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Monocyte distribution width as a pragmatic screen for SARS-CoV-2 or influenza infection. Scientific reports Monocyte distribution width (MDW) is a novel marker of monocyte activation, which is known to occur in the immune response to viral pathogens. Our objective was to determine the performance of MDW and other leukocyte parameters as screening tests for SARS-CoV-2 and influenza infection. This was a prospective cohort analysis of adult patients who underwent complete blood count (CBC) and SARS-CoV-2 or influenza testing in an Emergency Department (ED) between January 2020 and July 2021. The primary outcome was SARS-CoV-2 or influenza infection. Secondary outcomes were measures of severity of illness including inpatient hospitalization, critical care admission, hospital lengths of stay and mortality. Descriptive statistics and test performance measures were evaluated for monocyte percentage, MDW, white blood cell (WBC) count, and neutrophil to lymphocyte ratio (NLR). 3,425 ED patient visits were included. SARS-CoV-2 testing was performed during 1,922 visits with a positivity rate of 5.4%; influenza testing was performed during 2,090 with a positivity rate of 2.3%. MDW was elevated in patients with SARS-Cov-2 (median 23.0U; IQR 20.5-25.1) or influenza (median 24.1U; IQR 22.0-26.9) infection, as compared to those without (18.9U; IQR 17.4-20.7 and 19.1U; 17.4-21, respectively, P < 0.001). Monocyte percentage, WBC and NLR values were within normal range in patients testing positive for either virus. MDW identified SARS-CoV-2 and influenza positive patients with an area under the curve (AUC) of 0.83 (95% CI 0.79-0.86) and 0.83 (95% CI 0.77-0.88), respectively. At the accepted cut-off value of 20U for MDW, sensitivities were 83.7% (95% CI 76.5-90.8%) for SARS-CoV-2 and 89.6% (95% CI 80.9-98.2%) for influenza, compared to sensitivities below 45% for monocyte percentage, WBC and NLR. MDW negative predictive values were 98.6% (95% CI 98.0-99.3%) and 99.6% (95% CI 99.3-100.0%) respectively for SARS-CoV-2 and influenza. Monocyte Distribution Width (MDW), available as part of a routine complete blood count (CBC) with differential, may be a useful indicator of SARS-CoV-2 or influenza infection. 10.1038/s41598-022-24978-w
Study on the Value of Blood Biomarkers NLR and PLR in the Clinical Diagnosis of Influenza a Virus Infection in Children. Liao Yi,Liu Chenggui,He Weijun,Wang Dongmei Clinical laboratory BACKGROUND:The aim is to explore the value and significance of changes in neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR) in the diagnosis and treatment of patients with influenza virus infection. METHODS:A total of 1,330 cases of influenza A diagnosed with the nucleic acid testing were collected according to the 2019 version of the influenza diagnosis and treatment regimen in our hospital from September 2020 to December 2020. During the same period, 1,330 cases of healthy subjects were used as controls. The colloidal gold method and fluorescent PCR were used to detect influenza A virus. The sysmex800i was used for routine blood test, and statistical analysis was then performed. RESULTS:Comparing the relevant indicators between the research group and the healthy control group showed that the differences in NLR, PLR, PLT, absolute lymphocyte values, etc. were all statistically significant (p < 0.001). Among them, the average results of NLR and PLR of the research group were all larger than those of the control group; the mean values of the absolute value of lymphocytes (x 109) and PLT (x 109) in the research group were all smaller than those of the control group. The NLR, PLR, LMR, age, and other parameters of the research group and the healthy control group were analyzed to determine whether there was influenza infection according to a binary logistic regression model. The results showed that the differences were not statistically significant except for age and LMR (p > 0.05) and did not enter the regression model. The differences in other parameters such as NLR and PLR were all statistically significant (all p < 0.001), which were all entered into the regression model. They were very significant for predictive diagnosis of influenza A. The areas under the ROC curve of NLR and PLR were 0.961 (95% CI: 0.953-0.968) and 0.749 (95% CI: 0.730-0.769), respectively; the sensitivity and specificity of NLR were 88.4% and 93.1%, respectively, and Youden's index was 0.815, the optimal diagnostic cutoff value was 1.478; the sensitivity and specificity of PLR were 56.70% and 89.60%, respectively; the Youden's index was 0.463, and the optimal diagnostic cutoff value was 124. CONCLUSIONS:NLR and PLR had a certain degree of accuracy in the diagnosis of viral infections in children with influenza A. The diagnostic effect of NLR was particularly good. In the early stage of the disease, cheap and easily available blood biomarkers can be used to diagnose influenza A. However, LMR had no diagnostic value for influenza A because the area under the curve was too small. 10.7754/Clin.Lab.2021.210319
