scholarly journals Lymphocyte monocyte ratio is an effective and simple predictor for nosocomial influenza outbreaks

2021 ◽  
Author(s):  
Fan Junping ◽  
Ke Fanhang ◽  
Sun Fangyan ◽  
Tian Xinlun ◽  
Xiao Meng ◽  
...  
Keyword(s):  
Blood Cell ◽  
Symptom Onset ◽  
Peripheral Blood Cell ◽  
Outbreak Detection ◽  
Receiver Operating Curve ◽  
Influenza Outbreak ◽  
Cell Parameters ◽  
Specificity And Sensitivity ◽  
Tertiary Teaching ◽  
Influenza Outbreaks

Abstract ObjectivesNosocomial influenza outbreak detection remains challenging. We evaluated the diagnostic utility of blood cell parameters, along with their capacity to differentiate between hospital acquired influenza and coronavirus disease 2019 (COVID-19).MethodsWe retrospectively analyzed patients diagnosed with nosocomial influenza from January 2017 to December 2019, and patients with COVID-19 in early 2020 at a tertiary teaching hospital in Beijing, China. We compared the differences between blood cell count and ratios (lymphocyte-to-monocyte ratio [LMR], neutrophil-to-lymphocyte ratio [NLR], lymphocyte-to-platelet ratio [LPR]) at symptom onset, before (admission), and after (recovery) nosocomial influenza. We also compared the abovementioned parameters between influenza and COVID-19 patients.ResultsLymphocyte count, LMR, and LPR were significantly lower in the symptom onset than in the admission and recovery groups (p < 0.001), while NLR was higher (p < 0.001). LMR and NLR exhibited similar and consistent tendencies among different subgroups of patients with nosocomial influenza (p < 0.001). The area under the receiver operating curve (AUC) of LMR, NLR, LPR, and lymphocyte count were 0.914, 0.872, 0.806, and 0.866, respectively. The optimal LMR cut-off value was 2.50, with specificity and sensitivity of 92.0% and 81.3%, respectively. Peripheral blood cell ratios can help diagnose nosocomial influenza significantly earlier than conventional methods. For differentiating influenza and COVID-19, the AUCs of LMR was 0.825.ConclusionsLMR effectively predicts nosocomial influenza outbreaks, particularly during the COVID-19 pandemic when simultaneous transmission can be a substantial threat.

scholarly journals A Novel Data-driven Methodology for Influenza Outbreak Detection and Prediction

2020 ◽  
Author(s):  
Lin Du ◽  
Yan Pang
Keyword(s):  
Model Performance ◽  
Resource Planning ◽  
Data Driven ◽  
Outbreak Detection ◽  
Severe Illness ◽  
Prescription Data ◽  
Influenza Outbreak ◽  
Real World Evidence ◽  
Efficient Resource ◽  
Influenza Outbreaks

Abstract Influenza is an infectious disease that leads to an estimated 5 million severe illness cases and 650,000 respiratory deaths worldwide each year. Early detection and prediction of influenza outbreaks are crucial to efficient resource planning to save patients’ lives and healthcare costs. This paper proposes a novel data-driven methodology for influenza outbreaks detection and prediction. The doctor’s diagnosis-based prescription dataset of Influenza-Like Illness (ILI) from more than 3,000 clinics in Malaysia is used in this study because the prescription data are reliable and can be captured timely. A new Region Index (RI) of the influenza outbreak is proposed based on the prescription dataset. With the newly proposed RI metric, statistical and machine learning models are developed to detect and predict influenza outbreaks. Cross-validation is conducted to evaluate the prediction model performance. The proposed methods are also validated by real-world evidence. It is proved to be sensitive and accurate in influenza outbreak prediction with 80-90% accuracy, 70-80% recall, and 70-80% precision scores.

scholarly journals A novel data-driven methodology for influenza outbreak detection and prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lin Du ◽  
Yan Pang
Keyword(s):  
Resource Planning ◽  
Data Driven ◽  
Outbreak Detection ◽  
World Health ◽  
Severe Illness ◽  
Data Set ◽  
Influenza Outbreak ◽  
News Reports ◽  
Health Organization ◽  
Influenza Outbreaks

