Finding of the factors affecting the severity of COVID-19 based on mathematical models

Qu, Jiahao; Sumali, Brian; Lee, Ho; Terai, Hideki; Ishii, Makoto; Fukunaga, Koichi; Mitsukura, Yasue; Nishimura, Toshihiko

doi:10.1038/s41598-021-03632-x

Cited by 9 publications

(12 citation statements)

References 38 publications

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“…These data are similar to those from previously published studies. 25,26,34 The severity of lung tissue involvement was also associated with the severity of infection and increased mortality, which is consistent with the results of previous studies. In the analyzed cohort, only lactate dehydrogenase levels were significantly associated with the requirement for mechanical ventilation in COVID-19 patients, which is consistent with previous data on the effects of selected laboratory parameters on the severity of infection.…”

supporting

confidence: 91%

“…In the analyzed cohort, only lactate dehydrogenase levels were significantly associated with the requirement for mechanical ventilation in COVID-19 patients, which is consistent with previous data on the effects of selected laboratory parameters on the severity of infection. 25,26 The aforementioned reports regarding the effect of remdesivir treatment on mortality and mechanical ventilation are very ambiguous, while large meta-analyses have often indicated either a complete lack of an effect of remdesivir on the aforementioned parameters or shown discrepancies in the results. 21,33 A significant reduction in mortality was observed in hemato-oncology patients who received remdesivir.…”

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confidence: 99%

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Remdesivir Reduces Mortality in Hemato-Oncology Patients with COVID-19

Aksak‐Wąs¹,

Chober²,

Serwin³

et al. 2022

JIR

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Remdesivir is the first agent with proven clinical efficacy against coronavirus disease 2019 (COVID-19); however, its benefit is associated with early use, and its efficacy has been poorly studied in patients with hemato-oncological diseases, who have an increased risk of a severe course of infection. This study aimed to assess the effects of remdesivir on mortality, mechanical ventilation, and the duration of hospitalization in both the general population and in patients with hemato-oncological diseases. Materials and Methods: Longitudinal data for 4287 patients with confirmed COVID-19 were analyzed, including a subset of 200 individuals with hemato-oncological diseases. In total, 1285 (30.0%) patients received remdesivir, while the remaining patients were treated with other methods. Survival statistics for the 14-and 30-day observation time points were calculated using non-parametric and multivariate Cox models. Results: Mortality for the 14-and 30-day observation time points was notably lower among patients receiving remdesivir (7.2% vs 11.6%, p < 0.001 and 12.7% vs 16.0, p = 0.005, respectively); however, in multivariate models adjusted for age, sex, lung involvement, and lactate dehydrogenase and interleukin-6 levels, the administration of remdesivir did not reduce patient mortality at either the 14-day or 30-day time points. Among patients with haemato-oncological disease, significant survival benefit was observed at 14 and 30 days for patients treated with remdesivir (11.3% vs.16.7% and 24.2% vs 26.1%, respectively; p < 0.001). A favorable effect of remdesivir was also noted for the 14-day time point in multivariate survival analysis (HR:4.03 [95% confidence interval:1.37-11.88]; p = 0.01). Conclusion: Remdesivir significantly reduced the early mortality rate in COVID-19 patients with comorbid hemato-oncological disease, which emphasizes the need to administer this agent to immunosuppressed patients.

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supporting

confidence: 91%

mentioning

confidence: 99%

Remdesivir Reduces Mortality in Hemato-Oncology Patients with COVID-19

Aksak‐Wąs¹,

Chober²,

Serwin³

et al. 2022

JIR

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“…Severe cases of COVID-19 develop acute respiratory distress syndrome and often need mechanical ventilation. So, to limit the increase of severe cases, it seems essential to recognize the factors that promote the development of COVID-19 severity [ 5 ]. Although, the severity of COVID-19 is probably to be multifactorial, accumulating evidence has shown a high risk of poor prognosis and more severe complications among peoples with COVID-19 who have comorbidities [ 6 , 7 ] such as metabolic syndrome, hypertension, cardiovascular disease (CVD), and type 2 diabetes mellites (T2DM) [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Are lipid ratios and triglyceride-glucose index associated with critical care outcomes in COVID-19 patients?

