TRINETX COVID-19 · 9/17/2020 · 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

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


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


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



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

https://doi.org/10.1053/j.gastro.2020.06.003

https://doi.org/10.1101/2020.04.24.20079046

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.

https://doi.org/10.1016/j.amjcard.2020.06.010

https://doi.org/10.1111/apa.15412

https://ard.bmj.com/content/annrheumdis/early/2020/08/05/annrheumdis-2020-218500.full.pdf



https://doi.org/10.1053/j.gastro.2020.04.064

https://doi.org/10.1016/j.dhjo.2020.100942

https://doi.org/10.1016/j.mayocp.2020.05.014

Documents

TRINETX COVID-19 · 9/17/2020 · 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