AbstractInfluenza is an infectious disease that leads to an estimated 5 million cases of severe illness and 650,000 respiratory deaths worldwide each year. The early detection and prediction of influenza outbreaks are crucial for efficient resource planning to save patient’s lives and healthcare costs. We propose a new data-driven methodology for influenza outbreak detection and prediction at very local levels. A doctor’s diagnostic dataset of influenza-like illness from more than 3000 clinics in Malaysia is used in this study because these diagnostic data are reliable and can be captured promptly. A new region index (RI) of the influenza outbreak is proposed based on the diagnostic dataset. By analysing the anomalies in the weekly RI value, potential outbreaks are identified using statistical methods. An ensemble learning method is developed to predict potential influenza outbreaks. Cross-validation is conducted to optimize the hyperparameters of the ensemble model. A testing data set is used to provide an unbiased evaluation of the model. The proposed methodology is shown to be sensitive and accurate at influenza outbreak prediction, with average of 75% recall, 74% precision, and 83% accuracy scores across five regions in Malaysia. The results are also validated by Google Flu Trends data, news reports, and surveillance data released by World Health Organization.

scholarly journals Explore the Predictive Value of Peripheral Blood Cell Parameters in Refractory Mycoplasma pneumoniae Pneumonia in Children Over 6 Years Old

2021 ◽  
Vol 9 ◽  
Author(s):  
Yaoyao Ling ◽  
Jing Ning ◽  
Yongsheng Xu
Keyword(s):  
Pleural Effusion ◽  
Blood Cell ◽  
Mycoplasma Pneumoniae ◽  
Peripheral Blood ◽  
Predictive Value ◽  
Fiberoptic Bronchoscopy ◽  
Peripheral Blood Cell ◽  
Binary Logistic Regression Analysis ◽  
Cell Parameters ◽  
Lymphocyte Ratio

Background: To determine the predictive value of peripheral blood cell parameters for refractory Mycoplasma pneumoniae pneumonia (RMPP) in children over 6 years old.Methods: A retrospective study was conducted in children with RMPP admitted to the respiratory department of Tianjin Children's Hospital from September 2017 to September 2019, and non-refractory Mycoplasma pneumoniae pneumonia (NRMPP) was selected by the propensity score method and matched according to the ratio of 1:1.5. We analyzed the differences in clinical characteristics, peripheral blood cell parameters, imaging findings, and treatments between the two groups, and further determined the predictive value of peripheral blood cell parameters on RMPP.Results: There were 76 patients in the RMPP group and 114 patients in the NRMPP group. We found that the RMPP group has a longer clinical course and a higher incidence of intrapulmonary and extrapulmonary complications (p &lt; 0.01). Moreover, the proportion of children in the RMPP group who received immunotherapy (such as glucocorticoid, gamma immunoglobulin) and fiberoptic bronchoscopy intervention was higher than that in the NRMPP group (p &lt; 0.01). Meanwhile, the level of neutrophil, neutrophil/lymphocyte ratio (NLR), platelet count/lymphocyte ratio (PLR), mean platelet volume/lymphocyte ratio (MPVLR), C-reactive protein (CRP), lactic dehydrogenase (LDH), and interleukin (IL)-6 in the RMPP group was significantly higher (p &lt; 0.01) than those in the NRMPP group. The incidence of pulmonary consolidation, atelectasis, and pleural effusion was also higher in the RMPP group (p &lt; 0.05). ROC curve and binary logistic regression analysis showed that NLR &gt; 3.92 (OR = 3.243; 95% CI = 1.485–7.081; p = 0.003), MPVLR &gt; 5.29 (OR = 2.700; 95% CI = 1.258–5.795; p = 0.011), and pleural effusion (OR = 3.023; 95% CI = 1.424–6.420; p = 0.004) were significant factors in predicting RMPP. Our study showed that NLR had higher accuracy in predicting RMPP than CRP.Conclusions: The parameters of peripheral blood cells might be a predictor of RMPP. NLR &gt; 3.92, MPVLR &gt; 5.29, and pleural effusion might have important predictive value for RMPP in children over 6 years old.

Blood Cell Parameters and Predicting Coronary In-Stent Restenosis

Angiology ◽  
2019 ◽  
Vol 70 (8) ◽  
pp. 711-718 ◽  
Author(s):  
Zhichao Wang ◽  
Chi Liu ◽  
Hong Fang
Keyword(s):  
Blood Cell ◽  
Platelet Activation ◽  
Blood Cells ◽  
Inflammatory Marker ◽  
Distribution Width ◽  
Cell Parameters ◽  
Neutrophil Lymphocyte Ratio ◽  
Lymphocyte Ratio ◽  
Stent Restenosis ◽  
In Stent Restenosis

