Role of RDW in mathematical formulas aiding the differential diagnosis of microcytic anemia

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.

A prediction model of elderly hip fracture mortality including preoperative red cell distribution width constructed based on the random survival forest (RSF) and Cox risk ratio regression

An independent correlation between pre-RDW and 1-year mortality after surgery in elderly hip fracture can be used to predict mortality in elderly hip fracture patients and has predictive significance in anemia patients. With further research, a treatment algorithm can be developed to potentially identify patients at high risk of preoperative mortality.

INTRODUCTION

Red blood cell distribution width (RDW) is an independent predictor of various disease states in elderly individuals, but its association with the prognosis of elderly hip fracture patients is controversial. This study aimed to evaluate the prognostic value of RDW in such patients, construct a prediction model containing RDW using random survival forest (RSF) and Cox regression analysis, and compare RDW in patients with and without anemia.

METHODS

We retrospectively analyzed the data of elderly patients who underwent hip fracture surgery, selected the best variables using RSF, stratified the independent variables by Cox regression analysis, constructed a 1-year mortality prediction model of elderly hip fracture with RDW, and conducted internal validation and external validation.

RESULTS

Two thousand one hundred six patients were included in this study. The RSF algorithm selects 12 important influencing factors, and Cox regression analysis showed that eight variables including preoperative RDW (pre-RDW) were independent risk factors for death within 1-year after hip fracture surgery in elderly patients. Stratified analysis showed that pre-RDW was still independently associated with 1-year mortality in the non-anemia group and not in the anemia group. The nomogram prediction model had high differentiation and fit, and the prediction model constructed by the total cohort of patients was also used for validation of patients in the anemia patients and obtained good clinical benefits.

CONCLUSION

An independent correlation between pre-RDW and 1-year mortality after surgery in elderly hip fracture can be used to predict mortality in elderly hip fracture patients and has predictive significance in anemia patients.

Multi-criteria decision making to validate performance of RBC-based formulae to screen [Formula: see text]-thalassemia trait in heterogeneous haemoglobinopathies

BACKGROUND

India has the most significant number of children with thalassemia major worldwide, and about 10,000-15,000 children with the disease are born yearly. Scaling up e-health initiatives in rural areas using a cost-effective digital tool to provide healthcare access for all sections of people remains a challenge for government or semi-governmental institutions and agencies.

METHODS

We compared the performance of a recently developed formula SCS[Formula: see text] and its web application SUSOKA with 42 discrimination formulae presently available in the literature. 6,388 samples were collected from the Postgraduate Institute of Medical Education and Research, Chandigarh, in North-Western India. Performances of the formulae were evaluated by eight different measures: sensitivity, specificity, Youden's Index, AUC-ROC, accuracy, positive predictive value, negative predictive value, and false omission rate. Three multi-criteria decision-making (MCDM) methods, TOPSIS, COPRAS, and SECA, were implemented to rank formulae by ensuring a trade-off among the eight measures.

RESULTS

MCDM methods revealed that the Shine & Lal and SCS[Formula: see text] were the best-performing formulae. Further, a modification of the SCS[Formula: see text] formula was proposed, and validation was conducted with a data set containing 939 samples collected from Nil Ratan Sircar (NRS) Medical College and Hospital, Kolkata, in Eastern India. Our two-step approach emphasized the necessity of a molecular diagnosis for a lower number of the population. SCS[Formula: see text] along with the condition MCV[Formula: see text] 80 fl was recommended for a higher heterogeneous population set. It was found that SCS[Formula: see text] can classify all BTT samples with 100% sensitivity when MCV[Formula: see text] 80 fl.

CONCLUSIONS

We addressed the issue of how to integrate the higher-ranked formulae in mass screening to ensure higher performance through the MCDM approach. In real-life practice, it is sufficient for a screening algorithm to flag a particular sample as requiring or not requiring further specific confirmatory testing. Implementing discriminate functions in routine screening programs allows early identification; consequently, the cost will decrease, and the turnaround time in everyday workflows will also increase. Our proposed two-step procedure expedites such a process. It is concluded that for mass screening of BTT in a heterogeneous set of data, SCS[Formula: see text] and its web application SUSOKA can provide 100% sensitivity when MCV[Formula: see text] 80 fl.

Pediatric reference intervals for hematology parameters in healthy infants and young children in Iran

INTRODUCTION

Defining accurate age- and sex-specific reference intervals (RIs) for hematology parameters, especially for the pediatric population, is important for making an appropriate clinical diagnosis. To address gaps, we established age-specific RIs for 11 hematologic parameters in Iranian children younger than 30 months for the first time.

