Role of red blood cell distribution width, as a prognostic indicator in COVID-19: A systematic review and meta-analysis

Association between anion gap/calcium ratio and 30-day all-cause mortality in sepsis patients with diabetes mellitus

We aimed to determine the association between anion gap-to-calcium ratio (ACR) and 30-day mortality in sepsis patients with diabetes mellitus (DM). Data for sepsis patients diagnosed with DM was extracted from Medical Information Mart for Intensive Care Database IV. After screening, 4429 eligible subjects were included in our study finally. The receiver operating characteristic (ROC) curve was used to determine the cut-off value. According to the ROC curve analysis, the ACR demonstrated a higher area under the curve (AUC) of 0.622 compared to AG (0.598). Multivariable logistic regression with inverse probability of treatment weighting (IPTW) based on propensity score were used to detect the association between ACR and 30-day mortality. Our results showed that the patients with the high level of ACR had a higher risk of death within 30 days compared with those with low level of ACR (odds ratio 1.342, 95% confidence interval 1.180-1.526, P < 0.001). In a word, our results suggest that ACR may be an independent prognostic indicator for death with 30 days in critically ill patients with sepsis and DM.

Red blood cell distribution width-to-monocyte ratio for predicting 90-day mortality of COVID-19 in patients with chronic kidney disease during the Omicron period: a prospective single-center study

We aimed to test whether red blood cell distribution width (RDW) to monocyte percentage ratio (RMR) was associated with the acute-phase prognosis of coronavirus disease 2019 (COVID-19) in chronic kidney disease (CKD) patients. Prospective enrollment and 90-day follow-up of CKD patients with COVID-19 were conducted from December 1, 2022 to January 31, 2023. Demographics, clinical data, and laboratory and radiographic findings were collected, and multiple logistic regression, subgroup analysis, and receiver operating characteristic (ROC) curve analysis were performed. A total of 218 patients were enrolled, with a mean age of 59 years and 69.7% being male. The 90-day mortality rate was 24.8%. The lnRMR level was 5.18 (4.91-5.43) and emerged as an independent risk factor (OR: 3.01, 95% CI: 1.72-5.85). The lnRMR-mortality association was consistent across sex, age, CKD stage, COVID-19 vaccination, and comorbidity subgroups. The area under the ROC curve of lnRMR was 0.737 (95% CI: 0.655-0.819). Our findings indicate that lnRMR is a simple and practical predictor for identifying high-risk CKD patients during the acute phase of COVID-19.

Association Between Red Cell Distribution Width and Liver Injury after Cardiac and Aortic Aneurysm Surgery with Cardiopulmonary Bypass

OBJECTIVES

This study aimed to investigate the association between preoperative red blood cell distribution width (RDW) levels and liver injury (LI) after cardiac surgery, to highlight RDW's usefulness in the early identification and intervention for patients at high risk of LI.

DESIGN

A retrospective observational study.

SETTING

A university-affiliated teaching hospital and tertiary referral center.

PARTICIPANTS

Adult patients who underwent cardiac and aortic aneurysm surgery at Changhai Hospital in 2021.

INTERVENTIONS

Postoperative LI was defined by increased liver enzyme levels and/or hyperbilirubinemia, noted from the time of surgery to discharge. Logistic regression analyses were conducted to examine the RDW-LI relationship, with stratified analyses based on age, gender, and anemia. Survival within 30 days was assessed using the Kaplan-Meier method, with survival curve differences analyzed via the log-rank test. The study included 3 sets of sensitivity analyses.

MEASUREMENTS AND MAIN RESULTS

Postoperative LI was observed in 75 patients (10%). Multivariate regression analysis showed a significant association between high RDW levels and postoperative LI (adjusted odds ratio, 3.25; p = 0.033; 95% confidence intefal, 1.10-9.63), even after adjusting for all covariates. This association remained consistent across 3 sets of sensitivity analyses. Subgroup analysis showed men had a higher correlation with LI (p for interaction = 0.041). Kaplan-Meier analysis indicated a significantly lower survival rate in the LI group (76%) compared with the non-LI group (99.6%; p < 0.001).

CONCLUSIONS

Preoperative RDW levels are significantly associated with postoperative LI. RDW could serve as a significant useful marker for early detection and intervention in patients at high risk of LI, thereby potentially improving patient outcomes.

Indicators of stress hematopoiesis in the blood predict COVID-19 progression in patients over 65 years old

OBJECTIVES

Advanced age is a well-established risk factor for severe coronavirus disease 2019 (COVID-19). Exacerbated inflammation affects multiple organs, among which hematopoiesis responds by increased output of various cells. We aimed to determine the association between COVID-19 progression and large immature cell (LIC) counts, changes in erythrocyte and platelet distribution widths (RDW, PDW) with reference to patients' age.

METHODS

A total of 755 patients with complete blood cell (CBC) analysis in the first 24 h of hospitalization were enrolled. Patients were divided into two groups: under and above 65 years of age.

RESULTS

The LIC counts were different in both groups (p < 0.003). However, only the senior patients had markedly different values of RDW and PDW (p < 0.001). The receiver operating characteristic (ROC) curve analysis provided increased LIC (AUC = 0.600), RDW (AUC = 0.609), PDW (AUC = 0.556), and platelet to LIC ratio (AUC = 0.634) as significant in discriminating outcome in the older group. Importantly, these results were not repeated in the younger patients. In the elderly, the progression was predicted with LIC cut-off at ≥ 0.305 × 109/L (OR = 3.166) and RDW over 12.15% (OR = 2.081).

