scholarly journals qS-Ne2Mo Score – A New Risk Stratification Tool For Early Detection of Septic Shock in The Field of Emergency Medicine

2021 ◽  
Author(s):  
Peter Erdelyi ◽  
Laszlo Papp ◽  
Imre Foldesi ◽  
Katalin Farkas ◽  
Zsolt Molnar ◽  
...  
Keyword(s):  
Septic Shock ◽  
Risk Stratification ◽  
Cell Population ◽  
Complete Blood Count ◽  
Early Recognition ◽  
Population Data ◽  
The United States ◽  
Receiver Operating Curve ◽  
Suspected Infection ◽  
Risk Stratification Tool

Abstract Background Sepsis is one of the most significant healthcare concerns of the 21st century. In the United States sepsis affect 1.7 million adults, with 270,000 fatal cases, according to the estimation of Centers for Disease Control and Prevention. The management of sepsis relies on early recognition, therefore the emergency departments have distinctive role in sepsis care, hence the need for early reliable risk stratification tools.Methods A retrospective, quantitative study was performed in Department of Emergency, University of Szeged. Hungary. Patients with suspected infection were enrolled to four subgroups based on the results of patient examination and laboratory results. In all cases (N=276), cell population data markers were analyzed along with ordinary infection biomarkers, such as CRP, PCT and WBC. Performance of cell population data parameters were investigated with ROC (Receiver Operating Curve) analysis.Results Almost all cell population biomarkers showed significant differences in the subgroup analysis. Remarkable performance was found in three markers (NE-SFL/M, MO-X/M and NE-WY/M) in patients having septic shock. Combining quick SOFA with these biomarkers (qS-Ne2Mo score) resulted in excellent diagnostic ability for septic shock (AUC 0.914, p<0.001), with good sensitivity (73.9%) and excellent specificity (89 %).Conclusions Since determination of cell population data requires complete blood count analysis, turn-around time of this novel indicator is significantly lower than other methods. qS-Ne2Mo score might be used as an initial screening tool to select only those patients that need more extensive laboratory investigations for their proper treatment and spare inadequate, time and money consuming laboratory requests.Trial Registration: University of Szeged, Ethical Committee ref. nr. 25/2016-SZTE

scholarly journals Cell Population Data (CPD) for Early Recognition of Sepsis and Septic Shock in Children: A Pilot Study

2021 ◽  
Vol 9 ◽  
Author(s):  
Paolo Biban ◽  
Martina Teggi ◽  
Marcella Gaffuri ◽  
Pierantonio Santuz ◽  
Diletta Onorato ◽  
...  
Keyword(s):  
Septic Shock ◽  
Cell Population ◽  
Fluorescence Intensity ◽  
Elective Surgery ◽  
Early Recognition ◽  
Pediatric Intensive Care ◽  
Population Data ◽  
Sepsis Diagnosis ◽  
Reactive Protein ◽  
Sepsis And Septic Shock

Objectives: Innovative Cell Population Data (CPD) have been used as early biomarkers for diagnosing sepsis in adults. We assessed the usefulness of CPD in pediatric patients with sepsis/septic shock, in terms of early recognition and outcome prediction. We revised 54 patients (0–15 y) admitted to our Pediatric Intensive Care Unit (PICU) for sepsis/septic shock during a 4-year period. Twenty-eight patients were excluded, 26 septic patients were enrolled (G1). Forty children admitted for elective surgery served as controls (G2). Data on five selected CPD parameters, namely neutrophils fluorescence intensity (NE-SFL), monocytes cells complexity (MO-X), monocytes fluorescence intensity (MO-Y), monocytes complexity and width of dispersion of events measured (MO-WX), and monocytes cells size and width dispersion (MO-WZ), were obtained at time of PICU admission (t0) by a hematological analyzer (Sysmex XN 9000®). As the primary outcome we evaluated the relevance of CPD for diagnosing sepsis/septic shock on PICU admission. Furthermore, we investigated if CPD at t0 were correlated with C-reactive protein (CRP), patient survival, or complicated sepsis course.Results: On PICU admission (t0), NE-SFL, MO-WX, and MO-Y were higher in sepsis/septic shock patients compared to controls. NE-SFL values were correlated with CRP values in G1 patients (r = 0.83). None of the five CPD parameters was correlated with survival or complicated sepsis course.Conclusion: We found higher values of NE-SFL, MO-WX, and MO-Y in children with sepsis/septic shock upon PICU admission. These parameters may be a promising adjunct for early sepsis diagnosis in pediatric populations. Larger, prospective studies are needed to confirm our preliminary observations.

