Impact of Red Cell and Platelet Distribution Width in Patients of Medical Intensive Care Unit

Dubey, Aditya; Kumar, Sunil; Acharya, Sourya; Wanjari, Anil; Bawankule, Shilpa; Agrawal, Sachin; Shukla, Ashlesha

doi:10.1055/s-0041-1730883

Cited by 4 publications

(6 citation statements)

References 32 publications

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“…PDW is utilized to depict the distribution of PLT volume, and when PLT is excessively consumed, the bone marrow produces abundant immature PLT that is larger than mature PLT. PDW is also significantly associated with sepsis and other severe illnesses, which is closely linked to poor COVID-19 outcomes and death (31,32). In this study, the severe group showed a larger PDW, with a mean of 17.35 versus 16.88, which was significantly different from the non-severe group (p =0.001).…”

Section: Discussionmentioning

confidence: 55%

A predictive model for disease severity among COVID-19 elderly patients based on IgG subtypes and machine learning

Zhuang,

Qi,

Yao

et al. 2023

Front. Immunol.

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ObjectiveDue to the increased likelihood of progression of severe pneumonia, the mortality rate of the elderly infected with coronavirus disease 2019 (COVID-19) is high. However, there is a lack of models based on immunoglobulin G (IgG) subtypes to forecast the severity of COVID-19 in elderly individuals. The objective of this study was to create and verify a new algorithm for distinguishing elderly individuals with severe COVID-19.MethodsIn this study, laboratory data were gathered from 103 individuals who had confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using a retrospective analysis. These individuals were split into training (80%) and testing cohort (20%) by using random allocation. Furthermore, 22 COVID-19 elderly patients from the other two centers were divided into an external validation cohort. Differential indicators were analyzed through univariate analysis, and variable selection was performed using least absolute shrinkage and selection operator (LASSO) regression. The severity of elderly patients with COVID-19 was predicted using a combination of five machine learning algorithms. Area under the curve (AUC) was utilized to evaluate the performance of these models. Calibration curves, decision curves analysis (DCA), and Shapley additive explanations (SHAP) plots were utilized to interpret and evaluate the model.ResultsThe logistic regression model was chosen as the best machine learning model with four principal variables that could predict the probability of COVID-19 severity. In the training cohort, the model achieved an AUC of 0.889, while in the testing cohort, it obtained an AUC of 0.824. The calibration curve demonstrated excellent consistency between actual and predicted probabilities. According to the DCA curve, it was evident that the model provided significant clinical advantages. Moreover, the model performed effectively in an external validation group (AUC=0.74).ConclusionThe present study developed a model that can distinguish between severe and non-severe patients of COVID-19 in the elderly, which might assist clinical doctors in evaluating the severity of COVID-19 and reducing the bad outcomes of elderly patients.

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Section: Discussionmentioning

confidence: 55%

A predictive model for disease severity among COVID-19 elderly patients based on IgG subtypes and machine learning

Zhuang,

Qi,

Yao

et al. 2023

Front. Immunol.

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“…For the mortality time, the longest observation time published was 90 days in just one study ( 30 ), yet the shortest observation time published was 1 day ( 35 ). Eight studies ( 19 , 21 , 23 , 28 , 31 – 33 , 37 ) did not provide a definite mortality time and just defined it as “during ICU hospitalization” or “during hospitalization.” The characteristics of the included studies are shown in Table 1 .…”

Section: Resultsmentioning

confidence: 99%

“…According to which of the included studies are using the Sepsis-2 definition and which the Sepsis-3 definition, we also made a sensitivity analysis of this research, the aim of which was to investigate the stability of the results. We did sensitivity analysis using “METANINF.” Nine studies ( 13 , 19 , 22 , 25 , 28 , 29 , 31 , 35 , 36 ) that were defined as Sepsis-s and had potential influence on the results were excluded after removing the above four studies that had significantly impacted on sensitivity. The rest of the included studies were distributed between upper and lower 95% confidence intervals with the estimation line as the center.…”

Section: Resultsmentioning

confidence: 99%

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

Liao

Cao

et al. 2022

Front. Immunol.

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BackgroundRed 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.MethodsWe 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.ResultsTwenty-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 (I2 =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.ConclusionsRWD is an available and valuable biomarker for prediction of mortality in adult sepsis patients.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022357712.

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“…A study by Jawaharani et al has shown that music therapy has a better outcome in critically ill patients with sepsis [ 11 ]. One study studied red cell distribution width and PDW as significant indicators for the period of stay in the MICU, the requirement for mechanical ventilation, and mortality [ 10 ]. There are few studies conducted in India that have assessed the role of platelet indices in sepsis [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…Various studies have evaluated different scoring systems as well as music therapy regarding the outcomes of critically ill patients admitted to the medical intensive care unit (MICU) [7][8][9][10]. A study by Jawaharani et al has shown that music therapy has a better outcome in critically ill patients with sepsis [11].…”

Section: Introductionmentioning

confidence: 99%

Utility of Platelet Indices as Prognostic Markers of Sepsis: A Medical Intensive Care Unit-Based Cross-Sectional Study at a Rural Setup

Gupta,

Kumar,

Acharya

et al. 2024

Cureus

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Introduction Even after the breakthrough advancements in the management and prognostic scoring of sepsis, it remains an important cause of morbidity and mortality encountered in intensive care units (ICUs) throughout the globe. This study highlights the utility of platelet indices as prognostic markers of sepsis. Methods In the present prospective cross-sectional study, a total of 177 patients with sepsis were enrolled using the Sepsis-3 criteria. The platelet indices were then linked to severity using the Acute Physiology and Chronic Health Evaluation (APACHE) II score. The correlation of platelet indices to morbidity in terms of the length of ICU stay, need for a mechanical ventilator, types of infection, and mortality was also assessed. Results The results showed that mean platelet volume (p = 0.004) and platelet distribution width (PDW; p = 0.009) were positively correlated with the severity of sepsis. Among all the parameters, plateletcrit (%) was the best predictor of the need for an invasive mechanical ventilator at a cutoff point of ≤0.22 with a 60.90% chance of correctly predicting the need for an invasive mechanical ventilator, as was mortality at a cutoff point of ≤0.22 with a 67.30% chance of correct prediction. Among the platelet indices, only PDW showed a significant association with growth in culture because patients with growth had significantly higher PDW as compared to those who did not have growth (22.4 ± 4.47 vs 20.81 ± 4.29, p = 0.011). Conclusion The difference between the survivors and non-survivor groups was statistically significant for platelet indices, making them easily available, cost-effective, and useful prognostic markers for patients in septic shock. This will help in easy understanding and preventing its morbid complications, even at the primary care physician level.

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Impact of Red Cell and Platelet Distribution Width in Patients of Medical Intensive Care Unit

Cited by 4 publications

References 32 publications

A predictive model for disease severity among COVID-19 elderly patients based on IgG subtypes and machine learning

A predictive model for disease severity among COVID-19 elderly patients based on IgG subtypes and machine learning

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

Utility of Platelet Indices as Prognostic Markers of Sepsis: A Medical Intensive Care Unit-Based Cross-Sectional Study at a Rural Setup

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