Routine blood tests as a potential diagnostic tool for COVID-19

Ferrari, Davide; Motta, Andréa; Strollo, Marta; Banfi, Giuseppe; Locatelli, Massimo

doi:10.1515/cclm-2020-0398

Cited by 241 publications

(256 citation statements)

References 15 publications

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“…For all of these parameters, lower averages were found in the group of patients with COVID-19. Such findings are consistent with those described by Ferrari et al [29] and Dorn et al [24].…”

Section: Discussionsupporting

confidence: 94%

Prediction of COVID-19 From Hemogram Results and Age Using Machine Learning

Silveira

2020

Front Health Inform

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Introduction: The rapid global dissemination of COVID-19 culminated in the mobilization of great technological efforts aimed at its better understanding and control. In this context, Machine Learning gains notoriety, and its application has been widely documented for pathophysiological, diagnostic, therapeutic, prognostic and monitoring of COVID-19 purposes. The present study aimed to build a model for the prediction of the diagnosis of COVID-19 based on blood count results and age of patients and to identify the main characteristics taken into account by the algorithm for the predictive decision.Material and Methods: Anonymous data from 1157 patients made available by the COVID-19 Data Sharing / BR repository were used. The work took place in two distinct stages: description and analysis of the data; and construction of the predictive model. Results: With the exception of hemoglobin measurement, mean corpuscular volume, red cell distribution width, mean platelet volume and neutrophil-lymphocyte ratio, there was a statistically significant association of all other hematological parameters assessed with COVID-19. The predictive model developed from the XGBoost classifier reached an accuracy of 80.0% with a sensitivity of 75.6% and specificity of 82.0%. The variables that had the greatest influence on the predictive decision were basophil, eosinophil and leukocyte measurements. The present study confirms the potential of using blood count results, a widely available and accessible test, in the context of the diagnostic evaluation and pathophysiological investigation of COVID-19.Conclusion: This work highlights the relevance of the systematization and dissemination of data related to COVID-19 for use in new research.

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“…For all of these parameters, lower averages were found in the group of patients with COVID-19. Such findings are consistent with those described by Ferrari et al [29] and Dorn et al [24].…”

Section: Discussionsupporting

confidence: 94%

Prediction of COVID-19 From Hemogram Results and Age Using Machine Learning

Silveira

2020

Front Health Inform

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“…In a subset of patients with routine laboratory parameters obtained at COVID-19 diagnosis, we observed differential distribution of hematologic and biochemical parameters in relationship with patients' mortality (8). As shown in Supplementary Fig.…”

Section: Factors Associated With Mortality From Covid-19 In Patientsmentioning

confidence: 80%

“…In 120 cases (13.6%), admission to hospital was not deemed necessary, and patients were managed with domiciliary self-isolation. The median length of hospitalization in admitted patients was 10 days (IQR, [5][6][7][8][9][10][11][12][13][14][15][16][17][18], and median permanence in intensive/ subintensive care unit was 6 days (IQR, 3-12). Oxygen therapy was administered to 527 patients (59.2%), including high-flow delivery in 244 (27.4%).…”

Section: Features Of Covid-19 Diseasementioning

confidence: 99%

Clinical Portrait of the SARS-CoV-2 Epidemic in European Patients with Cancer

et al. 2020

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The SARS-CoV-2 pandemic significantly affected oncology practice across the globe. There is uncertainty as to the contribution of patients' demographics and oncologic features to severity and mortality from COVID-19 and little guidance as to the role of anticancer and anti-COVID-19 therapy in this population. In a multicenter study of 890 patients with cancer with confirmed COVID-19, we demonstrated a worsening gradient of mortality from breast cancer to hematologic malignancies and showed that male gender, older age, and number of comorbidities identify a subset of patients with significantly worse mortality rates from COVID-19. Provision of chemotherapy, targeted therapy, or immunotherapy did not worsen mortality. Exposure to antimalarials was associated with improved mortality rates independent of baseline prognostic factors. This study highlights the clinical utility of demographic factors for individualized risk stratification of patients and supports further research into emerging anti-COVID-19 therapeutics in SARS-CoV-2-infected patients with cancer. SIGNIFICANCE: In this observational study of 890 patients with cancer diagnosed with SARS-CoV-2, mortality was 33.6% and predicted by male gender, age ≥65, and comorbidity burden. Delivery of cancer therapy was not detrimental to severity or mortality from COVID-19. These patients should be the focus of shielding efforts during the SARS-CoV-2 pandemic. Research.

