Prognostic role of red blood cell distribution width in patients with sepsis: a systematic review and meta-analysis

Development of a Simple Scoring System for Predicting Discharge Safety from the Medical ICU to Low-Acuity Wards: The Role of the Sequential Organ Failure Assessment Score, Albumin, and Red Blood Cell Distribution Width

Despite advancements in artificial intelligence-based decision-making, transitioning patients from intensive care units (ICUs) to low-acuity wards is challenging, especially in resource-limited settings. This study aimed to develop a simple scoring system to predict ICU discharge safety. We retrospectively analyzed patients admitted to a tertiary hospital's medical ICU (MICU) between July 2016 and December 2021. This period was divided into two phases for model development and validation. We identified risk factors associated with unexpected death within 14 days of MICU discharge and developed a predictive scoring system that incorporated these factors. We verified the system's performance using validation data. In the development cohort, 522 patients were discharged from the MICU, and 42 (8.04%) died unexpectedly. In multivariate analysis, the Sequential Organ Failure Assessment (SOFA) score (odds ratio [OR] 1.26, 95% confidence interval [CI] 1.13-1.41), red blood cell distribution width (RDW) (OR 1.20, 95% CI 1.07-1.36), and albumin (OR 0.37, 95% CI 0.16-0.84) were predictors of unexpected death. Each variable was assigned a weighted point in the scoring system, and the area under the curve (AUC) was 0.788 (95% CI 0.714-0.855). The scoring system was performed using an AUC of 0.738 (95% CI 0.653-0.822) in the validation cohort of 343 patients with 9.62% of unexpected deaths. When a cut-off of 0.032 was applied, a sensitivity and a specificity of 81.8% and 55.2%, respectively, were achieved. This simple bedside predictive score for ICU discharge uses the SOFA score, albumin level, and RDW to aid in timely decision-making and optimize critical care facility allocation in resource-limited settings.

Prognostic value of red blood cell distribution width to albumin ratio for predicting mortality in adult patients meeting sepsis-3 criteria in intensive care units

BACKGROUND

Patients with sepsis with low albumin levels and high red blood cell distribution width levels have poor prognoses. Red blood cell distribution width to albumin ratio (RAR) has recently attracted attention as an innovative inflammation biomarker. We aimed to explore the association between RAR and the prognosis of patients with sepsis.

METHODS

This retrospective observational study included 402 patients meeting the sepsis-3 standards admitted to Yantai Yuhuangding Hospital's intensive care units (ICUs) between January 2020 and December 2022. The relationship between RAR and mortality in patients with sepsis was examined using regression analysis, Kaplan-Meier analyses, and a receiver operating characteristic curve. Subgroup and sensitivity analyses were conducted to assess the results' robustness.

RESULTS

RAR, when considered as a continuous variable, was a significant independent in-hospital mortality risk factor (adjusted odds ratio [OR]: 1.383; 95% confidence interval [CI]: 1.164-1.645; P < 0.001). When considering RAR as a categorical variable, the ORs (95% CIs) of hospital mortality for quartile 2 (Q2), Q3, and Q4 compared with Q1 were 1.027 (0.413-2.551), 3.632 (1.579-8.354), and 4.175 (1.625-10.729), respectively, P < 0.001. Similar outcomes were observed for 28- and 90-day mortalities.

CONCLUSIONS

RAR may indicate clinical prognosis for patients with sepsis in the ICU, potentially providing a low-cost, easily repeatable, and accessible biomarker for risk categorization for these patients.

Red cell distribution width to albumin ratio predicts treatment failure in peritoneal dialysis-associated peritonitis

BACKGROUND

This study aims to investigate the potential correlation between baseline red cell distribution width (RDW) to albumin ratio (RAR) levels and treatment failure in peritoneal dialysis-associated peritonitis (PDAP) patients.

METHODS

A retrospective single-center study was conducted on 286 PDAP patients. Logistic regression and generalized estimation equation (GEE) analyses were employed to assess the relationship between RAR and treatment failure.

RESULTS

RAR emerged as a robust predictor of treatment failure in PDAP patients. Elevated RAR levels were associated with an increased risk of treatment failure, exhibiting a linear relationship. Even after adjusting for demographic and clinical variables, this association remained statistically significant. ROC analysis revealed that RAR outperformed RDW and albumin individually in predicting PDAP prognosis.

CONCLUSION

This study highlights RAR as a superior prognostic marker for treatment failure in PDAP patients, offering new insights into risk assessment and management strategies for this challenging condition.

LncRNA HOXA‑AS2 is a prognostic and clinicopathological predictor in patients with cancer: A meta‑analysis

Elevated expression of long non-coding RNA homeobox A cluster antisense RNA 2 (lncRNA HOXA-AS2) is known to have prognostic value in various solid tumors. The present meta-analysis aimed to comprehensively quantify its prognostic significance across a wider spectrum of malignancies and to provide an updated synthesis of evidence that could refine prognostic models. To achieve this aim, multiple databases were carefully searched for lncRNA HOXA-AS2-related articles published in the past 10 years. Hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to demonstrate the prognostic value of lncRNA HOXA-AS2 using Stata 15.0 software. The function of lncRNA HOXA-AS2 was inferred from its associations with key clinical outcomes such as lymph node metastasis, distant metastasis, tumor stage and tumor size, which may reflect its role in tumor biology. In the present systematic review and meta-analysis of 454 patients across 7 studies, it was found that high lncRNA HOXA-AS2 expression was significantly associated with a shorter overall survival (OS) time in patients with cancer (HR=2.14; 95% CI, 1.40-3.27; P<0.001). High lncRNA HOXA-AS2 expression was also associated with lymph node metastasis [odds ratio (OR)=2.06; 95% CI, 1.07-3.99; P=0.032], distant metastasis (OR=2.11; 95% CI, 1.15-3.88; P=0.016), advanced tumor stage (OR=2.71; 95% CI, 1.50-4.89; P=0.001) and larger tumor size (OR=2.02; 95% CI, 0.86-4.78; P=0.006). However, no significant association was observed with age (OR=1.00; 95% CI, 0.63-1.59; P=0.991) or sex (OR=1.55; 95% CI, 0.72-3.34; P=0.258). In conclusion, elevated expression of lncRNA HOXA-AS2 was significantly related to poor clinical outcomes in various cancer types, such as osteosarcoma, non-small cell lung cancer and papillary thyroid carcinoma, a finding that was further confirmed by the present study. Specifically, the potential of lncRNAHOXA-AS2 as a biomarker in assessing tumor stage, metastasis risk and OS in patients was demonstrated. However, the results of the present study also indicated that the expression of lncRNA HOXA-AS2 was not significantly associated with age or sex, suggesting its role in cancer progression might be independent of these factors. This insight may direct future research to place more focus on the relationship between lncRNA HOXA-AS2 and specific cancer types and clinical characteristics.

