Red Blood Cell Volume Distribution Research Articles

Risk of low levels of blood group antibodies mediating hemolysis in ABO-incompatible neonates with negative three hemolysis tests.

To explore the risk of low-level blood group antibody-mediated hemolysis in ABO-incompatible newborns with negative three hemolysis tests, aiming to assist in the identification and management of neonatal jaundice. A retrospective case-control study was performed in 892 children with jaundice. The patients were divided into three groups: group I, ABO compatible, negative three hemolysis tests; group II, ABO incompatible, negative three hemolysis tests; and group III, ABO incompatible, positive three hemolysis tests. We analyzed the differences in clinical data, blood routine and biochemical laboratory results. (1) Patients in group II had higher levels of mean corpuscular volume (MCV), standard deviation of red blood cell volume distribution width (RDW-SD), alanine aminotransferase (ALT), lactate dehydrogenase (LDH), alkaline phosphatase (ALP), and bile acid (BA) than those in group I (P < 0.05). However, there were no statistically significant differences in the MCV, ALT, ALP and BA levels between groups II and III (P > 0.05). (2) Mean corpuscular hemoglobin concentration (MCHC) >359.5 g/L, cell volume distribution width (RDW-CV) >15.95%, and reticulocyte count (RET) >4.235% were identified as independent predictors of positive hemolysis test results (P < 0.001). The combination of MCHC, RDW-CV, and RET% yielded an AUC of 0.841. Low-level blood group antibody-mediated hemolysis may occur in ABO-incompatible neonates even when three hemolysis tests are negative. Changes in liver function parameters must be monitored. The combination of MCHC, RDW-CV, and RET% can be used to improve the detection rate of HDN.

Risk factors for ischemic stroke in China: a case-control study

Background Stroke is a primary cause of disability and death in adults worldwide. This study aimed to determine whether uric acid (UA), total bilirubin (TBIL), serum creatinine (Scr), homocysteine (Hcy), platelet large cell ratio (P-LCR), mean platelet volume (MPV), red blood cell volume distribution width (RDW), and common carotid artery intima-media thickness (CCA-IMT) were risk factors for ischemic stroke and whether there were sex differences in their associations. Methods This was a case-control study, and all data were collected from the medical records at Fuzhou First People’s Hospital of Jiangxi Province, China. The case group consisted of 400 patients treated for ischemic stroke at the Neurology Department, Fuzhou First People’s Hospital from January 2017 to June 2023. The control group included 200 patients with non-ischemic stroke in the same period and at the same hospital. In this study, clinical history, physical examination, and biochemical indices such as UA, TBIL, Scr, Hcy, P-LCR, MPV, and RDW were obtained from the patients’ medical records. Craniocerebral imaging was performed using magnetic resonance imaging (MRI) or computerized tomography (CT). CCA-IMT was measured using ultrasonography of the carotid arteries. Results In the multivariate binary logistic regression analysis, Hcy, FBS, right CCA-IMT and Hypertension were associated with ischemic stroke (p&lt;0.001). The odds of ischemic stroke increased with Hcy (adjusted OR=1.217, 95% CI=1.129-1.311, p&lt;0.001), fasting blood sugar (FBS) (adjusted OR=1.313, 95%CI=1.169-1.475, p&lt;0.001), and right CCA-IMT (adjusted OR=45.273, 95%CI=6.693-306.232, p&lt;0.001). Hypertensive persons had a higher possibility of ischemic stroke than persons without hypertension (adjusted OR=3.161, 95%CI=2.086-4.790, p&lt;0.001). Conclusions Hcy, FBS, right CCA-IMT, and hypertension are independent risk factors for ischemic stroke in China. There was no association between TBIL, Scr, P-LCR, MPV, RDW, and the incidence of ischemic stroke. There was no sex difference in the association between Hcy, TBIL, Scr, P-LCR, and the incidence of ischemic stroke.

Accuracy of Haematology Analyzer in the Diagnosis of Malaria in Comparison with Gold Standard Microscopy