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“…12,27 Routine Laboratory Tests Lymphopenia occurs in over 50% of COVID-19 patients. 10,11,28 Neutrophil-to-lymphocyte and plateletto-lymphocyte ratios do not distinguish COVID-19. 29 Elevated lactate dehydrogenase (LDH) is also frequently described.…”

Section: Clinical Examinationmentioning

confidence: 97%

“…29 Elevated lactate dehydrogenase (LDH) is also frequently described. 10,28,30,31 None of these laboratory findings are commonly utilized in the diagnosis of influenza, but their prevalence and accuracy to distinguish COVID-19 from other viral mimics merit further evaluation. 23,24 Elevated prothrombin time (PT), ferritin, D-dimer, or IL-6 are associated with severe COVID-19.…”

Section: Clinical Examinationmentioning

confidence: 99%

Diagnosing COVID‐19 in the Emergency Department: A Scoping Review of Clinical Examinations, Laboratory Tests, Imaging Accuracy, and Biases

Carpenter

Mudd

West

et al. 2020

Academic Emergency Medicine

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Objective Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) emerged as a global pandemic in early 2020 with rapidly evolving approaches to diagnosing the clinical illness called coronavirus disease (COVID‐19). The primary objective of this scoping review is to synthesize current research of the diagnostic accuracy of history, physical examination, routine laboratory tests, real‐time reverse transcription–polymerase chain reaction (rRT‐PCR), immunology tests, and computed tomography (CT) for the emergency department (ED) diagnosis of COVID‐19. Secondary objectives included a synopsis of diagnostic biases likely with current COVID‐19 research as well as corresponding implications of false‐negative and false‐positive results for clinicians and investigators. Methods A Preferred Reporting Items for Systematic Reviews and Meta‐Analyses–Scoping Review (PRISMA‐ScR)–adherent synthesis of COVID‐19 diagnostic accuracy through May 5, 2020, was conducted. The search strategy was designed by a medical librarian and included studies indexed by PubMed and Embase since January 2020. Results A total of 1,907 citations were screened for relevance. Patients without COVID‐19 are rarely reported, so specificity and likelihood ratios were generally unavailable. Fever is the most common finding, while hyposmia and hypogeusia appear useful to rule in COVID‐19. Cough is not consistently present. Lymphopenia is the mostly commonly reported laboratory abnormality and occurs in over 50% of COVID‐19 patients. rRT‐PCR is currently considered the COVID‐19 criterion standard for most diagnostic studies, but a single test sensitivity ranges from 60% to 78%. Multiple reasons for false‐negatives rRT‐PCR exist, including sample site tested and disease stage during which sample was obtained. CT may increase COVID‐19 sensitivity in conjunction with rRT‐PCR, but guidelines for imaging patients most likely to benefit are emerging. IgM and IgG serology levels are undetectable in the first week of COVID‐19, but sensitivity (range = 82% to 100%) and specificity (range = 87% to 100%) are promising. Whether detectable COVID‐19 antibodies correspond to immunity remains unanswered. Current studies do not adhere to accepted diagnostic accuracy reporting standards and likely report significantly biased results if the same tests were to be applied to general ED populations with suspected COVID‐19. Conclusions With the exception of fever and disorders of smell/taste, history and physical examination findings are unhelpful to distinguish COVID‐19 from other infectious conditions that mimic SARS‐CoV‐2 like influenza. Routine laboratory tests are also nondiagnostic, although lymphopenia is a common finding and other abnormalities may predict severe disease. Although rRT‐PCR is the current criterion standard, more inclusive consensus‐based criteria will likely emerge because of the high false‐negative rate of PCR tests. The role of serology and CT in ED assessments remains undefined.

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Routine blood tests as a potential diagnostic tool for COVID-19

Cited by 241 publications

References 15 publications

Prediction of COVID-19 From Hemogram Results and Age Using Machine Learning

Prediction of COVID-19 From Hemogram Results and Age Using Machine Learning

Clinical Portrait of the SARS-CoV-2 Epidemic in European Patients with Cancer

Diagnosing COVID‐19 in the Emergency Department: A Scoping Review of Clinical Examinations, Laboratory Tests, Imaging Accuracy, and Biases

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