, and 30-day outcomes. RESULTS: We included 366,050 and 119,597 patients diagnosed and hospitalized with COVID-19, respectively. Prostate and breast cancers were the most frequent cancers (range: 5%-18% and 1%-14% in the diagnosed cohort, respectively). Hematologic malignancies were also frequent, with non-Hodgkin's lymphoma being among the five most common cancer subtypes in the diagnosed cohort. Overall, patients were aged above 65 years and had multiple comorbidities. Occurrence of death ranged from 2% to 14% and from 6% to 26% in the diagnosed and hospitalized COVID-19 cohorts, respectively. Patients hospitalized with influenza (n = 67,743) had a similar distribution of cancer subtypes, sex, age, and comorbidities but lower occurrence of adverse events. CONCLUSIONS: Patients with a history of cancer and COVID-19 had multiple comorbidities and a high occurrence of COVID-19-related events. Hematologic malignancies were frequent. IMPACT: This study provides epidemiologic characteristics that can inform clinical care and etiologic studies.


Subject(s)
COVID-19/mortality , Neoplasms/epidemiology , Outcome Assessment, Health Care/statistics & numerical data , Adolescent , Adult , Aged , Aged, 80 and over , Child , Cohort Studies , Comorbidity , Databases, Factual , Female , Hospitalization/statistics & numerical data , Humans , Influenza, Human/epidemiology , Male , Middle Aged , Pandemics , Prevalence , Risk Factors , SARS-CoV-2 , Spain/epidemiology , United States/epidemiology , Young Adult
10.
Int J Obes (Lond) ; 45(11): 2347-2357, 2021 11.
Article in English | MEDLINE | ID: covidwho-1315585

ABSTRACT

BACKGROUND: A detailed characterization of patients with COVID-19 living with obesity has not yet been undertaken. We aimed to describe and compare the demographics, medical conditions, and outcomes of COVID-19 patients living with obesity (PLWO) to those of patients living without obesity. METHODS: We conducted a cohort study based on outpatient/inpatient care and claims data from January to June 2020 from Spain, the UK, and the US. We used six databases standardized to the OMOP common data model. We defined two non-mutually exclusive cohorts of patients diagnosed and/or hospitalized with COVID-19; patients were followed from index date to 30 days or death. We report the frequency of demographics, prior medical conditions, and 30-days outcomes (hospitalization, events, and death) by obesity status. RESULTS: We included 627 044 (Spain: 122 058, UK: 2336, and US: 502 650) diagnosed and 160 013 (Spain: 18 197, US: 141 816) hospitalized patients with COVID-19. The prevalence of obesity was higher among patients hospitalized (39.9%, 95%CI: 39.8-40.0) than among those diagnosed with COVID-19 (33.1%; 95%CI: 33.0-33.2). In both cohorts, PLWO were more often female. Hospitalized PLWO were younger than patients without obesity. Overall, COVID-19 PLWO were more likely to have prior medical conditions, present with cardiovascular and respiratory events during hospitalization, or require intensive services compared to COVID-19 patients without obesity. CONCLUSION: We show that PLWO differ from patients without obesity in a wide range of medical conditions and present with more severe forms of COVID-19, with higher hospitalization rates and intensive services requirements. These findings can help guiding preventive strategies of COVID-19 infection and complications and generating hypotheses for causal inference studies.


Subject(s)
COVID-19/epidemiology , Obesity/epidemiology , Adolescent , Adult , Aged , COVID-19/mortality , Cohort Studies , Comorbidity , Female , Hospitalization , Humans , Male , Middle Aged , Prevalence , Risk Factors , Spain/epidemiology , United Kingdom/epidemiology , United States/epidemiology , Young Adult
11.
Kidney Int ; 100(4): 894-905, 2021 10.
Article in English | MEDLINE | ID: covidwho-1260814

