Comparative study of hematological and radiological feature of severe/critically ill patients with COVID‐19, influenza A H7N9, and H1N1 pneumonia

Kong, Jindan; Hu, Yan; Wan, Shouhong; Li, Zheng; Di, Zou; Zhang, Leisi; Lu, Yin; Wang, Jun; Chen, Xiaochen; Fu, Jianhong

doi:10.1002/jcla.24100

Cited by 5 publications

(7 citation statements)

References 28 publications

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“…Interestingly, our finding that SARS-CoV-2 negative patients often had higher parameters of infection or tissue injury such as WBC or CRP than SARS-CoV-2 positive patients is in agreement with the results from previous studies comparing COVID-19 with other known infections of bacterial or viral origin [ 19 , 20 ]. This reflects that SARS-CoV-2 initiated a lower inflammatory response in comparison to other bacterial and viral infections.…”

Section: Discussionsupporting

confidence: 92%

Machine Learning Successfully Detects Patients with COVID-19 Prior to PCR Results and Predicts Their Survival Based on Standard Laboratory Parameters in an Observational Study

et al. 2022

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Introduction In the current COVID-19 pandemic, clinicians require a manageable set of decisive parameters that can be used to (i) rapidly identify SARS-CoV-2 positive patients, (ii) identify patients with a high risk of a fatal outcome on hospital admission, and (iii) recognize longitudinal warning signs of a possible fatal outcome. Methods This comparative study was performed in 515 patients in the Maria Skłodowska-Curie Specialty Voivodeship Hospital in Zgierz, Poland. The study groups comprised 314 patients with COVID-like symptoms who tested negative and 201 patients who tested positive for SARS-CoV-2 infection; of the latter, 72 patients with COVID-19 died and 129 were released from hospital. Data on which we trained several machine learning (ML) models included clinical findings on admission and during hospitalization, symptoms, epidemiological risk, and reported comorbidities and medications. Results We identified a set of eight on-admission parameters: white blood cells, antibody-synthesizing lymphocytes, ratios of basophils/lymphocytes, platelets/neutrophils, and monocytes/lymphocytes, procalcitonin, creatinine, and C-reactive protein. The medical decision tree built using these parameters differentiated between SARS-CoV-2 positive and negative patients with up to 90–100% accuracy. Patients with COVID-19 who on hospital admission were older, had higher procalcitonin, C-reactive protein, and troponin I levels together with lower hemoglobin and platelets/neutrophils ratio were found to be at highest risk of death from COVID-19. Furthermore, we identified longitudinal patterns in C-reactive protein, white blood cells, and D dimer that predicted the disease outcome. Conclusions Our study provides sets of easily obtainable parameters that allow one to assess the status of a patient with SARS-CoV-2 infection, and the risk of a fatal disease outcome on hospital admission and during the course of the disease. Supplementary Information The online version contains supplementary material available at 10.1007/s40121-022-00707-8.

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Section: Discussionsupporting

confidence: 92%

Machine Learning Successfully Detects Patients with COVID-19 Prior to PCR Results and Predicts Their Survival Based on Standard Laboratory Parameters in an Observational Study

et al. 2022

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“…Most of the data were from retrospective studies, 5,10,[15][16][17][18][19][20][22][23][24][25][26][28][29][30][32][33][34][35][37][38][39][40][41][42][43][44][45][46][47] 2 articles were prospective studies, 31,34 and 3 articles did not specify the type of study. 21,27,36 Four articles specifically studied patients with influenza A, 10,21,31,36 15 articles studied patients with type A/B, 5,15,16,20,[24][25][26][27]30,32,33,…”

Section: Characteristics and Results Of Individual Studiesmentioning

confidence: 99%

“…In addition, SARS-CoV-2 and influenza virus share a common route of interpersonal transmission through aerosolized or respiratory droplets, and the diagnosis of both mainly depends on reverse transcription polymerase chain reaction. 9,10 Before the COVID-19 pandemic, influenza viruses were the most common cause of severe viral pneumonia. Influenza-associated hospitalizations for critical illness (between 5% and 19% of all influenza hospitalizations) have increased considerably over the past several decades.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Clinical Characteristics and Outcomes in Intensive Care Units Between Patients with Coronavirus Disease 2019 (COVID-19) and Patients with Influenza: A Systematic Review and Meta-Analysis