Plasma IL-8 As A Risk Stratification Tool For Adults With Septic Shock

2010 ◽  
Author(s):  
Carolyn S. Calfee ◽  
B. T. Thompson ◽  
Polly E. Parsons ◽  
Lorraine B. Ware ◽  
Michael A. Matthay ◽  
...  
Keyword(s):  
Septic Shock ◽  
Risk Stratification ◽  
Risk Stratification Tool

scholarly journals Bioinformatic and Machine Learning Applications in Melanoma Risk Assessment and Prognosis: A Literature Review

Genes ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1751
Author(s):  
Emily Z. Ma ◽  
Karl M. Hoegler ◽  
Albert E. Zhou
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Risk Stratification ◽  
Metastatic Melanoma ◽  
The United States ◽  
Clinical Staging ◽  
Machine Learning Applications ◽  
Risk Stratification Tool ◽  
Melanoma Treatment ◽  
Melanoma Patients

Over 100,000 people are diagnosed with cutaneous melanoma each year in the United States. Despite recent advancements in metastatic melanoma treatment, such as immunotherapy, there are still over 7,000 melanoma-related deaths each year. Melanoma is a highly heterogenous disease, and many underlying genetic drivers have been identified since the introduction of next-generation sequencing. Despite clinical staging guidelines, the prognosis of metastatic melanoma is variable and difficult to predict. Bioinformatic and machine learning analyses relying on genetic, clinical, and histopathologic inputs have been increasingly used to risk stratify melanoma patients with high accuracy. This literature review summarizes the key genetic drivers of melanoma and recent applications of bioinformatic and machine learning models in the risk stratification of melanoma patients. A robustly validated risk stratification tool can potentially guide the physician management of melanoma patients and ultimately improve patient outcomes.

Automated screening for myelodysplastic syndromes through analysis of complete blood count and cell population data parameters

2014 ◽  
Vol 89 (4) ◽  
pp. 369-374 ◽  
Author(s):  
Philipp W. Raess ◽  
Gert-Jan M. van de Geijn ◽  
Tjin L. Njo ◽  
Boudewijn Klop ◽  
Dmitry Sukhachev ◽  
...  
Keyword(s):  
Cell Population ◽  
Myelodysplastic Syndromes ◽  
Blood Count ◽  
Complete Blood Count ◽  
Population Data ◽  
Automated Screening

scholarly journals A Novel Marker for Screening Paroxysmal Nocturnal Hemoglobinuria Using Routine Complete Blood Count and Cell Population Data

2015 ◽  
Vol 35 (1) ◽  
pp. 35-40 ◽  
Author(s):  
Jimin Kahng ◽  
Yonggoo Kim ◽  
Jung Ok Kim ◽  
Kwangsang Koh ◽  
Jong Wook Lee ◽  
...  
Keyword(s):  
Cell Population ◽  
Paroxysmal Nocturnal Hemoglobinuria ◽  
Blood Count ◽  
Complete Blood Count ◽  
Population Data

Spontaneous Knee Effusion in an Adolescent Soccer Player

2021 ◽  
pp. 1-4
Author(s):  
Bernadette D. Buckley ◽  
Christopher J. Joyce
Keyword(s):  
Lyme Disease ◽  
Soccer Player ◽  
Complete Blood Count ◽  
Early Recognition ◽  
The United States ◽  
Disease Diagnosis ◽  
Left Knee ◽  
Lyme Arthritis ◽  
Radiographic Imaging ◽  
Knee Effusion

A 13-year-old female recreational soccer player presented with spontaneous left knee effusion approximately 2 weeks after the start of soccer season. Radiographic imaging was negative, and a complete blood count identified an increase in inflammatory markers. Additional two-tier testing confirmed a Lyme disease diagnosis, which was unusual for an athlete residing in the southeast region of the United States. The presentation of knee effusion, and subsequently Lyme arthritis, is a common clinical manifestation of late-stage Lyme disease. Early recognition of this infection and prompt treatment by a variety of specialists reduce the severity or chronicity of symptoms.

scholarly journals Genomic Classifier Augments the Role of Pathological Features in Identifying Optimal Candidates for Adjuvant Radiation Therapy in Patients With Prostate Cancer: Development and Internal Validation of a Multivariable Prognostic Model

2017 ◽  
Vol 35 (18) ◽  
pp. 1982-1990 ◽  
Author(s):  
Deepansh Dalela ◽  
María Santiago-Jiménez ◽  
Kasra Yousefi ◽  
R. Jeffrey Karnes ◽  
Ashley E. Ross ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Lymph Node ◽  
Risk Stratification ◽  
The United States ◽  
Cumulative Number ◽  
Clinical Recurrence ◽  
Cox Regression Analysis ◽  
Initial Observation ◽  
Risk Stratification Tool

