COVID-19 Report • Run on April 3, 2020 TRINETX COVID-19 _______________________________________________________________________________ 2019-nCoV (COVID-19) Real-World Data Report EMEA Issue 6 Run on September 17, 2020
COVID-19 Report • Run on April 3, 2020 Run by Juliet Winfred on JULY 31, 2019 – 4:15 PM
TRINETX
COVID-19 _______________________________________________________________________________
2019-nCoV (COVID-19) Real-World Data Report
EMEA Issue 6 Run on September 17, 2020
COVID-19 Report • Run on September 17, 2020
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CONTENTS
OVERVIEW ................................................................................................................................... 3
NETWORK CHARACTERISTICS ......................................................................................................... 3
COHORT SUMMARY ...................................................................................................................... 3
CLINICAL FINDINGS ....................................................................................................................... 4
COVID-19 Patient Density Map ................................................................................................... 4
Demographics and Prior/Coexisting Conditions of COVID-19 Patients ............................................. 5
Clinical Characteristics During COVID-19 Episode .......................................................................... 6
Treatment Pathway of COVID-19 Patients .................................................................................... 7
MAJOR OUTCOMES ...................................................................................................................... 8
Kaplan-Meier Survival Curve for All-Cause Mortality ..................................................................... 9
CLINICAL SPOTLIGHT ..................................................................................................................... 9
APPENDIX ...................................................................................................................................10
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OVERVIEW TriNetX is the global health research network that connects the world of drug discovery and
development from pharmaceutical company to study site, and investigator to patient by
sharing real-world data to make clinical and observational research easier and more efficient.
This report summarizes critical information about the characteristics, treatments, and
outcomes of COVID-19 patients identified in our network and will be updated on an ongoing
basis.
NETWORK CHARACTERISTICS
This report includes data from the TriNetX EMEA network, representing electronic medical
record (EMR) data from 26 healthcare organizations (HCOs) across 8 countries in Europe and
the Middle East, representing over 12 million patients. A subset of HCOs on the EMEA network
allow for advanced analytics to be run.
COHORT SUMMARY Potential COVID-19 patients were identified using on a combination of ICD-10 diagnostic terms
and confirmatory laboratory results occurring on or after January 1, 2020 (See Appendix A).
TriNetX identified 42,950 potential COVID-19 patients as of September 17, 2020. From this
cohort of all potential COVID-19 patients, we identified a sub-cohort of 12,850 severe patients
who were hospitalized within one month on or after the first instance of COVID-19 in their
EMR.
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CLINICAL FINDINGS
COVID-19 Patient Density Map
Country Patients Percent of Cohort
Spain 30,470 70.9%
United Kingdom 4,710 11.0%
Italy 4,120 9.6%
Israel 2,200 5.1%
Belgium 950 2.2%
Germany 470 1.1%
Bulgaria 50 0.1%
United Kingdom Germany
Spain
Israel
Italy
Bulgaria
Belgium
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PATIENT CHARACTERISTICS
Demographics and Prior/Coexisting Conditions of COVID-19 Patients
All COVID-19 Patients Severe COVID-19 Patients
Demographics n=42,950 n=12,850
Age, years (mean ± SD) 55 ± 21 65 ± 18
10 - 19 (n, %) 1,840 4.3 200 1.6
20 – 29 (n, %) 4,160 9.7 530 4.1
30 – 39 (n, %) 5,270 12.3 800 6.2
40 – 49 (n, %) 6,440 15.0 1,260 9.8
50 – 59 (n, %) 7,090 16.5 2,080 16.2
60 – 69 (n, %) 5,910 13.8 2,260 17.6
70 – 79 (n, %) 5,590 13.0 2,580 20.1
≥80 (n, %) 6,690 15.6 3,170 24.7
Male Sex (n, %) 21,440 49.9 7,240 56.3
Female Sex (n, %) 21,510 50.1 5,620 43.7
n=33,010 n=11,780
Prior or Coexisting Condition1 n % n %
Respiratory diseases 4,700 14.2 3,420 29.0
COPD 860 2.6 680 5.8
Asthma 710 2.2 430 3.7
Seasonal allergies* 50 0.1 20 0.2
Cardiovascular diseases 5,260 15.9 3,950 33.5
Hypertension 3,300 10.0 2,430 20.6
Congestive heart failure 1,080 3.3 880 7.5
Angina pectoris 550 1.7 390 3.3
Myocardial infarction 370 1.1 310 2.6
Cancer* 2,540 5.9 1,450 11.3
Diabetes 1,820 5.5 1,380 11.7
Kidney disease 1,060 3.2 820 7.0
HIV 40 0.1 30 0.3
1 Data as of September 17, 2020. Diagnoses captured any time to one day before first instance of COVID-19 in EMR. Except
where noted with an asterisk (*), diagnosis data was captured using Characteristics in the TriNetX platform from a subset of
HCOs that allow for the platform’s advanced analytics to be run. For the conditions marked with an asterisk, data was
captured using Query Builder and the percentage is calculated based on the total number of patients in the base cohort and
sub-cohort of severe patients.