Objective: To assess the accuracy of Haematology analyzer in the diagnosis of malaria in comparison with gold standard microscopy.&#x0D; Methodology: A cross-sectional study was conducted at the Pakistan Institute of Medical Sciences and the Pakistan Atomic Energy Commission in Islamabad, Pakistan. The study was conducted for around six months, from May 2023 to October 2023. The Mindray BC-6200 haematology analyzer was utilised to evaluate a total of 191 samples, comprising 127 samples from that were infected with malaria and 64 samples from healthy controls. When the presence of malaria parasites, identified as Plasmodium vivax and Plasmodium falciparum, was detected in dyed thick blood film, a microscopy examination was carried out as a reference. Analyse-it v4.92.3 was used to create the receiver operating characteristic (ROC) curve analysis. The agreement between BC-6200 and light microscopy was assessed using the Kappa value.&#x0D; Results: The InR by BC-6200's sensitivity and specificity for P. falciparum and P. vivax infections. The sensitivity of the InR by BC-600 for P. falciparum and P. vivax was 27.9% and 85.5%, respectively. The specificity of the InR by BC-6200 for P. vivax and P. falciparum was 82.7% and 86.5%, respectively. The infection densities in microscopy varied statistically significantly between the various InR groups (χ2 = 14.50, P &lt; 0.005).An analysis was conducted on the correlation between the cell blood count and the count of InR in both the P. vivax and P. falciparum-infected patient groups. The results showed a clear correlation between InR (P. vivax group R2 = 0.87) and ΔWBC (WBCDIFF–WBCBASO). In the Mindray BC-6200 haematology analyzer, WBCBASO represents the number of WBC counting in the BASO channel with severe membrane degradation, while WBCDIFF represents the number of WBC counting in the DIFF channel with mild lyse. The volume distribution widths of RBC, HGB, and red blood cells did not differ substantially. The reticulocyte characteristics of the P. vivax/P. falciparum patient group and control group differed significantly (P&lt;0.02), although RBC, HGB, and red blood cell volume distribution width (RDW) did not differ significantly (P &gt; 0.05). There was a significant difference (P&lt;0.02) in the PLT count between the P. vivax/P. falciparum patient groups and the control group.&#x0D; Conclusion: The results imply that malaria might be screened for in a clinical setting using the BC-6200 haematology analyzer's "InR" flag and "InR#/InR‰" parameters.

Changes in expression levels of erythrocyte and immune-related genes are associated with high altitude polycythemia

BackgroundAs a chronic mountain sickness(CMS) with the highest incidence and the greatest harm, the pathogenesis of high altitude polycythemia (HAPC) is still not fully understood.Methods37 HAPC patients and 42 healthy subjects were selected from plateau, and peripheral venous blood samples were collected for transcriptome sequencing on Illumina NovaSeq platform. The sequenced data were analyzed by bioinformatics and phenotypic association analysis.ResultsThe results showed significant differences in multiple clinical indicators including RBC and HGB et al. existed between HAPC and control. Based on the RNA-seq data, 550 genes with significant differential expression were identified in HAPC patients. GO and KEGG pathway enrichment analysis showed that the up-regulated genes were mainly enriched in processes such as erythrocyte differentiation and development and homeostasis of number of cells, while the down-regulated genes were mainly enriched in categories such as immunoglobulin production, classical pathway of complement activation and other biological processes. The coupling analysis of differential expression genes(DEGs) and pathological phenotypes revealed that 91 DEGs were in close correlation with in the phenotype of red blood cell volume distribution (width-CV and width-SD), and they were all up-regulated in HAPC and involved in the process of erythrocyte metabolism. Combined with the functional annotation of DEGs and literature survey, we found that the expression of several potential genes might be responsible for pathogenesis of HAPC. Besides, cell type deconvolution analysis result suggested that the changes in the number of some immune cell types was significantly lower in HAPC patients than control, implying the autoimmune level of HAPC patients was affected to a certain extent.ConclusionThis study provides an important data source for understanding the pathogenesis and screening pathogenic genes of HAPC. We found for the first time that there was a significant correlation between HAPC and the pathological phenotype of width-CV and width-SD, wherein the enriched genes were all up-regulated expressed and involved in the process of erythrocyte metabolism. Although the role of these genes needs to be further studied, the candidate genes can provide a starting point for functionally pinning down the underlying mechanism of HAPC.

Machine learning for the prediction of all-cause mortality in patients with sepsis-associated acute kidney injury during hospitalization.

Sepsis-associated acute kidney injury (S-AKI) is considered to be associated with high morbidity and mortality, a commonly accepted model to predict mortality is urged consequently. This study used a machine learning model to identify vital variables associated with mortality in S-AKI patients in the hospital and predict the risk of death in the hospital. We hope that this model can help identify high-risk patients early and reasonably allocate medical resources in the intensive care unit (ICU). A total of 16,154 S-AKI patients from the Medical Information Mart for Intensive Care IV database were examined as the training set (80%) and the validation set (20%). Variables (129 in total) were collected, including basic patient information, diagnosis, clinical data, and medication records. We developed and validated machine learning models using 11 different algorithms and selected the one that performed the best. Afterward, recursive feature elimination was used to select key variables. Different indicators were used to compare the prediction performance of each model. The SHapley Additive exPlanations package was applied to interpret the best machine learning model in a web tool for clinicians to use. Finally, we collected clinical data of S-AKI patients from two hospitals for external validation. In this study, 15 critical variables were finally selected, namely, urine output, maximum blood urea nitrogen, rate of injection of norepinephrine, maximum anion gap, maximum creatinine, maximum red blood cell volume distribution width, minimum international normalized ratio, maximum heart rate, maximum temperature, maximum respiratory rate, minimum fraction of inspired O2, minimum creatinine, minimum Glasgow Coma Scale, and diagnosis of diabetes and stroke. The categorical boosting algorithm model presented significantly better predictive performance [receiver operating characteristic (ROC): 0.83] than other models [accuracy (ACC): 75%, Youden index: 50%, sensitivity: 75%, specificity: 75%, F1 score: 0.56, positive predictive value (PPV): 44%, and negative predictive value (NPV): 92%]. External validation data from two hospitals in China were also well validated (ROC: 0.75). After selecting 15 crucial variables, a machine learning-based model for predicting the mortality of S-AKI patients was successfully established and the CatBoost model demonstrated best predictive performance.

