Can routine laboratory variables predict survival in COVID-19? An artificial neural network-based approach

Abstract

To the Editor, As of August 23, 2020, a cumulative total of over 23 million cases of coronavirus disease 2019 (COVID-19) infections and 800,000 related deaths has been reported [1]. Although most infected people present with mild-tomoderate symptoms, about one-third require hospitalization [2] (Last accessed 27 Aug 2020). Identification of valid prognostic factors for patients with COVID-19 might be helpful in the early diagnosis of "high-risk" individuals [3]. Some demographic and clinical variablesnotably age, male sex, smoking or comorbidities such as cardiovascular disease, obesity or diabeteshave been associated with a worse prognosis By contrast, while some potential blood biomarkers (e.g., lactate dehydrogenase [LDH], C-reactive protein, coagulation parameters or lymphopenia) are emerging The use of artificial intelligence (e.g., artificial neural network [ANN]) as a form of predictive analysis could help in this regard, and its combination with standard observation at triage might help to correctly identify those patients at a higher risk We have studied the prognostic value (in terms of survival) of potential "early" routine biochemistry and hematological biomarkers in patients with COVID-19. This is a retrospective study of all admitted patients diagnosed with COVID-19 (by polymerase chain reaction) in a large public Hospital of Madrid, Spain (Hospital 12 de Octubre) from February 28 to March 30. The protocol was approved by the Ethics Committee of the aforementioned institution (reference #20/222) and adhered to the Declaration of Helsinki. The predictive value (i.e., odds of dying in the hospital versus discharge) of routine serum biochemistry (Cobas 8000 platform; Roche Diagnostics, Risch-Rotkreuz, Switzerland) and hematological parameters (DxH 900 hematology analyzer, Beckman Coulter,

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