Routine blood parameters as auxiliary diagnostic tools for infection in children. Journal of medical microbiology Recently, the incidence of () infection in children has been increasing annually. Early differential diagnosis of infection can not only avoid the abuse of antibiotics, but also is essential for early treatment and reduction of transmission. The change of routine blood parameters may have important clinical significance for the diagnosis of infection, but it has not been reported so far. This study aims to establish a predictive model for infection and explore the changes and clinical value of routine blood parameters in children with infection, serving as auxiliary indicators for the diagnosis and differentiation of clinical infection. A total of 770 paediatric patients with respiratory tract infections were enrolled in this study, including 360 in the group, 40 in the SARS-CoV-2 group, 200 in the influenza A virus group, and 170 in the control group. The differences of routine blood parameters among all groups were compared, and risk factors were analysed using multivariate logistics analysis, and the diagnostic efficacy of differential indicators using ROC curves. This study revealed that Mono% (OR: 3.411; 95% CI: 1.638-7.102; =0.001) was independent risk factor associated with infection, and Mono% (AUC=0.786, the optimal cutoff at 7.8%) had a good discriminative ability between patients with infection and healthy individuals. Additionally, Mono% (OR: 0.424; 95% CI: 0.231-0.781; =0.006) and Lymp% (OR: 0.430; 95% CI: 0.246-0.753; =0.003) were independent risk factors for distinguishing infection from influenza A virus infection, and the Lymp% (AUC=0.786, the optimal cutoff at 22.1%) and Net% (AUC=0.761, the optimal cutoff at 65.2%) had good discriminative abilities between infection and influenza A infection. Furthermore, platelet distribution width (OR: 0.680; 95% CI: 0.538-0.858; =0.001) was independent risk factor for distinguishing infection from SARS-CoV-2 infection. Meanwhile, the ROC curve demonstrated that PDW (AUC=0.786, the optimal cutoff at 15%) has a good ability to differentiate between infection and SARS-CoV-2 infection. This study demonstrates that routine blood parameters can be used as auxiliary diagnostic indicators for infection and provide reference for the diagnosis and differentiation of clinical infection. 10.1099/jmm.0.001885
Use of common blood parameters for the differential diagnosis of childhood infections. PloS one BACKGROUND:Routine laboratory investigations are not rapidly available to assist clinicians in the diagnosis of pediatric acute infections. Our objective was to evaluate some common blood parameters and use them for the differential diagnosis of childhood infections. METHODS:This retrospective study was conducted between October 2019 and September 2020 at Guangzhou Women and Children's Medical Center, China. We performed blood tests in patients infected with DNA viruses (n = 402), RNA viruses (n = 602), gram-positive organisms (G+; n = 421), gram-negative organisms (G-; n = 613), or Mycoplasma pneumoniae (n = 387), as well as in children without infection (n = 277). The diagnostic utility of blood parameters to diagnose various infections was evaluated by logistic regression analysis. RESULTS:The most common G+ organism, G- organism, and virus were Streptococcus pneumoniae (39.7%), Salmonella typhimurium (18.9%), and influenza A virus (40.2%), respectively. The value of logit (P) = 0.003 × C-reactive protein (CRP) - 0.011 × hemoglobin (HGB) + 0.001 × platelets (PLT) was significantly different between the control, RNA virus, DNA virus, M. pneumoniae, G- organism, and G+ organism groups (2.46 [95% CI, 2.41-2.52], 2.60 [2.58-2.62], 2.70 [2.67-2.72], 2.78 [2.76-2.81], 2.88 [2.85-2.91], and 2.97 [2.93-3.00], respectively; p = 0.00 for all). The logistic regression-based model showed significantly greater accuracy than the best single discriminatory marker for each group (logit [Pinfection] vs. CRP, 0.90 vs. 0.84, respectively; logit [PRNA] vs. lymphocytes, 0.83 vs. 0.77, respectively; p = 0.00). The area under curve values were 0.72 (0.70-0.74) for HGB and 0.81 (0.79-0.82) for logit (Pvirus/bacteria) to diagnose bacterial infections, whereas they were 0.72 (0.68-0.74) for eosinophils and 0.80 (0.78-0.82) for logit (Pvirus/bacteria) to diagnose viral infections. Logit (Pvirus/bacteria) < -0.45 discriminated bacterial from viral infection with 78.9% specificity and 70.7% sensitivity. CONCLUSIONS:The combination of CRP, HGB, PLT, eosinophil, monocyte, and lymphocyte counts can distinguish between the infectious pathogens in children. 10.1371/journal.pone.0273236
