Prognostic accuracy of visual lung damage computed tomography score for mortality prediction in patients with COVID-19 pneumonia: a systematic review and meta-analysis

Zakariaee, Seyed Salman; Naderi, Negar; Rezaee, Danial

doi:10.1186/s43055-022-00741-z

Cited by 6 publications

(11 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A recent meta-analysis study showed that CT-SS index with and without an optimal cutoff was positively associated with mortality of COVID-19 patients (OR 7.124; 95% CI 5.307–9.563 and OR 1.244; 95% CI 1.157–1.337, respectively) [ 17 ]. Sensitivity, specificity, and AUC of this predictive parameter were 0.67 (95%CI: 0.59–0.75), 0.79 (95%CI: 0.74–0.84), and 0.8248, respectively [ 16 ]. Although the association between CT-SS and mortality of COVID-19 patients was reported [ 16 , 17 ], its prognosis significance in combination with other prognostic parameters was not evaluated yet.…”

Section: Discussionmentioning

confidence: 99%

“…Sensitivity, specificity, and AUC of this predictive parameter were 0.67 (95%CI: 0.59–0.75), 0.79 (95%CI: 0.74–0.84), and 0.8248, respectively [ 16 ]. Although the association between CT-SS and mortality of COVID-19 patients was reported [ 16 , 17 ], its prognosis significance in combination with other prognostic parameters was not evaluated yet. Therefore, in this study, kNN, MLP, SVM, and J48 decision tree algorithms were developed based on the most relevant features in determining the risk of COVID-19 mortality.…”

Section: Discussionmentioning

confidence: 99%

“…These studies mainly used demographics, risk factors, clinical manifestations, and laboratory results [ 12 – 15 ]. In recent meta-analysis studies, it was shown that chest computed tomography severity score (CT-SS) is an appropriate prognostic factor for mortality prediction in COVID-19 patients [ 16 , 17 ]. Therefore, it might improve the prognostic performances of the ML algorithms for predicting clinical outcomes of patients with COVID-19 pneumonia.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prognostic significance of chest CT severity score in mortality prediction of COVID-19 patients, a machine learning study

Zakariaee

Abdi

Naderi

et al. 2023

Egypt J Radiol Nucl Med

Self Cite

View full text Add to dashboard Cite

Background The high mortality rate of COVID-19 makes it necessary to seek early identification of high-risk patients with poor prognoses. Although the association between CT-SS and mortality of COVID-19 patients was reported, its prognosis significance in combination with other prognostic parameters was not evaluated yet. Methods This retrospective single-center study reviewed a total of 6854 suspected patients referred to Imam Khomeini hospital, Ilam city, west of Iran, from February 9, 2020 to December 20, 2020. The prognostic performances of k-Nearest Neighbors (kNN), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and J48 decision tree algorithms were evaluated based on the most important and relevant predictors. The metrics derived from the confusion matrix were used to determine the performance of the ML models. Results After applying exclusion criteria, 815 hospitalized cases were entered into the study. Of these, 447(54.85%) were male and the mean (± SD) age of participants was 57.22(± 16.76) years. The results showed that the performances of the ML algorithms were improved when they are fed by the dataset with CT-SS data. The kNN model with an accuracy of 94.1%, sensitivity of 100. 0%, precision of 89.5%, specificity of 88.3%, and AUC around 97.2% had the best performance among the other three ML techniques. Conclusions The integration of CT-SS data with demographics, risk factors, clinical manifestations, and laboratory parameters improved the prognostic performances of the ML algorithms. An ML model with a comprehensive collection of predictors could identify high-risk patients more efficiently and lead to the optimal use of hospital resources.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prognostic significance of chest CT severity score in mortality prediction of COVID-19 patients, a machine learning study

Zakariaee

Abdi

Naderi

et al. 2023

Egypt J Radiol Nucl Med

Self Cite

View full text Add to dashboard Cite

show abstract

“…In another study based on similar features, AUC values ranging between 0.84 and 0.89 were obtained with the XGBoost model, which is an advanced version of the GB model [ 21 ]. Zakariaee et al added CT parenchyma score to the dataset consisting of demographics, clinical data, comorbid diseases, and laboratory results [ 22 ]. In the study comparing kNN, multilayer perceptron, SVM, and J48 decision tree algorithms, an AUC of 0.97 was obtained with the kNN model, and it was observed that the addition of CT parenchyma score improved model performance.…”

Section: Discussionmentioning

confidence: 99%

Predicting COVID-19 Outcomes: Machine Learning Predictions Across Diverse Datasets

Panç,

Hürsoy,

Başaran

et al. 2023

Cureus

View full text Add to dashboard Cite

BackgroundThe COVID-19 infection has spread rapidly since its emergence and has affected a large part of the global population. With the increasing number of cases, researchers are trying to predict the prognosis of patients by using different data with artificial intelligence methods such as machine learning (ML). In this study, we aimed to predict mortality risk in COVID-19 patients using ML algorithms with different datasets. MethodologyIn this retrospective study, we evaluated the fever, oxygen saturation, laboratory results, thorax computed tomography (CT) findings, and comorbid diseases at admission to the hospital of 404 patients whose diagnosis was confirmed by the reverse transcription polymerase chain reaction test. Different datasets were created by combining the data. The Synthetic Minority Oversampling Technique was used to reduce the imbalance in the dataset. K-nearest neighbors, support vector machine, stochastic gradient descent, random forest, neural network, naive Bayes, logistic regression, gradient boosting, XGBoost, and AdaBoost models were used to create the ML algorithm, and the accuracy rates of mortality prediction were compared. ResultsWhen the dataset was created with CT parenchyma score, pulmonary artery and inferior vena cava diameters, and laboratory results, mortality was predicted with an accuracy of 98.4% with the gradient boosting model. ConclusionsThe study demonstrates that patient prognosis can be accurately predicted using simple measurements from thorax CT scans and laboratory findings.

show abstract

“…CT severity score (CT-SS) is determined according to the extent of lung involvement on the CT images and is an appropriate prognostic factor for mortality prediction in patients with COVID-19 pneumonia. 8 In Cao Y et al study 9 , it was reported that deceased patients had higher CT-SSs than discharged patients (20.9 ± 3.0 vs. 15.6 ± 5.0, p < 0.001). Similar results were also observed in several countries.…”

mentioning

confidence: 93%

Influence of threshold selection strategy on the prognostic accuracy of chest CT severity score for mortality prediction of COVID-19 patients

Zakariaee¹,

Abdi²

2022

Heart & Lung

Self Cite

View full text Add to dashboard Cite

Prognostic accuracy of visual lung damage computed tomography score for mortality prediction in patients with COVID-19 pneumonia: a systematic review and meta-analysis

Cited by 6 publications

References 41 publications

Prognostic significance of chest CT severity score in mortality prediction of COVID-19 patients, a machine learning study

Prognostic significance of chest CT severity score in mortality prediction of COVID-19 patients, a machine learning study

Predicting COVID-19 Outcomes: Machine Learning Predictions Across Diverse Datasets

Influence of threshold selection strategy on the prognostic accuracy of chest CT severity score for mortality prediction of COVID-19 patients

Contact Info

Product

Resources

About