METHODS

Fresh whole blood samples collected from a total of 344 participants (males: 158 and females: 186) ages 3 days to 30 months, with a mean age of 12.91 ± 7.15 months, were recruited from healthcare centers in Mashhad, Iran. Hematologic parameters, including complete blood count (CBC), were analyzed on the Sysmex auto-analyzer system (KX-21 N). RIs were calculated with 90% confidence intervals using the direct method based on CLSI Ep28-A3 and C28-A3 guidelines.

RESULTS

None of the CBC parameters required sex partitioning. Of 11 CBC parameters, six required age partitions of 3 days-<4 months, 4-<10, 10-<15, and 4-<30 months. Five parameters (i.e., white blood cell count, mean corpuscular hemoglobin concentration, mean platelet volume, red cell distribution width, and platelet distribution width) did not demonstrate age-specific changes. RIs of red blood cell count and hematocrit, as well as hemoglobin, increased with age, while mean corpuscular volume, mean corpuscular hemoglobin, and platelet count, decreased with age.

CONCLUSION

In this study, we established RIs for 11 hematology parameters in young children. Age partitioning was required for six parameters demonstrating marked changes during the early period of growth and development and necessitating the use of pediatric-specific reference standards.

RDW-SD is Superior to RDW-CV in Reflecting Liver Fibrosis Stage in Patients with Chronic Hepatitis B

Purpose

The clinical significance of the red blood cell distribution width (RDW)-coefficient of variation (RDW-CV) has been recognized in numerous diseases, but few studies have investigated the usefulness of RDW-standard deviation (RDW-SD). This study aimed to compare the utility of RDW-SD and RDW-CV in evaluating liver fibrosis stage in patients with chronic hepatitis B (CHB).

Patients and Methods

In this retrospective study, we enrolled 720 treatment-naïve CHB patients and 578 healthy controls, and evaluated their clinical parameters. In CHB patients, the associations between RDW-CV and liver fibrosis stage were analyzed as compared to RDW-SD using one-way analysis of variance (ANOVA), Spearman's rank correlation, student's t-test, binary logistic regression, and receiver operating characteristic (ROC) curve.

Results

RDW-SD, rather than RDW-CV was significantly elevated in CHB patients compared with healthy controls. Correlation analysis showed a stronger association between RDW-SD and liver fibrosis stage than RDW-CV in CHB patients. RDW-CV and RDW-SD are both independent predictors of significant fibrosis. For the diagnosis of significant fibrosis, the area under the receiver operating characteristic curve (AUC) for RDW-CV was 0.599, while for RDW-SD, it was 0.706. RDW-to-platelet ratio (RPR), a novel index for liver fibrosis calculated as RDW-CV/platelet, exhibited an AUC of 0.730. This AUC increased to 0.752 when RDW-CV in the RPR formula was replaced with RDW-SD. Additionally, subgroup analyses based on age, gender, and HBeAg status showed that the AUC for RDW-SD in diagnosing significant fibrosis was significantly greater than that for RDW-CV, with statistically significant differences.

Conclusion

RDW-SD showed superiority in reflecting liver fibrosis stage and diagnosing liver significant fibrosis than RDW-CV in treatment-naïve CHB patients.

Predicting thalassemia using deep neural network based on red blood cell indices

BACKGROUND AND OBJECTIVE

The traditional statistical screening method for thalassemia based on red blood cell (RBC) indices is being replaced by machine learning. Here, we developed deep neural networks (DNNs) that outperformed the traditional method for predicting thalassemia.

METHOD

Using a dataset of 8693 records comprising genetic tests and other 11 features we constructed 11 DNN models and 4 traditional statistical models and then compared their performances and analysed feature importance for interpreting DNN models.

RESULTS

The area under the receiver operating characteristic curve, accuracy, Youden's index, F1 score, sensitivity, specificity, positive predictive value and negative predictive value, were 0.960, 0.897, 0.794, 0.897, 0.883, 0.911, 0.914, and 0.882, respectively, for our best model, and compared with the traditional statistical model based on the mean corpuscular volume, these values were increased by 10.22%, 10.09%, 26.55%, 8.92%, 4.13%, 16.90%, 13.86% and 6.07%, respectively, and by 15.38%, 11.70%, 31.70%, 9.89%, 3.05%, 22.13%, 17.11% and 5.94%, respectively, for the mean cellular haemoglobin model. The DNN model performance will reduce without age, RBC distribution width (RDW), sex, or both WBC and PLT.