DISCUSSION

Aging is characterized by a decline in immunological competence with a compromised control of inflammation leading to a proinflammatory state. This background together with the actions of pathogens may lead to emergency myelopoiesis.

CONCLUSION

Our results point to the important differences between age groups regarding CBC-related parameters of stress hematopoiesis during severe infection. Higher LIC, RDW and PDW levels were reliable in the early identification of COVID-19 progression only in the elderly.

Enhancing the Understanding of Abdominal Trauma During the COVID-19 Pandemic Through Co-Occurrence Analysis and Machine Learning

BACKGROUND

This study examines the impact of the COVID-19 pandemic on abdominal trauma management by comparing pre-pandemic (17 February 2018-26 February 2020) and pandemic periods (27 February 2020-7 March 2022).

METHODS

Analyzing data from 118 patients at the Emergency County Clinical Hospital of Craiova, we identified significant shifts in clinical practices affecting patient outcomes.

RESULTS

During the pandemic, a moderate increase in surgical interventions for specific abdominal traumas indicated the effective adaptation of the medical system. Prioritizing critical cases and deferring non-urgent procedures optimized limited resources. Demographic and clinical factors-including age, sex, body mass index (BMI), and red cell distribution width (RDW)-significantly influenced the hospitalization duration and recovery outcomes. Gender disparities in mortality lessened during the pandemic, possibly due to standardized interventions and the physiological effects of SARS-CoV-2. The link between occupation and obesity highlighted how work environments impact trauma severity, especially as lifestyle changes affect BMI. While age remained a major predictor of mortality, its influence slightly decreased, potentially due to improved protocols for elderly patients. RDW emerged as an important prognostic marker for disease severity and mortality risk.

CONCLUSIONS

Employing advanced co-occurrence analysis enhanced with machine learning, we uncovered complex relationships between clinical and demographic variables often overlooked by traditional methods. This innovative approach provided deeper insights into the collective impact of various factors on patient outcomes. Our findings demonstrate the healthcare system's rapid adaptations during the pandemic and offer critical insights for optimizing medical strategies and developing personalized interventions in global crises.

Accounting for Red Cell Distribution Width Improves Risk Stratification by Commonly Used Mortality/Deterioration Risk Scores in Adult Patients Hospitalized Due to COVID-19

Higher red blood cell distribution width (RDW) levels have gained attention in the prognostication of many chronic metabolic and malignant diseases, as well as coronavirus disease 2019 (COVID-19). We aimed to evaluate whether accounting for RDW might contribute to risk stratification when added to commonly used risk scoring systems in adult COVID-19 patients. We retrospectively analyzed a cohort of 3212 non-critical COVID-19 patients hospitalized in a tertiary-level institution from March 2020 to June 2021. Admission RDW values were considered normal if they were ≤14.5% in males or ≤16.1% in females. The Modified Early Warning Score (MEWS), International Severe Acute Respiratory and Emerging Infections Consortium Coronavirus Clinical Characterisation Consortium score (ISARIC 4C), and Veterans Health Administration COVID-19 (VACO) index were evaluated as prognostic scores. RDW exceeded the upper limit in 628 (19.6%) of the patients. When RDW was accounted for, risks of the predicted outcomes were considerably different within the same MEWS, 4C score, and VACO index levels. The same patterns applied equally to patients who started, and those who did not start, remdesivir before deterioration. RDW may be a useful tool for stratifying risk when considered on top of commonly used prognostic scores in non-critical COVID-19 patients.

Association between red blood cell distribution width and 30-day mortality in critically ill septic patients: a propensity score-matched study

BACKGROUND

Sepsis is the leading cause of death worldwide, and a number of biomarkers have been developed for early mortality risk stratification. Red blood cell distribution width (RDW) is a routinely available hematological data and has been found to be associated with mortality in a number of diseases; therefore, we aim to address the association between RDW and mortality in critically ill patients with sepsis.

METHODS

We analyzed data of critically ill adult patients with sepsis on the TriNetX platform, excluding those with hematologic malignancies, thalassemia, and iron deficiency anemia. Propensity score-matching (PSM) (1:1) was used to mitigate confounding effects, and hazard ratio (HR) with 95% confidence (CI) was calculated to determine the association between RDW and 30-day mortality. We further conducted sensitivity analyses through using distinct cut-points of RDW and severities of sepsis.

RESULTS

A total of 256,387 critically ill septic patients were included in the analysis, and 40.0% of them had RDW equal to or higher than 16%. After PSM, we found that high RDW was associated with an increased 30-day mortality rate (HR: 1.887, 95% CI 1.847-1.928). The associations were consistent using distinct cut-points of RDW, with the strength of association using cut-points of 12%, 14%, 16%, 18% and 20% were 2.098, 2.204, 1.887, 1.809 and 1.932, respectively. Furthermore, we found consistent associations among critically ill septic patients with distinct severities, with the association among those with shock, receiving mechanical ventilation, bacteremia and requirement of hemodialysis being 1.731, 1.735, 2.380 and 1.979, respectively.

CONCLUSION

We found that RDW was associated with 30-day mortality in critically ill septic patients, underscoring the potential as a prognostic marker in sepsis. More studies are needed to explore the underlying mechanisms.

Increased red cell distribution width predicts mortality in COVID-19 patients admitted to a Dutch intensive care unit

BACKGROUND

Abnormal red blood cell distribution width (RDW) is associated with poor cardiovascular, respiratory, and coronavirus disease 2019 (COVID-19) outcomes. However, whether RDW provides prognostic insights regarding COVID-19 patients admitted to the intensive care unit (ICU) was unknown. Here, we retrospectively investigated the association of RDW with 30-day and 90- day mortalities, duration of mechanical ventilation, and length of ICU and hospital stay in patients with COVID-19.

