Dataset Information

Alkaline phosphatase and score of HRCT as indicators for predicting the severity of COVID-19.

ABSTRACT:

Background

The clinical symptoms, blood laboratory data, O2 saturation and high-resolution computed tomography (HRCT) findings are critical factors in diagnosis of COVID-19 infection.

Methods

In this study, 105 hospitalized patients suspected of having COVID-19 were evaluated. Finally, the laboratory and HRCT and related factors data of 83 confirmed cases by HRCT and RT-PCR were analyzed. To compare the median of quantitative variables in the two groups, the Mann-Whitney U test was used. Also, to determine the factors associated with the positiveness of the HRCT result, a univariate logistic model was fitted. Moreover, receiver operating characteristic (ROC) curves were constructed to test the ability of the final model to predict the positiveness of HRCT result.

Results

61.40% of the patients had a comorbidity disease. 89.20% had fever, 92.00% cough, 91.40% dyspnea. Abnormal CRP was seen in 77.80% of the patients, followed by 66.70% lymphopenia, and 60.30% neutrophilia. Also, ALP (abnormal vs. normal) and score of HRCT assessment variables had a significant effect on the positiveness of HRCT findings. 87.95% had abnormal HRCT with 41% bilateral multi lobar patchy ground glass opacity (GGO). Moreover, there was a statistically significant association between the level of O2 saturation and HRCT results.

Conclusion

Our findings showed that male patients with middle age and comorbidity disease were more susceptible to the COVID-19 infection. Additionally, clinical features, blood laboratory findings, O2 saturation and HRCT findings are critical factors in the prognosis of COVID-19 infection.

SUBMITTER: Sharifpour A

PROVIDER: S-EPMC8222983 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCoronavirus disease 2019 (COVID-19) is still ongoing spreading globally, machine learning techniques were used in disease diagnosis and to predict treatment outcomes, which showed favorable performance. The present study aims to predict COVID-19 severity at admission by different machine learning techniques including random forest (RF), support vector machine (SVM), and logistic regression (LR). Feature importance to COVID-19 severity were further identified.MethodsA retrospective design was adopted in the JinYinTan Hospital from January 26 to March 28, 2020, eighty-six demographic, clinical, and laboratory features were selected with LassoCV method, Spearman's rank correlation, experts' opinions, and literature evaluation. RF, SVM, and LR were performed to predict severe COVID-19, the performance of the models was compared by the area under curve (AUC). Additionally, feature importance to COVID-19 severity were analyzed by the best performance model.ResultsA total of 287 patients were enrolled with 36.6% severe cases and 63.4% non-severe cases. The median age was 60.0 years (interquartile range: 49.0-68.0 years). Three models were established using 23 features including 1 clinical, 1 chest computed tomography (CT) and 21 laboratory features. Among three models, RF yielded better overall performance with the highest AUC of 0.970 than SVM of 0.948 and LR of 0.928, RF also achieved a favorable sensitivity of 96.7%, specificity of 69.5%, and accuracy of 84.5%. SVM had sensitivity of 93.9%, specificity of 79.0%, and accuracy of 88.5%. LR also achieved a favorable sensitivity of 92.3%, specificity of 72.3%, and accuracy of 85.2%. Additionally, chest-CT had highest importance to illness severity, and the following features were neutrophil to lymphocyte ratio, lactate dehydrogenase, and D-dimer, respectively.ConclusionsOur results indicated that RF could be a useful predictive tool to identify patients with severe COVID-19, which may facilitate effective care and further optimize resources.