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1.
J Med Virol ; 94(1): 298-302, 2022 01.
Article in English | MEDLINE | ID: covidwho-1513873

ABSTRACT

For preventing the spread of the coronavirus disease 2019 (COVID-19) pandemic, measures like wearing masks, social distancing, and hand hygiene played crucial roles. These measures may also have affected the expansion of other infectious diseases like respiratory tract infections (RTI) and gastro-intestinal infections (GII). Therefore, we aimed to investigate non-COVID-19 related RTI and GII during the COVID-19 pandemic. Patients with a diagnosis of an acute RTI (different locations) or acute GII documented anonymously in 994 general practitioner (GP) or 192 pediatrician practices in Germany were included. We compared the prevalence of acute RTI and GII between April 2019-March 2020 and April 2020-March 2021. In GP practices, 715,440 patients were diagnosed with RTI or GII in the nonpandemic period versus 468,753 in the pandemic period; the same trend was observed by pediatricians (275,033 vs. 165,127). By GPs, the strongest decrease was observed for the diagnosis of influenza (-71%, p < 0.001), followed by acute laryngitis (-64%, p < 0.001), acute lower respiratory infections (bronchitis) (-62%, p < 0.001), and intestinal infections (-40%, p < 0.001). In contrast, the relatively rare viral pneumonia strongly increased by 229% (p < 0.001). In pediatrician practices, there was a strong decrease in infection diagnoses, especially influenza (-90%, p < 0.001), pneumonia (-73%, p < 0.001 viral; -76%, p < 0.001 other pneumonias), and acute sinusitis (-66%, p < 0.001). No increase was observed for viral pneumonia in children. The considerable limitations concerning social life implemented during the COVID-19 pandemic to combat the spread of SARS-CoV-2 also resulted in an inadvertent but welcome reduction in other non-Covid-19 respiratory tract and gastro-intestinal infections.


Subject(s)
COVID-19/prevention & control , Communicable Disease Control/methods , Gastrointestinal Diseases/epidemiology , Respiratory Tract Infections/epidemiology , Adolescent , Adult , Aged , Aged, 80 and over , Child , Child, Preschool , Cross-Sectional Studies , Electronic Health Records/statistics & numerical data , Female , Germany/epidemiology , Hand Hygiene/methods , Humans , Male , Masks , Middle Aged , Physical Distancing , Prevalence , Young Adult
3.
Crit Care ; 25(1): 304, 2021 08 23.
Article in English | MEDLINE | ID: covidwho-1370943

ABSTRACT

BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic has underlined the urgent need for reliable, multicenter, and full-admission intensive care data to advance our understanding of the course of the disease and investigate potential treatment strategies. In this study, we present the Dutch Data Warehouse (DDW), the first multicenter electronic health record (EHR) database with full-admission data from critically ill COVID-19 patients. METHODS: A nation-wide data sharing collaboration was launched at the beginning of the pandemic in March 2020. All hospitals in the Netherlands were asked to participate and share pseudonymized EHR data from adult critically ill COVID-19 patients. Data included patient demographics, clinical observations, administered medication, laboratory determinations, and data from vital sign monitors and life support devices. Data sharing agreements were signed with participating hospitals before any data transfers took place. Data were extracted from the local EHRs with prespecified queries and combined into a staging dataset through an extract-transform-load (ETL) pipeline. In the consecutive processing pipeline, data were mapped to a common concept vocabulary and enriched with derived concepts. Data validation was a continuous process throughout the project. All participating hospitals have access to the DDW. Within legal and ethical boundaries, data are available to clinicians and researchers. RESULTS: Out of the 81 intensive care units in the Netherlands, 66 participated in the collaboration, 47 have signed the data sharing agreement, and 35 have shared their data. Data from 25 hospitals have passed through the ETL and processing pipeline. Currently, 3464 patients are included in the DDW, both from wave 1 and wave 2 in the Netherlands. More than 200 million clinical data points are available. Overall ICU mortality was 24.4%. Respiratory and hemodynamic parameters were most frequently measured throughout a patient's stay. For each patient, all administered medication and their daily fluid balance were available. Missing data are reported for each descriptive. CONCLUSIONS: In this study, we show that EHR data from critically ill COVID-19 patients may be lawfully collected and can be combined into a data warehouse. These initiatives are indispensable to advance medical data science in the field of intensive care medicine.