CONCLUSIONS

Our DNN model outperformed the current screening model. In 8 features, RDW and age were the most useful, followed by sex and the combination of WBC and PLT, the remaining nearly useless.

Recent advances in laboratory hematology reflected by a decade of CCLM publications

On the occasion of the 60th anniversary of Clinical Chemistry and Laboratory Medicine (CCLM) we present a review of recent developments in the discipline of laboratory hematology as these are reflected by papers published in CCLM in the period 2012–2022. Since data on CCLM publications from 1963 to 2012 are also available, we were able to make a comparison between the two periods. This interestingly revealed that the share of laboratory hematology papers has steadily increased and reached now 16% of all papers published in CCLM. It also became evident that blood coagulation and fibrinolysis, erythrocytes, platelets and instrument and method evaluation constituted the ‘hottest’ topics with regard to number of publications. Some traditional, characteristic CCLM categories like reference intervals, standardization and harmonization, were more stable and probably will remain so in the future. With the advent of important newer topics, like new coagulation assays and drugs and cell population data generated by hematology analyzers, laboratory hematology is anticipated to remain a significant discipline in CCLM publications.

Performance analysis of machine learning algorithms and screening formulae for β-thalassemia trait screening of Indian antenatal women

BACKGROUND

Currently, more than forty discrimination formulae based on red blood cell (RBC) parameters and some supervised machine learning algorithms (MLAs) have been recommended for β-thalassemia trait (BTT) screening. The present study was aimed to evaluate and compare the performance of 26 such formulae and 13 MLAs on antenatal woman data with a recently developed formula SCS_BTT, which is available for evaluation in over seventy countries as an Android app, called SUSOKA[16].

METHODS

A diagnostic database of 2942 antenatal females were collected from PGIMER, Chandigarh, India and was used for this analysis. The data set consists of hypochromic microcytic anemia, BTT, Hemoglobin E trait, double heterozygote for Hemoglobin S and BTT, heterozygote for Hemoglobin D Punjab and normal subjects. Performance of the formulae and the MLAs were assessed by Sensitivity, Specificity, Youden's Index, and AUC-ROC measures. A final recommendation was made from the ranking obtained through two Multiple Criteria Decision-Making (MCDM) techniques, namely, Simultaneous Evaluation of Criteria and Alternatives (SECA) and TOPSIS.

RESULTS

It was observed that Extreme Learning Machine (ELM) and Gradient Boosting Classifier (GBC) showed maximum Youden's index and AUC-ROC measures compared to all discriminating formulae. Sensitivity remains maximum for SCS_BTT. K-means clustering and the ranking from MCDM methods show that SCS_BTT, Shine & Lal and Ravanbakhsh-F4 formula ensures higher performance among all formulae. The discriminant power of some MLAs and formulae was found considerably lower than that reported in original studies.

CONCLUSION

Comparative information on MLAs can aid researchers in developing new discriminating formulae that simultaneously ensure higher sensitivity and specificity. More multi-centric verification of the formulae on heterogeneous data is indispensable. SCS_BTT and Shine & Lal formula, and ELM and GBC are recommended for screening BTT based on MCDM. SCS_BTT can be used with certainty as a tangible cost-saving screening tool for mass screening for antenatal women in India and other countries.

Clinical Utility of Red Blood Cell Distribution Width for the Diagnosis and Prognosis of Cervical Cancer

Background

The width of red blood cell distribution (RDW) is correlated with some diseases, but its clinical value and prognostic role in cervical cancer is unclear.

Methods

We used receiver operating characteristic curves to evaluate the diagnostic ability of RDW and other clinical parameters in cervical cancer based on a case–control design. Using retrospective data, we explored the correlation of RDW with overall (OS) and progression-free (PFS) survival using Kaplan–Meier analysis and univariate and multivariate Cox regression with the hazard ratio (HR) and 95% confidence interval (CI). A restricted cubic plot was used to evaluate the nonlinear association between RDW and prognosis risk.

Results

RDW was significantly higher in cases than in controls (14.6±1.7 vs 12.5±1.8, P<0.001). It showed high diagnostic accuracy for cervical cancer, with a sensitivity of 79.3%, specificity of 65.6%, and area under the curve of 0.802 (95% CI, 0.775–0.827) with a cutoff value of 13.88. There was a significant positive correlation between RDW and C-reactive protein (r=0.434, P=0.023). Multivariate Cox regression indicated that it was independently associated with a poorer PFS (HR, 2.05; 95% CI, 1.25–3.18, P<0.001) and OS (HR, 2.73; 95% CI, 1.61–4.64, P<0.001). RDW>14.66 showed a nonlinear increased risk for a poor PFS and OS.

