Last update: May 21, 2020.

What is DxCOVID-19

DxCOVID-19 is a test based on a routine blood analysis, and an Artificial Intelligence algorithm capable to suggest a potential COVID-19 infection from a footprint in the bloodstream. Designed to detect those SARS-CoV-2 positive cases to be confirmed later by a RT-PCR test. Also measures the risk of bad outcomes.
DxCOVID-19 demonstrated the feasibility and clinical soundness of using routine blood tests analysis ―such as Leukocytes, Neutrophil, Lymphocyte, Monocyte, Eosinophil, Basophil, Platelet Count, C-Reactive Protein (CRP), Aspartate Aminotransferase (AST), Alanine Aminotransferase (ALT) or Serum Creatinine, among others―, and Machine Learning (ML) as an alternative to reverse transcription polymerase chain reaction (RT-PCR) tests and CT scans for identifying COVID-19 positive patients. This is especially useful as a quick and inexpensive screening test, which can be performed in the presence of any of the symptoms or signs related to a COVID-19 infection (such as fever, cough, shortness of breath, or sudden loss of smell or taste), but also in the case of fatigue, conjunctivitis, nausea, vomiting, abdominal discomfort or diarrhea, with which it is possible to reserve the RT-PCR tests ―more rare, laborious and expensive―, for those cases that the algorithm classifies as positive.
DxCOVID-19 is based on an algorithm that jointly processes clinical data of patients (sex, race/ethnicity and age), symptoms and signs (fever, cough, shortness of breath, sudden loss of smell or taste, fatigue, conjunctivitis, nausea, vomiting, abdominal discomfort or diarrhea), as well as several basic laboratory determinations, obtained in a simple and rapid blood test. It has been developed from data from real patients ―2,884 of them positive for SARS-CoV-2― all of them confirmed by RT-PCR. In this way, the performance achieved by DxCOVID-19 (sensitivity between 92% and 95%, and 65% especificity) provides enough proof that it can be used to discriminate among potential COVID-19 infectious patients with sufficient reliability, and similar sensitivity to the current gold standard, that is, the RT-PCR.

How does DxCOVID-19 work?

SARS-CoV-2 (COVID-19) infection creates a footprint in the blood that DxCOVID-19 can detect, since COVID-19 is associated with:

  • Lymphopenia (abnormally low level of white blood cells in the blood).
  • Thrombocytopenia (abnormally low level of platelets).
  • Increased aspartate aminotransferase (AST) levels.
  • Increased alanine aminotransferase (ALT) levels.
  • Significantly increased C-Reactive Protein (CRP) levels.

In this way, DxCOVID-19 computes two different and independent algorithms: one exclusively with clinical data and laboratory results; and another, which also includes those symptoms and signs reported by the patients, creating two different scores.
Then, the algorithm generates a final score related to positivity and negativity by comparing the previous scores with the footprint already learned in the modeling process. If any of the symptoms and signs are not reported and the algorithm does not obtain a clear enough score, an indeterminate result may occur ―in this case, the algorithm performs several simulations for all the symptoms and signs, determining each of them as positive and negative, obtaining a score―.

Main features



Artificial Intelligence (AI) algorithm trained with real positive and negative SARS-CoV-2 patients and focused on enhancing doctor–patient relationship.


Based in Machine Learning (ML) and Deep Learning (DL) to develop powerful and accurate interpretative commenting through Natural Language Processing (NLP).


Only a swift visit to your preferred local laboratory or medical center to drawn the blood sample is required to generate the final report.



Complete list of several ―carefully selected―, blood analytes already bundled for an easy order.


Specially designed to be performed as many times should be required thanks to its high diagnostic capabilities and low price.


An easy-to-understand report with suggestions of further actions when a SARS-CoV-2 positive or negative is found specially written both for doctors and patients.


How the report looks like?

Once blood has been analyzed by you laboratory, our servers compute all values in an innovative algorithm and generate a final report with results, comments and suggestions.

Excerpt from a 6-page report*


* The report has been specially written to be interpreted by both doctors and patients.


Uses and purposes



Detect those SARS-CoV-2 positive cases to suggest a RT-PCR test to confirm positivity, and recommend isolation and/or medical treatment.


Discard those SARS-CoV-2 negative cases in order to don’t waste unnecessary and expensive RT-PCR tests.


Detect those positive results with a potential worst prognosis to allow quick interventions for better outcomes.


For whom is it intended?


Symptomatic people

Men and women with those symptoms and signs linked to COVID-19 ―mainly fever, cough, shortness of breath, sudden loss of smell or taste, but also fatigue, conjunctivitis, nausea, vomiting, abdominal discomfort or diarrhea―, that need to confirm or discard a potential SARS-CoV-2 infection for an early diagnosis.

Asymptomatic people

Men and women without those symptoms and signs linked to COVID-19 who may have been in contact with a previous SARS-CoV-2 infected person, in order to confirm or discard any potential infection, since one of the biggest challenges in COVID-19 pandemic is asymptomatic transmission.