Major advances in coronary interventional techniques and pharmacotherapy as well as the use of drug-eluting stents (DESs) have considerably reduced the risk of in-stent restenosis (ISR). However, ISR remains a major clinical challenge. Inflammation and platelet activation are important processes that underlie the pathophysiology of ISR. Parameters related to blood cells, entailing both cell count and morphology, are useful markers of the inflammatory response and platelet activation in clinical practice. Recent studies have highlighted several new combined or derived parameters related to blood cells that independently predict ISR after DES implantation. The neutrophil/lymphocyte ratio, an inflammatory marker, is regarded as a predictor of the risk of ISR and the stability of atherosclerotic plaques. The mean platelet volume, a widely used platelet activation parameter, has been shown to be a predictor of the risk of ISR and the efficacy of antiplatelet therapy. Other markers considered include the platelet/lymphocyte ratio, red blood cell distribution width, and platelet distribution width. This review provides an overview of these parameters that may help stratify the risk of coronary angiographic and clinical outcomes related to ISR.

scholarly journals Performance of age-adjusted D-dimer values for predicting DVT before the knee and hip arthroplasty

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Jian Xiang Wu ◽  
Jiang Hui Qing ◽  
Yao Yao ◽  
Dong Yang Chen ◽  
Qing Jiang
Keyword(s):  
Deep Vein Thrombosis ◽  
Odds Ratio ◽  
Joint Arthroplasty ◽  
Receiver Operating Curve ◽  
Multiple Logistic Regression ◽  
Vein Thrombosis ◽  
Specificity And Sensitivity ◽  
Knee And Hip Arthroplasty ◽  
Deep Vein ◽  
D Dimer

Abstract Purpose To compare the specificity and sensitivity of preoperative D-dimer and age-adjusted D-dimer value for predicting the incidence of the DVT preoperatively in total joint arthroplasty (TJA) patients. Methods We enrolled 406 patients finally above 50 years old. Everyone had done ultrasonography bedside, and D-dimer concentrations were collected before surgery. The D-dimer and age-adjusted D-dimer cut-off was calculated by multiple logistic regression and receiver operating curve (ROC) analyses. Results A total of 39 patients had found asymptomatic deep vein thrombosis (DVT) by ultrasonography. The age (odds ratio [OR] 1.067; p = 0.003) and D-dimer (OR 1.331; p = 0.025) were related to the existence of DVT. For conventional D-dimer and age-adjusted D-dimer value, the area under the curves (AUCs) were 0.685 (0.499–0.696) and 0.795 (0.611–0.881), respectively. Conclusion Compared to traditional D-dimer, age-adjusted D-dimer showed better performance in screening DVT, which was useful clinically.

scholarly journals A Comparison of Lung Ultrasound and Computed Tomography in the Diagnosis of Patients with COVID-19: A Systematic Review and Meta-Analysis

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1351
Author(s):  
Mengshu Wang ◽  
Xufei Luo ◽  
Ling Wang ◽  
Janne Estill ◽  
Meng Lv ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Interstitial Pneumonia ◽  
Lung Ultrasound ◽  
Meta Analysis ◽  
Diagnostic Odds Ratio ◽  
Receiver Operating Curve ◽  
Cochrane Library ◽  
Molecular Tests ◽  
Specificity And Sensitivity

Background Lung ultrasound (LUS) and computed tomography (CT) can both be used for diagnosis of interstitial pneumonia caused by coronavirus disease 2019 (COVID-19), but the agreement between LUS and CT is unknown. Purpose to compare the agreement of LUS and CT in the diagnosis of interstitial pneumonia caused by COVID-19. Materials and Methods We searched PubMed, Cochrane library, Embase, Chinese Biomedicine Literature, and WHO COVID-19 databases to identify studies that compared LUS with CT in the diagnosis of interstitial pneumonia caused by COVID-19. We calculated the pooled overall, positive and negative percent agreements, diagnostic odds ratio (DOR) and the area under the standard receiver operating curve (SROC) for LUS in the diagnosis of COVID-19 compared with CT. Results We identified 1896 records, of which nine studies involving 531 patients were finally included. The pooled overall, positive and negative percentage agreements of LUS for the diagnosis of interstitial pneumonia caused by COVID-19 compared with CT were 81% (95% confidence interval [CI] 43–99%), 96% (95% CI, 80–99%, I2 = 92.15%) and 80% (95%CI, 60–92%, I2 = 92.85%), respectively. DOR was 37.41 (95% CI, 9.43–148.49, I2 = 63.9%), and the area under the SROC curve was 0.94 (95% CI, 0.92–0.96). The quality of evidence for both specificity and sensitivity was low because of heterogeneity and risk of bias. Conclusion The level of diagnostic agreement between LUS and CT in the diagnosis of interstitial pneumonia caused by COVID-19 is high. LUS can be therefore considered as an equally accurate alternative for CT in situations where molecular tests are not available.

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