The application value of mean red blood cell volume and red blood cell volume distribution width combined with total serum bilirubin in the early screening of neonatal hemolytic disease

BackgroundThe hemolytic nature of hemolytic disease of the newborn (HDN) is described as the abnormal destruction and decomposition of red blood cells, causing heterogeneous manifestations such as abnormal red blood cell volume and morphology. Mean corpuscular volume (MCV) and red blood cell volume distribution width (RDW) are commonly used parameters related to red blood cell volume. Total serum bilirubin (TSB) is routinely monitored among newborns. This study aims to explore the value of MCV and RDW, combined with TSB, to improve the efficiency of HDN diagnosis.MethodsThree hundred eighty-eight children with HDN and 371 children with non-HDN pathological jaundice who were diagnosed and treated in the neonatal department of our hospital from January 2019 to December 2020 were included in the study. Clinical data collected include examination results of laboratory indicators, such as MCV, coefficient of variation of red blood cell volume distribution width (RDW-CV), standard deviation of red blood cell volume distribution width (RDW-SD), and TSB. The differences in the indicators between the two groups of children were retrospectively analyzed.Results1) The detection rate of HDN in children in the early group was higher than that in the late group (P < 0.001). 2) The early-stage group had lower TSB levels and higher values of MCV, RDW-CV and RDW-SD (P < 0.001). Compared with the children in the non-HDN group, the indices in the HDN group were higher in the early stage (P < 0.001). 3) In the early stage, the TSB, MCV, RDW-CV, and RDW-SD were positively correlated with the diagnosis of HDN (P < 0.001). Early monitoring of TSB, MCV, RDW-CV and RDW-SD was valuable for HDN detection, the area under the curve (AUC) was 0.729, 0.637, 0.715, and 0.685, respectively (P < 0.001). 4) After a binary logistic analysis at TSB > 163.3 μmol/L, MCV > 96.35fL, and RDW-CV > 16.05%, the diagnosis rate of HDN increased (P < 0.001). The AUC of the HDN detected using the combined indicators was 0.841.ConclusionAt MCV > 96.35fL or RDW-CV > 16.05%, children with jaundice in three days of birth (especially children with TSB > 163.3 μmol/L) should be screened for HDN. A combination of TSB, MCV, and RDW-CV can improve the early detection rate of HDN, contribute to reduce the readmission rate and risk of hyperbilirubinemia.

Related factors of urinary tract infections in inpatients based on real world data

To analyze the risk factors for urinary tract infection (UTI) among inpatients. The case data of 1 875 inpatients receiving urinary bacterial culture in Beijing Haidian Hospital from October 2019 to May 2021 were analyzed retrospectively. According to the etiological diagnostic criteria of UTI, they were divided into infection group and non-infection group. The species and distribution of pathogens in the infection group were analyzed, and the case data and laboratory indexes were subjected to univariate analysis. The variables with statistical significance were selected for binary logistic regression to analyze the risk factors of urinary tract infection and establish a prediction model. The receiver operating characteristic (ROC) curve was drawn for each parameter included in the model, and the area under the curve (AUC) was calculated. The diagnostic and predictive efficacy of each parameter alone and their combination for UTI were evaluated. So, a total of 1 162 patients with non-infection group and 713 patients with UTI were detected. Among the cultured pathogens, the constituent ratio of Gram-negative bacteria, Gram-positive bacteria and fungi was 57.2%(408/713), 35.9%(256/713) and 6.9%(49/713) respectively. Multivariate analysis showed that, Age, duration of urinary catheterization>7 d, stroke and orthopedic surgery were the risk factors of UTI among inpatients. The use of antibiotics is a protective factor for urinary tract infections. The prediction model of UTI was established by the risk factors, age, duration of urinary catheterization>7 d, stroke, orthopedic surgery, urinary leukocyte esterase, urinary nitrite and Coefficient of variability of red blood cell volume distribution width (RDW-CV). The AUC of the combination of the eight parameters in diagnosing and predicting UTI was 0.835 (95%CI: 0.816-0.855), with the sensitivity of 70.7% and the specificity of 82.8%. In conclusion,the combination of the eight parameters can better assist in the diagnosis and prediction of UTI, and provide an experimental basis for clinicians to judge UTI.