Bibliographic references


Scientific publications

  1. Assiri A, Al-Tawfiq JA, Al-Rabeeah AA, Al-Rabiah FA, Al-Hajjar S, Al-Barrak A, Flemban H, Al-Nassir WN, Balkhy HH, Al-Hakeem RF, Makhdoom HQ, Zumla AI, Memish ZA (2013) Epidemiological, demographic, and clinical characteristics of 47 cases of Middle East respiratory syndrome coronavirus disease from Saudi Arabia: a descriptive study. The Lancet Infectious diseases 13(9):752-761.
  2. Audia S, Mahévas M, Samson M, Godeau B, Bonnotte B (2017) Pathogenesis of immune thrombocytopenia. Autoimmunity reviews 16(6):620-632.
  3. Chen N, Zhou N, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 2020.
  4. Chen W, Li Z, Yang B, Wang P, Zhou O, Zhu J, Chen X, Yang P, Zhou H. Delayed-Phase Thrombocytopenia in Patients of Coronavirus Disease 2019 (COVID-19).
  5. Chng WJ, Lai HC, Earnest A, Kuperan P. Haematological parameters in severe acute respiratory syndrome. Clin Lab Haematol. 2005; 27:15-20.3.
  6. Ding J, Karp JE, and Emadi A. Elevated lactate dehydrogenase (LDH) can be a marker of immune suppression in cancer: Interplay between hematologic and solid neoplastic clones and their microenvironments. Cancer Biomark. 2017; 19(4):353-63.
  7. Feng G, Zheng KI, Yan QQ, et al. COVID-19 and Liver Dysfunction: Current Insights and Emergent Therapeutic Strategies. J Clin Transl Hepatol. 2020; 8(1):18‐24. doi:10.14218/JCTH.2020.00018.
  8. Grau GE, Morrow D, Izui S, Lambert PH (1986) Pathogenesis of the delayed phase of Rauscher virus-induced thrombocytopenia. Journal of immunology (Baltimore, Md: 1950) 136(2):686-691.
  9. Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med. 2020.
  10. Hajjar SA, Memish ZA, McIntosh K (2013) Middle East Respiratory Syndrome Coronavirus (MERS-CoV): a perpetual challenge. Annals of Saudi medicine 33(5):427-436.
  11. Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020; 395(10223):497-506.5.
  12. Karimi O, Goorhuis A, Schinkel J, Codrington J, Vreden SGS, Vermaat JS, Stijnis C, Grobusch MP (2016) Thrombocytopenia and subcutaneous bleedings in a patient with Zika virus infection. Lancet (London, England) 387(10022):939-940.
  13. Laham FR, Trott AA, Bennett BL, Kozinetz CA, Jewell AM, Garofalo RP, et al. LDH concentration in nasal-wash fluid as a biochemical predictor of bronchiolitis severity. Pediatrics. 2010; 125(2):e225-33.
  14. Lambert MP, Gernsheimer TB (2017) Clinical updates in adult immune thrombocytopenia. Blood 129(21):2829-2835.
  15. Lee IK, Liu JW, Wang L, Yang KD, Li CC, and Eng HL. 2009 pandemic influenza A (H1N1): clinical and laboratory characteristics in pediatric and adult patients and in patients with pulmonary involvement. Influenza Other Respir Viruses. 2012; 6(6):e152-61.
  16. Li J, Fan JG. Characteristics and Mechanism of Liver Injury in 2019 Coronavirus Disease. J Clin Transl Hepatol. 2020; 8(1):13‐17. doi:10.14218/JCTH.2020.00019.
  17. Li T, Qiu Z, Zhang L, Han Y, He W, Liu Z, et al. Significant changes of peripheral T lymphocyte subsets in patients with severe acute respiratory syndrome. J Infect Dis. 2004; 189(4):648-51.
  18. Lin L, Lu L, Cao W, Li T (2020) Hypothesis for potential pathogenesis of SARS-CoV-2 infection-a review of immune changes in patients with viral pneumonia. Emerging microbes & infections 9 (1):727-732.
  19. Lippi, G. & Plebani, M. (2020) Laboratory abnormalities in patients with COVID-2019 infection, Clinical Chemistry and Laboratory Medicine (CCLM).
  20. Maan R, van der Meer AJ, Hansen BE, Feld JJ, Wedemeyer H, Dufour JF, Zangneh HF, Lammert F, Manns MP, Zeuzem S, Janssen HL, de Knegt RJ, Veldt BJ (2014) Effect of thrombocytopenia on treatment tolerability and outcome in patients with chronic HCV infection and advanced hepatic fibrosis. Journal of hepatology 61(3):482-491.
  21. Méndez R, Menéndez R, Amara-Elori I, Feced L, Piró A, Ramírez P, et al. Lymphopenic community-acquired pneumonia is associted with a dysregulated immuneresponse and increased severity and mortality. J Infect 2019; 78(6):42331.
  22. Omrani-Nava V, Maleki I, Ahmadi A, Moosazadeh M, Hedayatizadeh-Omran A, et al. Evaluation of Hepatic Enzymes Changes and Association with Prognosis in COVID-19 Patients, Hepat Mon. 2020; 20(4):e103179.
  23. Pan L, Beverley PC, and Isaacson PG. Lactate dehydrogenase (LDH) isoenzymes and proliferative activity of lymphoid cells–an immunocytochemical study. Clin Exp Immunol. 1991; 86(2):240-5.
  24. Song YJ, Kim A, Kim GT, Yu HY, Lee ES, Park MJ, et al. Inhibition of lactate dehydrogenase A suppresses inflammatory response in RAW 264.7 macrophages. Mol Med Rep. 2019; 19(1):629-37.
  25. Xu XW, Wu XX, Jiang XG, et al. Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) out-side of Wuhan, China: retrospective case series. BMJ. 2020; 368:m606.6.
  26. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. Jama. 2020.
  27. Wong RSM, Wu A, To KF, et al. Haematological manifestations inpatients with severe acute respiratory syndrome: retrospective analysis. BMJ. 2003; 326:1358-1362.
  28. Yan, L., Zhang, H., Goncalves, J. et al. An interpretable mortality prediction model for COVID-19 patients. Nat Mach Intell (2020).
  29. Zhu N, Zhang D, Wang W, et al. A novel coronavirus from patientswith pneumonia in China, 2019. New Engl J Med. 2020; 382:727-733.2.

Social Media