ABSTRACT

Acute kidney injury is a common complication in patients hospitalized with SARSCoV-2 (COVID-19), with prior studies implicating multiple potential mechanisms of injury. Although COVID-19 is often compared to other respiratory viral illnesses, few formal comparisons of these viruses on kidney health exist. In this retrospective cohort study, we compared the incidence, features, and outcomes of acute kidney injury among Veterans hospitalized with COVID-19 or influenza and adjusted for baseline conditions using weighted comparisons. A total of 3402 hospitalizations for COVID-19 and 3680 hospitalizations for influenza admitted between October 1, 2019 and May 31, 2020 across 127 Veterans Administration hospitals nationally were studied using the electronic medical record. Acute kidney injury occurred more frequently among those with COVID-19 compared to those with influenza (40.9% versus 29.4%, weighted analysis) and was more severe. Patients with COVID-19 were more likely to require mechanical ventilation and vasopressors and experienced higher mortality. Proteinuria and hematuria were frequent in both groups but more common in COVID-19. Recovery of kidney function was less common in patients with COVID-19 and acute kidney injury but was similar among survivors. Thus, findings from this study confirm that acute kidney injury is more common and severe among patients hospitalized with COVID-19 compared to influenza, a finding that may be driven largely by illness severity. Hence, the combined impact of these two illnesses on kidney health may be significant and have important implications for resource allocation.


Subject(s)
Acute Kidney Injury , COVID-19 , Influenza, Human , Veterans , Acute Kidney Injury/diagnosis , Acute Kidney Injury/epidemiology , Acute Kidney Injury/therapy , Hospital Mortality , Humans , Incidence , Influenza, Human/epidemiology , Retrospective Studies , SARS-CoV-2 , United States/epidemiology
12.
J Am Med Inform Assoc ; 28(8): 1765-1776, 2021 07 30.
Article in English | MEDLINE | ID: covidwho-1246728

ABSTRACT

OBJECTIVE: To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online. MATERIALS AND METHODS: We developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data. RESULTS: Our public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems. DISCUSSION AND CONCLUSIONS: We present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems.


Subject(s)
Algorithms , COVID-19 , Computer Communication Networks , Confidentiality , Electronic Health Records , Information Storage and Retrieval/methods , Natural Language Processing , Common Data Elements , Female , Humans , Logistic Models , Male , Registries
13.
BMJ ; 373: n1038, 2021 05 11.
Article in English | MEDLINE | ID: covidwho-1223582

ABSTRACT

OBJECTIVE: To investigate the use of repurposed and adjuvant drugs in patients admitted to hospital with covid-19 across three continents. DESIGN: Multinational network cohort study. SETTING: Hospital electronic health records from the United States, Spain, and China, and nationwide claims data from South Korea. PARTICIPANTS: 303 264 patients admitted to hospital with covid-19 from January 2020 to December 2020. MAIN OUTCOME MEASURES: Prescriptions or dispensations of any drug on or 30 days after the date of hospital admission for covid-19. RESULTS: Of the 303 264 patients included, 290 131 were from the US, 7599 from South Korea, 5230 from Spain, and 304 from China. 3455 drugs were identified. Common repurposed drugs were hydroxychloroquine (used in from <5 (<2%) patients in China to 2165 (85.1%) in Spain), azithromycin (from 15 (4.9%) in China to 1473 (57.9%) in Spain), combined lopinavir and ritonavir (from 156 (<2%) in the VA-OMOP US to 2,652 (34.9%) in South Korea and 1285 (50.5%) in Spain), and umifenovir (0% in the US, South Korea, and Spain and 238 (78.3%) in China). Use of adjunctive drugs varied greatly, with the five most used treatments being enoxaparin, fluoroquinolones, ceftriaxone, vitamin D, and corticosteroids. Hydroxychloroquine use increased rapidly from March to April 2020 but declined steeply in May to June and remained low for the rest of the year. The use of dexamethasone and corticosteroids increased steadily during 2020. CONCLUSIONS: Multiple drugs were used in the first few months of the covid-19 pandemic, with substantial geographical and temporal variation. Hydroxychloroquine, azithromycin, lopinavir-ritonavir, and umifenovir (in China only) were the most prescribed repurposed drugs. Antithrombotics, antibiotics, H2 receptor antagonists, and corticosteroids were often used as adjunctive treatments. Research is needed on the comparative risk and benefit of these treatments in the management of covid-19.