Subject(s)
COVID-19/epidemiology , Critical Illness/epidemiology , Data Warehousing/statistics & numerical data , Electronic Health Records/statistics & numerical data , Hospitalization/statistics & numerical data , Intensive Care Units/statistics & numerical data , Critical Care , Humans , Netherlands
4.
Crit Care ; 25(1): 295, 2021 Aug 17.
Article in English | MEDLINE | ID: covidwho-1362062

ABSTRACT

BACKGROUND: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. METHODS: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. RESULTS: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict "survival". Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients' age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. CONCLUSIONS: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration "ClinicalTrials" (clinicaltrials.gov) under NCT04455451.


Subject(s)
COVID-19/epidemiology , Critical Illness/epidemiology , Electronic Health Records/statistics & numerical data , Intensive Care Units , Machine Learning , Adult , Aged , COVID-19/therapy , Cohort Studies , Critical Illness/therapy , Emergency Service, Hospital , Female , Germany , Humans , Male , Middle Aged , Outcome Assessment, Health Care
5.
Am J Public Health ; 111(S2): S93-S100, 2021 07.
Article in English | MEDLINE | ID: covidwho-1328024

ABSTRACT

Timely and accurate data on COVID-19 cases and COVID-19‒related deaths are essential for making decisions with significant health, economic, and policy implications. A new report from the National Academies of Sciences, Engineering, and Medicine proposes a uniform national framework for data collection to more accurately quantify disaster-related deaths, injuries, and illnesses. This article describes how following the report's recommendations could help improve the quality and timeliness of public health surveillance data during pandemics, with special attention to addressing gaps in the data necessary to understand pandemic-related health disparities.


Subject(s)
COVID-19/prevention & control , Disaster Planning/organization & administration , Disasters/prevention & control , Disease Outbreaks/prevention & control , Population Surveillance/methods , COVID-19/epidemiology , Communicable Disease Control/organization & administration , Disasters/statistics & numerical data , Disease Outbreaks/statistics & numerical data , Electronic Health Records/statistics & numerical data , Humans
6.
J Cancer Res Ther ; 17(2): 547-550, 2021.
Article in English | MEDLINE | ID: covidwho-1268381

ABSTRACT

Purpose: Health emergency due to COVID-19 started in Uruguay on March 13, 2020; our mastology unit tried to ensure adequate oncological care, and protect patients from the virus infection and complications. Objective: To assess the health care activities in the "peak" of the pandemic during 3 months. Materials and Methods: we collected data from the electronic health record. Results: There were a total of 293 medical appointments from 131 patients (221 face-to-face), that decreased by 16.7% compared to the same period in 2019 (352 appointments). The medical appointments were scheduled to evaluate the continuity of systemic treatment or modifications (95 patients; 72.5%), follow-up (17; 12.9%), first-time consultation (12; 9.1%), and assess paraclinical studies (7; 5.3%). The patients were on hormone therapy (81 patients; 74%), chemotherapy (CT) (21; 19%), and anti-HER2 therapies (9; 8%). New twenty treatments were initiated. Of the 14 patients that were on adjuvant/neoadjuvant CT, 9 (64.3%) continued with the same regimen with the addition of prophylactic granulocyte-colony-stimulating factors (G-CSF), and 5 (35.7%), who were receiving weekly paclitaxel, continued the treatment with no changes. Of the seven patients that were on palliative CT, 2 (28.5%) continued the treatment with the addition of G-CSF, 3 (42.8%) continued with weekly capecitabine or paclitaxel with no treatment changes, and 2 (28.5%) changed their treatment regimen (a less myelosuppressive regimen was selected for one and due to progression of the disease in the other patient). The ninety patients who were receiving adjuvant, neoadjuvant, or palliative criteria hormone therapy and/or anti-HER2 therapies, continued the treatment with no changes. Conclusions: The evidence suggests that, although medical appointments decreased by approximately 17%, we could maintain healthcare activities, continued most of the treatments while the most modified was CT with G-CSF to avoid myelosuppression.