Risk Factors and Scoring Systems to Predict the Mortality Risk of Afebrile Adult Patients with Monomicrobial Gram-Negative Bacteremia: A 10-Year Observational Study in the Emergency Department

BACKGROUND

The mortality rate of afebrile bacteremia has been reported to be as high as 45%. This investigation focused on the risk factors and predictive performance of scoring systems for the clinical outcomes of afebrile patients with monomicrobial gram-negative bacteria (GNB) in the emergency department (ED).

METHODS

We conducted a retrospective analysis of afebrile adult ED patients with monomicrobial GNB bacteremia from January 2012 to December 2021. We dissected the demographics, clinical pictures, and laboratory investigations. We applied five scoring systems and three revised systems to predict the clinical outcomes.

RESULTS

There were 600 patients included (358 males and 242 females), with a mean age of 69.6 ± 15.4 years. The overall mortality rate was 50.17%, reaching 68.52% (74/108) in cirrhotic patients. Escherichia coli was the leading pathogen (42.83%). The non-survivors had higher scores of the original MEDS (p < 0.001), NEWS (p < 0.001), MEWS (p < 0.001), qSOFA (p < 0.001), and REMS (p = 0.030). In univariate logistic regression analyses, several risk factors had a higher odds ratio (OR) for mortality, including liver cirrhosis (OR 2.541, p < 0.001), malignancy (OR 2.259, p < 0.001), septic shock (OR 2.077, p = 0.002), and male gender (OR 0.535, p < 0.001). The MEDS demonstrated that the best predictive power with the maximum area under the curve (AUC) was measured at 0.773 at the cut-off point of 11. The AUCs of the original NEWS, MEWS, qSOFA, and REMS were 0.663, 0.584, 0.572, and 0.553, respectively. We revised the original MEDS, NEWS, and qSOFA by adding red cell distribution width, albumin, and lactate scores and found a better predictive power of the AUC of 0.797, 0.719, and 0.694 on the revised MEDS ≥11, revised qSOFA ≥ 3, and revised NEWS ≥ 6, respectively.

CONCLUSIONS

The original MEDS, revised MEDS, revised qSOFA, and revised NEWS were valuable tools for predicting the mortality risk in afebrile patients with monomicrobial GNB bacteremia. We suggested that clinicians should explore patients with the risk factors mentioned above for possible severe infection, even in the absence of fever and initiate hemodynamic support and early adequate antibiotic therapy in patients with higher scores of the original MEDS (≥11), revised MEDS (≥11), revised NEWS (≥6), and revised qSOFA (≥3).

Red blood cell distribution width as a predictor of mortality and poor functional outcome after acute ischemic stroke: a meta-analysis and meta-regression

BACKGROUND

This study aimed to review evidence on the ability of red cell distribution width (RDW) to predict mortality and poor functional outcomes after acute ischemic stroke (AIS).

METHODS

Databases of PubMed, CENTRAL, Scopus, Embase, and Web of Science were searched online from inception to 25th Jul 2023 for all studies reporting the association between RDW and outcomes as adjusted ratios. A random-effects meta-analysis was done. Meta-regression was conducted using multiple moderators.

RESULTS

15 studies with 14,968 patients were included. Meta-analysis found that RDW, both as a categorical variable (OR: 2.10 95% CI: 1.74, 2.55 I2 = 42%) and continuous variable OR: 1.16 95% CI: 1.05, 1.28 I2 = 64%) was a significant predictor of mortality after AIS. Age and number of hypertensives were found to be significant moderators in the meta-regression. Also, high RDW, as a categorical variable (OR: 1.68 95% CI: 1.20, 2.35 I2 = 84%), was associated with significantly higher odds of poor functional outcomes after AIS, but not as a continuous variable (OR: 1.07 95% CI: 0.99, 1.16 I2 = 61%). Meta-regression showed that the association was stronger in small sample-sized studies.

CONCLUSION

RDW can be a useful, readily available, and cost-effective biomarker to rapidly stratify AIS patients at risk of poor outcomes. High RDW was consistently associated with an increased risk of mortality after AIS, however, its ability to predict poor functional outcomes needs to be verified by further studies.

Red blood cell distribution width (RDW) - A new nutritional biomarker to assess nutritional risk and response to nutritional therapy?

BACKGROUND & AIMS

Red cell distribution width (RDW) has been proposed as a surrogate marker for acute and chronic diseases and may be influenced by nutritional deficits. We assessed the prognostic value of RDW regarding clinical outcomes and nutritional treatment response among medical inpatients at nutritional risk.

METHODS

This is a secondary analysis of EFFORT, a randomized, controlled, prospective, multicenter trial investigating the effects of nutritional support in patients at nutritional risk in eight Swiss hospitals. We examined the association between RDW and mortality in regression analysis.

RESULTS

Among 1,244 included patients (median age 75 years, 46.6 % female), high RDW (≥15 %) levels were found in 38 % of patients (n = 473) with a significant association of higher malnutrition risk [OR 1.48 (95%CI 1.1 to 1.98); p = 0.009]. Patients with high RDW had a more than doubling in short-term (30 days) mortality risk [adjusted HR 2.12 (95%CI 1.44 to 3.12); p < 0.001] and a signficant increase in long-term (5 years) mortality risk [adjusted HR 1.73 (95%CI 1.49 to 2.01); p < 0.001]. Among patients with high RDW, nutritional support reduced morality within 30 days [adjusted OR 0.56 (95%CI 0.33 to 0.96); p = 0.035], while the effect of the nutritional intervention in patients with low RDW was markedly smaller.

CONCLUSIONS

Among medical patients at nutritional risk, RDW correlated with several nutritional parameters and was a strong prognostic marker for adverse clinical outcomes at short- and long-term, respectively. Patients with high baseline RDW levels also showed a strong benefit from the nutritional intervention. Further research is needed to understand whether monitoring of RDW over time severs as a nutritional biomarker to assess effectiveness of nutritional treatment in the long run.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02517476.