The effect of age on the clinical characteristics and innate immune cell function in the patients with abdominal sepsis.

Sepsis is a life-threatening dysregulated host response to infection that compromises organ health, and abdominal sepsis is a commonly presenting critical illness in intensive care units (ICU). In this study, we investigate the effect of age on clinical sepsis characteristics and innate immune cells (neutrophils and monocytes) functionality in abdominal sepsis patients. We recruited 32 patients with abdominal sepsis from the Beijing Ditan Hospital’s ICU from February 2021 to September 2021, and selected 18 healthy volunteers that were age- and sex-matched as controls for a prospective cohort study. Elderly abdominal sepsis patients (age >65 years) had the following altered characteristics compared to nonelderly patient controls: lower mean arterial pressure, monocytes percentage, and red blood cell volume distribution width (p < 0.05); higher neutrophils percentage and neutrophils-to-lymphocytes ratio (p < 0.05); significantly increased monocyte-produced reactive oxygen (p < 0.05); increases neutrophilic secretion of TNF-α, as well as lower monocytic secretion of TNF-α (p < 0.05); higher neutrophil percentage (which was significantly higher in peripheral blood than monocyte percentage). Elderly patients also had significantly increased phagocytic activity in their neutrophils and monocytes (p < 0.05), significantly reduced neutrophils-produced reactive oxygen (p < 0.001), and significantly increased TNF-α secretion by monocytes and neutrophils (p < 0.05). We found that elderly patients have decreased immune cell function and increased release of cytokines compared to younger patients, suggesting individualized treatment plans targeting the elderly septic microenvironment could help prevent organ failure in elderly septic patients and improves patient survival.

Clinical characteristics of patients with confirmed and asymptomatic SARS-CoV-2 infection in China.

To examine the clinical characteristics of patients with asymptomatic novel coronavirus disease 2019 (COVID-19) and compare them with those of patients with mild disease. A retrospective cohort study. Multiple medical centers in Wuhan, Hubei, China. A total of 3,263 patients with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) infection between February 4, 2020, and April 15, 2020. Patient demographic characteristics, medical history, vital signs, and laboratory and chest computed tomography (CT) findings. A total of 3,173 and 90 patients with mild and moderate, and asymptomatic COVID-19, respectively, were included. A total of 575 (18.2%) symptomatic patients and 4 (4.4%) asymptomatic patients developed the severe illness. All asymptomatic patients recovered; no deaths were observed in this group. The median duration of viral shedding in asymptomatic patients was 17 (interquartile range, 9.25-25) days. Patients with higher levels of ultrasensitive C-reactive protein (odds ratio [OR] = 1.025, 95% confidence interval [CI], 1.01-1.04), lower red blood cell volume distribution width (OR = 0.68, 95% CI 0.51-0.88), lower creatine kinase Isoenzyme(0.94, 0.89-0.98) levels, or lower lesion ratio (OR = 0.01, 95% CI 0.00-0.33) at admission were more likely than their counterparts to have asymptomatic disease. Patients with younger ages and fewer comorbidities are more likely to be asymptomatic. Asymptomatic patients had similar laboratory characteristics and longer virus shedding time than symptomatic patients; screen and isolation during their infection were helpful to reduce the risk of SARS-CoV-2 transmission.

Melatonin promotes the growth and development of lambs by increasing growth hormone and testosterone, targeting on apoptosis signaling pathway and intestinal microflora.

Melatonin is an indole-like neuroendocrine hormone. A large number of studies have shown that melatonin can improve production performance of ewes, but it is not clear in lambs. In this study, the growth and development of the 2-month-old lambs implanted with melatonin were monitored for 60 days. The results showed that the growth rate of body weight and body skew length of lambs with melatonin treatment were significantly improved compared to the controls. The similar results were also observed in red blood cell count, hematocrit, red blood cell volume distribution width, the levels of growth hormone, testosterone, immunoglobulin A, immunoglobulin M and albumin. In addition, the cross sectional area of muscle fibers and adipose cells of lambs with melatonin implantation were also significantly increased compared to the controls (P<0.05). To further explore the potential mechanisms, the muscle and adipose tissue were selected for transcriptome sequencing. KEGG enrichment results showed that melatonin regulated the expression of genes related to apoptotic signaling pathway in muscle and adipocytes. Since the intestinal microbiota are involved in the nutritional balance and animal growth, the 16SrRNA sequencing related to the intestinal microbiota was also performed. The data indicated that the structural differences of fecal microflora mainly occur in the pathways of Cardiovascular disease, Excretory system and Signaling molecules and interaction. In brief, melatonin promotes the growth and development of lambs. The potential mechanisms may be that melatonin increased the growth hormone and testosterone mediated apoptosis signaling pathway and regulated intestinal microbial flora. Our results provide valuable information for melatonin to improve the production of sheep husbandry in the future.

Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated encephalopathy

ObjectiveOur study aimed to identify predictors as well as develop machine learning (ML) models to predict the risk of 30-day mortality in patients with sepsis-associated encephalopathy (SAE).Materials and methodsML models were developed and validated based on a public database named Medical Information Mart for Intensive Care (MIMIC)-IV. Models were compared by the area under the curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values, and Hosmer–Lemeshow good of fit test.ResultsOf 6994 patients in MIMIC-IV included in the final cohort, a total of 1232 (17.62%) patients died following SAE. Recursive feature elimination (RFE) selected 15 variables, including acute physiology score III (APSIII), Glasgow coma score (GCS), sepsis related organ failure assessment (SOFA), Charlson comorbidity index (CCI), red blood cell volume distribution width (RDW), blood urea nitrogen (BUN), age, respiratory rate, PaO2, temperature, lactate, creatinine (CRE), malignant cancer, metastatic solid tumor, and platelet (PLT). The validation cohort demonstrated all ML approaches had higher discriminative ability compared with the bagged trees (BT) model, although the difference was not statistically significant. Furthermore, in terms of the calibration performance, the artificial neural network (NNET), logistic regression (LR), and adapting boosting (Ada) models had a good calibration—namely, a high accuracy of prediction, with P-values of 0.831, 0.119, and 0.129, respectively.ConclusionsThe ML models, as demonstrated by our study, can be used to evaluate the prognosis of SAE patients in the intensive care unit (ICU). Online calculator could facilitate the sharing of predictive models.

Diuretic Resistance Prediction and Risk Factor Analysis of Patients with Heart Failure During Hospitalization

Objectives:This study performed a prediction and risk factor analysis of diuretic resistance (DR) in patients with decompensated heart failure during hospitalization.Methods:The data of patients with decompensated heart failure treated in 2010–2018 with DR (n = 3,383) or without DR (n = 15,444) were retrospectively collected from Chinese PLA General Hospital medical records. Statistical analysis of baseline was performed on two groups of people, and the risk factor of DR was analyzed through logic regression. Six machine learning models were built accordingly, and the adjustment of model super parameters was performed by using Bayesian optimization method. Finally, the optimal algorithm was selected according to prediction efficiency.Results:The preliminary analysis of variance showed significant differences in the incidence of DR among patients with lung infection, hyperlipidemia, type 2 diabetes, and kidney disease. There were significant differences in estimated glomerular filtration rate (eGFR) (P < 0.001). In addition, some physical indicators like BMI were different, the laboratory results like mean red blood cell volume or C-reactive protein assay were also significantly different. The optimal classification model indicated that the best cutoff points for risk factors were vein carbon dioxide, 21 mmol/L and 29 mmol/L; total protein, 64 g/L; pro-brain natriuretic peptide (pro-BNP), 7,600 pg/mL; eGFR, 50 mL/(min ∙ 1.73 m2); serum albumin, 33 g/L; hematocrit, 0.32% and 0.56%; red blood cell volume distribution width, 13; and age, 59 years. The optimal area under the curve was 0.9512. The ranked features derived from the model were age, abnormal sodium level, pro-BNP level, serum albumin level, D-dimer level, direct bilirubin level, and eGFR.Conclusions:The DR risk prediction model based on a gradient boosting decision tree created here identified its important risk factors. The model made very accurate predictions using simple indicators and simultaneously calculated cutoff values to help doctors predict the occurrence of DR.

Machine learning based on routine laboratory indicators promoting the discrimination between active tuberculosis and latent tuberculosis infection

BackgroundDiscriminating active tuberculosis (ATB) from latent tuberculosis infection (LTBI) remains challenging. The present study aims to evaluate the performance of diagnostic models established using machine learning based on routine laboratory indicators in differentiating ATB from LTBI. MethodsParticipants were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Diagnostic models were established based on routine laboratory indicators using machine learning. ResultsA total of 2619 participants (1025 ATB and 1594 LTBI) were enrolled in discovery cohort and another 942 subjects (388 ATB and 554 LTBI) were recruited in validation cohort. ATB patients had significantly higher levels of tuberculosis-specific antigen/phytohemagglutinin ratio and coefficient variation of red blood cell volume distribution width, and lower levels of albumin and lymphocyte count than those of LTBI individuals. Six models were built and the optimal performance was obtained from GBM model. GBM model derived from training set (n = 1965) differentiated ATB from LTBI in the test set (n = 654) with a sensitivity of 84.38% (95% CI, 79.42%-88.31%) and a specificity of 92.71% (95% CI, 89.73%-94.88%). Further validation by an independent cohort confirmed its encouraging value with a sensitivity of 87.63% (95% CI, 83.98%-90.54%) and specificity of 91.34% (95% CI, 88.70%-93.40%), respectively. ConclusionsWe successfully developed a model with promising diagnostic value based on machine learning for the first time. Our study proposed that GBM model may be of great benefit served as a tool for the accurate identification of ATB.

