Incorporation of dynamic segmented neutrophil-to-monocyte ratio with leukocyte count for sepsis risk stratification

Fang, Wenfeng; Chen, Yu‐Mu; Wang, Yi‐Hsi; Huang, Chung‐Cheng; Hung, Kuo‐Chi; Fang, Ying-Tang; Chang, Yu‐Ling; Lin, Chiung-Yu; Chang, Ya‐Ting; Chen, Hung-Cheng; Huang, Kuo‐Tung; Chen, Yun-Che; Wang, Chin‐Chou; Lin, Meng-Chih

doi:10.1038/s41598-019-56368-0

Cited by 17 publications

(17 citation statements)

References 34 publications

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“…These peripheral blood in ammatory cells are often studied together, NLR and NMR are easily calculated and serve as systemic in ammation biomarkers [11,39]. NLR has been studied in the severity and prognosis of COVID-19 [12-14, 18, 40], in our study, NLR was also higher in severe COVID-19.…”

Section: Discussionsupporting

confidence: 50%

“…NLR has been studied in the severity and prognosis of COVID-19 [12-14, 18, 40], in our study, NLR was also higher in severe COVID-19. According to Fang [11], segmented NMR can act as an immune dysfunction score to predict the 28-day mortality of patients with sepsis. In present study, levels of NMR were signi cantly higher in severe COVID-19 than non-severe patients, which implied that NMR might be a potential serum marker for severity of COVID-19.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, if early identi cations and interventions can be taken in time through recognizing the warning signals of severe COVID-19, it is possible to boost cure rate, reduce mortality and shorten length of hospital stay. White blood cell count, neutrophil percentage, neutrophil-tomonocyte ratio (NMR) and neutrophil-to-lymphocyte ratio (NLR) are good biomarkers for in ammation and immune status in vivo, which are also simple, reproducible and cost-effective [11][12][13][14][15][16][17][18]. Given this situation, we wondered if these parameters at admission could help re ect the clinical severity in patients with COVID-19.…”

Section: Introductionmentioning

confidence: 99%

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Neutrophil Percentage and Neutrophil-to-Monocyte Ratio as Independent Risk Factors in the Severity of COVID-19

Peng

et al. 2020

Preprint

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BackgroundInflammation plays an important role in progression of the various viral pneumonia containing COVID-19, severe inflammatory responses could lead to an imbalance of immune response. The purpose of this study was to explore the possibility of the white blood count, neutrophil percentage, neutrophil-to-monocyte ratio (NMR) and neutrophil-to-lymphocyte ratio (NLR) at admission to reflect the clinical severity in patients with COVID‐19.MethodsClinical and laboratory data of adult COVID-19 patients in Changsha, China, were collected and analyzed on admission. A logistic regression model was adopted to analyze the association between the disease severity and related risk factors. The receiver operating characteristic (ROC) curve was utilized to analyze the abilities of potential risk factors in the prediction of COVID-19 severity.ResultsCompared with non-severe patients, the severe ones had significantly higher levels of neutrophil percentage (74.9% vs. 62.1%; P < 0.001), NLR (4.1 vs. 2.1; P < 0.001) and NMR (12.4 vs. 8.0; P < 0.001). A regression analysis showed that neutrophil percentage (OR,1.113; 95% CI, 1.020-1.213; P=0.016) and NMR (OR, 1.110; 95% CI, 1.002-1.230; P = 0.046) were significantly associated with severity of COVID-19 patients. ROC curve showed that the area under the curves of neutrophil percentage, NMR and the combination of them were 0.842 (95% confidence interval (CI), 0.782-0.902), 0.790 (95% CI, 0.710-0.871) and 0.851 (95% CI, 0.790-0.911), respectively.ConclusionsNeutrophil percentage and NMR may act as independent risk factors in the severity of COVID-19.

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Section: Discussionsupporting

confidence: 50%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neutrophil Percentage and Neutrophil-to-Monocyte Ratio as Independent Risk Factors in the Severity of COVID-19

Peng

et al. 2020

Preprint

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“…Specific pathogens induce specific responses, such as an increase in circulating neutrophils and monocytes typically increase during sepsis as part of the immune defence mechanism, and several studies have indicated that ratios between different blood cells can be used to predict outcome. The ratio of monocyte: neutrophil can be used to determine sepsis severity [24], and platelet: monocyte aggregation has been reported to be increased in patients with Influenza A (H1N1) [25]. Indeed, the clinical interest in using the relationship between different parameters of the full blood counts is of great value and interest due to its simplicity and readily measurable parameters.…”

