Red Blood Cell Distribution Width in Hospitalized COVID-19 Patients

Ramachandran, Preethi; Gajendran, Mahesh; Perisetti, Abhilash; Elkholy, Karim O; Chakraborti, Abhishek; Lippi, Giuseppe; Goyal, Hemant

doi:10.3389/fmed.2021.582403

Cited by 18 publications

(15 citation statements)

References 29 publications

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“…The extracted probabilistic relationships inform evidence-based knowledge on the course of COVID-19 pneumonia with inpatient treatment. Some of these relationships matched with the results of other researchers [ 38 , 39 , 40 , 41 , 42 , 43 , 44 ]. However, other extracted results are new, such as the influence of monocytes from the third time interval (10–17 days) to lethality.…”

Section: Discussionsupporting

confidence: 87%

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

Derevitskii

Mramorov

Usoltsev

et al. 2022

JPM

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The primary goal of this paper is to develop an approach for predicting important clinical indicators, which can be used to improve treatment. Using mathematical predictive modeling algorithms, we examined the course of COVID-19-based pneumonia (CP) with inpatient treatment. Algorithms used include dynamic and ordinary Bayesian networks (OBN and DBN), popular ML algorithms, the state-of-the-art auto ML approach and our new hybrid method based on DBN and auto ML approaches. Predictive targets include treatment outcomes, length of stay, dynamics of disease severity indicators, and facts of prescribed drugs for different time intervals of observation. Models are validated using expert knowledge, current clinical recommendations, preceding research and classic predictive metrics. The characteristics of the best models are as follows: MAE of 3.6 days of predicting LOS (DBN plus FEDOT auto ML framework), 0.87 accuracy of predicting treatment outcome (OBN); 0.98 F1 score for predicting facts of prescribed drug (DBN). Moreover, the advantage of the proposed approach is Bayesian network-based interpretability, which is very important in the medical field. After the validation of other CP datasets for other hospitals, the proposed models can be used as part of the decision support systems for improving COVID-19-based pneumonia treatment. Another important finding is the significant differences between COVID-19 and non-COVID-19 pneumonia.

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Section: Discussionsupporting

confidence: 87%

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

Derevitskii

Mramorov

Usoltsev

et al. 2022

JPM

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“…Interestingly, RDW increased at the end of the study period, indicating a more diverse RBC population. Altered RDW in COVID-19 was already observed by other groups, associating elevated RDW with all-cause mortality [ 37 ] and RDW during hospitalization with a significantly increased risk of mortality and septic shock [ 8 , 38 ]. An increased RBC turnover rate by SARS-CoV-2 with accelerated lysis of old RBCs and synthesis of new cells would explain the observed reversed hemorheological alterations at T30 when the initially affected cells are gradually replaced.…”

Section: Discussionmentioning

confidence: 80%

SARS-CoV-2 Altered Hemorheological and Hematological Parameters during One-Month Observation Period in Critically Ill COVID-19 Patients

Bizjak

John

Matits

et al. 2022

IJMS

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Hematological and hemorheological parameters are known to be altered in COVID-19; however, the value of combined monitoring in order to deduce disease severity is only scarcely examined. A total of 44 acute SARS-CoV-2-infected patients (aCOV) and 44 age-matched healthy controls (Con) were included. Blood of aCOV was sampled at admission (T0), and at day 2 (T2), day 5 (T5), day 10 (T10), and day 30 (T30) while blood of Con was only sampled once. Inter- and intra-group differences were calculated for hematological and hemorheological parameters. Except for mean cellular volume and mean cellular hemoglobin, all blood cell parameters were significantly different between aCOV and Con. During the acute disease state (T0–T5), hematological and hemorheological parameters were highly altered in aCOV; in particular, anemic conditions and increased immune cell response/inflammation, oxidative/nitrosative stress, decreased deformability, as well as increased aggregation, were observed. During treatment and convalescence until T30, almost all abnormal values of aCOV improved towards Con values. During the acute state of the COVID-19 disease, the hematological, as well as the hemorheological system, show fast and potentially pathological changes that might contribute to the progression of the disease, but changes appear to be largely reversible after four weeks. Measuring RBC deformability and aggregation, as well as oxidative stress induction, may be helpful in monitoring critically ill COVID-19 patients.

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“…In recent years, a number of diagnostic and prognostic markers, both alone and in combination, have been tested on individuals diagnosed with AP [16][17][18]. After elevated RDW was established as a predictor of poor outcomes in chronic heart failure in 2007, multiple studies have shown that it is signifcantly related to short-term mortality outcomes in a range of infammatory disorders [19,20]. Additionally, Jin [21] and Zhang's [22] studies demonstrated a strong correlation between RDW and acute pancreatitis mortality.…”

Section: Discussionmentioning

confidence: 99%

Development of a Novel Nomogram Incorporating Red Blood Cell Distribution Width-Albumin Ratio for the Prediction of 30-day Mortality in Acute Pancreatitis Patients

Pan

Lei

et al. 2022

Emergency Medicine International

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Purpose. The available nomograms used to predict acute pancreatitis (AP) are not comprehensive. We sought to investigate the effect of red blood cell distribution width (RDW)-albumin ratio (RA) on prognosis of patients with AP and develop a new nomogram to identify AP patients at high risk for mortality. Methods. We used data from the Medical Information Mart for Intensive Care IV version 2.0 (MIMIC-IV v2.0). A total of 487 patients with acute pancreatitis were included. Patients enrolled in the study were randomly assigned to the training set and validation set at a 7 : 3 ratio. According to the 30-day mortality rate, the data were divided into a survival group and a death group. Multivariate logistic regression was used to establish a prognostic nomogram for predicting the 30-day mortality in AP patients. The area under the receiver operating characteristic curve (AUC), calibration curve, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), and a decision curve analysis (DCA) are used to verify the overall performance of the model. Results. Among 487 patients, 54 patients died (11.1%). 338 patients were assigned to the training cohort and 149 were assigned to the validation cohort. The multivariate analysis results showed that RA, age, heart rate, temperature, AST/ALT, BUN, hemoglobin, potassium, and bilirubin were independent risk factors. The prediction performance of the newly established nomogram was better than those of other common scoring systems (including SOFA, OASIS, and APSIII). The nomogram suggests that RA (OR = 1.706, 95% CI: 1.367–2.185) is the most significant laboratory test indicator influencing prognosis. Conclusion. The new nomogram incorporating RA performed well in predicting AP short-term mortality. A prospective study with a larger sample is needed to validate our findings.

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Red Blood Cell Distribution Width in Hospitalized COVID-19 Patients

Cited by 18 publications

References 29 publications

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

SARS-CoV-2 Altered Hemorheological and Hematological Parameters during One-Month Observation Period in Critically Ill COVID-19 Patients

Development of a Novel Nomogram Incorporating Red Blood Cell Distribution Width-Albumin Ratio for the Prediction of 30-day Mortality in Acute Pancreatitis Patients

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