METHODS

This study included 321 patients with COVID-19 aged >18 years who were admitted to the ICU between March 2020 and July 2022. The outcomes were mortality, duration of mechanical ventilation, and length of stay. RDW >14.5% was assessed in blood samples within 24 hours of admission.

RESULTS

The mortality rate was 30.5%. Multivariable Cox regression analysis showed an association between increased RDW and 30-day mortality (hazard ratio [HR], 3.64; 95% CI, 1.54-8.65), 90-day mortality (HR, 3.66; 95% CI, 1.59-8.40), and shorter duration of invasive ventilation (2.7 ventilator-free days, P=0.033).

CONCLUSIONS

Increased RDW in COVID-19 patients at ICU admission was associated with increased 30-day and 90-day mortalities, and shorter duration of invasive ventilation. Thus, RDW can be used as a surrogate biomarker for clinical outcomes in COVID-19 patients admitted to the ICU.

Hematological parameters to predict post-COVID-19 immune response among vaccinated and nonvaccinated individuals: a retrospective cross-sectional study

Background

This study finds the changes in the hematological parameters of healthy individuals to predict the immune status against coronavirus disease 2019 (COVID-19) among COVID -19 vaccinated and nonvaccinated individuals.

Methods

A comparative cross-sectional study among 210 healthy individuals was conducted. All individuals were divided into three groups, that is, IgG positive, IgG negative, and IgG and IgM positive, based on ELISA. Data analysis was done using SPSS version 25 for Windows.

Results

A statistically significant effect was found among the three groups in terms of mean levels of hemoglobin (Hb), hematocrit (Hct), mean corpuscular hemoglobin concentration (MCHC), red blood cells (RBC), RDW-CV, lymphocyte, neutrophil, eosinophils, and basophil count. The study also showed that 52.8% (n=74) had neither taken vaccination nor had any history of previous COVID-19 infection but were IgG antibody positive.

Conclusion

There was a statistically significant difference among hematological parameters between immune and nonimmune groups, and it can predict the COVID-19 immune status.

Utilizing red blood cell distribution width (RDW) as a reliable biomarker to predict treatment effects after chimeric antigen receptor T cell therapy

Chimeric antigen receptor T cell (CAR-T) therapy is an effective treatment for B cell malignancies. A certain fraction of patients, however, experience post-CAR-T relapse, and due to the difficulty of precise relapse prediction, biomarkers that can predict the strength and duration of CAR-T efficacy are needed before CAR-T infusion. Therefore, we performed a single-center cohort study including 91 diffuse large B cell lymphoma (DLBCL) patients treated with CAR-T in order to identify such a new prognostic biomarker. After confirming that each of the already reported prognostic parameters (disease status at leukapheresis, primary refractoriness, number of treatment lines, CD3+ cell counts at leukapheresis) has only limited predictive performance, we established a new composite parameter by integrating these four variables, and found that it predicts progression-free survival (PFS) after CAR-T infusion with statistical significance. Moreover, after comprehensive correlation analyses of this new composite parameter with all individual laboratory variables, we determined that the standard deviation of red blood cell distribution width (RDW-SD) at leukapheresis shows significant correlation with the composite parameter and may be a prognostic biomarker (R2 = 0.76, p = 0.02). Validation analysis indicated that a higher RDW-SD is significantly associated with poorer PFS after CAR-T cell therapy (HR, 3.46, P = 0.03). Thus, this study suggests that a single parameter, RDW-SD at leukapheresis, is a novel, useful biomarker that can be obtained early to predict therapeutic effects of CAR-T cell therapy. Post-CAR-T maintenance or re-induction therapies should be adopted for higher risk patients, who may relapse after CAR-T therapy.

The relationship between red cell distribution width, serum calcium ratio, and in-hospital mortality among patients with acute respiratory failure: A retrospective cohort study of the MIMIC-IV database

To investigate the impact of RDW/CA (the ratio of red cell distribution width to calcium) on in-hospital mortality in patients with acute respiratory failure (ARF). This retrospective cohort study analyzed the data of 6981 ARF patients from the Medical Information Mart for Intensive Care (MIMIC-IV) database 2.0. Critically ill participants between 2008 and 2019 at the Beth Israel Deaconess Medical Center in Boston. The primary outcome of interest was in-hospital mortality. A Cox proportional hazards regression model was used to determine whether the RDW/CA ratio independently correlated with in-hospital mortality. The Kaplan-Meier method was used to plot the survival curves of the RDW/CA. Subgroup analyses were performed to measure the mortality across various subgroups. After adjusting for potential covariates, we found that a higher RDW/CA was associated with an increased risk of in-hospital mortality (HR = 1.17, 95% CI: 1.01-1.35, P = .0365) in ARF patients. A nonlinear relationship was observed between RDW/CA and in-hospital mortality, with an inflection point of 1.97. When RDW/CA ≥ 1.97 was positively correlated with in-hospital mortality in patients with ARF (HR = 1.554, 95% CI: 1.183-2.042, P = .0015). The Kaplan-Meier curve indicated the higher survival rates for RDW/CA < 1.97 and the lower for RDW/CA ≥ 1.97 after adjustment for age, gender, body mass index, and ethnicity. RDW/CA is an independent predictor of in-hospital mortality in patients with ARF. Furthermore, a nonlinear relationship was observed between RDW/CA and in-hospital mortality in patients with ARF.

