Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU

  1. Elham Jamshidi
  2. Amirhossein Asgary
  3. Nader Tavakoli
  4. Alireza Zali
  5. Soroush Setareh
  6. Hadi Esmaily
  7. Seyed Hamid Jamaldini
  8. Amir Daaee
  9. Amirhesam Babajani
  10. Mohammad Ali Sendani Kashi
  11. Masoud Jamshidi
  12. Sahand Jamal Rahi
  13. Nahal Mansouri

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Abstract

Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies.

Objectives: Early prediction of mortality using machine learning based on typical laboratory results and clinical data registered on the day of ICU admission.

Methods: We retrospectively studied 797 patients diagnosed with COVID-19 in Iran and the United Kingdom (U.K.). To find parameters with the highest predictive values, Kolmogorov-Smirnov and Pearson chi-squared tests were used. Several machine learning algorithms, including Random Forest (RF), logistic regression, gradient boosting classifier, support vector machine classifier, and artificial neural network algorithms were utilized to build classification models. The impact of each marker on the RF model predictions was studied by implementing the local interpretable model-agnostic explanation technique (LIME-SP).

Results: Among 66 documented parameters, 15 factors with the highest predictive values were identified as follows: gender, age, blood urea nitrogen (BUN), creatinine, international normalized ratio (INR), albumin, mean corpuscular volume (MCV), white blood cell count, segmented neutrophil count, lymphocyte count, red cell distribution width (RDW), and mean cell hemoglobin (MCH) along with a history of neurological, cardiovascular, and respiratory disorders. Our RF model can predict patient outcomes with a sensitivity of 70% and a specificity of 75%. The performance of the models was confirmed by blindly testing the models in an external dataset.

Conclusions: Using two independent patient datasets, we designed a machine-learning-based model that could predict the risk of mortality from severe COVID-19 with high accuracy. The most decisive variables in our model were increased levels of BUN, lowered albumin levels, increased creatinine, INR, and RDW, along with gender and age. Considering the importance of early triage decisions, this model can be a useful tool in COVID-19 ICU decision-making.

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  1. Version published to 10.3389/fdgth.2021.681608
  2. ScreenIT

    SciScore for 10.1101/2021.02.04.21251131: (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
    Machine Learning Model: Logistic regression (LR) and random forest (RF) methods were used to build classification models using the Python scikit-learn package 21.
    Python
    suggested: (IPython, RRID:SCR_001658)
    scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)

    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:
    Another limitation of this study may be the lack of an objective criterion for ICU admission. Moreover, different treatment strategies can change the survival outcome for patients who may have had similar profiles when admitted to the ICU. In future studies, the accuracy of this model may be further improved by adding chest imaging data and by using a larger dataset. Possible targets for our ML framework include the prediction of other crucial information such as the patients’ need for mechanical ventilation, the occurrence of cytokine release syndrome, the severity of acute respiratory disease syndrome, the cause of death, and the right treatment strategy. In conclusion, we evaluated 66 parameters in COVID-19 patients at the time of ICU admission. Of those parameters, 15 metrics with the highest prediction values were identified: gender, age, BUN, Cr, INR, albumin, MCV, RDW, MCH, WBC, segmented neutrophil count, lymphocyte count, and past medical history of neurological, respiratory, and cardiovascular disorders. In addition, by using the LIME-SP method, we identified different submodules clarifying distinct clinical manifestations of severe COVID-19. The ML model trained in this study could help clinicians determine rapidly which patients are likely to have worse outcomes, and given the limited resources and reliance on supportive care allow physicians to make more informed decisions.

    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.

    About SciScore

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  3. Version published to 10.1101/2021.02.04.21251131 on medRxiv