Subject(s)
COVID-19/drug therapy , Chemotherapy, Adjuvant/methods , Drug Repositioning/methods , Administrative Claims, Healthcare/statistics & numerical data , Adolescent , Adrenal Cortex Hormones/therapeutic use , Adult , Aged , Aged, 80 and over , Azithromycin/therapeutic use , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19/virology , Ceftriaxone/therapeutic use , Child , Child, Preschool , China/epidemiology , Cohort Studies , Drug Combinations , Electronic Health Records/statistics & numerical data , Enoxaparin/therapeutic use , Female , Fluoroquinolones/therapeutic use , Humans , Hydroxychloroquine/therapeutic use , Infant , Infant, Newborn , Inpatients , Lopinavir/therapeutic use , Male , Middle Aged , Republic of Korea/epidemiology , Ritonavir/therapeutic use , SARS-CoV-2/drug effects , SARS-CoV-2/genetics , Safety , Spain/epidemiology , Treatment Outcome , United States/epidemiology , Vitamin D/therapeutic use , Young Adult
14.
Journal of Health Care for the Poor and Underserved ; 32(2 Supplement):300-317, 2021.
Article in English | ProQuest Central | ID: covidwho-1208109

ABSTRACT

The COVID-19 pandemic has created multiple opportunities to deploy artificial intelligence (AI)-driven tools and applied interventions to understand, mitigate, and manage the pandemic and its consequences. The disproportionate impact of COVID-19 on racial/ethnic minority and socially disadvantaged populations underscores the need to anticipate and address social inequalities and health disparities in AI development and application. Before the pandemic, there was growing optimism about AI's role in addressing inequities and enhancing personalized care. Unfortunately, ethical and social issues that are encountered in scaling, developing, and applying advanced technologies in health care settings have intensified during the rapidly evolving public health crisis. Critical voices concerned with the disruptive potentials and risk for engineered inequities have called for reexamining ethical guidelines in the development and application of AI. This paper proposes a framework to incorporate ethical AI principles into the development process in ways that intentionally promote racial health equity and social justice. Without centering on equity, justice, and ethical AI, these tools may exacerbate structural inequities that can lead to disparate health outcomes.

15.
JMIR Med Inform ; 9(4): e21547, 2021 Apr 05.
Article in English | MEDLINE | ID: covidwho-1195972

ABSTRACT

BACKGROUND: SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision-making during the pandemic. However, the model is at high risk of bias according to the "prediction model risk of bias assessment" criteria, and it has not been externally validated. OBJECTIVE: The aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases. METHODS: We followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia. RESULTS: The internal validation performance of the C-19 index had a C statistic of 0.73, and the calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data, the model obtained C statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US, and South Korean data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OHDSI network, the C statistics ranged between 0.40 and 0.68. CONCLUSIONS: Our results show that the discriminative performance of the C-19 index model is low for influenza cohorts and even worse among patients with COVID-19 in the United States, Spain, and South Korea. These results suggest that C-19 should not be used to aid decision-making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model.

16.
Rheumatology (Oxford) ; 60(SI): SI37-SI50, 2021 10 09.
Article in English | MEDLINE | ID: covidwho-1135892

ABSTRACT

OBJECTIVE: Patients with autoimmune diseases were advised to shield to avoid coronavirus disease 2019 (COVID-19), but information on their prognosis is lacking. We characterized 30-day outcomes and mortality after hospitalization with COVID-19 among patients with prevalent autoimmune diseases, and compared outcomes after hospital admissions among similar patients with seasonal influenza. METHODS: A multinational network cohort study was conducted using electronic health records data from Columbia University Irving Medical Center [USA, Optum (USA), Department of Veterans Affairs (USA), Information System for Research in Primary Care-Hospitalization Linked Data (Spain) and claims data from IQVIA Open Claims (USA) and Health Insurance and Review Assessment (South Korea). All patients with prevalent autoimmune diseases, diagnosed and/or hospitalized between January and June 2020 with COVID-19, and similar patients hospitalized with influenza in 2017-18 were included. Outcomes were death and complications within 30 days of hospitalization. RESULTS: We studied 133 589 patients diagnosed and 48 418 hospitalized with COVID-19 with prevalent autoimmune diseases. Most patients were female, aged ≥50 years with previous comorbidities. The prevalence of hypertension (45.5-93.2%), chronic kidney disease (14.0-52.7%) and heart disease (29.0-83.8%) was higher in hospitalized vs diagnosed patients with COVID-19. Compared with 70 660 hospitalized with influenza, those admitted with COVID-19 had more respiratory complications including pneumonia and acute respiratory distress syndrome, and higher 30-day mortality (2.2-4.3% vs 6.32-24.6%). CONCLUSION: Compared with influenza, COVID-19 is a more severe disease, leading to more complications and higher mortality.