Association of mean RDW values and changes in RDW with in-hospital mortality in ventilator-associated pneumonia (VAP): Evidence from MIMIC-IV database

INTRODUCTION

Ventilator-associated pneumonia (VAP) is a hospital-acquired infection with high mortality, and remains a challenge for clinical treatment. Red blood cell distribution width (RDW) was associated with worse outcomes in several diseases. The purpose of this study was to investigate the relationship between mean RDW values, changes in RDW (delta RDW), and in-hospital mortality among patients with VAP.

METHODS

In the present study, we enrolled 1266 VAP patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. All patients were categorized into low group, medium group, and high group according to tertiles of mean RDW values. The primary outcome was all-cause in-hospital mortality. Univariate logistic regression analysis, multivariate logistic regression analysis, and restricted cubic spline (RCS) curve were performed to determine the association between mean RDW values and in-hospital mortality in VAP. Moreover, RCS curve was plotted to explore the dose-response relationship between delta RDW and in-hospital mortality in VAP.

RESULTS

Among the VAP patients included in the study, the in-hospital mortality was 20.85% with 264 non-survivors and 1002 survivors. The non-survivors exhibited significantly higher mean RDW values and delta RDW values compared to survivors. Multivariate logistic regression analysis indicated that mean RDW values were positively associated with in-hospital mortality in VAP after adjusting for relevant covariates. The RCS curve demonstrated a dose-response relationship between mean RDW and the mortality in VAP. Moreover, a linear relationship was observed between delta RDW and in-hospital mortality in VAP.

CONCLUSION

Higher mean RDW values were significantly associated with an increased risk of in-hospital mortality in VAP. Additionally, a linear relationship was found between delta RDW values and in-hospital mortality. These findings suggest that RDW can be used to identify high-risk patients with poorer outcomes in VAP.

It's in the blood: a review of the hematological system in SARS-CoV-2-associated COVID-19

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an unprecedented global healthcare crisis. While SARS-CoV-2-associated COVID-19 affects primarily the respiratory system, patients with COVID-19 frequently develop extrapulmonary manifestations. Notably, changes in the hematological system, including lymphocytopenia, neutrophilia and significant abnormalities of hemostatic markers, were observed early in the pandemic. Hematological manifestations have since been recognized as important parameters in the pathophysiology of SARS-CoV-2 and in the management of patients with COVID-19. In this narrative review, we summarize the state-of-the-art regarding the hematological and hemostatic abnormalities observed in patients with SARS-CoV-2-associated COVID-19, as well as the current understanding of the hematological system in the pathophysiology of acute and chronic SARS-CoV-2-associated COVID-19.

COVID-19 diagnosis using clinical markers and multiple explainable artificial intelligence approaches: A case study from Ecuador

The COVID-19 pandemic erupted at the beginning of 2020 and proved fatal, causing many casualties worldwide. Immediate and precise screening of affected patients is critical for disease control. COVID-19 is often confused with various other respiratory disorders since the symptoms are similar. As of today, the reverse transcription-polymerase chain reaction (RT-PCR) test is utilized for diagnosing COVID-19. However, this approach is sometimes prone to producing erroneous and false negative results. Hence, finding a reliable diagnostic method that can validate the RT-PCR test results is crucial. Artificial intelligence (AI) and machine learning (ML) applications in COVID-19 diagnosis has proven to be beneficial. Hence, clinical markers have been utilized for COVID-19 diagnosis with the help of several classifiers in this study. Further, five different explainable artificial intelligence techniques have been utilized to interpret the predictions. Among all the algorithms, the k-nearest neighbor obtained the best performance with an accuracy, precision, recall and f1-score of 84%, 85%, 84% and 84%. According to this study, the combination of clinical markers such as eosinophils, lymphocytes, red blood cells and leukocytes was significant in differentiating COVID-19. The classifiers can be utilized synchronously with the standard RT-PCR procedure making diagnosis more reliable and efficient.

Integrating Medical Domain Knowledge for Early Diagnosis of Fever of Unknown Origin: An Interpretable Hierarchical Multimodal Neural Network Approach

Accurate and interpretable differential diagnostic technologies are crucial for supporting clinicians in decision-making and treatment-planning for patients with fever of unknown origin (FUO). Existing solutions commonly address the diagnosis of FUO by transforming it into a multi-classification task. However, after the emergence of COVID-19 pandemic, clinicians have recognized the heightened significance of early diagnosis in patients with FUO, particularly for practical needs such as early triage. This has resulted in increased demands for identifying a wider range of etiologies, shorter observation windows, and better model interpretability. In this article, we propose an interpretable hierarchical multimodal neural network framework (iHMNNF) to facilitate early diagnosis of FUO by incorporating medical domain knowledge and leveraging multimodal clinical data. The iHMNNF comprises a top-down hierarchical reasoning framework (Td-HRF) built on the class hierarchy of FUO etiologies, five local attention-based multimodal neural networks (La-MNNs) trained for each parent node of the class hierarchy, and an interpretable module based on layer-wise relevance propagation (LRP) and attention mechanism. Experimental datasets were collected from electronic health records (EHRs) at a large-scale tertiary grade-A hospital in China, comprising 34,051 hospital admissions of 30,794 FUO patients from January 2011 to October 2020. Our proposed La-MNNs achieved area under the receiver operating characteristic curve (AUROC) values ranging from 0.7809 to 0.9035 across all five decomposed tasks, surpassing competing machine learning (ML) and single-modality deep learning (DL) methods while also providing enhanced interpretability. Furthermore, we explored the feasibility of identifying FUO etiologies using only the first N-hour time series data obtained after admission.

Red blood cell distribution width to platelet ratio in neonatal foals with sepsis

BACKGROUND

Rapid and accurate markers to aid diagnosis of sepsis are needed in neonatal foals. The CBC variable red blood cell distribution width (RDW) to platelet ratio (RPR) is associated with inflammatory response and linked to poor outcomes of sepsis in human patients.

