Models Maastricht University

Description of the tool for severity risk assessment and triage for COVID-19 patients at hospital admission: This tool indicates the risk that a COVID-19 patient will develop severe illness during hospitalization. Severe illness is defined as meeting at least one of the following criteria during hospitalization:

  • respiratory failure requiring mechanical ventilation
  • shock
  • ICU admission
  • organ failure
  • death
To which patients does this tool apply?
The tool is developed for risk stratification and triage of patients who are diagnosed with COVID-19 and are being admitted to the hospital.
What information will you need?
Age (years)
C-reactive protein (mg/dL)
Lactate dehydrogenase (U/L)
Creatine kinase (U/L)
Urea (mg/dL)
Calcium (mg/dL)
Lymphocyte (%)
How was this tool developed?
The prognostic model is based on retrospective data of 299 patients, treated at one hospital of Wuhan, China, and admitted to the hospital between December 2019 and February 2020.

In total 364 COVID-19 patients were admitted to the hospital.

Applying the exclusion criteria resulted in removal of 65 patients

14 patients had already severe illness at hospital admission
13 patients had a time interval between admission and examinations > 2 days
38 patients were excluded because of missing data.
The predicted outcome is the probability that a patient will develop severe illness during hospitalization (as defined above)

Imputation of missing data was not applied (complete case analysis)

The data was split into a training set of 239 patients and a testing set of 60 patients. Five additional datasets were available for external validation. Test 1: a Wuhan hospital from Hubei province (n=36), Test 2: a non-Wuhan hospital in Hubei province (n=90), Test 3: three hospitals from a non-Hubei province (n=32), Test 4: a hospital from Roma, Italy (n=48), and Test 5: another hospital from Genoa, Italy (n=42).

The data collection consisted of 29 clinical variables (including comorbidities), 42 laboratory measurements and 5 semantic CT characteristics (assessed by 2 radiologists).

Features showing high pairwise Spearman correlation (r > 0.8) and the highest mean correlation with all remaining features were removed, and features with high importance and robustness were selected using the Boruta method. Recursive feature elimination based on bagged tree models with a cross-validation technique (10 folds, 10 times) was performed to select the best combination of features. The variable selection process was used to select the optimal combination of clinical, laboratory, and CT semantic variables.

The selected variables were used to build a logistic regression model. The performance in terms of AUC was optimized using the testing set of 60 patients (split sample set). The maximum value of the AUC is 1.0, indicating a perfect prediction model. A value of 0.5 indicates that patients are correctly classified in 50% of the cases, i.e. as good as chance. The performance of the model on the external validation sets, ranged from 0.83 – 0.93.

Further details about the modeling approach can be found in the published article.

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