Red Cell Distribution Width at Admission Predicts the Frequency of Acute Kidney Injury and 28-Day Mortality in Patients With Acute Respiratory Distress Syndrome

Cai, Nan; Jiang, Min; Wu, Chao; He, Fei

doi:10.1097/shk.0000000000001840

Cited by 14 publications

(12 citation statements)

References 30 publications

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“…During organ transplantation, acute blood loss, or toxic shock, ischemia/reperfusion injury has become an important pathogenic mechanism which leads to acute kidney injury [ 18 ]. As the most important excretory organ in the human body, drugs are often excreted through the kidney, and their massive use or even abuse is likely to cause drug-induced acute kidney injury [ 19 ]. Sepsis complicated with acute kidney injury refers to acute renal parenchymal injury occurring to patients with sepsis, and other factors may cause kidney damage like renal ischemia or nephrotoxic substances are excluded.…”

Section: Introductionmentioning

confidence: 99%

Account of Deep Learning-Based Ultrasonic Image Feature in the Diagnosis of Severe Sepsis Complicated with Acute Kidney Injury

Zhijia

2022

Computational and Mathematical Methods in Medicine

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This study was aimed at analyzing the diagnostic value of convolutional neural network models on account of deep learning for severe sepsis complicated with acute kidney injury and providing an effective theoretical reference for the clinical use of ultrasonic image diagnoses. 50 patients with severe sepsis complicated with acute kidney injury and 50 healthy volunteers were selected in this study. They all underwent ultrasound scans. Different deep learning convolutional neural network models dense convolutional network (DenseNet121), Google inception net (GoogLeNet), and Microsoft’s residual network (ResNet) were used for training and diagnoses. Then, the diagnostic results were compared with professional image physicians’ artificial diagnoses. The results showed that accuracy and sensitivity of the three deep learning algorithms were significantly higher than professional image physicians’ artificial diagnoses. Besides, the error rates of the three algorithm models for severe sepsis complicated with acute kidney injury were significantly lower than professional physicians’ artificial diagnoses. The areas under curves (AUCs) of the three algorithms were significantly higher than AUCs of doctors’ diagnosis results. The loss function parameters of DenseNet121 and GoogLeNet were significantly lower than that of ResNet, with the statistically significant difference ( P < 0.05 ). There was no significant difference in training time of ResNet, GoogLeNet, and DenseNet121 algorithms under deep learning, as the convergence was reached after 700 times, 700 times, and 650 times, respectively ( P > 0.05 ). In conclusion, the value of the three algorithms on account of deep learning in the diagnoses of severe sepsis complicated with acute kidney injury was higher than professional physicians’ artificial judgments and had great clinical value for the diagnoses and treatments of the disease.

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Section: Introductionmentioning

confidence: 99%

Account of Deep Learning-Based Ultrasonic Image Feature in the Diagnosis of Severe Sepsis Complicated with Acute Kidney Injury

Zhijia

2022

Computational and Mathematical Methods in Medicine

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“…24 In previous studies, elevated RDW values were found to be related to prognosis in patients with community-acquired pneumonia (CAP), ARDS and COVID-19. [25][26][27] In a meta-analysis, RDW values were found to be significantly higher in dying COVID-19 patients compared to survivors (95% CI=0.63-1.23; I 2 =85.58%). In addition, elevated RDW values were reported to be associated with the disease severity (30-day mortality, duration of hospital stays and the need for vasopressor support) (95% CI=0.47-0.97; I 2 =89.51%).…”

Section: Discussionmentioning

confidence: 99%

The Relationship Between Prognosis and Red Cell Distribution Width (RDW) and RDW-Albumin Ratio (RAR) in Patients with Severe COVID-19 Disease

Ertekin¹,

Acar²

2022

IJGM

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Purpose The present study aimed to investigate the relationship between prognosis and the red cell distribution width (RDW) and the RDW-albumin ratio (RAR) in patients with coronavirus diseases 2019 (COVID-19), since serum albumin and RDW levels may reflect inflammatory conditions. Patients and Methods A total of 289 patients who had been diagnosed with severe COVID-19 in the emergency department were retrospectively analyzed. The RAR levels were calculated by dividing RDW-CV by albumin. Patient groups (survivors, dying patients, those who received mechanical ventilation (MV) support or not, and those who needed vasopressors or not) were compared with regard to RDW-SD, RDW-CV and the RAR levels. Results RDW-SD, RDW-CV and the RAR levels were found to be statistically significantly higher in patients who died, and who received MV and vasopressor support, compared to those who survived and did not receive support (p<0.001 for all). In addition, while the cut-off value of RAR was >5.43, the sensitivity was 91.6%, the specificity was 93.7%, NPV was 93.1% and the AUC was 0.965 in predicting mortality (p<0.001). Logistic regression analysis showed that RDW-SD and RAR were independent risk factors for mortality in patients with severe COVID-19. Conclusion Elevated RDW and RAR levels at the time of admission may independently predict mortality and the need for vasopressor or MV support.

