Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements

Zhou, Kai; Sun, Yaoting; Li, Lü; Zang, Zelin; Wang, Jing; Li, Jun; Liang, Junbo; Zhang, Fangfei; Zhang, Qiushi; Ge, Weigang; Chen, Hao; Sun, Xindong; Liang, Yue; Wu, Xiaomai; Shen, Bo; Xu, Jiaqin; Zhu, Hongying; Chen, Shiyong; Yang, Hai; Huang, Shigao; Peng, Minfei; Lv, Dongqing; Zhang, Chao; Zhao, Haihong; Hong, Luxiao; Zhou, Zhehan; Chen, Haixiao; Dong, Xuejun; Tu, Chunyu; Li, Minghui; Zhu, Yi; Chen, Baofu; Li, Stan Z.; Guo, Tiannan

doi:10.1016/j.csbj.2021.06.022

Cited by 35 publications

(29 citation statements)

References 34 publications

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“…A total of 10 dysregulated proteins highly correlated with 16 clinical indexes ( Table S2B ) have been previously reported to be associated with COVID-19 severity ( Figure S4 ). 11 , 31 − 36 This result further corroborates these proteins as potential biomarkers for the monitoring of the disease progression.…”

Section: Resultssupporting

confidence: 73%

Potential Use of Serum Proteomics for Monitoring COVID-19 Progression to Complement RT-PCR Detection

Zhang

Cai

et al. 2021

J. Proteome Res.

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RT-PCR is the primary method to diagnose COVID-19 and is also used to monitor the disease course. This approach, however, suffers from false negatives due to RNA instability and poses a high risk to medical practitioners. Here, we investigated the potential of using serum proteomics to predict viral nucleic acid positivity during COVID-19. We analyzed the proteome of 275 inactivated serum samples from 54 out of 144 COVID-19 patients and shortlisted 42 regulated proteins in the severe group and 12 in the non-severe group. Using these regulated proteins and several key clinical indexes, including days after symptoms onset, platelet counts, and magnesium, we developed two machine learning models to predict nucleic acid positivity, with an AUC of 0.94 in severe cases and 0.89 in non-severe cases, respectively. Our data suggest the potential of using a serum protein-based machine learning model to monitor COVID-19 progression, thus complementing swab RT-PCR tests. More efforts are required to promote this approach into clinical practice since mass spectrometry-based protein measurement is not currently widely accessible in clinic.

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Section: Resultssupporting

confidence: 73%

Potential Use of Serum Proteomics for Monitoring COVID-19 Progression to Complement RT-PCR Detection

Zhang

Cai

et al. 2021

J. Proteome Res.

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“…Zhou et al . [35] employed a ML-based model to predict the progression of sickness severity. They used a genetic algorithm (GA) and support vector machine algorithm for feature selection and prediction, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…With an overall accuracy of 0.90, the Neural Network technique performed significantly better in predicting the death rate. Zhou et al [35] employed a ML-based model to predict the progression of sickness severity. They used a genetic algorithm (GA) and support vector machine algorithm for feature selection and prediction, respectively.…”

Section: Page16mentioning

confidence: 99%

Two-Step Machine Learning to Diagnose and Predict Involvement of Lungs in COVID-19 and Pneumonia using CT Radiomics

Khaniabadi

Bouchareb

Dhuhli

et al. 2022

Preprint

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Objective: We aimed to develop a two-step machine learning (ML) based model to diagnose and predict involvement of lungs in COVID-19 and non COVID-19 pneumonia patients using CT chest radiomic features. Methods: Three hundred CT scans (3-classes: 100 COVID-19, 100 pneumonia, and 100 healthy subjects) were enrolled in this study. Diagnostic task included 3-class classification. For severity prediction, two radiologists scored involvement of lungs in COVID-19 and pneumonia scans based on percentage of involvement in all 5 lobes. Datasets were classified into mild (0–25%), moderate (26–50%), and severe (>50%). Whole lungs were segmented utilizing deep learning-based segmentation method. Altogether, 107 features including shape, first-order histogram, second and high order texture features were extracted. For both tasks, datasets were randomly divided into 90% training sets (70% and 30% for training and validation, respectively) and 10% test sets. Pearson correlation coefficient (PCC≥90%) was performed to exclude highly correlated features. Subsequently, different feature selection algorithms (Correlation attribute evaluation, Information gain attribute, Wrapper Subset selection algorithm, Relief method, and Correlation-based feature selection) were assessed. The most pertinent features were finally selected using voting method based on the evaluation of all algorithms. Several ML-based supervised algorithms were utilized, namely Naïve Bays, Support Vector Machine, Bagging, Random Forest, K-nearest neighbors, Decision Tree and Ensemble Meta voting. The synthetic minority oversampling technique (SMOTE) was used to balance the three classes in training sets. The optimal model was first selected based on precision, recall and area-under-curve (AUC) by randomizing the training/validation sets 20 times, followed by testing using the test set. To ensure the repeatability of the results, the entire process was repeated 50 times. Results: Nine pertinent features (2 shape, 1 first-order, and 6 second-order features) were obtained after feature selection for both phases. In diagnostic task, the performance of 3-class classification using Random Forest was 0.909±0.026, 0.907±0.056, 0.902±0.044, 0.939±0.031, and 0.982±0.010 for precision, recall, F1-score, accuracy, and AUC, respectively. The severity prediction task using Random Forest achieved 0.868±0.123 precision, 0.865±0.121 recall, 0.853±0.139 F1-score, 0.934±0.024 accuracy, and 0.969±0.022 AUC. Conclusion: The two-phase ML-based model accurately classified COVID-19 and pneumonia patients using CT radiomics, and adequately predicted severity of lungs involvement. This 2-steps model showed great potential in assessing COVID-19 CT images towards improved management of patients.

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“…Consistent with previously published studies scoring methodology. A CT score was calculated as follows: numbers of lobes involved×1 + patchy/GGO×1 + consolidation×2 + fibrosis×3 [10] .…”

Section: Methodsmentioning

confidence: 99%

The evaluation of the immune status of COVID-19 recovered subjects with persistent abnormal lung CT after one year: A longitudinal cohort study

Chi

Zhou²,

Shen³

et al. 2022

International Immunopharmacology

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Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements

Cited by 35 publications

References 34 publications

Potential Use of Serum Proteomics for Monitoring COVID-19 Progression to Complement RT-PCR Detection

Potential Use of Serum Proteomics for Monitoring COVID-19 Progression to Complement RT-PCR Detection

Two-Step Machine Learning to Diagnose and Predict Involvement of Lungs in COVID-19 and Pneumonia using CT Radiomics

The evaluation of the immune status of COVID-19 recovered subjects with persistent abnormal lung CT after one year: A longitudinal cohort study

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