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Clinical Characteristics During COVID-19 Episode
All COVID-19 Patients Severe COVID-19 Patients
n=42,950 n=12,850
Diagnosis2 n % n %
Pneumonia 16,720 38.9 8,110 63.1
Renal failure 2,200 5.1 1,210 9.4
Hypotension 1,240 2.9 1,030 8.0
Acute lower respiratory infections 1,200 2.8 470 3.7
Acute respiratory distress syndrome (ARDS) 800 1.9 360 2.8
Fever 890 2.1 590 4.6
Diarrhea 650 1.5 270 2.1
Shortness of breath 540 1.3 370 2.9
Cough 500 1.2 330 2.6
Pain in throat and chest 450 1.0 210 1.6
Bronchitis 120 0.3 40 0.3
Hepatic failure 120 0.3 60 0.5
Loss of taste or smell 90 0.2 40 0.3
Clinical Setting2 n % n %
Inpatient 14,070 32.8 12,850 100.0
Emergency 13,720 31.9 7,270 56.6
Medication3 n % n %
Antibiotics 5,950 13.9 5,220 40.6
Antimalarials 3,430 8.0 3,050 23.7
Glucocorticoids 2,830 6.6 2,530 19.7
Antivirals 1,470 3.4 1,280 10.0
Interleukin Inhibitors 600 1.4 540 4.2
2 Diagnoses and clinical setting captured in EMR one week before to one month after first instance of COVID-19 in EMR.
3 Medications captured in EMR one day before to one month after first instance of COVID-19 in EMR.
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Treatment Pathway of COVID-19 Patients
The sunburst diagram shows the top ten individual or combination therapies used to treat potential COVID-19 patients. Here a line of therapy is defined as any treatments taken within 1 day. Treatment pathways were analyzed from the first instance of COVID-19 in EMR until September 17, 2020.
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MAJOR OUTCOMES4
All COVID-19 Patients Severe COVID-19 Patients
n=25,550 n=6,180
Laboratory Data5 Mean ± SD Mean ± SD
Complete Blood Count
Hemoglobin, g/dL 13.4 ± 1.9 13.0 ± 1.9
Hematocrit, % 39.8 ± 5.2 38.5 ± 5.4
RBC, 106 cells/µL 4.4 ± 0.7 4.3 ± 0.7
Platelet Count, 103 cells/µL 262.4 ± 121.4 281.9 ± 139.7
WBC, 103 cells/µL 7.1 ± 4.7 7.4 ± 5.7
Eosinophils, % 2.1 ± 2.4 2.1 ± 2.6
Metabolic
Creatinine, mg/dL 0.9 ± 0.8 1.0 ± 1.0
Hepatic
ALT, U/L 38.7 ± 46.6 45.6 ± 55.7
AST, U/L 30.6 ± 53.2 35.5 ± 68.8
Alk Phos, U/L 83.9 ± 59.0 88.8 ± 68.7
Total bilirubin, mg/dL 0.5 ± 0.4 0.5 ± 0.5
Inflammatory
C Reactive Protein, mg/L 6.7 ± 23.6 8.1 ± 25.5
IL-6, pg/mL 176.0 ± 823.9 206.2 ± 892.6
Mortality6 n % n %
All-cause mortality 1,440 5.6 1,200 19.4
n=42,950 N=12,850
Care and Management5 n % n %
Hospitalization 17,990 41.9 12,850 100.0
Chest radiology (e.g., x-ray, CT, MRI) 11,090 25.8 4,340 33.8
Abnormal finding on imaging of lung7 110 1.0 60 1.4
Mechanical ventilation (including ECMO) 1,050 2.4 490 3.8
Follow-up time at least 14 days 12,800 29.8 6,600 51.4
Follow-up time at least 21 days 11,550 26.9 6,110 47.5
Follow-up time at least 28 days 10,700 24.9 5,750 44.7
4 Laboratory data and mortality are calculated from a subset of HCOs that allow for the platform’s advanced analytics to be
run. 5 Laboratory and care and management variables captured in EMR on same day to one month after first instance of COVID-19
in EMR. Laboratory data are of patients’ most recent laboratory results in this time window. Not all patients have laboratory data.
6 All-cause mortality captured in EMR on same day to two months after first instance of COVID-19 in EMR. 7 Abnormal finding on imaging of lung is an ICD-10 term (R91). Percentages are calculated among patients with chest
radiology performed.
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Kaplan-Meier Survival Curve for All-Cause Mortality
The Kaplan-Meier curve shows the survival probability among all COVID-19 patients and severe COVID-19 patients. All-cause mortality was analyzed from the first instance of COVID-19 up to 2 months after, through September 17, 2020.