Conclusion

RDW is an easy, quick, and inexpensive tool for the early detection and risk management of cervical cancer. A greater RDW is associated with a poor prognosis in cervical cancer.

Factors associated with in-hospital mortality in adult sepsis with Escherichia coli infection

Background

Escherichia coli (E. coli) is an important pathogen in sepsis. This study aimed to explore the factors which were associated with in-hospital mortality in adult sepsis with E. coli infection based on a public database.

Methods

All sepsis patients with E. coli infection in MIMIC-III were included in this study. Clinical characteristics between the survivor and non-survivor groups were analyzed. Factors associated with in-hospital mortality were identified by multivariate logistic regression.

Results

A total of 199 patients were eventually included and divided into two groups: a survivor group (n = 167) and a non-survivor group (n = 32). RDW and HCT were identified as the factors with clinical outcomes. The area under the ROC curve (AUC) were 0.633 and 0.579, respectively. When combined RDW and HCT for predicting in-hospital mortality, the AUC was 0.772, which was significantly superior to SOFA and APACHEII scores.

Conclusion

RDW and HCT were identified as factors associated with in-hospital mortality in adult sepsis patients with E. coli infection. Our findings will be of help in early and effective evaluation of clinical outcomes in those patients.

Red blood cell distribution width-standard deviation but not red blood cell distribution width-coefficient of variation as a potential index for the diagnosis of iron-deficiency anemia in mid-pregnancy women

The aim of this study was to compare the diagnostic values of red blood cell distribution width-coefficient of variation (RDW-CV) and red blood cell distribution width-standard deviation (RDW-SD) in mid-pregnancy women with iron deficiency anemia (IDA). To obtain the results, 115 mid-pregnancy women with IDA, defined as the IDA group, and 142 healthy mid-pregnancy women, selected as the control group, were enrolled in this study. Hematological parameters and ferritin concentrations in the serum were analyzed. The efficiency of RDW-CV and RDW-SD to distinguish IDA from mid-pregnancy women was evaluated using receiver operating characteristic (ROC) curves. The RDW-SD value in the IDA group was significantly higher than that in the control group (p < 0.05), while the RDW-CV value did not differ between them (p = 0.84). Significantly negative correlations were found between RDW-CV (r = −0.297, p = 0.001), RDW-SD (r = −0.404, p = 0.000), and serum ferritin in the IDA group but not in the control group. For the diagnosis of IDA, RDW-CV and RDW-SD produced areas under the ROC curves of 0.58 and 0.84. To conclude, our results suggest that RDW-SD, but not RDW-CV, can be used as a diagnostic index of IDA for mid-pregnancy women.

Application of Bayesian Decision Tree in Hematology Research: Differential Diagnosis of β-Thalassemia Trait from Iron Deficiency Anemia

OBJECTIVE

Several discriminating techniques have been proposed to discriminate between β-thalassemia trait (βTT) and iron deficiency anemia (IDA). These discrimination techniques are essential clinically, but they are challenging and typically difficult. This study is the first application of the Bayesian tree-based method for differential diagnosis of βTT from IDA.

METHOD

This cross-sectional study included 907 patients with ages over 18 years old and a mean (±SD) age of 25 ± 16.1 with either βTT or IDA. Hematological parameters were measured using a Sysmex KX-21 automated hematology analyzer. Bayesian Logit Treed (BLTREED) and Classification and Regression Trees (CART) were implemented to discriminate βTT from IDA based on the hematological parameters.

RESULTS

This study proposes an automatic detection model of beta-thalassemia carriers based on a Bayesian tree-based method. The BLTREED model and CART showed that mean corpuscular volume (MCV) was the main predictor in diagnostic discrimination. According to the test dataset, CART indicated higher sensitivity and negative predictive value than BLTREED for differential diagnosis of βTT from IDA. However, the CART algorithm had a high false-positive rate. Overall, the BLTREED model showed better performance concerning the area under the curve (AUC).

CONCLUSIONS

The BLTREED model showed excellent diagnostic accuracy for differentiating βTT from IDA. In addition, understanding tree-based methods are easy and do not need statistical experience. Thus, it can help physicians in making the right clinical decision. So, the proposed model could support medical decisions in the differential diagnosis of βTT from IDA to avoid much more expensive, time-consuming laboratory tests, especially in countries with limited recourses or poor health services.