HYPOTHESIS

Explore the correlation of RPR with sepsis in neonatal foals and evaluate RPR predictive and prognostic value.

ANIMALS

Three hundred seventeen hospitalized neonatal foals ≤7 days of age that had a CBC and physical exam performed at admission between 2012 and 2021.

METHODS

Retrospective case-control study. Clinical records were used to calculate sepsis scores and define groups. Red blood cell distribution width to platelet ratio was calculated and compared between groups (septic vs nonseptic) based on Kruskal-Wallis and Wilcoxon signed-rank tests. A multivariate logistic regression model to predict sepsis was created. The cutoff for RPR was obtained based on the maximal Youden Index. The Kaplan-Meier method and the log-rank test were used to estimate survival curves and compare survival rates based on RPR.

RESULTS

Red blood cell distribution width to platelet ratio was significantly higher in septic foals (Median = 0.099, confidence interval [CI] [0.093; 0.108]) than in sick nonseptic (0.085, CI [0.083; 0.089]) and healthy foals (0.081, CI [0.077; 0.086]; P < .0001). Red blood cell distribution width to platelet ratio was able to predict sepsis with high accuracy (AUC = 82.1%). The optimal RPR cutoff for sepsis was 0.09.

CONCLUSIONS AND CLINICAL IMPORTANCE

Red blood cell distribution width to platelet ratio calculation is practical, inexpensive, and based on CBC-derived data. Calculation of RPR along with CBC can aid in the diagnosis of sepsis and estimation of outcome.

Increased red blood cell deformation in children and adolescents after SARS-CoV-2 infection

Severe coronavirus disease 2019 (COVID-19) is associated with hyperinflammation, hypercoagulability and hypoxia. Red blood cells (RBCs) play a key role in microcirculation and hypoxemia and are therefore of special interest in COVID-19 pathophysiology. While this novel disease has claimed the lives of many older patients, it often goes unnoticed or with mild symptoms in children. This study aimed to investigate morphological and mechanical characteristics of RBCs after SARS-CoV-2 infection in children and adolescents by real-time deformability-cytometry (RT-DC), to investigate the relationship between alterations of RBCs and clinical course of COVID-19. Full blood of 121 students from secondary schools in Saxony, Germany, was analyzed. SARS-CoV-2-serostatus was acquired at the same time. Median RBC deformation was significantly increased in SARS-CoV-2-seropositive compared to seronegative children and adolescents, but no difference could be detected when the infection dated back more than 6 months. Median RBC area was the same in seropositive and seronegative adolescents. Our findings of increased median RBC deformation in SARS-CoV-2 seropositive children and adolescents until 6 months post COVID-19 could potentially serve as a progression parameter in the clinical course of the disease with an increased RBC deformation pointing towards a mild course of COVID-19.

A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, RT-PCR customarily outputs a lot of false-negative and incorrect results. Current works indicate that COVID-19 can also be diagnosed using imaging resolutions, including CT scans, X-rays, and blood tests. Nevertheless, X-rays and CT scans cannot always be used for patient screening because of high costs, radiation doses, and an insufficient number of devices. Therefore, there is a requirement for a less expensive and faster diagnostic model to recognize the positive and negative cases of COVID-19. Blood tests are easily performed and cost less than RT-PCR and imaging tests. Since biochemical parameters in routine blood tests vary during the COVID-19 infection, they may supply physicians with exact information about the diagnosis of COVID-19. This study reviewed some newly emerging artificial intelligence (AI)-based methods to diagnose COVID-19 using routine blood tests. We gathered information about research resources and inspected 92 articles that were carefully chosen from a variety of publishers, such as IEEE, Springer, Elsevier, and MDPI. Then, these 92 studies are classified into two tables which contain articles that use machine Learning and deep Learning models to diagnose COVID-19 while using routine blood test datasets. In these studies, for diagnosing COVID-19, Random Forest and logistic regression are the most widely used machine learning methods and the most widely used performance metrics are accuracy, sensitivity, specificity, and AUC. Finally, we conclude by discussing and analyzing these studies which use machine learning and deep learning models and routine blood test datasets for COVID-19 detection. This survey can be the starting point for a novice-/beginner-level researcher to perform on COVID-19 classification.

Association between Red Blood Cell Distribution Width and Obstructive Sleep Apnea Syndrome: A Systematic Review and Meta-Analysis

Although polysomnography is the gold standard method to diagnose obstructive sleep apnea syndrome (OSAS), there is an ongoing quest for simpler and relatively inexpensive biomarkers of disease presence and severity. To address this issue, we conducted a systematic review of the potential diagnostic role of the red blood cell distribution width (RDW), a routine hematological parameter of red blood cell volume variability, in OSAS. A total of 1478 articles were initially identified in the databases PubMed, Web of Science, Scopus, Embase, and Google Scholar, from their inception to February 2023, and 20 were selected for final analysis. The RDW was significantly higher in OSAS than in non-OSAS subjects (SMD = 0.44, 95% CI 0.20 to 0.67, p < 0.001; low certainty of evidence). In univariate meta-regression, the mean oxygen saturation (SpO₂) was significantly associated with the effect size. No significant between-group differences were observed in subgroup analyses. Notably, in OSAS subjects, the RDW SMD progressively increased with disease severity. In conclusion, these results suggest that the RDW is a promising biomarker of OSAS (PROSPERO registration number: CRD42023398047).