The Association Between High CHA2DS2-VASc Scores and Short and Long-Term Mortality for Coronary Care Unit Patients

BackgroundThe CHA2DS2-VASc score has been associated with the prognosis of cardiovascular diseases. This study aimed to explore the association between the CHA2DS2-VASc score and all-cause mortality in coronary care unit (CCU) patients.MethodsThe study was based on the Medical Information Mart for Intensive Care (MIMIC) III database. CCU patients were divided into two groups according to CHA2DS2-VASc score: 0-3 (low risk)，4-9 (intermediate and high risk). The primary outcome was 30-day mortality, and the secondary endpoints included in-hospital, 1-year, and 5-year mortality. Propensity score matching (PSM) and sensitivity analyzes for the confounders were also performed. The restricted cubic splines flexibility model was used to demonstrate the relation between red blood cell volume distribution width (RDW), blood urea nitrogen (BUN), platelet, white blood cell (WBC), hemoglobin, phosphorus, glucose, potassium, sodium and 30-day mortality in the 0-3 score versus the 4-9 score groups after PSM.ResultsAmong 4491 eligible patients, 988 patients with low CHA2DS2-VASc scores and 988 patients with intermediate and high CHA2DS2-VASc scores had similar propensity scores and were included in the analyzes. In the survival analysis, the patients with intermediate and high CHA2DS2-VASc scores were associated with higher 30-day mortality [hazard ratio (HR): 1.11; 95% confidence interval (CI), 1.02–1.20, P = .014], 1-year mortality [HR: 1.13; 95%CI, 1.06–1.19, P < .001], and 5-year mortality [HR: 1.13; 95%CI, 1.07–1.18, P < .001]. The interaction for 30-day mortality among subgroups was not significant between the 0-3 score versus the 4-9 score groups. The restricted cubic splines for 30-day mortality demonstrated an L-shaped trajectory for platelets and hemoglobin, a J-shaped trajectory for WBC, glucose and potassium, and a U-shaped trajectory for sodium, respectively (all nonlinear P <.001).ConclusionsA high CHA2DS2-VASc score was an independent risk for 30-day, 1-year, and 5-year mortality for CCU patients.

A Machine Learning Approach for the Prediction of Traumatic Brain Injury Induced Coagulopathy.

Background: Traumatic brain injury-induced coagulopathy (TBI-IC), is a disease with poor prognosis and increased mortality rate.Objectives: Our study aimed to identify predictors as well as develop machine learning (ML) models to predict the risk of coagulopathy in this population.Methods: ML models were developed and validated based on two public databases named Medical Information Mart for Intensive Care (MIMIC)-IV and the eICU Collaborative Research Database (eICU-CRD). Candidate predictors, including demographics, family history, comorbidities, vital signs, laboratory findings, injury type, therapy strategy and scoring system were included. Models were compared on area under the curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values, and decision curve analysis (DCA) curve.Results: Of 999 patients in MIMIC-IV included in the final cohort, a total of 493 (49.35%) patients developed coagulopathy following TBI. Recursive feature elimination (RFE) selected 15 variables, including international normalized ratio (INR), prothrombin time (PT), sepsis related organ failure assessment (SOFA), activated partial thromboplastin time (APTT), platelet (PLT), hematocrit (HCT), red blood cell (RBC), hemoglobin (HGB), blood urea nitrogen (BUN), red blood cell volume distribution width (RDW), creatinine (CRE), congestive heart failure, myocardial infarction, sodium, and blood transfusion. The external validation in eICU-CRD demonstrated that adapting boosting (Ada) model had the highest AUC of 0.924 (95% CI: 0.902–0.943). Furthermore, in the DCA curve, the Ada model and the extreme Gradient Boosting (XGB) model had relatively higher net benefits (ie, the correct classification of coagulopathy considering a trade-off between false- negatives and false-positives)—over other models across a range of threshold probability values.Conclusions: The ML models, as indicated by our study, can be used to predict the incidence of TBI-IC in the intensive care unit (ICU).