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“…Mechanistically,the progression and overall survival in ARDS is primary determined by the overlapping of excessive systemic in ammatory cascade,alveolar capillary damage, increased permeability and microthrombosis, alveolar epithelial damage, dysregulated coagulation,and reduced or absent of pulmonary surfactant in various degrees (19,48),disturbances of multifarious biological process of synthesis and secretion of in ammatory factors and signal transduction pathways allows for the exploration of potential biomarkers which correlated strongly with risk of death or prognosis of ARDS (49).Considering that PT and PTT as biomaker involved in the pathway of coagulation and brinolysis,we further explore the ROC curves of PT and PTT for predicting the 28-day mortality of patients with ARDS.It turned out that PT and PTT have lager AUCs when compared with PLT,which might also can be a potentially important object in future clinical research to identify individuals with poor prognosis,and a combination of PLT and PT may be attractive in order to provide better diagnostic and prognostic capacity of ARDS.A accumulating body of studies indicated that red blood cell distribution width as a biomarker of oxidative stress and in ammation maintained association with the prognosis of ARDS (50)(51)(52)(53).Our data also obtained the similar results,but a single RDW does not fully re ect the in ammatory status,the composites of RDW and PLT may take it a step further identify the relationship among the in ammatory conditions and prognosis,therefore,prospective investigation is necessary to validate the impact of combination of RDW and PLT on predicting ARDS.…”

Section: Discussionmentioning

confidence: 99%

Platelet count as a prognostic marker for acute respiratory distress syndrome

Wang

Zhang

2023

Preprint

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Background The purpose of the present study was to evaluate the role of platelet count(PLT) in the prognosis of acute respiratory distress syndrome (ARDS) patients. Methods We extracted data from the MIMIC-IV database, patients who diagnosed with ARDS according to criteria from Berlin Definition and had the PLT taken within the first day after intensisve care unit admission were analyzed.ARDS patients were assigned to one of four categories based on platelet counts (PLT ≤ 100×10^9/L,PLT 101–200×10^9/L,PLT 201–300×10^9/L,PLT > 300×10^9/L).The primary outcome was 28-day mortaily.Survival probabilities was anayled by Kaplan-Meier.Further,the association between platelets count and mortality in ARDS patients was assessed by univariate and multivariable cox proportional hazards model. Results Overall, 3207 eligible participants with ARDS were included in the final analysis. From the Kaplan–Meier curves for 28-day mortality of PLT, PLT ≤ 100×10^9/L was associated with higher incidence of mortality(P = 0.001),the same trends were found in the 60-day(P = 0.001) and 90‐day mortality(P = 0.001). In the multivariate model adjusted for the potential factors, the adjusted hazard ratio (aHR) at PLT 101–200×10^9/L group,PLT 201–300×10^9/L,and PLT > 300×10^9/L was 0.522 [95% confidence interval (CI), 0.433–0.613,P = 0.000],0.572(95%CI,0.472–0.692,P = 0.000),and 0.681(95%CI,0.541–0.859,P = 0.000) compared with the reference group (PLT ≤ 100×10^9/L) relatively.In most subgroups,similar associations between PLT ≤ 100×10^9/L group and 28-day mortality were obtained. Conclusion Platelet count ≤ 100×10^9/L was strongly correlated with mortality in ARDS patients.These findings promoted the use of PLT as a prognostic biomarker with high predictive value for ARDS patients.

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Red Cell Distribution Width at Admission Predicts the Frequency of Acute Kidney Injury and 28-Day Mortality in Patients With Acute Respiratory Distress Syndrome

Cited by 14 publications

References 30 publications

Account of Deep Learning-Based Ultrasonic Image Feature in the Diagnosis of Severe Sepsis Complicated with Acute Kidney Injury

Account of Deep Learning-Based Ultrasonic Image Feature in the Diagnosis of Severe Sepsis Complicated with Acute Kidney Injury

The Relationship Between Prognosis and Red Cell Distribution Width (RDW) and RDW-Albumin Ratio (RAR) in Patients with Severe COVID-19 Disease

Platelet count as a prognostic marker for acute respiratory distress syndrome

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