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

2020 ◽  
Vol 58 (7) ◽  
pp. 1095-1099 ◽  
Author(s):  
Davide Ferrari ◽  
Andrea Motta ◽  
Marta Strollo ◽  
Giuseppe Banfi ◽  
Massimo Locatelli
Keyword(s):  
Blood Test ◽  
Hematological Parameters ◽  
False Negative ◽  
White Blood Cells ◽  
Turnaround Time ◽  
Test Analysis ◽  
Infected People ◽  
Reactive Protein ◽  
Routine Blood ◽  
Glutamyl Transpeptidase

AbstractObjectivesThe outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to date, the epidemic has gradually spread to 209 countries worldwide with more than 1.5 million infected people and 100,000 deaths. Amplification of viral RNA by rRT-PCR serves as the gold standard for confirmation of infection, yet it needs a long turnaround time (3–4 h to generate results) and shows false-negative rates as large as 15%–20%. In addition, the need of certified laboratories, expensive equipment and trained personnel led many countries to limit the rRT-PCR tests only to individuals with pronounced respiratory syndrome symptoms. Thus, there is a need for alternative, less expensive and more accessible tests.MethodsWe analyzed the plasma levels of white blood cells (WBCs), platelets, C-reactive protein (CRP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), γ-glutamyl transpeptidase (GGT), alkaline phosphatase and lactate dehydrogenase (LDH) of 207 patients who, after being admitted to the emergency room of the San Raffaele Hospital (Milan, Italy) with COVID-19 symptoms, were rRT-PCR tested. Of them, 105 tested positive, whereas 102 tested negative.ResultsStatistically significant differences were observed for WBC, CRP, AST, ALT and LDH. Empirical thresholds for AST and LDH allowed the identification of 70% of either COVID-19-positive or -negative patients on the basis of routine blood test results.ConclusionsCombining appropriate cutoffs for certain hematological parameters could help in identifying false-positive/negative rRT-PCR tests. Blood test analysis might be used as an alternative to rRT-PCR for identifying COVID-19-positive patients in those countries which suffer from a large shortage of rRT-PCR reagents and/or specialized laboratory.

scholarly journals A Clinical Study of Patients with Acute Epiglottitis

2018 ◽  
Vol 01 (01) ◽  
pp. e1-e5 ◽  
Author(s):  
Michihisa Kono ◽  
Takashi Goto ◽  
Nobuyuki Bandoh ◽  
Yasuaki Harabuchi
Keyword(s):  
Airway Management ◽  
Median Duration ◽  
Blood Test ◽  
White Blood Cells ◽  
Acute Epiglottitis ◽  
Reactive Protein ◽  
Number Of Patients ◽  
Duration Of Hospitalization ◽  
Upper Respiratory

AbstractAcute epiglottitis is an acute inflammation of the upper respiratory airway that rarely causes airway obstruction. A retrospective study was conducted on 115 patients with acute epiglottitis from April 2007 to December 2017 (65 males and 50 females; aged 12–85 years old, median age of 45 years). When counting the number of patients according to the month, from April to September more than 10 patients were treated. Median duration from symptom onset to first visit was 3 days (1–14 days). Eight (7%) of 115 patients had diabetes, and 16 (13.9%) had epiglottic cyst. We divided all the patients into six groups by laryngeal findings according to the classification of Katori and Tsukuda. Number of patients classified as IA, IB, IIA, IIB, IIIA, and IIIB was 41 (35.7%), 21 (18.3%), 22 (19.1%), 15 (13%), 8 (7%), and 8 (7%), respectively. Median duration of hospitalization was 5 days (2–26 days). In the blood test on the first day of hospitalization, the number of white blood cells (WBC) ranged from 3,400 to 25,350/μL (median 10,350/μL) and the C-reactive protein (CRP) ranged from 0.01 to 23.3 mg/dL (median, 2.5 mg/dL). The number of WBC and CRP at the fourth day after the hospitalization was significantly lower than those at the first day. Eight (7%) patients required the airway management such as tracheotomy or cricothyroidotomy. Age, laryngeal finding (severe epiglottis swelling and arytenoid edema; Katori and Tsukuda's classification IIIB), and high inflammatory reaction in blood test (WBC ≥ 20,000/μL and CRP ≥ 20 mg/dL) were the factors that significantly correlated with the airway management.