Subject(s)
Breast Neoplasms/drug therapy , COVID-19/epidemiology , Continuity of Patient Care/statistics & numerical data , Delivery of Health Care/statistics & numerical data , Medical Oncology/statistics & numerical data , Adult , Aged , Aged, 80 and over , Antineoplastic Combined Chemotherapy Protocols/adverse effects , Bone Marrow/drug effects , Breast Neoplasms/complications , Breast Neoplasms/diagnosis , Breast Neoplasms/immunology , COVID-19/immunology , COVID-19/prevention & control , COVID-19/transmission , Communicable Disease Control/standards , Continuity of Patient Care/organization & administration , Delivery of Health Care/organization & administration , Delivery of Health Care/standards , Electronic Health Records/statistics & numerical data , Female , Granulocyte Colony-Stimulating Factor/administration & dosage , Hematopoiesis/drug effects , Hematopoiesis/immunology , Humans , Medical Oncology/organization & administration , Medical Oncology/standards , Middle Aged , Pandemics/prevention & control , Referral and Consultation/standards , Referral and Consultation/statistics & numerical data , Retrospective Studies , Telemedicine/organization & administration , Telemedicine/standards , Telemedicine/statistics & numerical data , Triage/organization & administration , Triage/standards , Uruguay/epidemiology
7.
J Am Geriatr Soc ; 69(10): 2745-2751, 2021 10.
Article in English | MEDLINE | ID: covidwho-1268122

ABSTRACT

BACKGROUND/OBJECTIVES: Transitional care management (TCM) visits delivered following hospitalization have been associated with reductions in mortality, readmissions, and total costs; however, uptake remains low. We sought to describe trends in TCM visit delivery during the COVID-19 pandemic. DESIGN: Cross-sectional study of ambulatory electronic health records from December 30, 2019 and January 3, 2021. SETTING: United States. PARTICIPANTS: Forty four thousand six hundred and eighty-one patients receiving transitional care management services. MEASUREMENTS: Weekly rates of in-person and telehealth TCM visits before COVID-19 was declared a national emergency (December 30, 2019 to March 15, 2020), during the initial pandemic period (March 16, 2020 to April 12, 2020) and later period (April 12, 2020 to January 3, 2021). Characteristics of patients receiving in-person and telehealth TCM visits were compared. RESULTS: A total of 44,681 TCM visits occurred during the study period with the majority of patients receiving TCM visits age 65 years and older (68.0%) and female (55.0%) Prior to the COVID-19 pandemic, nearly all TCM visits were conducted in-person. In the initial pandemic, there was an immediate decline in overall TCM visits and a rise in telehealth TCM visits, accounting for 15.4% of TCM visits during this period. In the later pandemic, the average weekly number of TCM visits was 841 and 14.0% were telehealth. During the initial and later pandemic periods, 73.3% and 33.6% of COVID-19-related TCM visits were conducted by telehealth, respectively. Across periods, patterns of telehealth use for TCM visits were similar for younger and older adults. CONCLUSION: The study findings highlight a novel and sustained shift to providing TCM services via telehealth during the COVID-19 pandemic, which may reduce barriers to accessing a high-value service for older adults during a vulnerable transition period. Further investigations comparing outcomes of in-person and telehealth TCM visits are needed to inform innovation in ambulatory post-discharge care.


Subject(s)
Aftercare , Ambulatory Care/statistics & numerical data , COVID-19 , Telemedicine , Transitional Care , Aftercare/methods , Aftercare/trends , Aged , COVID-19/mortality , COVID-19/prevention & control , COVID-19/therapy , Costs and Cost Analysis , Cross-Sectional Studies , Electronic Health Records/statistics & numerical data , Female , Humans , Male , Massachusetts/epidemiology , Mortality , Patient Discharge , Patient Readmission/statistics & numerical data , SARS-CoV-2 , Telemedicine/organization & administration , Telemedicine/statistics & numerical data , Telemedicine/trends , Transitional Care/organization & administration , Transitional Care/trends
8.
JAMA Netw Open ; 4(6): e2112596, 2021 06 01.
Article in English | MEDLINE | ID: covidwho-1265355