Clinical value of the red blood cell distribution width to albumin ratio in the assessment of prognosis in critically ill patients with sepsis: a retrospective analysis

Background

Red blood cell (RBC) distribution width (RDW) to albumin ratio is a novel biomarker and its prognostic effect on critically ill patients with sepsis has not been extensively investigated. The objective of this study was to identify the prognostic value of the RDW to albumin ratio in these patients.

Methods

Data were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. A Cox proportional hazards model and restricted cubic spline model were used to determine the association of RDW to albumin ratio with mortality. Receiver operating characteristic (ROC) curves and Kaplan-Meier survival curves were applied, and the area under the curve (AUC) was used to compare the predictive value.

Results

A total of 3,969 eligible patients were enrolled. The median RDW to albumin ratio was significantly higher in non-survivors than in survivors at 30 and 90 days. Patients were divided into groups according to the RDW to albumin ratio, and the risk of 30- and 90-day mortality markedly increased in the group with a higher ratio. The relationship between the RDW to albumin ratio as a continuous variable and 30-day mortality also showed an upward trend in the restricted cubic spline. The AUC of the RDW to albumin ratio was 0.633 in discriminating 30-day mortality which was similar to that of the lactate to albumin ratio (AUC =0.617; P=0.133) and higher than that of the neutrophil percentage to albumin ratio (AUC =0.559; P<0.001).

Conclusions

The RDW to albumin ratio is a promising biomarker for assessing the prognosis of critically ill patients with sepsis. Its predictive value in determining mortality was found to be similar to that of the lactate to albumin ratio and superior to that of the neutrophil percentage to albumin ratio.

Big data insights into the diagnostic values of CBC parameters for sepsis and septic shock in burn patients: a retrospective study

Sepsis and septic shock are prevalent and life-threatening complications in burn patients. Despite their severity, existing diagnostic methods are limited. This study aims to evaluate the efficacy of Complete Blood Count (CBC) and CBC ratio markers in diagnosing sepsis and septic shock, and in predicting mortality among burn patients. A cohort of 2757 burn patients was examined to ascertain the correlation between various CBC parameters, their ratios, and the incidence of sepsis and related mortality. Key markers analyzed included Red Cell Distribution Width (RDW), Mean Platelet Volume (MPV), Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), and Mean Platelet Volume-to-Platelet Ratio (MPVPR). Our findings indicate that 65.5% of the patients developed sepsis, and 24.3% succumbed to their conditions. The CBC parameters RDW, MPV, NLR, MPVPR, and MPV-to-Lymphocyte Ratio (MPVLR) were significantly associated with sepsis and mortality. These markers showed considerable temporal variation and yielded an Area Under the Curve (AUC) of over 0.65 in an unadjusted Generalized Estimating Equations (GEE) model. This study underscores the potential of RDW, MPV, NLR, MPVPR, and MPVLR as vital prognostic tools for diagnosing sepsis, septic shock, and predicting mortality in burn patients. Although based on a single-center dataset, our results contribute to the enhancement of sepsis management by facilitating earlier, more precise diagnosis and treatment strategies. Further multi-center research is necessary to confirm these findings and broaden their applicability, establishing a solid base for future explorations in this crucial field.

Interpretable machine learning model for early prediction of 28-day mortality in ICU patients with sepsis-induced coagulopathy: development and validation

OBJECTIVE

Sepsis-induced coagulopathy (SIC) is extremely common in individuals with sepsis, significantly associated with poor outcomes. This study attempted to develop an interpretable and generalizable machine learning (ML) model for early predicting the risk of 28-day death in patients with SIC.

METHODS

In this retrospective cohort study, we extracted SIC patients from the Medical Information Mart for Intensive Care III (MIMIC-III), MIMIC-IV, and eICU-CRD database according to Toshiaki Iba's scale. And the overlapping in the MIMIC-IV was excluded for this study. Afterward, only the MIMIC-III cohort was randomly divided into the training set, and the internal validation set according to the ratio of 7:3, while the MIMIC-IV and eICU-CRD databases were considered the external validation sets. The predictive factors for 28-day mortality of SIC patients were determined using recursive feature elimination combined with tenfold cross-validation (RFECV). Then, we constructed models using ML algorithms. Multiple metrics were used for evaluation of performance of the models, including the area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), accuracy, sensitivity, specificity, negative predictive value, positive predictive value, recall, and F1 score. Finally, Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME) were employed to provide a reasonable interpretation for the prediction results.

RESULTS

A total of 3280, 2798, and 1668 SIC patients were screened from MIMIC-III, MIMIC-IV, and eICU-CRD databases, respectively. Seventeen features were selected to construct ML prediction models. XGBoost had the best performance in predicting the 28-day mortality of SIC patients, with AUC of 0.828, 0.913 and 0.923, the AUPRC of 0.807, 0.796 and 0.921, the accuracy of 0.785, 0.885 and 0.891, the F1 scores were 0.63, 0.69 and 0.70 in MIMIC-III (internal validation set), MIMIC-IV, and eICU-CRD databases. The importance ranking and SHAP analyses showed that initial SOFA score, red blood cell distribution width (RDW), and age were the top three critical features in the XGBoost model.

CONCLUSIONS

We developed an optimal and explainable ML model to predict the risk of 28-day death of SIC patients 28-day death risk. Compared with conventional scoring systems, the XGBoost model performed better. The model established will have the potential to improve the level of clinical practice for SIC patients.

Applying artificial neural network in predicting sepsis mortality in the emergency department based on clinical features and complete blood count parameters

A complete blood count (CBC) is routinely ordered for emergency department (ED) patients with infections. Certain parameters, such as the neutrophil-to-lymphocyte ratio (NLR), might have prognostic value. We aimed to evaluate the prognostic value of the presenting CBC parameters combined with clinical variables in predicting 30-day mortality in adult ED patients with infections using an artificial neural network (ANN). We conducted a retrospective study of ED patients with infections between 17 December 2021 and 16 February 2022. Clinical variables and CBC parameters were collected from patient records, with NLR, monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) calculated. We determined the discriminatory performance using the area under the receiver operating characteristic curve (AUROC) and performed a 70/30 random data split and supervised ANN machine learning. We analyzed 558 patients, of whom 144 (25.8%) had sepsis and 60 (10.8%) died at 30 days. The AUROCs of NLR, MLR, PLR, and their sum were 0.644 (95% CI 0.573-0.716), 0.555 (95% CI 0.482-0.628), 0.606 (95% CI 0.529-0.682), and 0.610 (95% CI 0.534-0.686), respectively. The ANN model based on twelve variables including clinical variables, hemoglobin, red cell distribution width, NLR, and PLR achieved an AUROC of 0.811 in the testing dataset.