Section: Discussionmentioning

confidence: 99%

Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population

Banerjee

Ray

Vorselaars

et al. 2020

International Immunopharmacology

157

151

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the novel coronavirus SARS-CoV-2 has been identified as the cause of the pandemic COVID-19. Early symptoms overlap with other common conditions such as common cold and Influenza, making early screening and diagnosis are crucial goals for health practitioners. The aim of the study was to use machine learning (ML), an artificial neural network (ANN) and a simple statistical test to identify SARS-CoV-2 positive patients from full blood counts without knowledge of symptoms or history of the individuals. The dataset included in the analysis and training contains anonymized full blood counts results from patients seen at the Hospital Israelita Albert Einstein, at São Paulo, Brazil, and who had samples collected to perform the SARS-CoV-2 rt-PCR test during a visit to the hospital. Patient data was anonymised by the hospital, clinical data was standardized to have a mean of zero and a unit standard deviation. This data was made public with the aim to allow researchers to develop ways to enable the hospital to rapidly predict and potentially identify SARS-CoV-2 positive patients. We find that with full blood counts random forest, shallow learning and a flexible ANN model predict SARS-CoV-2 patients with high accuracy between populations on regular wards (AUC = 94-95%) and those not admitted to hospital or in the community (AUC = 80-86%). Here, AUC is the Area Under the receiver operating characteristics Curve and a measure for model performance. Moreover, a simple linear combination of 4 blood counts can be used to have an AUC of 85% for patients within the community. The normalised data of different blood parameters from SARS-CoV-2 positive patients exhibit a decrease in platelets, leukocytes, eosinophils, basophils and lymphocytes, and an increase in monocytes. SARS-CoV-2 positive patients exhibit a characteristic immune response profile pattern and changes in different parameters measured in the full blood count that are detected from simple and rapid blood tests. While symptoms at an early stage of infection are known to overlap with other common conditions, parameters of the full blood counts can be analysed to distinguish the viral type at an earlier stage than current rt-PCR tests for SARS-CoV-2 allow at present. This new methodology has potential to greatly improve initial screening for patients where PCR based diagnostic tools are limited.

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“…The retrospective analysis study is a part of integrated research programs, consisting of prospective observational study (immune profile study) and retrospective medical record review (e.g., consecutive mechanically ventilated sepsis patients), that investigate the clinical factors, biomarkers, and immune response in predicting outcomes in sepsis patients 10 , 16 – 18 . All patients who met the Sepsis-3 criteria 8 and admitted to the medical ICUs with invasive mechanical ventilation support were screened.…”

Section: Methodsmentioning

confidence: 99%

Risk factors and associated outcomes of ventilator-associated events developed in 28 days among sepsis patients admitted to intensive care unit

Fang

Huang

et al. 2020

Sci Rep

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We hypothesized that Ventilator-Associated Event (VAE) within 28 days upon admission to medical intensive care units (ICUs) can be a predictor for poor outcomes in sepsis patients. We aimed to determine the risk factors and associated outcomes of VAE. A total of 453 consecutive mechanically ventilated (MV) sepsis patients were enrolled. Of them, 136 patients had immune profile study. Early VAE (< 7-day MV, n = 33) was associated with a higher mortality (90 days: 81.8% vs. 23.0% [non-VAE], P < 0.01), while late VAE (developed between 7 and 28 days, n = 85) was associated with longer MV day (43.8 days vs. 23.3 days [non-VAE], P < 0.05). The 90-day Kaplan-Meier survival curves showed three lines that separate the groups (non-VAE, early VAE, and late VAE). Cox regression models with time-varying coefficient covariates (adjusted for the number of days from intubation to VAE development) confirmed that VAE which occurred within 28 days upon admission to the medical ICUs can be associated with higher 90-day mortality. The risk factors for VAE development include impaired immune response (lower human leukocyte antigen D-related expression, higher interleukin-10 expression) and sepsis progression with elevated SOFA score (especially in coagulation sub-score). Ventilator-associated event (VAE) 1,2 was proposed to overcome the limitations of only focusing on ventilatorassociated pneumonia (VAP) 3 surveillance in mechanically ventilated (MV) patients in the context of surveying quality improvement. The limitations of previous VAP surveillance, which is proven to be neither sensitive nor specific 4 , hindered its application in quality improvement programs 5. Although VAE surveillance was a promising early warning tool for VAP prevention 6 , VAP prevention bundle compliance was not associated with a reduced risk of VAE 7. VAEs are defined as respiratory deterioration after a period of improved or stable gas exchange. The definition of VAE shifted the focus of surveillance from pneumonia to all conditions caused by mechanical ventilation including infectious or noninfectious conditions. The purpose was to expand the scope of surveillance to include multiple serious complications in ventilated patients, not just pneumonia, as well as to make surveillance more objective, efficient, and suitable for electronic implementation. Sepsis patients developed life-threatening organ dysfunctions (e.g., acute respiratory failure) caused by dysregulated host immune response to infection 8-10. An increase in sequential organ failure assessment (SOFA) score (including respiratory and other five sub-scores) has prognostic accuracy for in-hospital mortality in sepsis patients in the intensive care units (ICUs) 11. Since mechanical ventilator support and aggressive fluid resuscitation

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Incorporation of dynamic segmented neutrophil-to-monocyte ratio with leukocyte count for sepsis risk stratification

Cited by 17 publications

References 34 publications

Neutrophil Percentage and Neutrophil-to-Monocyte Ratio as Independent Risk Factors in the Severity of COVID-19

Neutrophil Percentage and Neutrophil-to-Monocyte Ratio as Independent Risk Factors in the Severity of COVID-19

Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population

Risk factors and associated outcomes of ventilator-associated events developed in 28 days among sepsis patients admitted to intensive care unit

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