Routine blood parameters are helpful for early identification of influenza infection in children. Zhu Ronghe,Chen Cuie,Wang Qiu,Zhang Xixi,Lu Chaosheng,Sun Yuanyuan BMC infectious diseases BACKGROUND:Routine blood parameters, such as the lymphocyte (LYM) count, platelet (PLT) count, lymphocyte-to-monocyte ratio (LMR), neutrophil-to-lymphocyte ratio (NLR), lymphocytes multiplied by platelets (LYM*PLT) and mean platelet volume-to-platelet ratio (MPV/PLT), are widely used to predict the prognosis of infectious diseases. We aimed to explore the value of these parameters in the early identification of influenza virus infection in children. METHODS:We conducted a single-center, retrospective, observational study of fever with influenza-like symptoms in pediatric outpatients from different age groups and evaluated the predictive value of various routine blood parameters measured within 48 h of the onset of fever for influenza virus infection. RESULTS:The LYM count, PLT count, LMR and LYM*PLT were lower, and the NLR and MPV/PLT were higher in children with an influenza infection (PCR-confirmed and symptomatic). The LYM count, LMR and LYM*PLT in the influenza infection group were lower in the 1- to 6-year-old subgroup, and the LMR and LYM*PLT in the influenza infection group were lower in the > 6-year-old subgroup. In the 1- to 6-year-old subgroup, the cutoff value of the LMR for predicting influenza A virus infection was 3.75, the sensitivity was 81.87%, the specificity was 84.31%, and the area under the curve (AUC) was 0.886; the cutoff value of the LMR for predicting influenza B virus infection was 3.71, the sensitivity was 73.58%, the specificity was 84.31%, and the AUC was 0.843. In the > 6-year-old subgroup, the cutoff value of the LMR for predicting influenza A virus infection was 3.05, the sensitivity was 89.27%, the specificity was 89.61%, and the AUC was 0.949; the cutoff value of the LMR for predicting influenza B virus infection was 2.88, the sensitivity was 83.19%, the specificity was 92.21%, and the AUC was 0.924. CONCLUSIONS:Routine blood tests are simple, inexpensive and easy to perform, and they are useful for the early identification of influenza virus infection in children. The LMR had the strongest predictive value for influenza virus infection in children older than 1 year, particularly in children older than 6 years with influenza A virus infection. 10.1186/s12879-020-05584-5
The blood routine test holds screening values for influenza A in 2023: a retrospective study. Translational pediatrics Background:Influenza A is the most common viral pathogen isolated from pediatric clinics during influenza seasons. Some young patients with influenza manifest rapid progression with high fever and severe sequelae, such as pneumonia and meningitis. Therefore, early diagnosis and prompt treatment are highly important. Specific diagnostic tests currently include antigen detection, antibody detection, nucleic acid test and virus isolation. Rapid antigen testing is the most commonly adopted method in the outpatient setting, but false negative results are frequently observed, which causes delayed treatment and severe outcome. Routine blood test is the most commonly used detection for the outpatients. Incorporating specific blood cell counts into rapid antigen test may overcome some technical issues and enable accurate early diagnosis. Methods:We enrolled 537 children with influenza-like symptoms like fever or respiratory symptoms from pediatric outpatients and 110 children without infectious diseases for control. Routine blood tests detected by a routine analyzer and influenza A virus antigen detection were performed in the patients. Significant blood routine parameters between groups were examined by statistical tests. Parameters in routine blood test were assessed by the receiver operating characteristic curve to find the screening indicators of influenza A. Multivariate logistic regression were used to establish the optimal combinations of blood routine parameters in our screening model. Results:Two subgroups were set according to age: ≤6 years old group and >6 years old group. In each group, patients were further divided into three subgroups: the influenza A-positive-result group (A+ group) (n=259), influenza A-negative-result group (A- group) (n=277) and healthy control group (H group) (n=110). Most routine blood parameters showed significant differences among the three subgroups in each age group. Notably, lymphocyte (LYM) number, platelet (PLT) number, lymphocyte-to-monocyte ratio (LMR) and LYM multiplied by PLT (LYM*PLT) exhibited extremely significant differences. Using A- group as a reference based on the area under the curve (AUC), both age groups had a similar trend. For A- group, the optimal cutoff value of LYM*PLT was 221.6, the AUC, the sensitivity and specificity were 0.6830, 55.71% and 76.92% in the ≤6 years old group. Meanwhile, the cutoff value of LYM*PLT was 196.7, and the AUC, the sensitivity and specificity were 0.6448, 53.97% and 70.81%, respectively in the >6 years old group. Screening model based on multivariate logistic regression model revealed that LYM*PLT was the optimal parameter combinations in ≤6 years old group (AUC =0.7202), while LYM and PLT were the optimal parameter combinations in >6 years old group (AUC =0.6760). Conclusions:Several blood routine parameters in children with influenza A demonstrate differential levels in both age subgroups. The LYM*PLT exhibits the potential screening value of influenza infection. 10.21037/tp-23-435