Association between red cell distribution width to albumin ratio and acute kidney injury in patients with sepsis: a MIMIC population-based study

PURPOSE

To investigate the association between red cell distribution width (RDW) to albumin (ALB) ratio and acute kidney injury (AKI) in sepsis.

METHODS

This was a retrospective cohort study. Data were collected from the Medical Information Mart for Intensive Care Database IV (MIMIC-IV) from 2008 to 2019. The incidence of AKI was the primary outcome, which was defined based on the improving Global Outcomes (KDIGO). The association of RDW/ALB ratio with AKI in sepsis was assessed by multivariate logistic regression analysis using relative risk (RR) and a 95% confidence interval (CI). Subgroup group analyses were applied according to age, use of ventilation, and use of vasopressor, SAPS II, and SOFA.

RESULTS

Of 1810 sepsis patients involved in this study, 563 (31.10%) sepsis patients developed AKI after ICU admission. The results suggested an increase in RDW/ALB was associated with a rise in the risk of AKI in sepsis (RR 1.09, 95% CI 1.02 to 1.16, P = 0.013).Based on the subgroup analysis, RDW/ALB ratio was significantly associated with the risk of AKI in sepsis patients using the treatment of ventilation (RR: 1.07, 95% CI 1.01 to 1.14, P = 0.041)) and in patients with SAPS II < 43 (RR: 1.16, 95% CI 1.04 to 1.29, P = 0.007).

CONCLUSION

RDW/ALB ratio was independently associated with the risk of AKI in sepsis patients.

Evaluation of red blood cell distribution width, neutrophil-to-lymphocyte ratio, and other hematologic parameters in canine acute pancreatitis

OBJECTIVE

To determine if RBC distribution width (RDW), neutrophil-to-lymphocyte ratio (NLR), and other hematological parameters are associated with increased odds of in-hospital mortality, increased length of hospitalization (LOH), or disease severity as measured by the Canine Acute Pancreatitis Severity (CAPS) score in dogs with acute pancreatitis (AP).

DESIGN

Retrospective, multicenter study from January 2016 to August 2020.

SETTING

Four private emergency and specialty referral centers.

ANIMALS

On initial case search, 118 client-owned dogs were identified with a clinical diagnosis of AP. Out of these cases, 114 dogs met inclusion criteria, defined as sudden onset of ≥2 compatible clinic signs (lethargy, anorexia, vomiting, or abdominal pain), a specific canine pancreatic lipase concentration >400 μg/L, hospital admission, as well as CBC and serum biochemistry run within 48 hours of initial hospitalization. Disease severity was calculated and measured using the CAPS score, in addition to LOH and in-hospital mortality.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Clinical endpoints were in-hospital mortality, LOH, and disease severity, as evaluated by the CAPS score. Overall in-hospital mortality was 36.8%. NLR was significantly associated with survival, with a higher percentage being associated with an increased likelihood of nonsurvival (odds ratio: 1.1, 95% confidence interval: 1.0-1.2; P = 0.006, adjusted P = 0.04). Increased NLR was found to be significantly associated with a longer LOH based on the unadjusted P-value (P = 0.02) but was not statistically significant based on a P-value adjusted for multiple comparisons (P = 0.12). No significant associations were noted when RDW, platelet-to-lymphocyte ratio, WBC count, mean platelet volume, RDW-to-platelet ratio, or RDW-to-total serum calcium ratio was evaluated against outcome measures.

CONCLUSIONS

This study retrospectively evaluated the prognostic utility of several readily available hematological parameters in dogs hospitalized for AP. Dogs with an increased NLR may have a higher risk of in-hospital mortality and increased LOH, although future prospective studies are necessary to confirm these findings.

Interpretable Machine Learning to Optimize Early In-Hospital Mortality Prediction for Elderly Patients with Sepsis: A Discovery Study

Sepsis-related mortality rates are high among elderly patients, especially those in intensive care units (ICUs). Early prediction of the prognosis of sepsis is critical, as prompt and effective treatment can improve prognosis. Researchers have predicted mortality and the development of sepsis using machine learning algorithms; however, few studies specifically focus on elderly patients with sepsis. This paper proposes a viable model for early prediction of in-hospital mortality among elderly patients diagnosed with sepsis. We extracted patient information from the Medical Information Mart for Intensive Care IV database. We employed several machine learning algorithms to predict the in-hospital mortality of elderly ICU patients with sepsis. The performance of the model was evaluated by using the AUROC and F1 score. Furthermore, the SHAP algorithm was used to explain the model, analyze how the individual features affect the model output, and visualize the Shapley value for a single individual. Our study included 18522 elderly patients, with a mortality of 15.4%. After screening, 59 clinical variables were extracted to develop models. Feature importance analysis showed that age, PO2, RDW, SPO2, WBC, and urine output were significantly related to the in-hospital mortality. According to the results of AUROC (0.871 (95% CI: 0.854-0.888)) and F1 score (0.547 (95% CI: 0.539-0.661)) analyses, the extreme gradient boosting (XGBoost) model outperformed the other models (i.e., LGBM, LR, RF, DT, and KNN). Furthermore, SHAP force analysis illustrated how the constructed model visualized the individualized prediction of death. XGBoost machine learning framework gives good in-hospital mortality prediction of elderly patients with sepsis and can maximize prediction model accuracy. The XGBoost model could be an effective tool to assist doctors in identifying high-risk cases of in-hospital mortality among elderly patients with sepsis. This could be used to create a clinical decision support system in the future.

Diagnostic value of RDW for the prediction of mortality in adult sepsis patients: A systematic review and meta-analysis

Background

Red blood cell distribution width (RDW) is a common biomarker of bacterial infections, and it can be easily obtained from a routine blood test. We investigate the diagnostic value of RDW for the prediction of mortality in adult sepsis patients through a review and meta-analysis. We registered this review in PROSPERO (Registration Number: CRD42022357712), and the details of the registration are included in Appendix 1.