Dynamics of hematological parameters in patients with asymptomatic and mild course of COVID19 infection

2021 ◽  
pp. 25-30
Author(s):  
R. R. Ismagilov ◽  
F. S. Bilalov ◽  
Yu. A. Ahmadullina ◽  
M. N. Sitdikova ◽  
A. Zh. Gilmanov
Keyword(s):  
Flow Cytometry ◽  
Blood Cells ◽  
Blood Test ◽  
Hematological Parameters ◽  
White Blood Cells ◽  
Clinical Manifestations ◽  
Reference Intervals ◽  
Mild Degree ◽  
Hematological Changes ◽  
Degree Of Severity

We studied changes in the parameters of the general (clinical) blood test in dynamics in outpatient patients with an asymptomatic course and a mild degree of severity of COVID-19. The study involved 67 patients. 30 men and 37 women were examined, their average age was 35 years. All the interviewed patients did not take specific antiviral drugs. Clinical manifestations were evaluated at the time of the survey on the 1st and 7th day from the day of receiving a positive PCR result. The parameters of the general (clinical) blood test were determined by flow cytometry in the CBC + 5Diff mode on a Unicel® DxH 800 hematological analyzer (Beckman Coulter, USA) in dynamics on the 1st and 7th days of observation. The features of clinical manifestations in patients with an asymptomatic course and mild severity coincided with the data of other authors. Hematological changes were mainly characterized by changes in the number of white blood cells and their subpopulations. On the 7th day of observation, there was a significant tendency to increase the number of white blood cells, lymphocytes, eosinophils, neutrophils and platelets within the reference intervals.

scholarly journals Evaluation of C-reactive protein and hematological parameters in smokeless tobacco users: A comparative cross-sectional study

2021 ◽  
Vol 37 (4) ◽  
Author(s):  
Sikander Munir Memon ◽  
Naresh Kumar ◽  
Aneela Atta Ur Rahman ◽  
Binafsha Manzoor Syed
Keyword(s):  
Smokeless Tobacco ◽  
Hematological Parameters ◽  
White Blood Cells ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Long Term Effects ◽  
C Reactive Protein ◽  
Cross Sectional ◽  
Reactive Protein ◽  
Significant Difference

Objective: To investigate the changes in levels of C-reactive protein (CRP) and hematological parameters among smokeless tobacco (SLT) users. Methods: A comparative cross-sectional study was conducted at the community level in the coastal districts of Sindh province namely Badin, Thatta, and Sujawal from January 2017 to December 2019. The CRP and hematological parameters were evaluated by well-established methods among SLT and non-SLT users. Results: There was a statistically significant difference between SLT users (mean CRP = 0.77) versus non-users (mean CRP = 0.18), p = <0.001. Among hematological parameters, white blood cells (SLT users median = 7.85 versus non-SLT users median = 8.50, p = 0.004), monocytes (SLT users median = 6.00 versus non-SLT users median = 6.00, p = 0.001) and erythrocyte sedimentation rate (SLT users median = 15.00 versus non-SLT users median = 10.00, p = 0.006) showed statistically significant difference. Conclusions: Significantly elevated CRP was observed in SLT users similarly hematological parameters also showed changes. WBCs, monocytes and ESR were significantly deranged among SLT users. Further studies looking into long term effects of these changes would be helpful. doi: https://doi.org/10.12669/pjms.37.4.3841 How to cite this:Memon SM, Kumar N, Atta-Ur-Rahman A, Syed BM. Evaluation of C-reactive protein and hematological parameters in smokeless tobacco users: A comparative cross-sectional study. Pak J Med Sci. 2021;37(4):---------.    doi: https://doi.org/10.12669/pjms.37.4.3841 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

scholarly journals Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: a Feasibility Study

2020 ◽  
Author(s):  
Davide Brinati ◽  
Andrea Campagner ◽  
Davide Ferrari ◽  
Massimo Locatelli ◽  
Giuseppe Banfi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gold Standard ◽  
False Negative ◽  
White Blood Cells ◽  
Standard Test ◽  
Tree Model ◽  
Web Based ◽  
Machine Learning Classification ◽  
Blood Tests ◽  
Routine Blood