et al. 2022

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Lipid ratios and the triglyceride and glucose index (TyG) could be a simple biochemical marker of insulin resistance (IR). The current study was carried out to examine the correlation between triglyceride to high-density lipoprotein-cholesterol (TG/HDL-C), total cholesterol to HDL-C (TC/HDL-C), low-density lipoprotein-cholesterol to HDL-C ratio (LDL-C/HDL-C), as well as TyG index with the severity and mortality of severe coronavirus disease 2019 (COVID-19). A total of 1228 confirmed COVID-19 patients were included in the current research. Regression models were performed to evaluate the correlation between the lipid index and severity and mortality of COVID-19. The TyG index and TG/HDL-C levels were significantly higher in the severe patients (P<0.05). TG/HDL-C, LDL-C/HDL-C, TC/HDL-C ratios, and TyG index were significantly lower in survivor cases (P<0.05). Multivariate logistic regression analysis demonstrated that predictors of the severity adjusted for age, sex and BMI were TyG index, TG/HDL-C ratio (OR = 1.42 CI:1.10–1.82, OR = 1.06 CI: 1.02–1.11, respectively). This analysis showed that TG/HDL-C, TC/HDL-C, LDL-C/HDL-C ratios, and TyG index statistically are correlated with COVID-19 mortality (OR = 1.12 CI:1.06–1.18, OR = 1.24 CI:1.05–1.48, OR = 1.47 CI:1.19–1.80, OR = 1.52 CI:1.01–2.31, respectively). In summary, the TyG index and lipid ratios such as TC/HDL-C, TG/HDL-C, LDL-C/HDL-C could be used as an early indicator of COVID-19 mortality. Furthermore, the study revealed that TyG index and TG/HDL-C indices are biochemical markers of COVID-19 severe prognosis.

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“…Qu et al [ 50 ] used a logistic regression model to analyze the results of the blood test. The best prognostic indications for severe COVID-19 were lymphocyte count, hemoglobin, and ferritin levels.…”

Section: Methodsmentioning

confidence: 99%

“…This study is built on different neural network models using different levels of hidden layers 1, 2 trained with two different numbers of epochs (10,50). The activation function used in this study is ReLu and we used Keras and the Tensorflow library.…”

Section: Convolutional Neural Network (First Stage Of Ensemble Learning)mentioning

confidence: 99%

An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples

Abayomi‐Alli

Damaševičius

Maskeliūnas

et al. 2022

Sensors

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Current research endeavors in the application of artificial intelligence (AI) methods in the diagnosis of the COVID-19 disease has proven indispensable with very promising results. Despite these promising results, there are still limitations in real-time detection of COVID-19 using reverse transcription polymerase chain reaction (RT-PCR) test data, such as limited datasets, imbalance classes, a high misclassification rate of models, and the need for specialized research in identifying the best features and thus improving prediction rates. This study aims to investigate and apply the ensemble learning approach to develop prediction models for effective detection of COVID-19 using routine laboratory blood test results. Hence, an ensemble machine learning-based COVID-19 detection system is presented, aiming to aid clinicians to diagnose this virus effectively. The experiment was conducted using custom convolutional neural network (CNN) models as a first-stage classifier and 15 supervised machine learning algorithms as a second-stage classifier: K-Nearest Neighbors, Support Vector Machine (Linear and RBF), Naive Bayes, Decision Tree, Random Forest, MultiLayer Perceptron, AdaBoost, ExtraTrees, Logistic Regression, Linear and Quadratic Discriminant Analysis (LDA/QDA), Passive, Ridge, and Stochastic Gradient Descent Classifier. Our findings show that an ensemble learning model based on DNN and ExtraTrees achieved a mean accuracy of 99.28% and area under curve (AUC) of 99.4%, while AdaBoost gave a mean accuracy of 99.28% and AUC of 98.8% on the San Raffaele Hospital dataset, respectively. The comparison of the proposed COVID-19 detection approach with other state-of-the-art approaches using the same dataset shows that the proposed method outperforms several other COVID-19 diagnostics methods.

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Finding of the factors affecting the severity of COVID-19 based on mathematical models

Cited by 9 publications

References 38 publications

Remdesivir Reduces Mortality in Hemato-Oncology Patients with COVID-19

Remdesivir Reduces Mortality in Hemato-Oncology Patients with COVID-19

Are lipid ratios and triglyceride-glucose index associated with critical care outcomes in COVID-19 patients?

An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples

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