Methods

We searched PubMed, Cochrane Library, Springer, and Embase between Jan. 1, 2000, and May 30, 2022, for primary studies about this research. We collected articles that investigated RDW for varying degrees of sepsis patients—those who suffered from sepsis, severe sepsis, or sepsis shock. Studies of healthy people and sepsis of children and neonates were excluded from our research. The definition of study characteristics and data extraction were finished by two independent researchers and discrepancies resolved by consensus. The combined sensitivities and specificities were calculated by meta-analysis using STATA14.0. The sensitivity of the included studies was analyzed by excluding studies that had potential heterogeneity. A summary operating characteristic curve was made to evaluate the diagnostic value for the prediction of mortality in adult sepsis patients. The Fagan test was used to explore likelihood ratios and posttest probabilities. Finally, we investigated the source of heterogeneity using meta-regression.

Results

Twenty-four studies, including 40,763 cases altogether, were included in this analysis. Bivariate analysis indicated a combined sensitivity of 0.81 (95% CI 0.73–0.86) and specificity of 0.65 (95% CI 0.54–0.75). The area under the summary receiver operating characteristic curve was 0.81 (95% CI 0.77–0.84). Substantial heterogeneity resided in the studies (I^{2 =}96.68, 95% CI 95.95–97.4). Meta-regression showed that the reference description, prospective design, and blinded interpretation of the included studies could be responsible for the heterogeneity.

Conclusions

RWD is an available and valuable biomarker for prediction of mortality in adult sepsis patients.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022357712.

Red blood cell distribution width as prognostic factor in sepsis: A new use for a classical parameter

PURPOSE

To evaluate Red blood cell distribution width (RDW) as a sepsis prognostic biomarker.

METHODS

203 septic patients admitted to the ICU. Analysis of RDW dynamics, hospital mortality discrimination ability and the added value when incorporated to the SOFA, LODS, SAPS-II and APACHE-II scores using the AUC-ROC.

RESULTS

Non-survivors presented higher RDW values during the first week after ICU admission (p = 0.048). Only SOFA and RDW were independently associated with mortality when adjusted by Charlson, immunosuppression, nosocomial infection, NEWS2, SAPS-II, septic shock and haemoglobin (p < 0.05). After adjustment, AUC-ROC was 0.827, 0.822, 0.824, 0.834 and 0.812 for each model including admission, 24, 48 and 72-h and 7-days RDW, respectively. When added to the scores, 24-h RDW and admission RDW improved their discrimination ability (SOFA AUC-ROC = 0.772 vs 0.812 SOFA + admission RDW, p = 0.041; LODS AUC-ROC = 0.687 vs 0.710, p = 0.002; SAPS-II AUC-ROC = 0.734 vs 0.785, p = 0.021; APACHE-II AUC-ROC = 0.672 vs 0.755, p = 0.003). Admission RDW with SOFA presented the better discrimination ability for mortality.

CONCLUSION

RDW is an independent prognostic marker of death in septic patients admitted in the ICU that improves SOFA, LODS, APACHE-II and SAPS-II discrimination ability. This parameter could be incorporated to the prognostic scores as a marker of systemic dysfunction and dysregulated inflammatory response.

The Emerging Clinical Significance of the Red Cell Distribution Width as a Biomarker in Chronic Obstructive Pulmonary Disease: A Systematic Review

There is an intense focus on the identification of novel biomarkers of chronic obstructive pulmonary disease (COPD) to enhance clinical decisions in patients with stable disease and acute exacerbations (AECOPD). Though several local (airway) and circulatory inflammatory biomarkers have been proposed, emerging evidence also suggests a potential role for routine haematological parameters, e.g., the red cell distribution width (RDW). We conducted a systematic literature search in PubMed, Web of Science, and Scopus, from inception to April 2022, for articles investigating the diagnostic and prognostic role of the RDW in stable COPD and AECOPD. The risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist. Significant associations between the RDW and the presence and severity of disease, outcomes (mortality, hospital readmission), and other relevant clinical parameters (right heart failure, pulmonary arterial hypertension) were reported in 13 out of 16 studies in stable COPD (low risk of bias in 11 studies), and 17 out of 21 studies of AECOPD (low risk of bias in 11 studies). Pending further research, our systematic review suggests that the RDW might be useful, singly or in combination with other parameters, for the diagnosis and risk stratification of patients with stable COPD and AECOPD (PROSPERO registration number: CRD42022348304).

Enhanced red blood cell distribution width to platelet ratio is a predictor of mortality in patients with sepsis: a propensity score matching analysis based on the MIMIC-IV database

OBJECTIVE

To explore the association between dynamic changes in red blood cell distribution width to platelet count ratio (RPR) during hospitalisation and short-term mortality in patients with sepsis.

DESIGN

A retrospective cohort study using propensity score matching.

SETTING

Intensive care units (ICUs) of Beth Israel Deaconess Medical Center.

PARTICIPANTS

A total of 8731 adult patients with sepsis were included in the study. The patients were identified from the ICU of the Medical Information Mart for Intensive Care database. The observed group included patients who experienced an increase in RPR of more than 30% during the first week of ICU admission, whereas the control group included the rest.

MAIN OUTCOME AND MEASURE

Using propensity score matching, a matched control group was created. The primary outcome was 28-day mortality, and the length of hospital stay and in-hospital mortality were the secondary outcomes.

RESULTS

The difference was evident in 28-day mortality between the two groups (85.8% vs 74.5%, p<0.001, Kaplan-Meier analysis, and HR=1.896, 95% CI=1.659 to 2.168, p<0.001, Cox regression). In the secondary outcomes, there was a significant difference in in-hospital mortality (p<0.001). In addition, the study discovered that the observed groups had a significantly longer hospital stay (p<0.001). Meanwhile, the results of subgroup analyses were consistent with those of the primary analyses.

CONCLUSIONS

In patients with sepsis, a significantly increased RPR is positively associated with the short-term death rate. Continuous RPR monitoring could be a valuable measure for predicting short-term mortality in patients with sepsis.

Association between red blood cell distribution width to albumin ratio and prognosis of patients with sepsis: A retrospective cohort study

Background

Red blood cell distribution width (RDW) to albumin ratio (RAR) is associated with poor prognosis in diabetic comorbidities and cancer. However, the association between RAR and prognosis in patients with sepsis remains unclear, which was investigated in this study.

