Reliable Diagnosis and Prognosis of COVID-19

Mansourian, Marjan; Marateb, Hamid Reza; Cube, Maja von; Khademi, Sadaf; Jordanic, Mislav; Mañanas, Miguel Ángel; Binder, Harald; Wolkewitz, Martin

doi:10.1201/9781003121152-14

Cited by 1 publication

(4 citation statements)

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“…Also, preparing the COVID-19 diagnosis risk chart, which is a valuable tool, is the focus of our feature study. Finally, the RT-PCR and chest CT-scan used as gold standard are not 100% accurate, and the agreement rate (Cohen's Kappa) reported in the study is, thus, a better index than traditional predictive indices (83).…”

Section: Discussionmentioning

confidence: 67%

“…The combination of the PCR and CT-scan results was used as the ground truth in our study. It was shown that the PCR test is not 100% correct to be considered as gold standard (70). Mainly, it was shown to have a false negative rate (FNR) ranging from 0.018 to 0.58, with a median of 0.11 (71), in addition to a sensitivity of 83.3% (15).…”

Section: Discussionmentioning

confidence: 99%

“…In addition to gender and symptom duration, signs and symptoms, blood biomarkers, and comorbidities were selected. Performance indices of the cross-validated confusion matrix for dataset 1 were as follows: sensitivity of 96% [confidence interval, CI, 95%: 94-98], specificity of 95% [90-99], positive predictive Marateb et alComputer-Aided Diagnosis of COVID-19 value (PPV) of 99% [98-100], negative predictive value (NPV) of 82% [76][77][78][79][80][81][82][83][84][85][86][87][88][89], diagnostic odds ratio (DOR) of 496 245], area under the ROC (AUC) of 0.96 [0.94-0.97], Matthews Correlation Coefficient (MCC) of 0.87 [0.85-0.88], accuracy of 96% [94][95][96][97][98], and Cohen's Kappa of 0.86 [0.81-0.91]. The proposed algorithm showed excellent diagnosis accuracy and class-labeling agreement, and fair discriminant power.…”

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confidence: 99%

“…Computer-Aided Diagnosis of COVID-19 value (PPV) of 99% [98-100], negative predictive value (NPV) of 82% [76][77][78][79][80][81][82][83][84][85][86][87][88][89], diagnostic odds ratio (DOR) of 496 245], area under the ROC (AUC) of 0.96 [0.94-0.97], Matthews Correlation Coefficient (MCC) of 0.87 [0.85-0.88], accuracy of 96% [94][95][96][97][98], and Cohen's Kappa of 0.86 [0.81-0.91]. The proposed algorithm showed excellent diagnosis accuracy and class-labeling agreement, and fair discriminant power.…”

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confidence: 99%

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Automatic Classification Between COVID-19 and Non-COVID-19 Pneumonia Using Symptoms, Comorbidities, and Laboratory Findings: The Khorshid COVID Cohort Study

et al. 2021

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Coronavirus disease-2019, also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was a disaster in 2020. Accurate and early diagnosis of coronavirus disease-2019 (COVID-19) is still essential for health policymaking. Reverse transcriptase-polymerase chain reaction (RT-PCR) has been performed as the operational gold standard for COVID-19 diagnosis. We aimed to design and implement a reliable COVID-19 diagnosis method to provide the risk of infection using demographics, symptoms and signs, blood markers, and family history of diseases to have excellent agreement with the results obtained by the RT-PCR and CT-scan. Our study primarily used sample data from a 1-year hospital-based prospective COVID-19 open-cohort, the Khorshid COVID Cohort (KCC) study. A sample of 634 patients with COVID-19 and 118 patients with pneumonia with similar characteristics whose RT-PCR and chest CT scan were negative (as the control group) (dataset 1) was used to design the system and for internal validation. Two other online datasets, namely, some symptoms (dataset 2) and blood tests (dataset 3), were also analyzed. A combination of one-hot encoding, stability feature selection, over-sampling, and an ensemble classifier was used. Ten-fold stratified cross-validation was performed. In addition to gender and symptom duration, signs and symptoms, blood biomarkers, and comorbidities were selected. Performance indices of the cross-validated confusion matrix for dataset 1 were as follows: sensitivity of 96% [confidence interval, CI, 95%: 94–98], specificity of 95% [90–99], positive predictive value (PPV) of 99% [98–100], negative predictive value (NPV) of 82% [76–89], diagnostic odds ratio (DOR) of 496 [198–1,245], area under the ROC (AUC) of 0.96 [0.94–0.97], Matthews Correlation Coefficient (MCC) of 0.87 [0.85–0.88], accuracy of 96% [94–98], and Cohen's Kappa of 0.86 [0.81–0.91]. The proposed algorithm showed excellent diagnosis accuracy and class-labeling agreement, and fair discriminant power. The AUC on the datasets 2 and 3 was 0.97 [0.96–0.98] and 0.92 [0.91–0.94], respectively. The most important feature was white blood cell count, shortness of breath, and C-reactive protein for datasets 1, 2, and 3, respectively. The proposed algorithm is, thus, a promising COVID-19 diagnosis method, which could be an amendment to simple blood tests and screening of symptoms. However, the RT-PCR and chest CT-scan, performed as the gold standard, are not 100% accurate.

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Section: Discussionmentioning

confidence: 67%