ABSTRACT

Importance: Additional sources of pediatric epidemiological and clinical data are needed to efficiently study COVID-19 in children and youth and inform infection prevention and clinical treatment of pediatric patients. Objective: To describe international hospitalization trends and key epidemiological and clinical features of children and youth with COVID-19. Design, Setting, and Participants: This retrospective cohort study included pediatric patients hospitalized between February 2 and October 10, 2020. Patient-level electronic health record (EHR) data were collected across 27 hospitals in France, Germany, Spain, Singapore, the UK, and the US. Patients younger than 21 years who tested positive for COVID-19 and were hospitalized at an institution participating in the Consortium for Clinical Characterization of COVID-19 by EHR were included in the study. Main Outcomes and Measures: Patient characteristics, clinical features, and medication use. Results: There were 347 males (52%; 95% CI, 48.5-55.3) and 324 females (48%; 95% CI, 44.4-51.3) in this study's cohort. There was a bimodal age distribution, with the greatest proportion of patients in the 0- to 2-year (199 patients [30%]) and 12- to 17-year (170 patients [25%]) age range. Trends in hospitalizations for 671 children and youth found discrete surges with variable timing across 6 countries. Data from this cohort mirrored national-level pediatric hospitalization trends for most countries with available data, with peaks in hospitalizations during the initial spring surge occurring within 23 days in the national-level and 4CE data. A total of 27 364 laboratory values for 16 laboratory tests were analyzed, with mean values indicating elevations in markers of inflammation (C-reactive protein, 83 mg/L; 95% CI, 53-112 mg/L; ferritin, 417 ng/mL; 95% CI, 228-607 ng/mL; and procalcitonin, 1.45 ng/mL; 95% CI, 0.13-2.77 ng/mL). Abnormalities in coagulation were also evident (D-dimer, 0.78 ug/mL; 95% CI, 0.35-1.21 ug/mL; and fibrinogen, 477 mg/dL; 95% CI, 385-569 mg/dL). Cardiac troponin, when checked (n = 59), was elevated (0.032 ng/mL; 95% CI, 0.000-0.080 ng/mL). Common complications included cardiac arrhythmias (15.0%; 95% CI, 8.1%-21.7%), viral pneumonia (13.3%; 95% CI, 6.5%-20.1%), and respiratory failure (10.5%; 95% CI, 5.8%-15.3%). Few children were treated with COVID-19-directed medications. Conclusions and Relevance: This study of EHRs of children and youth hospitalized for COVID-19 in 6 countries demonstrated variability in hospitalization trends across countries and identified common complications and laboratory abnormalities in children and youth with COVID-19 infection. Large-scale informatics-based approaches to integrate and analyze data across health care systems complement methods of disease surveillance and advance understanding of epidemiological and clinical features associated with COVID-19 in children and youth.


Subject(s)
COVID-19/epidemiology , Electronic Health Records/statistics & numerical data , Hospitalization/statistics & numerical data , Pandemics , SARS-CoV-2 , Adolescent , Child , Child, Preschool , Female , Global Health , Humans , Infant , Infant, Newborn , Male , Retrospective Studies
9.
Appl Clin Inform ; 12(3): 507-517, 2021 05.
Article in English | MEDLINE | ID: covidwho-1254109

ABSTRACT

OBJECTIVES: This article investigates the association between changes in electronic health record (EHR) use during the coronavirus disease 2019 (COVID-19) pandemic on the rate of burnout, stress, posttraumatic stress disorder (PTSD), depression, and anxiety among physician trainees (residents and fellows). METHODS: A total of 222 (of 1,375, 16.2%) physician trainees from an academic medical center responded to a Web-based survey. We compared the physician trainees who reported that their EHR use increased versus those whose EHR use stayed the same or decreased on outcomes related to depression, anxiety, stress, PTSD, and burnout using univariable and multivariable models. We examined whether self-reported exposure to COVID-19 patients moderated these relationships. RESULTS: Physician trainees who reported increased use of EHR had higher burnout (adjusted mean, 1.48 [95% confidence interval [CI] 1.24, 1.71] vs. 1.05 [95% CI 0.93, 1.17]; p = 0.001) and were more likely to exhibit symptoms of PTSD (adjusted mean = 15.09 [95% CI 9.12, 21.05] vs. 9.36 [95% CI 7.38, 11.28]; p = 0.035). Physician trainees reporting increased EHR use outside of work were more likely to experience depression (adjusted mean, 8.37 [95% CI 5.68, 11.05] vs. 5.50 [95% CI 4.28, 6.72]; p = 0.035). Among physician trainees with increased EHR use, those exposed to COVID-19 patients had significantly higher burnout (2.04, p < 0.001) and depression scores (14.13, p = 0.003). CONCLUSION: Increased EHR use was associated with higher burnout, depression, and PTSD outcomes among physician trainees. Although preliminary, these findings have implications for creating systemic changes to manage the wellness and well-being of trainees.