Methods

We conducted a retrospective cohort study based on the Medical Information Mart for Intensive Care (MIMIC) IV version 2.0 database. The primary outcome of this study was 28-day mortality. Secondary outcomes included 90-day mortality, in-hospital mortality, length of hospital stay, and length of intensive care unit (ICU) stay. Multivariate regression analysis and subgroup analysis were performed to investigate the association between RAR and prognosis in patients with sepsis.

Results

A total of 14,639 participants were included in this study. The mean age of the participants was 65.2 ± 16.3 years and the mean RAR was 5.5 ± 1.9 % /g/dl. For 28-day mortality, after adjusting for covariates, HRs [95% confidence intervals (CIs)] for tertiles 2 (4.4–5.8) and 3 (RAR > 5.8) were 1.33 (1.20, 1.46) and 1.98 (1.79, 2.19), respectively. Similar results were observed for 90-day mortality and in-hospital mortality. According to Kaplan-Meier curve analysis, the higher RAR group had higher 28-day mortality and 90-day mortality.

Conclusion

Our study shows that RAR is significantly associated with poor clinical prognosis in sepsis. The higher the RAR, the higher the 28-day, 90-day, and in-hospital mortality.

A nomogram to predict prolonged stay of obesity patients with sepsis in ICU: Relevancy for predictive, personalized, preventive, and participatory healthcare strategies

Objective

In an era of increasingly expensive intensive care costs, it is essential to evaluate early whether the length of stay (LOS) in the intensive care unit (ICU) of obesity patients with sepsis will be prolonged. On the one hand, it can reduce costs; on the other hand, it can reduce nosocomial infection. Therefore, this study aimed to verify whether ICU prolonged LOS was significantly associated with poor prognosis poor in obesity patients with sepsis and develop a simple prediction model to personalize the risk of ICU prolonged LOS for obesity patients with sepsis.

Method

In total, 14,483 patients from the eICU Collaborative Research Database were randomized to the training set (3,606 patients) and validation set (1,600 patients). The potential predictors of ICU prolonged LOS among various factors were identified using logistic regression analysis. For internal and external validation, a nomogram was developed and performed.

Results

ICU prolonged LOS was defined as the third quartile of ICU LOS or more for all sepsis patients and demonstrated to be significantly associated with the mortality in ICU by logistic regression analysis. When entering the ICU, seven independent risk factors were identified: maximum white blood cell, minimum white blood cell, use of ventilation, Glasgow Coma Scale, minimum albumin, maximum respiratory rate, and minimum red blood cell distribution width. In the internal validation set, the area under the curve was 0.73, while in the external validation set, it was 0.78. The calibration curves showed that this model predicted probability due to actually observed probability. Furthermore, the decision curve analysis and clinical impact curve showed that the nomogram had a high clinical net benefit.

Conclusion

In obesity patients with sepsis, we created a novel nomogram to predict the risk of ICU prolonged LOS. This prediction model is accurate and reliable, and it can assist patients and clinicians in determining prognosis and making clinical decisions.

Laboratory Features of Hospitalised Patients with COVID-19 in Jersey, UK

COVID-19 is an acute respiratory infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To date, more than 550 million cases and 6 million deaths have been reported worldwide. This study investigated the laboratory features in hospitalised patients with COVID-19 and determined risk factors for in-hospital mortality. This retrospective observational study included laboratory results of confirmed cases of hospitalised patients with SARS-CoV-2 infection in Jersey (UK) between March-December 2020 (subject to inclusion criteria), and a control group. Furthermore, COVID-19 patients were split into two sub-groups, based on outcome (non-survivors vs. survivors). Logistic regression was used to determine risk factors for in-hospital mortality. A total of 81 COVID-19 cases and 100 controls were included in this study. In the COVID-19 group, 59.3% of subjects were male, and the overall mortality was 33.3%. The main laboratory changes were the following: 95.1% of patients presented with raised C-reactive protein (p<0.001), 85% showed increased fibrinogen (p<0.001), 70% had prolonged prothrombin time (p=0.014), 51.9% suffered from lymphopenia (p<0.001), 42% had elevated gamma glutamyl transferase (p=0.011) and 35.8% demonstrated raised creatinine concentration (p=0.002). Non-survivors were older than survivors (median age: 82 vs. 74 years, p=0.003) with substantial lymphopenia (p=0.018), high creatinine level (p=0.009), and leukocytosis (p=0.018). Increased in-hospital mortality risk was 6.7-fold in patients presenting with a lymphocyte count <0.85 x10⁹/L, 5.3-fold with red blood cell distribution width >14%, 4.9-fold with white cell count >9.5 x10⁹/L, and 3.3-fold for those presenting with creatinine >100 μmol/L. Age ≥82 years was significantly associated with death, and male gender a risk factor for hospital admission in COVID-19. These results demonstrate that routine haematology and biochemistry tests may allow for risk-stratification of hospitalised patients with COVID-19.

MorbidGCN: prediction of multimorbidity with a graph convolutional network based on integration of population phenotypes and disease network

Exploring multimorbidity relationships among diseases is of great importance for understanding their shared mechanisms, precise diagnosis and treatment. However, the landscape of multimorbidities is still far from complete due to the complex nature of multimorbidity. Although various types of biological data, such as biomolecules and clinical symptoms, have been used to identify multimorbidities, the population phenotype information (e.g. physical activity and diet) remains less explored for multimorbidity. Here, we present a graph convolutional network (GCN) model, named MorbidGCN, for multimorbidity prediction by integrating population phenotypes and disease network. Specifically, MorbidGCN treats the multimorbidity prediction as a missing link prediction problem in the disease network, where a novel feature selection method is embedded to select important phenotypes. Benchmarking results on two large-scale multimorbidity data sets, i.e. the UK Biobank (UKB) and Human Disease Network (HuDiNe) data sets, demonstrate that MorbidGCN outperforms other competitive methods. With MorbidGCN, 9742 and 14 010 novel multimorbidities are identified in the UKB and HuDiNe data sets, respectively. Moreover, we notice that the selected phenotypes that are generally differentially distributed between multimorbidity patients and single-disease patients can help interpret multimorbidities and show potential for prognosis of multimorbidities.

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.

Cardiometabolic Morbidity and Other Prognostic Factors for Mortality in Adult Hospitalized COVID-19 Patients in North Jakarta, Indonesia

Background:

Although there have been several studies investigating prognostic factors for mortality in COVID-19, there have been lack of studies in low- and middle-income countries, including Indonesia. To date, the country has the highest mortality rate among Asian countries.