The relationship between monocyte-to-lymphocyte ratio and the risk of gastrointestinal system involvement in children with IgA vasculitis: A preliminary report.

Immunoglobulin A (IgA) vasculitis is the most common systemic vasculitis of childhood. It can affect the gastrointestinal system (GS) and the renal system. To evaluate the monocyte-to-lymphocyte ratio (MLR) and other hematological markers in predicting GS and renal system complications of IgA vasculitis in children. One hundred and fifteen children with IgA vasculitis and 95 healthy children were included in this study. Demographic characteristics, organ involvement, and laboratory findings, including neutrophil, lymphocyte, monocyte and platelet (Plt) counts, red blood cell volume distribution width (RDW), platelet distribution width (PDW), mean platelet volumes (MPV), monocyte/platelet counts, neutrophil/lymphocyte ratio (NLR), and platelet/lymphocyte ratio (PLR), were evaluated. Among 115 children with IgA vasculitis, 34 (29.5%) cases had GS involvement, and renal involvement was observed in 12 children (10.4%). Neutrophil, monocyte and Plt count and MLR, NLR and PLR values were higher in the IgA vasculitis group than in control groups. Moreover, the neutrophil count and NLR and MLR levels were significantly higher in children with GS involvement than in those without GS involvement. Logistic regression analysis showed MLR was the sole risk factor for GS involvement among these parameters. Furthermore, a cut-off MLR value of 0.245 differentiated children with IgA vasculitis with GS involvement from those without GS involvement (area under the curve (AUC) 0.694, with a sensitivity of 52.9% and specificity of 77.8%). An elevated MLR value could serve as a useful marker in predicting GS involvement in IgA vasculitis in children. Therefore, monitoring the blood MLR value may serve as an important novel indicator to pediatricians regarding the involvement of GS and disease severity of IgA vasculitis.

Evaluation of hematological parameters as an indicator of disease severity in Covid-19 patients: Pakistan's experience.

BackgroundThe severity of COVID‐19 could be evaluated by examining several blood parameters mainly white blood cell (WBC) count, granulocytes, platelet, and novel hemocytometric markers neutrophils to lymphocyte ratio (NLR), platelet‐to‐lymphocyte (PLR), and lymphocyte to monocyte ratio (LMR). The current study was conducted to investigate alteration in blood parameters and their association with the severity and mortality of COVID‐19 patients.MethodologyAn observational cross‐sectional study was conducted retrospectively, a total of 101 COVID‐19 positive patients were examined: 52 were mild, 24 were moderate, 09 were severe, and 16 were critically diseased patients. We also recorded 16 deaths associated with the critical group. The overall mean age observed in our study was 48.94 years, where the mean age for critical individuals was 62.12 ± 14.35 years.ResultsA significant association between the disease severity and elevation in blood parameters were observed. The WBC's and granulocyte count were significantly increased (p value <0.001) while the mean platelet count (165.0 × 109/L) and red blood cell volume distribution width (RDW) were decreased in the critical group (57.86%) compared to mild group's patients (177.3%) (p = 0.83). The lymphocytes count was decreased in critical patients (1.40 × 109/L) compared to mild patients (1.92 × 109/L) (p = 0.28). A significant association was observed in platelet‐lymphocyte ratio (p < 0.001), Neutrophil‐Lymphocyte ratio (p = <0.001), and Lymphocyte‐Monocyte ratio (0.011).ConclusionThese blood parameters could be used as a suitable biomarker for the prognosis and severity of COVID‐19. Evaluating novel hemograms NLR, PLR, and LMR can aid clinicians to identify potentially severe cases at early stages, initiate effective management in time, and conduct early triage which may reduce the overall mortality of COVID‐19 patients.

Red blood cell distribution width is associated with frailty in older inpatients in China: Sex differences in a cross-sectional study.

Although some studies have investigated an association between inflammatory marker and frailty, little is known about the relationship of red blood cell volume distribution width (RDW) and frailty. This study aimed to determine whether there were sex-specific associations of RDW and frailty among older hospitalized patients. A cross-sectional study was conducted in a comprehensive tertiary hospital in mainland China between February 2015 and November 2017. Hospitalized patients aged 60years and above were included. Frailty was defined according to Fried's frailty phenotype. Data on patients' demographics, clinical characteristics, and inflammatory markers were collected. The association was evaluated by using the linear and logistic regression modeling. The frailty prevalence was 26.65% (89/334) in men and 28.07% (80/285) in women. After adjusting for all covariates, patients in the 4th RDW quartile exhibited an increased risk of frailty compared with those of the 1st RDW quartile [men: odds ratio (OR)=2.26; 95%CI=1.01, 5.07; women: OR=6.29; 95%CI=2.11, 18.71]. P for trend were<0.05 for all models. An increased RDW among older inpatients was more strongly associated with a corresponding increased risk of frailty in women than in men. Our findings suggest that there were sex differences in the association between elevated RDW levels and frailty among older inpatients. These results may provide support for the intervention decision-making.