Purpose Despite documented oncologic benefit, use of postoperative adjuvant radiotherapy (aRT) in patients with prostate cancer is still limited in the United States. We aimed to develop and internally validate a risk-stratification tool incorporating the Decipher score, along with routinely available clinicopathologic features, to identify patients who would benefit the most from aRT. Patient and Methods Our cohort included 512 patients with prostate cancer treated with radical prostatectomy at one of four US academic centers between 1990 and 2010. All patients had ≥ pT3a disease, positive surgical margins, and/or pathologic lymph node invasion. Multivariable Cox regression analysis tested the relationship between available predictors (including Decipher score) and clinical recurrence (CR), which were then used to develop a novel risk-stratification tool. Our study adhered to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines for development of prognostic models. Results Overall, 21.9% of patients received aRT. Median follow-up in censored patients was 8.3 years. The 10-year CR rate was 4.9% vs. 17.4% in patients treated with aRT versus initial observation ( P < .001). Pathologic T3b/T4 stage, Gleason score 8-10, lymph node invasion, and Decipher score > 0.6 were independent predictors of CR (all P < .01). The cumulative number of risk factors was 0, 1, 2, and 3 to 4 in 46.5%, 28.9%, 17.2%, and 7.4% of patients, respectively. aRT was associated with decreased CR rate in patients with two or more risk factors (10-year CR rate 10.1% in aRT v 42.1% in initial observation; P = .012), but not in those with fewer than two risk factors ( P = .18). Conclusion Using the new model to indicate aRT might reduce overtreatment, decrease unnecessary adverse effects, and reduce risk of CR in the subset of patients (approximately 25% of all patients with aggressive pathologic disease in our cohort) who benefit from this therapy.

Assessment of sepsis in the critically ill

2016 ◽  
Author(s):  
Osamudiamen Idahosa ◽  
David T. Huang
Keyword(s):  
Septic Shock ◽  
Severe Sepsis ◽  
Organ Dysfunction ◽  
Early Identification ◽  
Signs And Symptoms ◽  
The United States ◽  
Medical Emergency ◽  
Suspected Infection ◽  
Portal Of Entry ◽  
Acute Organ Dysfunction

Sepsis is the presence of a known or suspected infection and a systemic inflammatory response. Severe sepsis is sepsis with acute organ dysfunction. Septic shock is a subset of severe sepsis characterized by systemic arterial hypotension or occult hypoperfusion. Severe sepsis is common, affecting more than 750 000 individuals in the United States each year with a hospital mortality of about 30%.Severe sepsis is a medical emergency that requires early identification, prompt evaluation, and treatment. The signs and symptoms of sepsis are influenced by the virulence of the pathogen, the portal of entry, the degree of organ dysfunction as well as the susceptibility and response of the host.

Role of leucocytes cell population data in the early detection of sepsis

2017 ◽  
Vol 71 (3) ◽  
pp. 259-266 ◽  
Author(s):  
Eloísa Urrechaga ◽  
Oihane Bóveda ◽  
Urko Aguirre
Keyword(s):  
Logistic Regression ◽  
Early Diagnosis ◽  
Cell Population ◽  
Predictive Value ◽  
Complete Blood Count ◽  
Area Under The Curve ◽  
Population Data ◽  
Risk Groups ◽  
Multivariate Logistic Regression Model ◽  
Morphological Alterations

AimsThe cell population data (CPD) parameters reported by XN analyser (Sysmex, Kobe, Japan) reflect the size and internal structure of leucocytes. We aimed to assess the clinical utility of these parameters as biomarkers for the early diagnosis of sepsis.MethodsThe study group (G1) included 586 controls (no quantitative or morphological alterations in the complete blood count) and 137 patients diagnosed with sepsis. The reliability of the model was evaluated using a validation group (G2) of 212 controls and 60 patients with sepsis. The optimal cut-off for the diagnosis of sepsis and the OR for CPD were established using a univariate logistic regression. A multivariate logistic regression model was then created. The OR and area under the curve were recorded. A risk stratification scale (neutrophils and monocytes (NEMO)) for diagnosing sepsis was established on the basis of the coefficients of the multivariate model.ResultsMO-X and neutrophils fluorescence intensity (NE-SFL) were found to be the most relevant of the CPD in predicting sepsis applying multivariate analysis to G1.NEMO score was composed using the above-mentioned CPD and subsequently stratified into three risk groups: mild (≤3), moderate (4≤NEMO≤5) and high (≥6). The OR for patients with a score of 4–5 was 10 and 249 for a score of ≥6. When applied to G2, the positive predictive value was 84.8 % and the negative predictive value was 96.0%.ConclusionsCPD are potentially useful for the early diagnosis of sepsis. Their values were used to compose in NEMO score can help in rapid and reliable decision making.

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