AbstractBackgroundThe COVID-19 pandemia due to the SARS-CoV-2 coronavirus, in its first 4 months since its outbreak, has to date reached more than 200 countries worldwide with more than 2 million confirmed cases (probably a much higher number of infected), and almost 200,000 deaths. Amplification of viral RNA by (real time) reverse transcription polymerase chain reaction (rRT-PCR) is the current gold standard test for confirmation of infection, although it presents known shortcomings: long turnaround times (3-4 hours to generate results), potential shortage of reagents, false-negative rates as large as 15-20%, the need for certified laboratories, expensive equipment and trained personnel. Thus there is a need for alternative, faster, less expensive and more accessible tests.Material and methodsWe developed two machine learning classification models using hematochemical values from routine blood exams (namely: white blood cells counts, and the platelets, CRP, AST, ALT, GGT, ALP, LDH plasma levels) drawn from 279 patients who, after being admitted to the San Raffaele Hospital (Milan, Italy) emergency-room with COVID-19 symptoms, were screened with the rRT-PCR test performed on respiratory tract specimens. Of these patients, 177 resulted positive, whereas 102 received a negative response.ResultsWe have developed two machine learning models, to discriminate between patients who are either positive or negative to the SARS-CoV-2: their accuracy ranges between 82% and 86%, and sensitivity between 92% e 95%, so comparably well with respect to the gold standard. We also developed an interpretable Decision Tree model as a simple decision aid for clinician interpreting blood tests (even off-line) for COVID-19 suspect cases.DiscussionThis study demonstrated the feasibility and clinical soundness of using blood tests analysis and machine learning as an alternative to rRT-PCR for identifying COVID-19 positive patients. This is especially useful in those countries, like developing ones, suffering from shortages of rRT-PCR reagents and specialized laboratories. We made available a Web-based tool for clinical reference and evaluation1.

scholarly journals Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests

2020 ◽  
Author(s):  
Cabitza Federico ◽  
Campagner Andrea ◽  
Ferrari Davide ◽  
Di Resta Chiara ◽  
Ceriotti Daniele ◽  
...  
Keyword(s):  
Machine Learning ◽  
Complete Blood Count ◽  
Characteristic Curve ◽  
External Validation ◽  
False Negative ◽  
Turnaround Time ◽  
Training Data ◽  
Data Set ◽  
Blood Tests ◽  
Routine Blood

AbstractBackgroundThe rRT-PCR test, the current gold standard for the detection of coronavirus disease (COVID-19), presents with known shortcomings, such as long turnaround time, potential shortage of reagents, false-negative rates around 15–20%, and expensive equipment. The hematochemical values of routine blood exams could represent a faster and less expensive alternative.MethodsThree different training data set of hematochemical values from 1,624 patients (52% COVID-19 positive), admitted at San Raphael Hospital (OSR) from February to May 2020, were used for developing machine learning (ML) models: the complete OSR dataset (72 features: complete blood count (CBC), biochemical, coagulation, hemogasanalysis and CO-Oxymetry values, age, sex and specific symptoms at triage) and two sub-datasets (COVID-specific and CBC dataset, 32 and 21 features respectively). 58 cases (50% COVID-19 positive) from another hospital, and 54 negative patients collected in 2018 at OSR, were used for internal-external and external validation.ResultsWe developed five ML models: for the complete OSR dataset, the area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.83 to 0.90; for the COVID-specific dataset from 0.83 to 0.87; and for the CBC dataset from 0.74 to 0.86. The validations also achieved good results: respectively, AUC from 0.75 to 0.78; and specificity from 0.92 to 0.96.ConclusionsML can be applied to blood tests as both an adjunct and alternative method to rRT-PCR for the fast and cost-effective identification of COVID-19-positive patients. This is especially useful in developing countries, or in countries facing an increase in contagions.

Role of time-normalized laboratory findings in predicting COVID-19 outcome

Diagnosis ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 387-394
Author(s):  
Davide Ferrari ◽  
Andrea Seveso ◽  
Eleonora Sabetta ◽  
Daniele Ceriotti ◽  
Anna Carobene ◽  
...  
Keyword(s):  
White Blood Cells ◽  
Critical Issue ◽  
Time Dependent ◽  
Prognostic Indicators ◽  
Prognostic Information ◽  
Laboratory Findings ◽  
Laboratory Parameters ◽  
Infected People ◽  
Reactive Protein ◽  
Hospitalization Period

AbstractObjectivesThe pandemic COVID-19 currently reached 213 countries worldwide with nearly 9 million infected people and more than 460,000 deaths. Although several Chinese studies, describing the laboratory findings characteristics of this illness have been reported, European data are still scarce. Furthermore, previous studies often analyzed the averaged laboratory findings collected during the entire hospitalization period, whereas monitoring their time-dependent variations should give more reliable prognostic information.MethodsWe analyzed the time-dependent variations of 14 laboratory parameters in two groups of COVID-19 patients with, respectively, a positive (40 patients) or a poor (42 patients) outcome, admitted to the San Raffaele Hospital (Milan, Italy). We focused mainly on laboratory parameters that are routinely tested, thus, prognostic information would be readily available even in low-resource settings.ResultsStatistically significant differences between the two groups were observed for most of the laboratory findings analyzed. We showed that some parameters can be considered as early prognostic indicators whereas others exhibit statistically significant differences only at a later stage of the disease. Among them, earliest indicators were: platelets, lymphocytes, lactate dehydrogenase, creatinine, alanine aminotransferase, C-reactive protein, white blood cells and neutrophils.ConclusionsThis longitudinal study represents, to the best of our knowledge, the first study describing the laboratory characteristics of Italian COVID-19 patients on a normalized time-scale. The time-dependent prognostic value of the laboratory parameters analyzed in this study can be used by clinicians for the effective treatment of the patients and for the proper management of intensive care beds, which becomes a critical issue during the pandemic peaks.

