Rapid Assessment of COVID-19 Mortality Risk with GASS Classifiers

  1. Salvatore Greco
  2. Alessandro Salatiello
  3. Nicolò Fabbri
  4. Fabrizio Riguzzi
  5. Emanuele Locorotondo
  6. Riccardo Spaggiari
  7. Alfredo De Giorgi
  8. Angelina Passaro

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Abstract

Risk prediction models are fundamental to effectively triage incoming COVID-19 patients. However, current triaging methods often have poor predictive performance, are based on variables that are expensive to measure, and often lead to hard-to-interpret decisions. We introduce two new classification methods that can predict COVID-19 mortality risk from the automatic analysis of routine clinical variables with high accuracy and interpretability. SVM22-GASS and Clinical-GASS classifiers leverage machine learning methods and clinical expertise, respectively. Both were developed using a derivation cohort of 499 patients from the first wave of the pandemic and were validated with an independent validation cohort of 250 patients from the second pandemic phase. The Clinical-GASS classifier is a threshold-based classifier that leverages the General Assessment of SARS-CoV-2 Severity (GASS) score, a COVID-19-specific clinical score that recently showed its effectiveness in predicting the COVID-19 mortality risk. The SVM22-GASS model is a binary classifier that non-linearly processes clinical data using a Support Vector Machine (SVM). In this study, we show that SMV22-GASS was able to predict the mortality risk of the validation cohort with an AUC of 0.87 and an accuracy of 0.88, better than most scores previously developed. Similarly, the Clinical-GASS classifier predicted the mortality risk of the validation cohort with an AUC of 0.77 and an accuracy of 0.78, on par with other established and emerging machine-learning-based methods. Our results demonstrate the feasibility of accurate COVID-19 mortality risk prediction using only routine clinical variables, readily collected in the early stages of hospital admission.

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  1. Version published to 10.3390/biomedicines11030831
  2. ScreenIT

    SciScore for 10.1101/2021.12.07.21267425: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analysis: Data analyses were performed by using SPSS 26.0 software (IBM SPSS Statistics, IBM Corporation) and MATLAB (MATLAB 2020a, The MathWorks, Natick, MA).
    SPSS
    suggested: (SPSS, RRID:SCR_002865)
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)
    Feature selection: To remove potentially redundant and uninformative features, while reducing the computational cost of training the SVM model, we performed feature selection; this was accomplished by learning a regularized logistic regression model with LASSO penalty[19,20] 19,20.
    LASSO
    suggested: (LaSSO, RRID:SCR_003418)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    This makes them carry an intrinsic limitation represented by the fact that often it can be hard to give a “clinical” sense to the weight assigned to each feature considered in the development of the score. For this reason, in our first work, we decided to develop the GASS score, an informatic tool with the ability to accurately predict COVID-19 outcomes while keeping a clinical sense. The 11 features were chosen, in fact, based on both the strength of the associations with the outcomes and the clinical importance established for each of them in the current literature. Interpreting the SVM22-GASS classifier: The SVM22-GASS predicts the 30-day mortality outcome by nonlinearly processing 38 patient features. Our variable importance analysis isolated eight variables as the most important for the model: White Blood Cell Count (WBC), Lymphocyte Count (LYM), Brain Natriuretic Peptide (BNP), Creatine Phosphokinase (CPK), Lactate Dehydrogenase (LDH), Fibrinogen (FIBR), PaO2/FiO2 Ratio, and (PFR), and High-Sensitivity Troponin I (TnI). Interestingly, half of them, (namely, LYM, BNP, PFR, and TnI) are also part of the Clinical-GASS score, which confirms their strong information content and predictive power. Consistently, serum BNP has been recently found to be significantly elevated in critically ill COVID patients in a recent meta-analysis[47] 48; whether or not this peptide can help discriminate high-risk COVID-19 patients remains unclear and it merits further investigation. Similarly...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2021.12.07.21267425 on medRxiv