Subject(s)
COVID-19/epidemiology , Education, Medical , Electronic Health Records/statistics & numerical data , Mental Health/statistics & numerical data , Adult , Burnout, Professional/epidemiology , Female , Humans , Male , Pandemics , Stress, Psychological/epidemiology
10.
Eur Heart J Qual Care Clin Outcomes ; 7(4): 378-387, 2021 07 21.
Article in English | MEDLINE | ID: covidwho-1246705

ABSTRACT

AIMS: We hypothesized that a decline in admissions with heart failure during COVID-19 pandemic would lead to a reciprocal rise in mortality for patients with heart failure in the community. METHODS AND RESULTS: We used National Heart Failure Audit data to identify 36 974 adults who had a hospital admission with a primary diagnosis of heart failure between February and May in either 2018, 2019, or 2020. Hospital admissions for heart failure in 2018/19 averaged 160/day but were much lower in 2020, reaching a nadir of 64/day on 27 March 2020 [incidence rate ratio (IRR): 0.40, 95% confidence interval (CI): 0.38-0.42]. The proportion discharged on guideline-recommended pharmacotherapies was similar in 2018/19 compared to the same period in 2020. Between 1 February-2020 and 31 May 2020, there was a 29% decrease in hospital deaths related to heart failure (IRR: 0.71, 95% CI: 0.67-0.75; estimated decline of 448 deaths), a 31% increase in heart failure deaths at home (IRR: 1.31, 95% CI: 1.24-1.39; estimated excess 539), and a 28% increase in heart failure deaths in care homes and hospices (IRR: 1.28, 95% CI: 1.18-1.40; estimated excess 189). All-cause, inpatient death was similar in the COVID-19 and pre-COVID-19 periods [odds ratio (OR): 1.02, 95% CI: 0.94-1.10]. After hospital discharge, 30-day mortality was higher in 2020 compared to 2018/19 (OR: 1.57, 95% CI: 1.38-1.78). CONCLUSION: Compared with the rolling daily average in 2018/19, there was a substantial decline in admissions for heart failure but an increase in deaths from heart failure in the community. Despite similar rates of prescription of guideline-recommended therapy, mortality 30 days from discharge was higher during the COVID-19 pandemic period.


Subject(s)
COVID-19 , Communicable Disease Control , Heart Failure , Hospital Mortality/trends , Hospitalization/statistics & numerical data , Aged, 80 and over , COVID-19/epidemiology , COVID-19/prevention & control , Cause of Death , Clinical Audit/statistics & numerical data , Communicable Disease Control/methods , Communicable Disease Control/organization & administration , Electronic Health Records/statistics & numerical data , Female , Heart Failure/mortality , Heart Failure/therapy , Humans , Male , Mortality , Quality of Health Care , SARS-CoV-2 , Severity of Illness Index , State Medicine/standards , State Medicine/statistics & numerical data , United Kingdom/epidemiology
11.
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
12.
Clin Pharmacol Ther ; 110(1): 108-122, 2021 07.
Article in English | MEDLINE | ID: covidwho-1212738

ABSTRACT

Numerous drugs are currently under accelerated clinical investigation for the treatment of coronavirus disease 2019 (COVID-19); however, well-established safety and efficacy data for these drugs are limited. The goal of this study was to predict the potential of 25 small molecule drugs in clinical trials for COVID-19 to cause clinically relevant drug-drug interactions (DDIs), which could lead to potential adverse drug reactions (ADRs) with the use of concomitant medications. We focused on 11 transporters, which are targets for DDIs. In vitro potency studies in membrane vesicles or HEK293 cells expressing the transporters coupled with DDI risk assessment methods revealed that 20 of the 25 drugs met the criteria from regulatory authorities to trigger consideration of a DDI clinical trial. Analyses of real-world data from electronic health records, including a database representing nearly 120,000 patients with COVID-19, were consistent with several of the drugs causing transporter-mediated DDIs (e.g., sildenafil, chloroquine, and hydroxychloroquine). This study suggests that patients with COVID-19, who are often older and on various concomitant medications, should be carefully monitored for ADRs. Future clinical studies are needed to determine whether the drugs that are predicted to inhibit transporters at clinically relevant concentrations, actually result in DDIs.


