What’s known
  1. COVID-19 may cause a variety of clinical conditions from an asymptomatic disease to severe viral pneumonia that can result in respiratory failure or death.
  2. The gold standard diagnostic test in the diagnosis of COVID-19 is RT-PCR. Lung imaging is quite valuable, but having access to thoracic CT is not always possible.
  3. Deep learning and artificial intelligence started to be used in many areas in medicine with a high diagnostic accuracy.
What’s new
Formulas developed with WEKA software with Linear Regression and J48 Decision Tree algorithms can be used to determine the status of patients.
With these formulas, the presence and level of thoracic CT involvement and the need for intensive care can be predicted by using the laboratory data of COVID-19 patients.
INTRODUCTIONCoronavirus disease 2019 (COVID-19) is an alarming public health concern worldwide. This disease may cause a variety of clinical conditions from an asymptomatic disease to severe viral pneumonia that can result in respiratory failure or death.1Presence of accurate and fast diagnostic tests is of clinical importance in controlling the COVID-19 pneumonia. The gold standard diagnostic test in the diagnosis of COVID-19 is the reverse transcriptase polymerase chain reaction (RT-PCR). However, this test has several limitations such as potential false negative results, high cost, difficulties in collecting, storing and transport of sample materials. Serological tests have also attracted attention as alternative or complementary to RT-PCR and other nucleic acid tests in the diagnosis of acute infections.2 However, they are not useful to diagnose acute cases as the IgM antibody response can be detected 6-15 days after the disease onset.3 Studies have reported that the sensitivity of fast SARS-CoV-2 antigen diagnosis tests is between 45 and 97%.4 Lack of common standardization among serological tests causes difficulties in the use of the test. As another diagnostic method in this disease, lung imaging is quite valuable, but having access to thoracic CT is not always possible. Moreover, early interpretation of this imaging, which is valuable for the diagnosis of COVID-19, requires an extra workload. This situation necessitates methods that can predict lung involvement and provide high diagnostic performance.5Another problem with this disease is that the patients experience unpredictable progression, giving rise to a need for intensive care. Although the intensive care need of COVID-19 patients varies by country and institution, it ranges between approximately 5 and 32%.6 It is important in terms of disease morbidity and mortality to predict patients’ intensive care needs and to transfer such patients quickly from healthcare institutions having no intensive care facilities.7There is a need for defining clinical and laboratory predictors enabling early prediction of progression to serious clinical form and intensive care need when combating this disease which puts severe stress on many healthcare systems across the world. Defining such predictors will enable risk classification so that patients having high risk of developing severe disease can be identified and necessary interventions initiated at an early stage. This will optimise human and technical resources during this ongoing pandemic.8 It could be a good alternative in support of physicians to define, with the help of the deep learning technology, a system consisting of laboratory parameters to be used to predict presence of lung involvement, the extent of such involvement and intensive care need in COVID-19 patients. In recent years, technologies such as deep learning and artificial intelligence started to be used in many areas in medicine with a high diagnostic accuracy.9,10 Although there are studies using these methods in the thoracic CT interpretation of COVID-19, we have not encountered any study on the use of these technologies for the assessment and interpretation of laboratory parameters in COVID-19. The present study aimed at defining a system of formulas that may predict the presence of thoracic CT involvement, the extent of such involvement and the need for intensive care stay on the basis of patient laboratory data using the Waikato Environment for Knowledge Analysis (WEKA) software, Linear Regression and J48 Decision Tree algorithms and that may in this way contribute to the process of diagnosing and treating the disease.