Parameters and Morphological Changes of Erythrocytes and Platelets of COVID-19 Subjects: A Longitudinal Cohort Study

PURPOSE

Information about dynamic changes occurring in the parameters and morphology of erythrocytes and platelets during the coronavirus disease 2019 (COVID-19) infection and convalescence is scarce. To explore potential associations between dynamic erythrocyte and platelet parameters, morphological changes, and the course or severity of the disease is essential.

PATIENTS AND METHODS

From January 17th, 2020, to February 20th, 2022, we followed up on 35 patients with non-severe and 11 patients with severe COVID-19 following their discharge. We collected clinical features, dynamic complete blood count (CBC), and peripheral blood smears (PBS) and analyzed parameter and morphological changes of erythrocytes and platelets depending on the course or severity of the disease. The course of the disease included four periods, namely onset (T1), discharge (T2), 1-year follow-up (T3), and 2-year follow-up (T4).

RESULTS

Red blood cell (RBC) counts and hemoglobin were the lowest in T2, followed by T1, and lower in T1 and T2 than in T3 and T4. Inversely, the red blood cell distribution width (RDW) was the highest in T2, followed by T1, and higher than in T3 and T4. Compared to non-severe patients, the platelet of severe patients was lower in T1 and T2. In contrast, the mean platelet volume (MPV) and platelet distribution width (PDW) tended to be higher in severe patients. Similarly, anisocytosis was more common in peripheral blood smears at early stages and in severe patients. Finally, large platelets were more common in severe patients.

CONCLUSION

Anisocytosis of erythrocytes and large platelets are found in patients with severe COVID-19, these changes may help primary hospitals to identify patients with a high risk of severe COVID-19 at an early stage.

Red cell distribution width values and red cell distribution width-to-platelet ratio in Thoroughbred foals in the first 24 hours of life

OBJECTIVES

To report red cell distribution width (RDW) values, to calculate RDW-to-platelet ratio (RPR), and to investigate a possible correlation of RDW and RPR index values in neonatal foals classified as healthy or at risk based on clinical information from a population of foals up to 24 hours of life.

DESIGN

Retrospective study conducted from records and CBCs of foals born between June and November from 2018 to 2020 foaling seasons.

SETTING

Breeding farm.

ANIMALS

Three hundred and nine neonatal full-term Thoroughbred foals.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Foals were evaluated by a veterinarian within 15 minutes after birth, and a blood sample was collected within 24 hours of life. Based on clinical information, 88 of 309 foals (28.4%) were considered at risk of perinatal disease, and 201 were healthy. Mean gestational age for the foals was 346.3 ± 9.7 days. RDW values did not differ between groups. Gestational length demonstrated to have a negative correlation with RDW (r = -0.156, P = 0.005) and mean corpuscular volume (r = -0.135, P = 0.01), indicating a link of these variables to foal maturity. RPR index was higher for at-risk (0.073 ± 0.018) than for healthy foals (0.068 ± 0.014, P = 0.01).

CONCLUSION

RPR might be a promising early indicator of disease for the field triage of neonatal foals.

An Interplay between Oxidative Stress (Lactate Dehydrogenase) and Inflammation (Anisocytosis) Mediates COVID-19 Severity Defined by Routine Clinical Markers

Viral infections activate the innate immune response and the secretion of inflammatory cytokines. They also alter oxidative stress markers, which potentially can have an involvement in the pathogenesis of the disease. The aim of this research was to study the role of the oxidative stress process assessed through lactate dehydrogenase (LDH) on the severity of COVID-19 measured by oxygen saturation (SaO2) and the putative interaction with inflammation. The investigation enrolled 1808 patients (mean age of 68 and 60% male) with COVID-19 from the HM Hospitals database. To explore interactions, a regression model and mediation analyses were performed. The patients with lower SaO2 presented lymphopenia and higher values of neutrophils-to-lymphocytes ratio and on the anisocytosis coefficient. The regression model showed an interaction between LDH and anisocytosis, suggesting that high levels of LDH (>544 U/L) and an anisocytosis coefficient higher than 10% can impact SaO2 in COVID-19 patients. Moreover, analysis revealed that LDH mediated 41% (p value = 0.001) of the effect of anisocytosis on SaO2 in this cohort. This investigation revealed that the oxidative stress marker LDH and the interaction with anisocytosis have an important role in the severity of COVID-19 infection and should be considered for the management and treatment of the oxidative phenomena concerning this within a precision medicine strategy.

Red Cell Distribution Width as a Prognostic Indicator for Mortality and ICU Admission in Patients with COVID-19

BACKGROUND

COVID-19 disease caused by the SARS-CoV-2 virus can lead to an acute respiratory illness with a high hospitalization and mortality risk. Therefore, prognostic indicators are essential for early interventions. As a component of complete blood counts, the coefficient of variation (CV) of red blood cell distribution width (RDW) reflects cellular volume variations. It has been shown that RDW is associated with increased mortality risk in a wide range of diseases. This study aimed to determine the relationship between RDW and mortality risk in COVID-19 patients.

METHODS

This retrospective study was performed on 592 patients admitted to hospital between February 2020 and December 2020. Patients were divided into low and high RDW groups and the relationship between RDW and mortality, intubation, admission to intensive care unit (ICU), and need for oxygen therapy was investigated.

RESULTS

The mortality rate in the low RDW group was 9.4%, while that in the high group was 20% (p < 0.001). Also, ICU admission in the low group was 8%, whereas this was 10% in the high RDW group (p = 0.040). The results of the Kaplan-Meyer curve showed that the survival rate was higher in the low group compared to the high RDW group. Cox results in the crude model showed that higher RDW values were directly related to increased mortality, although this was not significant after adjustment for other covariates.