Subject(s)
Antiviral Agents , COVID-19 , Drug Interactions , Drug-Related Side Effects and Adverse Reactions , Membrane Transport Proteins/metabolism , Virus Internalization/drug effects , Virus Replication/drug effects , Antiviral Agents/pharmacokinetics , COVID-19/drug therapy , COVID-19/virology , Clinical Trials as Topic , Drug Monitoring/methods , Drug Monitoring/standards , Drug-Related Side Effects and Adverse Reactions/etiology , Drug-Related Side Effects and Adverse Reactions/metabolism , Drug-Related Side Effects and Adverse Reactions/prevention & control , Electronic Health Records/statistics & numerical data , HEK293 Cells , Humans , Hydroxychloroquine/pharmacokinetics , Risk Assessment/methods , SARS-CoV-2/drug effects , SARS-CoV-2/physiology
13.
JAMA Netw Open ; 4(4): e217498, 2021 04 01.
Article in English | MEDLINE | ID: covidwho-1196364

ABSTRACT

Importance: Acute ischemic stroke (AIS) is a known neurological complication in patients with respiratory symptoms of COVID-19 infection. However, AIS has not been described as a late sequelae in patients without respiratory symptoms of COVID-19. Objective: To assess AIS experienced by adults 50 years or younger in the convalescent phase of asymptomatic COVID-19 infection. Design, Setting, and Participants: This case series prospectively identified consecutive male patients who received care for AIS from public health hospitals in Singapore between May 21, 2020, and October 14, 2020. All of these patients had laboratory-confirmed asymptomatic COVID-19 infection based on a positive SARS-CoV-2 serological (antibodies) test result. These patients were individuals from South Asian countries (India and Bangladesh) who were working in Singapore and living in dormitories. The total number of COVID-19 cases (54 485) in the worker dormitory population was the population at risk. Patients with ongoing respiratory symptoms or positive SARS-CoV-2 serological test results confirmed through reverse transcriptase-polymerase chain reaction nasopharyngeal swabs were excluded. Main Outcomes and Measures: Clinical course, imaging, and laboratory findings were retrieved from the electronic medical records of each participating hospital. The incidence rate of AIS in the case series was compared with that of a historical age-, sex-, and ethnicity-matched national cohort. Results: A total of 18 male patients, with a median (range) age of 41 (35-50) years and South Asian ethnicity, were included. The median (range) time from a positive serological test result to AIS was 54.5 (0-130) days. The median (range) National Institutes of Health Stroke Scale score was 5 (1-25). Ten patients (56%) presented with a large vessel occlusion, of whom 6 patients underwent intravenous thrombolysis and/or endovascular therapy. Only 3 patients (17%) had a possible cardiac source of embolus. The estimated annual incidence rate of AIS was 82.6 cases per 100 000 people in this study compared with 38.2 cases per 100 000 people in the historical age-, sex-, and ethnicity-matched cohort (rate ratio, 2.16; 95% CI, 1.36-3.48; P < .001). Conclusions and Relevance: This case series suggests that the risk for AIS is higher in adults 50 years or younger during the convalescent period of a COVID-19 infection without respiratory symptoms. Acute ischemic stroke could be part of the next wave of complications of COVID-19, and stroke units should be on alert and use serological testing, especially in younger patients or in the absence of traditional risk factors.


Subject(s)
Asymptomatic Infections/epidemiology , COVID-19 , Ischemic Stroke , SARS-CoV-2 , Thrombectomy/methods , Thrombolytic Therapy/methods , Adult , COVID-19/complications , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19 Serological Testing/methods , Convalescence , Electronic Health Records/statistics & numerical data , Endovascular Procedures/methods , Humans , Incidence , Ischemic Stroke/diagnosis , Ischemic Stroke/ethnology , Ischemic Stroke/etiology , Male , Middle Aged , Outcome and Process Assessment, Health Care , Risk Factors , SARS-CoV-2/isolation & purification , SARS-CoV-2/pathogenicity , Singapore/epidemiology , Transients and Migrants/statistics & numerical data
14.
Pharmacoepidemiol Drug Saf ; 30(7): 827-837, 2021 07.
Article in English | MEDLINE | ID: covidwho-1192592

ABSTRACT

The US Food and Drug Administration's Sentinel System was established in 2009 to use routinely collected electronic health data for improving the national capability to assess post-market medical product safety. Over more than a decade, Sentinel has become an integral part of FDA's surveillance capabilities and has been used to conduct analyses that have contributed to regulatory decisions. FDA's role in the COVID-19 pandemic response has necessitated an expansion and enhancement of Sentinel. Here we describe how the Sentinel System has supported FDA's response to the COVID-19 pandemic. We highlight new capabilities developed, key data generated to date, and lessons learned, particularly with respect to working with inpatient electronic health record data. Early in the pandemic, Sentinel developed a multi-pronged approach to support FDA's anticipated data and analytic needs. It incorporated new data sources, created a rapidly refreshed database, developed protocols to assess the natural history of COVID-19, validated a diagnosis-code based algorithm for identifying patients with COVID-19 in administrative claims data, and coordinated with other national and international initiatives. Sentinel is poised to answer important questions about the natural history of COVID-19 and is positioned to use this information to study the use, safety, and potentially the effectiveness of medical products used for COVID-19 prevention and treatment.