Objective:

We sought to identify the prognostic factors of mortality in hospitalized patients with COVID-19 in Jakarta.

Methods:

In this retrospective cohort study, we included all adult inpatients (≥18 years old) with confirmed COVID-19 from Koja General Hospital (North Jakarta, Indonesia) who had been hospitalized between March 20^th and July 31^st, 2020. Demographic, clinical, laboratory, and radiology data were extracted from the medical records and compared between survivors and non-survivors. Univariate and multivariate logistic regression analysis were used to explore the prognostic factors associated with in-hospital death.

Results:

Two hundred forty-three patients were included in the study, of whom 32 died. Comorbid of hypertension (OR 3.59; 95% CI 1.12–11.48; p = 0.031), obesity (OR 6.34; 95% CI 1.68–23.98; p = 0.007), immediate need of HFNC and/or IMV (OR 64.93; 95% CI 11.08–380.61; p < 0.001), abnormal RDW (OR 3.68; 95% CI 1.09–12.34; p = 0.035), ALC < 1,000/µL (OR 3.51; 95% CI 1.08–11.44; p = 0.038), D-dimer > 500 ng/mL (OR 9.36; 95% CI 1.53–57.12; p = 0.015) on admission, as well as chloroquine treatment (OR 3.61; 95% CI 1.09–11.99; p = 0.036) were associated with greater risk of overall mortality in COVID-19 patients. The likelihood of mortality increased with increasing number of prognostic factors.

Conclusion:

The potential prognostic factors of hypertension, obesity, immediate need of HFNC and/or IMV, abnormal RDW, ALC < 1,000/µL, D-dimer > 500 ng/mL, and chloroquine treatment could help clinicians to identify COVID-19 patients with poor prognosis at an early stage.

Red Cell Distribution Width Is Independently Associated with Mortality in Sepsis

BACKGROUND

Mortality in sepsis remains high. Studies on small cohorts have shown that red cell distribution width (RDW) is associated with mortality. The aim of this study was to validate these findings in a large multicenter cohort.

METHODS

We conducted this retrospective analysis of the multicenter eICU Collaborative Research Database in 16,423 septic patients. We split the cohort in patients with low (≤15%; n = 7,129) and high (>15%; n = 9,294) RDW. Univariable and multivariable multilevel logistic regressions were used to fit regression models for the binary primary outcome of hospital mortality and the secondary outcome intensive care unit (ICU) mortality with hospital unit as random effect. Optimal cutoffs were calculated using the Youden index.

RESULTS

Patients with high RDW were more often older than 65 years (57% vs. 50%; p < 0.001) and had higher Acute Physiology and Chronic Health Evaluation (APACHE) IV scores (69 vs. 60 pts.; p < 0.001). Both hospital (adjusted odds ratios [aOR] 1.18; 95% CI: 1.16-1.20; p < 0.001) and ICU mortality (aOR 1.16; 95% CI: 1.14-1.18; p < 0.001) were associated with RDW as a continuous variable. Patients with high RDW had a higher hospital mortality (20 vs. 9%; aOR 2.63; 95% CI: 2.38-2.90; p < 0.001). This finding persisted after multivariable adjustment (aOR 2.14; 95% CI: 1.93-2.37; p < 0.001) in a multilevel logistic regression analysis. The optimal RDW cutoff for the prediction of hospital mortality was 16%.

CONCLUSION

We found an association of RDW with mortality in septic patients and propose an optimal cutoff value for risk stratification. In a combined model with lactate, RDW shows equivalent diagnostic performance to Sequential Organ Failure Assessment (SOFA) score and APACHE IV score.

Microfluidic Characterization of Red Blood Cells Microcirculation under Oxidative Stress

Microcirculation is one of the basic functional processes where the main gas exchange between red blood cells (RBCs) and surrounding tissues occurs. It is greatly influenced by the shape and deformability of RBCs, which can be affected by oxidative stress induced by different drugs and diseases leading to anemia. Here we investigated how in vitro microfluidic characterization of RBCs transit velocity in microcapillaries can indicate cells damage and its correlation with clinical hematological analysis. For this purpose, we compared an SU-8 mold with an Si-etched mold for fabrication of PDMS microfluidic devices and quantitatively figured out that oxidative stress induced by tert-Butyl hydroperoxide splits all RBCs into two subpopulations of normal and slow cells according to their transit velocity. Obtained results agree with the hematological analysis showing that such changes in RBCs velocities are due to violations of shape, volume, and increased heterogeneity of the cells. These data show that characterization of RBCs transport in microfluidic devices can directly reveal violations of microcirculation caused by oxidative stress. Therefore, it can be used for characterization of the ability of RBCs to move in microcapillaries, estimating possible side effects of cancer chemotherapy, and predicting the risk of anemia.

Evaluation of hematological parameters related to systemic inflammation in acute and subacute/chronic low back pain

Aim: To compare the hematological parameters associated with systemic inflammation between acute and subacute/chronic nonspecific low back pain and to evaluate their diagnostic roles in relation to chronicity in low back pain. Materials & methods: This retrospective case-control study included 150 participants aged 18-65 years with acute nonspecific low back pain, 150 with subacute/chronic nonspecific low back pain, 150 as the control group. Results: Red cell distribution width was significantly higher in the subacute/chronic pain group compared with the acute pain group (p = 0.003), and had a poor diagnostic value for chronicity (cutoff: 11.95, p = 0.003). There were no significant differences in terms of other parameters (p > 0.05). Conclusion: Red cell distribution width has a poor diagnostic value for chronicity in nonspecific low back pain.

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.

The Red Blood Cell Distribution Width-Albumin Ratio Was a Potential Prognostic Biomarker for Diabetic Ketoacidosis

Background

The red blood cell distribution width (RDW)–albumin ratio (RA) is a new biomarker, which is d-efined as RDW divided by albumin. This study aimed at determining the prognostic values of RA for diabetic ketoacidosis (DKA).

Methods

Data were obtained from Medical Information Mart for Intensive Care Database III V1.4 (MIMIC-III) and the RA calculated. Multivariate Cox regression analysis was performed to determine the correlation between RA and 90-day mortality or 365-day mortality. To further investigate the association with RA and mortality, the patients were divided into two groups. The second outcome was the association between the incidence of DKA-related infections and RA.