Subject(s)
Autoimmune Diseases/mortality , Autoimmune Diseases/virology , COVID-19/mortality , Hospitalization/statistics & numerical data , Influenza, Human/mortality , Adult , Aged , Aged, 80 and over , COVID-19/immunology , Cohort Studies , Female , Humans , Influenza, Human/immunology , Male , Middle Aged , Prevalence , Prognosis , Republic of Korea/epidemiology , SARS-CoV-2 , Spain/epidemiology , United States/epidemiology , Young Adult
17.
Lancet Digit Health ; 3(2): e98-e114, 2021 02.
Article in English | MEDLINE | ID: covidwho-1065706

ABSTRACT

BACKGROUND: Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) have been postulated to affect susceptibility to COVID-19. Observational studies so far have lacked rigorous ascertainment adjustment and international generalisability. We aimed to determine whether use of ACEIs or ARBs is associated with an increased susceptibility to COVID-19 in patients with hypertension. METHODS: In this international, open science, cohort analysis, we used electronic health records from Spain (Information Systems for Research in Primary Care [SIDIAP]) and the USA (Columbia University Irving Medical Center data warehouse [CUIMC] and Department of Veterans Affairs Observational Medical Outcomes Partnership [VA-OMOP]) to identify patients aged 18 years or older with at least one prescription for ACEIs and ARBs (target cohort) or calcium channel blockers (CCBs) and thiazide or thiazide-like diuretics (THZs; comparator cohort) between Nov 1, 2019, and Jan 31, 2020. Users were defined separately as receiving either monotherapy with these four drug classes, or monotherapy or combination therapy (combination use) with other antihypertensive medications. We assessed four outcomes: COVID-19 diagnosis; hospital admission with COVID-19; hospital admission with pneumonia; and hospital admission with pneumonia, acute respiratory distress syndrome, acute kidney injury, or sepsis. We built large-scale propensity score methods derived through a data-driven approach and negative control experiments across ten pairwise comparisons, with results meta-analysed to generate 1280 study effects. For each study effect, we did negative control outcome experiments using a possible 123 controls identified through a data-rich algorithm. This process used a set of predefined baseline patient characteristics to provide the most accurate prediction of treatment and balance among patient cohorts across characteristics. The study is registered with the EU Post-Authorisation Studies register, EUPAS35296. FINDINGS: Among 1 355 349 antihypertensive users (363 785 ACEI or ARB monotherapy users, 248 915 CCB or THZ monotherapy users, 711 799 ACEI or ARB combination users, and 473 076 CCB or THZ combination users) included in analyses, no association was observed between COVID-19 diagnosis and exposure to ACEI or ARB monotherapy versus CCB or THZ monotherapy (calibrated hazard ratio [HR] 0·98, 95% CI 0·84-1·14) or combination use exposure (1·01, 0·90-1·15). ACEIs alone similarly showed no relative risk difference when compared with CCB or THZ monotherapy (HR 0·91, 95% CI 0·68-1·21; with heterogeneity of >40%) or combination use (0·95, 0·83-1·07). Directly comparing ACEIs with ARBs demonstrated a moderately lower risk with ACEIs, which was significant with combination use (HR 0·88, 95% CI 0·79-0·99) and non-significant for monotherapy (0·85, 0·69-1·05). We observed no significant difference between drug classes for risk of hospital admission with COVID-19, hospital admission with pneumonia, or hospital admission with pneumonia, acute respiratory distress syndrome, acute kidney injury, or sepsis across all comparisons. INTERPRETATION: No clinically significant increased risk of COVID-19 diagnosis or hospital admission-related outcomes associated with ACEI or ARB use was observed, suggesting users should not discontinue or change their treatment to decrease their risk of COVID-19. FUNDING: Wellcome Trust, UK National Institute for Health Research, US National Institutes of Health, US Department of Veterans Affairs, Janssen Research & Development, IQVIA, South Korean Ministry of Health and Welfare Republic, Australian National Health and Medical Research Council, and European Health Data and Evidence Network.

