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Published on: December 19, 2020
Identification and validation of prognostic factors in patients with COVID-19: A retrospective study based on
Sheng Zhang1, Sisi Huang1, Jiao Liu1
1Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin 2nd Road, Shanghai 200025, China.
Insights
Artificial intelligence models accurately predicted COVID-19 mortality using key prognostic factors. These factors include illness severity, age, and various clinical markers, offering valuable insights for patient outcomes.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Medicine
- Epidemiology
Background:
- The COVID-19 pandemic presents a significant global health challenge with high mortality rates.
- Traditional analytical methods have identified risk factors for COVID-19 mortality, but the application of artificial intelligence (AI) remains less explored.
- This study addresses the need for advanced analytical approaches to understand COVID-19 prognosis.
Purpose of the Study:
- To investigate prognostic factors for mortality in COVID-19 patients using artificial intelligence (AI) methods.
- To compare the predictive performance of AI models with traditional regression techniques.
- To identify key clinical and demographic indicators associated with adverse outcomes in COVID-19.
Main Methods:
- A cohort of 1145 COVID-19 patients admitted to Wuhan Infectious Diseases Hospital was analyzed.
- Data were randomly divided into training (60%) and testing (40%) sets.
- Least Absolute Shrinkage and Selection Operator (LASSO) regression and LASSO-based Artificial Neural Network (ANN) models were employed to identify mortality predictors, with performance evaluated by Receiver Operating Characteristic (ROC) curve analysis.
Main Results:
- Nine independent prognostic factors for mortality were identified: severity of illness, age, platelet count, leukocyte count, prealbumin, C-reactive protein (CRP), total bilirubin, APACHE II score, and SOFA score.
- The LASSO regression model achieved a correct classification rate of 0.98 and an Area Under the ROC Curve (AUC) of 0.980 (training) and 0.990 (testing).
- The LASSO-based ANN model demonstrated a correct classification rate of 0.990 and an AUC of 0.980 in both training and testing cohorts.
Conclusions:
- Both LASSO regression and LASSO-based ANN models accurately predicted clinical outcomes in COVID-19 patients.
- Identified prognostic factors (illness severity, age, platelet count, leukocyte count, prealbumin, CRP, total bilirubin, APACHE II, SOFA scores) are crucial for assessing mortality risk.
- AI-driven models offer a powerful tool for predicting COVID-19 patient prognosis and informing clinical decision-making.
Background:
Novel coronavirus disease 2019 (COVID-19) is an ongoing global pandemic with high mortality. Although several studies have reported different risk factors for mortality in patients based on traditional analytics, few studies have used artificial intelligence (AI) algorithms. This study investigated prognostic factors for COVID-19 patients using AI methods.
Methods:
COVID-19 patients who were admitted in Wuhan Infectious Diseases Hospital from December 29, 2019 to March 2, 2020 were included. The whole cohort was randomly divided into training and testing sets at a 6:4 ratio. Demographic and clinical data were analyzed to identify predictors of mortality using least absolute shrinkage and selection operator (LASSO) regression and LASSO-based artificial neural network (ANN) models. The predictive performance of the models was evaluated using receiver operating characteristic (ROC) curve analysis.
Results:
A total of 1145 patients (610 male, 53.3%) were included in the study. Of the 1145 patients, 704 were assigned to the training set and 441 were assigned to the testing set. The median age of the patients was 57 years (range: 47-66 years). Severity of illness, age, platelet count, leukocyte count, prealbumin, C-reactive protein (CRP), total bilirubin, Acute Physiology and Chronic Health Evaluation (APACHE) II score, and Sequential Organ Failure Assessment (SOFA) score were identified as independent prognostic factors for mortality. Incorporating these nine factors into the LASSO regression model yielded a correct classification rate of 0.98, with area under the ROC curve (AUC) values of 0.980 and 0.990 in the training and testing cohorts, respectively. Incorporating the same factors into the LASSO-based ANN model yielded a correct classification rate of 0.990, with an AUC of 0.980 in both the training and testing cohorts.
Conclusions:
Both the LASSO regression and LASSO-based ANN model accurately predicted the clinical outcome of patients with COVID-19. Severity of illness, age, platelet count, leukocyte count, prealbumin, CRP, total bilirubin, APACHE II score, and SOFA score were identified as prognostic factors for mortality in patients with COVID-19.
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