scholarly journals EFFECT OF ECOLOGICAL FACTORS ON THE BLOOD TESTS FOR DOGS IN THE TUZLA CANTON AREA

2020 ◽  
Vol 20 (1-2) ◽  
Author(s):  
Jasmin FERIZBEGOVIĆ́ ◽  
Elvira HADŽIAHMETOVIĆ JURIDÁ ◽  
Marija VUČIĆEVIĆ́ ◽  
Sandra ĐAPÓ
Keyword(s):  
Environmental Factors ◽  
Body Mass ◽  
Blood Test ◽  
Hematological Parameters ◽  
Statistical Processing ◽  
Ecological Factors ◽  
Living Organism ◽  
Living Environment ◽  
Test Analysis ◽  
Purebred Dogs

Blood test analysis, i.e. determination of the number of erythrocytes, leucocytes, thrombocytes and other blood elements is nowadays used quite frequently for diagnostic purposes. Hematological parameters and body mass are reliable indicators of the general condition of the organism, and serve as an indirect indicator of the environment of the living organism. Hematological parameters and body mass were examined in a total of 60 dogs of various breeds, including mixed breeds. The purebred dogs included 30 dogs while the mixed breed group included another 30 dogs from the territory of Tuzla Canton. The examined dogs differed in the manner of nutrition and their living environment, and therefore, the results show a variety of deviations that were considered later on and further analysed in order to determine their causes. All examinations were conducted, observed and processed in accordance with relevant ecological environmental factors in the period of autumn of 2016. The aim of this study was to determine the influence of environmental factors in the study period on the hematological parameters of both examined groups of dogs. After the analysis of the blood test with the use of the hemogram for owned dogs and asylum dogs, that have been treated and kept in various manners, and the statistical processing of data obtained, it was concluded that the dogs with increased number of eosionophils had a parasitic disease. A large number of owned dogs had parasitic diseases due to incorrect usage of antiparasitics and not so frequent visits to veterinarians. The dogs that were under constant medical supervision were completely healthy.

scholarly journals Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Federico Cabitza ◽  
Andrea Campagner ◽  
Davide Ferrari ◽  
Chiara Di Resta ◽  
Daniele Ceriotti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Complete Blood Count ◽  
Characteristic Curve ◽  
External Validation ◽  
False Negative ◽  
Turnaround Time ◽  
Training Data ◽  
Data Set ◽  
Blood Tests ◽  
Routine Blood

AbstractObjectivesThe rRT-PCR test, the current gold standard for the detection of coronavirus disease (COVID-19), presents with known shortcomings, such as long turnaround time, potential shortage of reagents, false-negative rates around 15–20%, and expensive equipment. The hematochemical values of routine blood exams could represent a faster and less expensive alternative.MethodsThree different training data set of hematochemical values from 1,624 patients (52% COVID-19 positive), admitted at San Raphael Hospital (OSR) from February to May 2020, were used for developing machine learning (ML) models: the complete OSR dataset (72 features: complete blood count (CBC), biochemical, coagulation, hemogasanalysis and CO-Oxymetry values, age, sex and specific symptoms at triage) and two sub-datasets (COVID-specific and CBC dataset, 32 and 21 features respectively). 58 cases (50% COVID-19 positive) from another hospital, and 54 negative patients collected in 2018 at OSR, were used for internal-external and external validation.ResultsWe developed five ML models: for the complete OSR dataset, the area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.83 to 0.90; for the COVID-specific dataset from 0.83 to 0.87; and for the CBC dataset from 0.74 to 0.86. The validations also achieved good results: respectively, AUC from 0.75 to 0.78; and specificity from 0.92 to 0.96.ConclusionsML can be applied to blood tests as both an adjunct and alternative method to rRT-PCR for the fast and cost-effective identification of COVID-19-positive patients. This is especially useful in developing countries, or in countries facing an increase in contagions.

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