CONCLUSION

The results of our study reveal that high RDW is associated with increased hospitalization and risk of death and that RDW may be a reliable indicator of COVID-19 prognosis.

The Relationship Between Prognosis and Red Cell Distribution Width (RDW) and RDW-Albumin Ratio (RAR) in Patients with Severe COVID-19 Disease

Purpose

The present study aimed to investigate the relationship between prognosis and the red cell distribution width (RDW) and the RDW-albumin ratio (RAR) in patients with coronavirus diseases 2019 (COVID-19), since serum albumin and RDW levels may reflect inflammatory conditions.

Patients and Methods

A total of 289 patients who had been diagnosed with severe COVID-19 in the emergency department were retrospectively analyzed. The RAR levels were calculated by dividing RDW-CV by albumin. Patient groups (survivors, dying patients, those who received mechanical ventilation (MV) support or not, and those who needed vasopressors or not) were compared with regard to RDW-SD, RDW-CV and the RAR levels.

Results

RDW-SD, RDW-CV and the RAR levels were found to be statistically significantly higher in patients who died, and who received MV and vasopressor support, compared to those who survived and did not receive support (p<0.001 for all). In addition, while the cut-off value of RAR was >5.43, the sensitivity was 91.6%, the specificity was 93.7%, NPV was 93.1% and the AUC was 0.965 in predicting mortality (p<0.001). Logistic regression analysis showed that RDW-SD and RAR were independent risk factors for mortality in patients with severe COVID-19.

Conclusion

Elevated RDW and RAR levels at the time of admission may independently predict mortality and the need for vasopressor or MV support.

Platelet-derived immuno-inflammatory indices show best performance in early prediction of COVID-19 progression

BACKGROUND

Coronavirus disease 2019 (COVID-19) profoundly affects the immune and hematopoietic systems with various degrees of reactive changes in the blood cell counts. Immuno-inflammatory indices are considered a simple and effective tool in the prediction of COVID-19 outcomes. We aimed to evaluate and compare the usefulness of leukocyte and platelet counts-based immuno-inflammatory indices on admission to hospital in predicting COVID-19 progression and mortality.

METHODS

A total of 945 patients were enrolled. In addition to blood cell counts, we assessed hemogram-derived immuno-inflammatory indices in relation to COVID-19 progression and death. The indices were tested by analysis of variance, receiver operating characteristic curve analysis, and binomial logistic regressions.

RESULTS

Patients with severe COVID-19 had significantly higher counts of neutrophils, eosinophils, and large immature cells (LIC), while decreased counts of platelets and monocytes. Lymphopenia was found in all of the patients, but without significant association with the outcomes. Patients with a LIC count ≥0.265 x 09 /L had 54.7% more odds of having COVID-19 progression. In multivariable analyses, platelets/neutrophil-to-lymphocyte ratio (P/NLR) and platelets-to-neutrophil radio (P/N) were significant independent predictors of COVID-19 progression and mortality. The odds of a poor outcome were two times higher in cases with P/NLR < 43 x 109 /L and P/N < 29 x 109 /L.

CONCLUSION

Indices that include platelet count in combination with neutrophil and/or lymphocyte counts displayed the best discriminatory ability and prognostic value of COVID-19 outcomes. Additionally, LIC showed promising results in the early identification of severe COVID-19.

Red Blood Cell Morphologic Abnormalities in Patients Hospitalized for COVID-19

Peripheral blood smear is a simple laboratory tool, which remains of invaluable help for diagnosing primary and secondary abnormalities of blood cells despite advances in automated and molecular techniques. Red blood cells (RBCs) abnormalities are known to occur in many viral infections, typically in the form of mild normo-microcytic anemia. While several hematological alterations at automated complete blood count (including neutrophilia, lymphopenia, and increased red cell distribution width—RDW) have been consistently associated with severity of COVID-19, there is scarce information on RBCs morphological abnormalities, mainly as case-reports or small series of patients, which are hardly comparable due to heterogeneity in sampling times and definition of illness severity. We report here a systematic evaluation of RBCs morphology at peripheral blood smear in COVID-19 patients within the first 72 h from hospital admission. One hundred and fifteen patients were included, with detailed collection of other clinical variables and follow-up. A certain degree of abnormalities in RBCs morphology was observed in 75 (65%) patients. Heterogenous alterations were noted, with spiculated cells being the more frequent morphology. The group with &gt;10% RBCs abnormalities had more consistent lymphopenia and thrombocytopenia compared to those without abnormalities or &lt;10% RBCs abnormalities (p &lt; 0.018, and p &lt; 0.021, respectively), thus underpinning a possible association with an overall more sustained immune-inflammatory “stress” hematopoiesis. Follow-up analysis showed a different mortality rate across groups, with the highest rate in those with more frequent RBCs morphological alterations compared to those with &lt;10% or no abnormalities (41.9%, vs. 20.5%, vs. 12.5%, respectively, p = 0.012). Despite the inherent limitations of such simple association, our results point out towards further studies on erythropoiesis alterations in the pathophysiology of COVID-19.

