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Table 1 Recent studies on predicting the severity of Covid-19 patients

From: A novel reliability-based regression model to analyze and forecast the severity of COVID-19 patients

Author/[Ref.]

Scope

Attributes

Methods

Performance

Size

Country

Zhang et al. [48]

Severity of COVID-19

Clinical and laboratory variables

Univariable and multivariable logistic regression models

AUC=0.906

80

China

Hajiahmadi et al. [16]

ICU and death

CT severity score

Logistic regression model

AUC=0.764

192

Iran

Homayounieh et al. [18]

ICU and death

Interpretation of radiologists, clinical variables, lung radiomics

Multiple logistic regression model

AUC =0.84 (for ICU admission)

315

Iran

Huang et al. [19]

Severe cases

Clinical and laboratory data

Single-factor and multivariate logistic regression

AUC = 0.985 (95% CI 0.968–1.00)

125

China

Zhou et al. [52]

Severe cases

clinical, laboratory, and CT data

Multivariable logistic regression

AUC =0.952

134

China

Xiao et al. [44]

Severe illness

Demographic, clinical, laboratory, and radiological data

Univariable and multivariable logistic regression models

AUC= 0.861 (95% CI 0.811–0.902)

243

China

Wei et al. [10]

Common and severe patients

Clinical and CT data

Multivariate logistic regression

AUC=0.95

81

China

Dong et al. [12]

Survival

Clinical and laboratory findungs

Multivariable Cox regression model

AUC= 0.922 (14 days) AUC= 0.881 (21 days)

628

China

Bai et al. [5]

Severity of disease

Clinical, laboratory, and CT data

Logistic regression model, LDA, SVM, MLP and LSTM

AUC=0.954

133

China

Al-Najjar and Al-Rousan [2]

Recovered and death cases

Sex, birth year, country, region, group, infection reason, and confirmed date on the outcome

Neural network

Accuracy=0.938 Accuracy=0.995

1308

South Korea

Li et al. [27]

Severe cases

CT scan data and clinical biochemical attributes

Machine-learning models

AUC =0.93

46

China

Matos et al. [29]

Mechanical ventilation, death

CT scan and clinical attributes

GLM, PBR, CIT, and SVL

AUC =0.92

106

Italy

Ning et al. [31]

Negative, mild, and severe cases

CT images and clinical features

CNN, DNNs, and PLR

AUC = 0.944 (negative) AUC = 0.860 (mild) AUC = 0.884 (severe)

1521

China

Zhou et al. [51]

Severe cases

Clinical factors

GA and SVM

Accuracy: over 0.94 Accuracy= 0.80

144 25

China

Yan et al. [46]

Survival for severe cases

Clinical data

XGBoost algorithm

Accuracy=0.93

375

China

Shi et al. [37]

Severe cases

Clinical and radiological findings

LASSO logistic regression

AUC= 0.890

196

China

Bi et al. [6]

Severe illness

Fibrinogen-to-albumin ratio (FAR) and platelet count (PLT)

Multivariate cox analysis

AUC=0.754

113

China

Zhou et al. [53]

Severe cases

Body temperature, cough, dyspnea, hypertension, cardiovascular disease, chronic liver disease, and chronic kidney disease

Multivariable logistic regression

AUC= 0.862 (95% CI 0.801–0.925)

366

China

Cheng et al. [9]

ICU transfer

Signs, nursing assessments, laboratory features and electrocardiograms

Random forest

AUC= 0.799 (95% CI 0.752–0.846)

1987

USA

McRae et al. [30]

Death

CRP, NT-proBNP, MYO, D-dimer, PCT, CK-MB, cTnI

Logistic regression model

AUC= 0.94 (95% CI 0.89–0.99)

160

China