18.
Lancet Digit Health ; 3(2): e98-e114, 2021 02.
Article in English | MEDLINE | ID: covidwho-989557

ABSTRACT

BACKGROUND: Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) have been postulated to affect susceptibility to COVID-19. Observational studies so far have lacked rigorous ascertainment adjustment and international generalisability. We aimed to determine whether use of ACEIs or ARBs is associated with an increased susceptibility to COVID-19 in patients with hypertension. METHODS: In this international, open science, cohort analysis, we used electronic health records from Spain (Information Systems for Research in Primary Care [SIDIAP]) and the USA (Columbia University Irving Medical Center data warehouse [CUIMC] and Department of Veterans Affairs Observational Medical Outcomes Partnership [VA-OMOP]) to identify patients aged 18 years or older with at least one prescription for ACEIs and ARBs (target cohort) or calcium channel blockers (CCBs) and thiazide or thiazide-like diuretics (THZs; comparator cohort) between Nov 1, 2019, and Jan 31, 2020. Users were defined separately as receiving either monotherapy with these four drug classes, or monotherapy or combination therapy (combination use) with other antihypertensive medications. We assessed four outcomes: COVID-19 diagnosis; hospital admission with COVID-19; hospital admission with pneumonia; and hospital admission with pneumonia, acute respiratory distress syndrome, acute kidney injury, or sepsis. We built large-scale propensity score methods derived through a data-driven approach and negative control experiments across ten pairwise comparisons, with results meta-analysed to generate 1280 study effects. For each study effect, we did negative control outcome experiments using a possible 123 controls identified through a data-rich algorithm. This process used a set of predefined baseline patient characteristics to provide the most accurate prediction of treatment and balance among patient cohorts across characteristics. The study is registered with the EU Post-Authorisation Studies register, EUPAS35296. FINDINGS: Among 1 355 349 antihypertensive users (363 785 ACEI or ARB monotherapy users, 248 915 CCB or THZ monotherapy users, 711 799 ACEI or ARB combination users, and 473 076 CCB or THZ combination users) included in analyses, no association was observed between COVID-19 diagnosis and exposure to ACEI or ARB monotherapy versus CCB or THZ monotherapy (calibrated hazard ratio [HR] 0·98, 95% CI 0·84-1·14) or combination use exposure (1·01, 0·90-1·15). ACEIs alone similarly showed no relative risk difference when compared with CCB or THZ monotherapy (HR 0·91, 95% CI 0·68-1·21; with heterogeneity of >40%) or combination use (0·95, 0·83-1·07). Directly comparing ACEIs with ARBs demonstrated a moderately lower risk with ACEIs, which was significant with combination use (HR 0·88, 95% CI 0·79-0·99) and non-significant for monotherapy (0·85, 0·69-1·05). We observed no significant difference between drug classes for risk of hospital admission with COVID-19, hospital admission with pneumonia, or hospital admission with pneumonia, acute respiratory distress syndrome, acute kidney injury, or sepsis across all comparisons. INTERPRETATION: No clinically significant increased risk of COVID-19 diagnosis or hospital admission-related outcomes associated with ACEI or ARB use was observed, suggesting users should not discontinue or change their treatment to decrease their risk of COVID-19. FUNDING: Wellcome Trust, UK National Institute for Health Research, US National Institutes of Health, US Department of Veterans Affairs, Janssen Research & Development, IQVIA, South Korean Ministry of Health and Welfare Republic, Australian National Health and Medical Research Council, and European Health Data and Evidence Network.