Supporting Clinical COVID-19 Diagnosis with Routine Blood Tests Using Tree-Based Entropy Structured Self-Organizing Maps

Data classification is an automatic or semi-automatic process that, utilizing artificial intelligence algorithms, learns the variable and class relationships of a dataset for use a posteriori in situations where the class result is unknown. For many years, work on this topic has been aimed at increasing the hit rates of algorithms. However, when the problem is restricted to applications in healthcare, besides the concern with performance, it is also necessary to design algorithms whose results are understandable by the specialists responsible for making the decisions. Among the problems in the field of medicine, a current focus is related to COVID-19: AI algorithms may contribute to early diagnosis. Among the available COVID-19 data, the blood test is a typical procedure performed when the patient seeks the hospital, and its use in the diagnosis allows reducing the need for other diagnostic tests that can impact the detection time and add to costs. In this work, we propose using self-organizing map (SOM) to discover attributes in blood test examinations that are relevant for COVID-19 diagnosis. We applied SOM and an entropy calculation in the definition of a hierarchical, semi-supervised and explainable model named TESSOM (tree-based entropy-structured self-organizing maps), in which the main feature is enhancing the investigation of groups of cases with high levels of class overlap, as far as the diagnostic outcome is concerned. Framing the TESSOM algorithm in the context of explainable artificial intelligence (XAI) makes it possible to explain the results to an expert in a simplified way. It is demonstrated in the paper that the use of the TESSOM algorithm to identify attributes of blood tests can help with the identification of COVID-19 cases. It providing a performance increase in 1.489% in multiple scenarios when analyzing 2207 cases from three hospitals in the state of São Paulo, Brazil. This work is a starting point for researchers to identify relevant attributes of blood tests for COVID-19 and to support the diagnosis of other diseases.

Association Between Red Blood Cell Distribution Width and COVID-19 Severity in Delta Variant SARS-CoV-2 Infection

Studies have discovered that wild-type SARS-CoV-2 infections are commonly linked to abnormalities in the hematological profiles of COVID-19 patients, one such abnormality being characterized by elevations in red blood cell distribution width (RDW). Whether this linkage reoccurs in delta variant SARS-CoV-2 infection remains unexamined. Here we compared baseline blood parameters in COVID-19 patients infected by wild type and its delta variant, respectively. Our results here point to that although the delta variant has shown increased virulence, transmissibility, and vaccine escape, it has a minimally negative impact on RDW values that were previously found prognostic for COVID-19 severity.

Red Blood Cell Distribution Width (RDW) as a Predictor of In-Hospital Mortality in COVID-19 Patients; a Cross Sectional Study

INTRODUCTION

Red blood cell distribution width (RDW) has been introduced as a predictive factor for mortality in several critical illnesses and infectious diseases. This study aimed to assess the possible relationship between RDW on admission and COVID-19 in-hospital mortality.

METHOD

This cross-sectional study was performed using the Isfahan COVID-19 registry. Adult confirmed cases of COVID-19 admitted to four hospitals affiliated with Isfahan University of Medical Sciences in Iran were included. Age, sex, O2 saturation, RDW on admission, Intensive Care Unit admission, laboratory data, history of comorbidities, and hospital outcome were extracted from the registry. Cox proportional hazard regression was used to study the independent association of RDW with mortality.

RESULTS

4152 patients with the mean age of 61.1 ± 16.97 years were included (56.2% male). 597 (14.4%) cases were admitted to intensive care unit (ICU) and 477 (11.5%) cases died. The mortality rate of patients with normal and elevated RDW was 7.8% and 21.2%, respectively (OR= 3.1, 95%CI: 2.6-3.8), which remained statistically significant after adjusting for age, O2 saturation, comorbidities, and ICU admission (2.03, 95% CI: 1.68-2.44). Moreover, elevated RDW mortality Hazard Ratio in patients who were not admitted to ICU was higher than ICU-admitted patients (3.10, 95% CI: 2.35-4.09 vs. 1.47, 95% CI: 1.15-1.88, respectively).

CONCLUSION

The results support the presence of an association between elevated RDW and mortality in patients with COVID-19, especially those who were not admitted to ICU. It seems that elevated RDW can be used as a predictor of mortality in COVID-19 cases.

Evaluation of the Prognostic Value of Red Cell Distribution Width to Total Serum Calcium Ratio in Patients with Acute Pancreatitis

Introduction

Acute pancreatitis (AP) is a sudden inflammatory process in the pancreas with variable involvement of nearby organs or other organ systems, and it is a common cause for hospitalization of gastrointestinal origin. Early prediction of the prognosis of patients with AP is important to help physicians triage the patients and decrease mortality. Red cell distribution width (RDW) and total serum calcium (TSC) have been reported to be useful predictors of the severity of AP, but if these parameters are associated with the prognosis of AP is unknown. The objective of the study was to evaluate whether RDW/TSC can be used to predict the prognosis of patients with AP at an early stage.

Methods

We retrospectively enrolled AP patients admitted to the emergency department of West China Hospital of Sichuan University from January 1, 2016, to June 30, 2016. According to the prognosis, AP patients were divided into ICU group and non-ICU group, surgery group and nonsurgery group, and hospital survival group and hospital death group. Demographic information and clinical and laboratory parameters of all enrolled patients after being admitted to ED were compared between the groups. The receiver operator characteristic (ROC) curves were used to evaluate the prognostic values of RDW, TSC, and RDW/TSC in patients with AP.

Results

A total of 666 AP patients were enrolled in this study, with an average age of 47.99 ± 14.11 years, including 633 patients who survived to discharge and 33 patients who died during hospitalization. The areas under the curve (AUC) of RDW and RDW/TSC predict that patients need to be admitted to ICU (0.773 vs. 0.824 vs. 0.723), patients need surgery treatment (0.744 vs. 0.768 vs. 0.690), and patients survived to hospital discharge (0.809 vs. 0.855 vs. 0.780) were greater than that of TSC, with RDW/TSC being the greatest.

Conclusions

RDW/TSC may be a new method to identify the AP patients who need to be transferred to the ICU, accompanying complications which need surgery treatment, or may be died in hospital at an early stage, and we should pay more attention to RDW/TSC in patients with AP, for they may have a worse prognosis.