Subject(s)
COVID-19/therapy , Health Information Management/organization & administration , Product Surveillance, Postmarketing/methods , Public Health Surveillance/methods , United States Food and Drug Administration/organization & administration , Antiviral Agents/therapeutic use , COVID-19/epidemiology , COVID-19/virology , COVID-19 Vaccines/administration & dosage , COVID-19 Vaccines/adverse effects , Communicable Disease Control/legislation & jurisprudence , Databases, Factual/statistics & numerical data , Electronic Health Records/statistics & numerical data , Health Policy , Humans , Pandemics/prevention & control , Pandemics/statistics & numerical data , United States/epidemiology , United States Food and Drug Administration/legislation & jurisprudence
15.
Australas Psychiatry ; 29(3): 340-343, 2021 06.
Article in English | MEDLINE | ID: covidwho-1186470

ABSTRACT

OBJECTIVE: It has been widely predicted that the COVID-19 pandemic will have a detrimental impact on the mental health (MH) of individuals. This has been dubbed as the MH 'second wave'. In Australia, these impacts have been partly mitigated by institutional responses such as increased access to psychotherapy. Consultation Liaison (CL) psychiatry services provide MH care to acutely unwell patients in general hospitals. It was hypothesised that the number of referrals to the studied service had increased since the start of the pandemic. METHODS: From the Electronic medical records (eMRs), the authors collected daily referral numbers, over 3 consecutive years, to a large CL service in metropolitan Sydney. RESULTS: Referrals were significantly increased by 25%, 95% CI [1.14, 1.36], p < .001 since the start of the pandemic. This increase was delayed, and remained elevated despite a reduction in COVID-19 infections. CONCLUSION: This study adds evidence to the existence of the MH 'second wave', highlights a key impact on healthcare workers' well-being and will assist in guiding resource allocation decisions in the near future.


Subject(s)
COVID-19 , Hospitals, General/statistics & numerical data , Mental Disorders/therapy , Mental Health Services/statistics & numerical data , Psychiatry/statistics & numerical data , Referral and Consultation/statistics & numerical data , Electronic Health Records/statistics & numerical data , Humans , New South Wales
16.
Lancet Respir Med ; 9(4): 397-406, 2021 04.
Article in English | MEDLINE | ID: covidwho-1180129

ABSTRACT

BACKGROUND: Analysis of the effect of COVID-19 on the complete hospital population in England has been lacking. Our aim was to provide a comprehensive account of all hospitalised patients with COVID-19 in England during the early phase of the pandemic and to identify the factors that influenced mortality as the pandemic evolved. METHODS: This was a retrospective exploratory analysis using the Hospital Episode Statistics administrative dataset. All patients aged 18 years or older in England who completed a hospital stay (were discharged alive or died) between March 1 and May 31, 2020, and had a diagnosis of COVID-19 on admission or during their stay were included. In-hospital death was the primary outcome of interest. Multilevel logistic regression was used to model the relationship between death and several covariates: age, sex, deprivation (Index of Multiple Deprivation), ethnicity, frailty (Hospital Frailty Risk Score), presence of comorbidities (Charlson Comorbidity Index items), and date of discharge (whether alive or deceased). FINDINGS: 91 541 adult patients with COVID-19 were discharged during the study period, among which 28 200 (30·8%) in-hospital deaths occurred. The final multilevel logistic regression model accounted for age, deprivation score, and date of discharge as continuous variables, and sex, ethnicity, and Charlson Comorbidity Index items as categorical variables. In this model, significant predictors of in-hospital death included older age (modelled using restricted cubic splines), male sex (1·457 [1·408-1·509]), greater deprivation (1·002 [1·001-1·003]), Asian (1·211 [1·128-1·299]) or mixed ethnicity (1·317 [1·080-1·605]; vs White ethnicity), and most of the assessed comorbidities, including moderate or severe liver disease (5·433 [4·618-6·392]). Later date of discharge was associated with a lower odds of death (0·977 [0·976-0·978]); adjusted in-hospital mortality improved significantly in a broadly linear fashion, from 52·2% in the first week of March to 16·8% in the last week of May. INTERPRETATION: Reductions in the adjusted probability of in-hospital mortality for COVID-19 patients over time might reflect the impact of changes in hospital strategy and clinical processes. The reasons for the observed improvements in mortality should be thoroughly investigated to inform the response to future outbreaks. The higher mortality rate reported for certain ethnic minority groups in community-based studies compared with our hospital-based analysis might partly reflect differential infection rates in those at greatest risk, propensity to become severely ill once infected, and health-seeking behaviours. FUNDING: None.