19.
medRxiv ; 2021 Feb 12.
Article in English | MEDLINE | ID: covidwho-955711

ABSTRACT

OBJECTIVE: To estimate the proportion of patients hospitalized with COVID-19 who undergo dialysis, tracheostomy, and extracorporeal membrane oxygenation (ECMO). DESIGN: A network cohort study. SETTING: Seven databases from the United States containing routinely-collected patient data: HealthVerity, Premier, IQVIA Hospital CDM, IQVIA Open Claims, Optum EHR, Optum SES, and VA-OMOP. PATIENTS: Patients hospitalized with a clinical diagnosis or a positive test result for COVID-19. INTERVENTIONS: Dialysis, tracheostomy, and ECMO. MEASUREMENTS AND MAIN RESULTS: 842,928 patients hospitalized with COVID-19 were included (22,887 from HealthVerity, 77,853 from IQVIA Hospital CDM, 533,997 from IQVIA Open Claims, 36,717 from Optum EHR, 4,336 from OPTUM SES, 156,187 from Premier, and 10,951 from VA-OMOP). Across the six databases, 35,192 (4.17% [95% CI: 4.13% to 4.22%]) patients received dialysis, 6,950 (0.82% [0.81% to 0.84%]) had a tracheostomy, and 1,568 (0.19% [95% CI: 0.18% to 0.20%]) patients underwent ECMO over the 30 days following hospitalization. Use of ECMO was more common among patients who were younger, male, and with fewer comorbidities. Tracheostomy was broadly used for a similar proportion of patients regardless of age, sex, or comorbidity. While dialysis was generally used for a similar proportion among younger and older patients, it was more frequent among male patients and among those with chronic kidney disease. CONCLUSION: Use of dialysis among those hospitalized with COVID-19 is high at around 4%. Although less than one percent of patients undergo tracheostomy and ECMO, the absolute numbers of patients who have undergone these interventions is substantial.

20.
medRxiv ; 2020 Nov 27.
Article in English | MEDLINE | ID: covidwho-955714

ABSTRACT

OBJECTIVE: Patients with autoimmune diseases were advised to shield to avoid COVID-19, but information on their prognosis is lacking. We characterised 30-day outcomes and mortality after hospitalisation with COVID-19 among patients with prevalent autoimmune diseases, and compared outcomes after hospital admissions among similar patients with seasonal influenza. DESIGN: Multinational network cohort study. SETTING: Electronic health records data from Columbia University Irving Medical Center (CUIMC) (NYC, United States [US]), Optum [US], Department of Veterans Affairs (VA) (US), Information System for Research in Primary Care-Hospitalisation Linked Data (SIDIAP-H) (Spain), and claims data from IQVIA Open Claims (US) and Health Insurance and Review Assessment (HIRA) (South Korea). PARTICIPANTS: All patients with prevalent autoimmune diseases, diagnosed and/or hospitalised between January and June 2020 with COVID-19, and similar patients hospitalised with influenza in 2017-2018 were included. MAIN OUTCOME MEASURES: 30-day complications during hospitalisation and death. RESULTS: We studied 133,589 patients diagnosed and 48,418 hospitalised with COVID-19 with prevalent autoimmune diseases. The majority of participants were female (60.5% to 65.9%) and aged ≥50 years. The most prevalent autoimmune conditions were psoriasis (3.5 to 32.5%), rheumatoid arthritis (3.9 to 18.9%), and vasculitis (3.3 to 17.6%). Amongst hospitalised patients, Type 1 diabetes was the most common autoimmune condition (4.8% to 7.5%) in US databases, rheumatoid arthritis in HIRA (18.9%), and psoriasis in SIDIAP-H (26.4%).Compared to 70,660 hospitalised with influenza, those admitted with COVID-19 had more respiratory complications including pneumonia and acute respiratory distress syndrome, and higher 30-day mortality (2.2% to 4.3% versus 6.3% to 24.6%). CONCLUSIONS: Patients with autoimmune diseases had high rates of respiratory complications and 30-day mortality following a hospitalization with COVID-19. Compared to influenza, COVID-19 is a more severe disease, leading to more complications and higher mortality. Future studies should investigate predictors of poor outcomes in COVID-19 patients with autoimmune diseases. WHAT IS ALREADY KNOWN ABOUT THIS TOPIC: Patients with autoimmune conditions may be at increased risk of COVID-19 infection andcomplications.There is a paucity of evidence characterising the outcomes of hospitalised COVID-19 patients with prevalent autoimmune conditions. WHAT THIS STUDY ADDS: Most people with autoimmune diseases who required hospitalisation for COVID-19 were women, aged 50 years or older, and had substantial previous comorbidities.Patients who were hospitalised with COVID-19 and had prevalent autoimmune diseases had higher prevalence of hypertension, chronic kidney disease, heart disease, and Type 2 diabetes as compared to those with prevalent autoimmune diseases who were diagnosed with COVID-19.A variable proportion of 6% to 25% across data sources died within one month of hospitalisation with COVID-19 and prevalent autoimmune diseases.For people with autoimmune diseases, COVID-19 hospitalisation was associated with worse outcomes and 30-day mortality compared to admission with influenza in the 2017-2018 season.

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