CLINICAL SPOTLIGHT
Each issue of the 2019-nCoV (COVID-19) Real-World Data Report spotlights real-world insights
generated in the TriNetX platform or datasets.
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APPENDIX
Appendix A: COVID-19 query in TriNetX
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Appendix B: Distribution of age and sex
APPENDIX C: COVID-19 Publications Using TriNetX Data
• Harrison S, Fazio-Eynullayeva E, Lane D, et al. (2020.) Co-morbidities Associated with Mortality
in 31,461 Adults with COVID-19 in the United States: A Federated Electronic Medical Record
Analysis. Accepted to PLOS Medicine.
• Singh S, Khan A, Chowdhry M, et al. (2020). Risk of Severe COVID-19 in Patients with
Inflammatory Bowel Disease in United States. A Multicenter Research Network Study. Published
in Gastroenterology. DOI: https://doi.org/10.1053/j.gastro.2020.06.003
• Hadi Y, Naqvi S, Kupec J, et al. (2020). Characteristics and outcomes of COVID-19 in patients with
HIV, AIDS. Volume Publish Ahead of Print - Issue - doi: 10.1097/QAD.0000000000002666.
• Singh S, Chowdhry M, Chatterjee A, et al. (2020). Gender-Based Disparities in COVID-19 Patient
Outcomes: A Propensity-matched Analysis medRxiv preprint.
https://doi.org/10.1101/2020.04.24.20079046
• Griffith DM, Sharma G, Holliday CS, et al. (2020). Men and COVID-19: A Biopsychosocial
Approach to Understanding Sex Differences in Mortality and Recommendations for Practice and
Policy Interventions. Prev Chronic Dis. doi:10.5888/pcd17.200247
• Khan A, Chatterjee A, Singh S (2020). Comorbidities and Disparities in Outcomes of COVID-19
Among African American and White Patients. medRxiv 2020.05.10.20090167; doi:
https://doi.org/10.1101/2020.05.10.20090167
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• Shailendra Singh, Mohammad Bilal, Ahmad Khan, Monica Chowdhry, Sergio A. Sánchez-Luna,
Gursimran S. Kochhar, Diogo Turiani Hourneaux de Moura, Christopher C. Thompson.
• Singh S, Bilal M, Khan A, et al. (2020). Outcomes of COVID-19 in Patients with Obesity in United
States: A Large Research Network Study. Lancet pre-print.
• Onteddu SR, Nalleballe K, Sharma R., et al. (2020). Underutilization of Healthcare for strokes
during the COVID-19 outbreak. International Journal of Stroke, 1747493020934362.
• Annie F, Bates MC, Nanjundappa A, et al. (2020). Prevalence and outcomes of acute ischemic
stroke among patients≤ 50 years of age with laboratory confirmed COVID-19 infection. American
Journal of Cardiology. , doi: https://doi.org/10.1016/j.amjcard.2020.06.010
• Singh S, Khan A, Chowdhry M, et al. (2020). Outcomes of hydroxychloroquine treatment among
hospitalized COVID-19 patients in the United States - real-world evidence from a federated
electronic medical record network. medRxiv.
• Ranabothu S, Onteddu S, Nalleball K, et al. (2020). Spectrum of COVID‐19 in Children. Acta
Paediatrica https://doi.org/10.1111/apa.15412
• Nalleballe K, Reddy Onteddu S, et al. (2020) Spectrum of neuropsychiatric manifestations in
COVID-19 [published online ahead of print, 2020 Jun 17]. Brain Behav Immun. S0889-
1591(20)31008-4. https//doi:10.1016/j.bbi.2020.06.020
• London JW, Fazio-Eynullayeva E, Palchuk MB, Sankey P, McNair C. (2020). Effects of the COVID-
19 pandemic on cancer-related patient encounters. JCO Clinical Cancer Informatics, 4, 657-665.
• Singer ME, Kaelber DC, Antonelli MJ (2020). Hydroxychloroquin ineffective for COVID-19
prophylaxis in lupus and rheumatoid arthritis. Annals of the Rheumatic Diseases.
https://ard.bmj.com/content/annrheumdis/early/2020/08/05/annrheumdis-2020-
218500.full.pdf
• Singh S, Khan A. (2020) Clinical characteristics and outcomes of COVID-19 among patients with
pre-existing liver disease in United States: a multi-center research network study,
Gastroenterology, doi: https://doi.org/10.1053/j.gastro.2020.04.064.
• Turk MA, Landes SD, Formica MK, & Goss KD (2020). Intellectual and developmental disability
and COVID-19 case-fatality trends: TriNetX analysis. Disability and health journal, 100942.
Advance online publication. https://doi.org/10.1016/j.dhjo.2020.100942
• Alkhouli M, Nanjundappa A, Annie F, et al. (2020) Sex differences in COVID-19 case fatality rate:
insights from a multinational registry. Mayo Clin Proc. 2020;95(x):xx-xx. doi:
https://doi.org/10.1016/j.mayocp.2020.05.014.