Subject(s)
COVID-19/mortality , Hospital Mortality/trends , Minority Groups/statistics & numerical data , Pandemics/statistics & numerical data , Patient Acceptance of Health Care/statistics & numerical data , Adolescent , Adult , Age Factors , Aged , Aged, 80 and over , COVID-19/diagnosis , Comorbidity , Datasets as Topic , Electronic Health Records/statistics & numerical data , England/epidemiology , Female , Humans , Length of Stay/statistics & numerical data , Male , Middle Aged , Retrospective Studies , Risk Factors , Severity of Illness Index , Sex Factors , Young Adult
17.
JMIR Public Health Surveill ; 6(4): e22400, 2020 10 22.
Article in English | MEDLINE | ID: covidwho-1172949

ABSTRACT

BACKGROUND: Racial disparities in health care are well documented in the United States. As machine learning methods become more common in health care settings, it is important to ensure that these methods do not contribute to racial disparities through biased predictions or differential accuracy across racial groups. OBJECTIVE: The goal of the research was to assess a machine learning algorithm intentionally developed to minimize bias in in-hospital mortality predictions between white and nonwhite patient groups. METHODS: Bias was minimized through preprocessing of algorithm training data. We performed a retrospective analysis of electronic health record data from patients admitted to the intensive care unit (ICU) at a large academic health center between 2001 and 2012, drawing data from the Medical Information Mart for Intensive Care-III database. Patients were included if they had at least 10 hours of available measurements after ICU admission, had at least one of every measurement used for model prediction, and had recorded race/ethnicity data. Bias was assessed through the equal opportunity difference. Model performance in terms of bias and accuracy was compared with the Modified Early Warning Score (MEWS), the Simplified Acute Physiology Score II (SAPS II), and the Acute Physiologic Assessment and Chronic Health Evaluation (APACHE). RESULTS: The machine learning algorithm was found to be more accurate than all comparators, with a higher sensitivity, specificity, and area under the receiver operating characteristic. The machine learning algorithm was found to be unbiased (equal opportunity difference 0.016, P=.20). APACHE was also found to be unbiased (equal opportunity difference 0.019, P=.11), while SAPS II and MEWS were found to have significant bias (equal opportunity difference 0.038, P=.006 and equal opportunity difference 0.074, P<.001, respectively). CONCLUSIONS: This study indicates there may be significant racial bias in commonly used severity scoring systems and that machine learning algorithms may reduce bias while improving on the accuracy of these methods.


Subject(s)
Forecasting/methods , Hospital Mortality , Machine Learning/standards , APACHE , Adult , Aged , Algorithms , Cohort Studies , Early Warning Score , Electronic Health Records/statistics & numerical data , Female , Humans , Machine Learning/statistics & numerical data , Male , Middle Aged , Retrospective Studies , Simplified Acute Physiology Score
20.
J Biomed Inform ; 117: 103744, 2021 05.
Article in English | MEDLINE | ID: covidwho-1155518

ABSTRACT

Fast temporal query on large EHR-derived data sources presents an emerging big data challenge, as this query modality is intractable using conventional strategies that have not focused on addressing Covid-19-related research needs at scale. We introduce a novel approach called Event-level Inverted Index (ELII) to optimize time trade-offs between one-time batch preprocessing and subsequent open-ended, user-specified temporal queries. An experimental temporal query engine has been implemented in a NoSQL database using our new ELII strategy. Near-real-time performance was achieved on a large Covid-19 EHR dataset, with 1.3 million unique patients and 3.76 billion records. We evaluated the performance of ELII on several types of queries: classical (non-temporal), absolute temporal, and relative temporal. Our experimental results indicate that ELII accomplished these queries in seconds, achieving average speed accelerations of 26.8 times on relative temporal query, 88.6 times on absolute temporal query, and 1037.6 times on classical query compared to a baseline approach without using ELII. Our study suggests that ELII is a promising approach supporting fast temporal query, an important mode of cohort development for Covid-19 studies.


Subject(s)
Big Data , COVID-19 , Electronic Health Records/statistics & numerical data , Information Storage and Retrieval , Humans , SARS-CoV-2
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