Zhong,

Wang,

Guo

et al. 2024

J Intensive Care Med

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Background Severe infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or influenza virus can cause patients to be admitted to intensive care units (ICUs). It is necessary to understand the differences in clinical characteristics and outcomes between these two types of critically ill patients. Methods We searched Embase, PubMed, and Web of Science for articles and performed a meta-analysis using Stata 14.0 with a random-effects model. This paper was written in strict accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results Thirty-five articles involving 131,692 ICU patients with coronavirus disease 2019 (COVID-19) and 30,286 ICU patients with influenza were included in our meta-analysis. Compared with influenza patients, COVID-19 patients were more likely to be male (odds ratio (OR) = 1.75, 95% CI: 1.54-1.99) and older (standardized mean difference (SMD) = 0.16, 95% CI: 0.03-0.29). In terms of laboratory test results, COVID-19 patients had higher lymphocyte (SMD = 0.38, 95% CI: 0.17-0.59) and platelet counts (SMD = 0.52, 95% CI: 0.29-0.75) but lower creatinine (SMD = −0.29, 95% CI: −0.55-0.03) and procalcitonin levels (SMD = −0.78, 95% CI: −1.11-0.46). Diabetes (SMD = 1.27, 95% CI: 1.08-1.48) and hypertension (SMD = 1.30, 95% CI: 1.05-1.60) were more prevalent in COVID-19 patients, while influenza patients were more likely to have cancer (OR = 0.52, 95% CI: 0.44-0.62), cirrhosis (OR = 0.52, 95% CI: 0.44-0.62), immunodepression (OR = 0.38, 95% CI: 0.25-0.58), and chronic pulmonary diseases (OR = 0.35, 95% CI: 0.24-0.52). We also found that patients with COVID-19 had longer ICU stays (SMD = 0.20, 95% CI: 0.05-0.34), were more likely to develop acute respiratory distress syndrome (OR = 4.90, 95% CI: 2.77-8.64), and had higher mortality (OR = 1.35, 95% CI: 1.17-1.55). Conclusions There are some differences in the basic characteristics, comorbidities, laboratory test results and complications between ICU patients with COVID-19 and ICU patients with influenza. Critically ill patients with COVID-19 often require more medical resources and have worse clinical outcomes. PROSPERO Registration Number: CRD42023452238

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“…On the other hand, we measured a downregulation of several CCGs after administration of the primer dose, such as IL-17A, IL-8, IL-4, TSLP, VCAM-1, ICAM-1, Tie-2, and VEGF-D. Most of these analytes are crucially involved in the adaptive immune response or in cancer progression ( 26 , 27 ). For example, downregulation of TSLP, that has an important role in the maturation of T cell populations and in enhancing Th2 responses ( 28 ), and of IL-4, a key Th2 cytokine with profound effects on B cell function, could be detrimental to the development of an adaptive immune response in the studied cancer patients ( Figure 2A , left panel).…”

Section: Resultsmentioning

confidence: 99%

Predictive model for BNT162b2 vaccine response in cancer patients based on blood cytokines and growth factors

et al. 2022

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BackgroundPatients with cancer, especially hematological cancer, are at increased risk for breakthrough COVID-19 infection. So far, a predictive biomarker that can assess compromised vaccine-induced anti-SARS-CoV-2 immunity in cancer patients has not been proposed.MethodsWe employed machine learning approaches to identify a biomarker signature based on blood cytokines, chemokines, and immune- and non-immune-related growth factors linked to vaccine immunogenicity in 199 cancer patients receiving the BNT162b2 vaccine.ResultsC-reactive protein (general marker of inflammation), interleukin (IL)-15 (a pro-inflammatory cytokine), IL-18 (interferon-gamma inducing factor), and placental growth factor (an angiogenic cytokine) correctly classified patients with a diminished vaccine response assessed at day 49 with >80% accuracy. Amongst these, CRP showed the highest predictive value for poor response to vaccine administration. Importantly, this unique signature of vaccine response was present at different studied timepoints both before and after vaccination and was not majorly affected by different anti-cancer treatments.ConclusionWe propose a blood-based signature of cytokines and growth factors that can be employed in identifying cancer patients at persistent high risk of COVID-19 despite vaccination with BNT162b2. Our data also suggest that such a signature may reflect the inherent immunological constitution of some cancer patients who are refractive to immunotherapy.

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Comparative study of hematological and radiological feature of severe/critically ill patients with COVID‐19, influenza A H7N9, and H1N1 pneumonia

Abstract: This is an open access article under the terms of the Creat ive Commo ns Attri bution-NonCo mmercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Cited by 5 publications

References 28 publications

Machine Learning Successfully Detects Patients with COVID-19 Prior to PCR Results and Predicts Their Survival Based on Standard Laboratory Parameters in an Observational Study

Machine Learning Successfully Detects Patients with COVID-19 Prior to PCR Results and Predicts Their Survival Based on Standard Laboratory Parameters in an Observational Study

Comparison of Clinical Characteristics and Outcomes in Intensive Care Units Between Patients with Coronavirus Disease 2019 (COVID-19) and Patients with Influenza: A Systematic Review and Meta-Analysis

Predictive model for BNT162b2 vaccine response in cancer patients based on blood cytokines and growth factors

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