Discussion on the Correlation between Clinical Typing of Covid-19 Patients and Risk Factors and Peripheral Red Blood Cell Parameters

To investigate the correlation between clinical typing of coronavirus disease-19 patients and risk factors (age, sex, underlying disease), hematocrit (packed-cell volume), mean erythrocyte volume, standard deviation of erythrocyte volume distribution width, variation coefficient of erythrocyte distribution width and other peripheral erythrocyte parameters. Retrospective analysis from January 2020 to March 2020 were coronavirus disease-19, 82 cases of patients, according to the hospital condition grading assessment can be divided into normal group and the normal group (including heavy type and critically ill patients), using R*C chi-square statistics of gender and the presence of basic diseases, the binary classification Logistic regression analysis statistic average age, red blood cells deposited (packed-cell volume), red blood cell volume, red blood cell volume distribution width standard deviation, red blood cell distribution width variation coefficient, determine the correlation between. There were differences in age and presence of underlying diseases (p<0.05); There were no significant differences in hematocrit (packed-cell volume) or mean erythrocyte volume between the normal and non-normal groups (including severe and critically ill patients) (p<0.05); There were differences in standard deviation and variation coefficient of erythrocyte distribution width between the normal group and the non-normal group (including severe and critical patients) (p<0.05). In coronavirus disease-19 patients, increased age, accompanied by underlying disease, decreased standard deviation of red blood cell volume distribution width, and increased variation coefficient of red blood cell distribution width are correlated with the severity of the disease to a certain extent, and can be used as one of the predictive indicators of the severity of the disease in coronavirus disease-19 patients.

COVID-19 disease-Temporal analyses of complete blood count parameters over course of illness, and relationship to patient demographics and management outcomes in survivors and non-survivors: A longitudinal descriptive cohort study.

Detailed temporal analyses of complete (full) blood count (CBC) parameters, their evolution and relationship to patient age, gender, co-morbidities and management outcomes in survivors and non-survivors with COVID-19 disease, could identify prognostic clinical biomarkers. From 29 January 2020 until 28 March 2020, we performed a longitudinal cohort study of COVID-19 inpatients at the Italian National Institute for Infectious Diseases, Rome, Italy. 9 CBC parameters were studied as continuous variables [neutrophils, lymphocytes, monocytes, platelets, mean platelet volume, red blood cell count, haemoglobin concentration, mean red blood cell volume and red blood cell distribution width (RDW %)]. Model-based punctual estimates, as average of all patients' values, and differences between survivors and non-survivors, overall, and by co-morbidities, at specific times after symptoms, with relative 95% CI and P-values, were obtained by marginal prediction and ANOVA- style joint tests. All analyses were carried out by STATA 15 statistical package. 379 COVID-19 patients [273 (72% were male; mean age was 61.67 (SD 15.60)] were enrolled and 1,805 measures per parameter were analysed. Neutrophils' counts were on average significantly higher in non-survivors than in survivors (P<0.001) and lymphocytes were on average higher in survivors (P<0.001). These differences were time dependent. Average platelets' counts (P<0.001) and median platelets' volume (P<0.001) were significantly different in survivors and non-survivors. The differences were time dependent and consistent with acute inflammation followed either by recovery or by death. Anaemia with anisocytosis was observed in the later phase of COVID-19 disease in non-survivors only. Mortality was significantly higher in patients with diabetes (OR = 3.28; 95%CI 1.51-7.13; p = 0.005), obesity (OR = 3.89; 95%CI 1.51-10.04; p = 0.010), chronic renal failure (OR = 9.23; 95%CI 3.49-24.36; p = 0.001), COPD (OR = 2.47; 95% IC 1.13-5.43; p = 0.033), cardiovascular diseases (OR = 4.46; 95%CI 2.25-8.86; p = 0.001), and those >60 years (OR = 4.21; 95%CI 1.82-9.77; p = 0.001). Age (OR = 2.59; 95%CI 1.04-6.45; p = 0.042), obesity (OR = 5.13; 95%CI 1.81-14.50; p = 0.002), renal chronic failure (OR = 5.20; 95%CI 1.80-14.97; p = 0.002) and cardiovascular diseases (OR 2.79; 95%CI 1.29-6.03; p = 0.009) were independently associated with poor clinical outcome at 30 days after symptoms' onset. Increased neutrophil counts, reduced lymphocyte counts, increased median platelet volume and anaemia with anisocytosis, are poor prognostic indicators for COVID19, after adjusting for the confounding effect of obesity, chronic renal failure, COPD, cardiovascular diseases and age >60 years.