Results

For DKA patients in the ICU, RA was significantly correlated with 90-day mortality (HR: 2.1, 95% CI: 1.5, 3.0, p < 0.001) and 365-day mortality (HR: 1.9, 95% CI: 1.5, 2.5, p < 0.001). A high RA was independently correlated with increased 90-day mortality (HR: 7.8, 95% CI: 1.8, 34.0, p for trend <0.001) and 365-day mortality (HR: 5.2, 95% CI: 2.4, 11.3, p for trend <0.001). Moreover, RA was found to be an independent predictor for sepsis and septic shock in patients with DKA (HR: 2.9, 95% CI: 2.0, 4.1, p < 0.001). After adjusting for confounders, the statistical outcome was the same.

Conclusion

A high RA is significantly correlated with increased all-cause mortality of DKA as well as an increased incidence of DKA-related infections. RA is a potential prognostic marker for DKA.

Red Cell Distribution Width as a Prognostic Factor and Its Comparison with Lactate in Patients with Sepsis

Sepsis remains the leading cause of death in critically ill patients. Thus, regular measurement of lactate levels has been proposed in sepsis guidelines. Elevated red cell distribution width (RDW) is associated with mortality risk in patients with sepsis. This study aimed to investigate the association between RDW and the risk of other adverse outcomes in patients with sepsis and to compare the mortality discriminative ability between lactate and RDW levels. This is a single-centered, retrospective, case-control study that included 504 adult patients with sepsis in the emergency department between 1 January 2020 and 31 December 2020. Eligible patients were divided into normal (RDW ≤ 14.5%) and high (RDW > 14.5%) groups. The baseline characteristics and adverse outcomes were recorded and compared. Compared with the normal RDW group, the patients in the high RDW group had a significantly higher rate of ICU admission (48.8% vs. 32.4%, p = 0.03), septic shock (39.2% vs. 23.5%, p < 0.01), and 30-day in-hospital mortality (32.0% vs. 20.7%, p < 0.01). Furthermore, the RDW (area under curve (AUC) = 0.71) had superior mortality discriminative ability compared to lactate (AUC = 0.63) levels (p = 0.02). Clinicians could rely on this simple and rapid parameter for risk stratification to initiate prompt treatment for patients with sepsis.

Red Blood Cell Distribution Width, Disease Severity, and Mortality in Hospitalized Patients with SARS-CoV-2 Infection: A Systematic Review and Meta-Analysis

The identification of biomarkers predicting disease severity and outcomes is the focus of intense research in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 infection). Ideally, such biomarkers should be easily derivable from routine tests. We conducted a systematic review and meta-analysis of the predictive role of the red blood cell distribution width (RDW), a routine hematological test, in patients with SARS-CoV-2 infection. We searched the electronic databases PubMed, Web of Science and Scopus, from January 2020 to November 2020, for studies reporting data on the RDW and coronavirus disease 2019 (COVID-19) severity, defined as severe illness or admission to the intensive care unit (ICU), and mortality. Eleven studies in 4901 COVID-19 patients were selected for the meta-analysis. Pooled results showed that the RDW values were significantly higher in patients with severe disease and non-survivors (standard mean difference, SMD = 0.56, 95% CI 0.31 to 0.81, p < 0.001). Heterogeneity between studies was extreme (I² = 80.6%; p < 0.001). In sensitivity analysis, the effect size was not modified when each study was in turn removed (effect size range, between 0.47 and 0.63). The Begg’s (p = 0.53) and Egger’s tests (p = 0.52) showed no evidence of publication bias. No significant correlations were observed between SMD and age, gender, whole blood count, end point, study geographic area, or design. Our meta-analysis showed that higher RDW values are significantly associated with COVID-19 severity and mortality. This routine parameter might assist with early risk stratification in patients with SARS-CoV-2 infection.

Red Blood Cell Distribution Is a Significant Predictor of Severe Illness in Coronavirus Disease 2019

INTRODUCTION

As red blood cell distribution width (RDW) significantly predicts clinical outcomes in patients with respiratory tract infections and in those with critical illnesses, we performed a critical analysis of the literature to explore the potential prognostic role of this laboratory parameter in coronavirus disease 2019 (COVID-19).

METHODS

An electronic search was conducted in Medline, Scopus and Web of Science, using the keywords "coronavirus disease 2019" OR "COVID-19" AND "red blood cell distribution width" OR "RDW" in all fields, up to the present time, with no language restriction. Studies reporting the value of RDW-CV in CO-VID-19 patients with or without severe illness were included in a pooled analysis.

RESULTS

The pooled analysis included 3 studies, totaling 11,445 COVID-19 patients' samples (2,654 with severe disease; 23.2%). In all investigations RDW-CV was higher in COVID-19 patients with severe illness than in those with mild disease, with differences between 0.30 and 0.70%. The pooled analysis, despite consistent heterogeneity (I2: 88%), revealed that the absolute RDW-CV value was 0.69% higher (95% CI 0.40-0.98%; p < 0.001) in COVID-19 patients with severe illness compared to those with mild disease.

CONCLUSION

These results, along with data published in other studies, support the use of RDW for assessing the risk of unfavorable COVID-19 progression.

Red Blood Cell Distribution Width (RDW) Predicts COVID-19 Severity: A Prospective, Observational Study from the Cincinnati SARS-CoV-2 Emergency Department Cohort

Since previous evidence has demonstrated that red blood cell distribution width (RDW) may be a useful prognostic parameter in many critical illnesses and infectious diseases, we investigated the utility of RDW for monitoring patients with coronavirus disease 2019 (COVID-19). The study population consisted of 49 COVID-19 patients, including 16 (32.6%) with severe illness, 12 (24.5%) with severe acute kidney injury (AKI), and 8 (16.3%) requiring renal replacement therapy (RRT). The predictive value of blood tests, performed during emergency department evaluation, was then addressed. A progressive increase of RDW was observed with advancing COVID-19 severity. The area under the curve (AUC) of RDW was 0.73 for predicting severe illness, 0.80 for severe AKI, and 0.83 for RRT, respectively. In multivariate analysis, elevated RDW was associated with 9-fold and 16-fold increased odds of severe COVID-19 and AKI, respectively. The results of this study suggest that RDW should be part of routine laboratory assessment and monitoring of COVID-19.