61 - Using Machine Learning for Prediction of Poor Clinical Outcomes in Adult Patients Hospitalized with COVID-19

Background: As the ongoing COVID-19 pandemic develops, there is a need for prediction rules to guide clinical decisions. Previous reports have identified risk factors using statistical inference model. The primary goal of these models is to characterize the relationship between variables and outcomes, not to make predictions. In contrast, the primary purpose of machine learning is obtaining a model that can make repeatable predictions. The objective of this study is to develop decision rules tailored to our patient population to predict ICU admissions and death in patients with COVID-19.

Methods: We used a de-identified dataset of hospitalized adults with COVID-19 admitted to our community hospital between March 2020 and June 2020. We used a Random Forest algorithm to build the prediction models for ICU admissions and death. Random Forest is one of the most powerful machine learning algorithms; it leverages the power of multiple decision trees, randomly created, for making decisions.

Results: 313 patients were included; 237 patients were used to train each model, 26 were used for testing, and 50 for validation. A total of 16 variables, selected according to their availability in the Emergency Department, were fit into the models. For the survival model, the combination of age >57 years, the presence of altered mental status, procalcitonin ≥3.0 ng/mL, a respiratory rate >22, and a blood urea nitrogen >32 mg/dL resulted in a decision rule with an accuracy of 98.7% in the training model, 73.1% in the testing model, and 70% in the validation model (Table 1, Figure 1). For the ICU admission model, the combination of age < 82 years, a systolic blood pressure of ≤94 mm Hg, oxygen saturation of ≤93%, a lactate dehydrogenase >591 IU/L, and a lactic acid >1.5 mmol/L resulted in a decision rule with an accuracy of 99.6% in the training model, 80.8% in the testing model, and 82% in the validation model (Table 2, Figure 2).Table 1. Measures of Performance in Predicting Inpatient Mortality


Conclusion: We created decision rules using machine learning to predict ICU admission or death in patients with COVID-19. Although there are variables previously described with statistical inference, these decision rules are customized to our patient population; furthermore, we can continue to train the models fitting more data with new patients to create even more accurate prediction rules.Figure 1. Receiver Operating Characteristic (ROC) Curve for Inpatient Mortality
Table 2. Measures of Performance in Predicting Intensive Care Unit Admission
Figure 2. Receiver Operating Characteristic (ROC) Curve for Intensive Care Unit Admission

Presenting Author(s)

• 

  Guillermo Rodriguez-Nava, MD

  Internal Medicine Resident
  AMITA Health Saint Francis Hospital
  Evanston, IL

  Disclosure: I do not have any relevant financial / non-financial relationships with any proprietary interests.

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Co-Author(s)

• DT

  Daniela Patricia Trelles-Garcia, MD

  Internal Medicine Resident
  AMITA Health Saint Francis Hospital
  Evanston, Illinois

  Disclosure: I do not have any relevant financial / non-financial relationships with any proprietary interests.
• MY

  Maria Adriana Yanez-Bello, MD

  Internal Medicine Resident
  AMITA Health Saint Francis Hospital
  Evanston, Illinois

  Disclosure: I do not have any relevant financial / non-financial relationships with any proprietary interests.
• CC

  Chul Won Chung, MD

  Internal Medicine Resident
  AMITA Health Saint Francis Hospital
  Evanston, Illinois

  Disclosure: I do not have any relevant financial / non-financial relationships with any proprietary interests.
• SC

  Sana Chaudry, MD

  Internal Medicine Chief Resident
  AMITA Health Saint Francis Hospital
  Evanston, Illinois

  Disclosure: I do not have any relevant financial / non-financial relationships with any proprietary interests.
• AK

  Aimen Khan, MD

  Internal Medicine Resident
  AMITA Health Saint Francis Hospital
  Evanston, Illinois

  Disclosure: I do not have any relevant financial / non-financial relationships with any proprietary interests.
• HF

  Harvey Friedman, MD, FACP

  Co-director
  AMITA Health Saint Francis Hospital
  Evanston, Illinois

  Disclosure: I do not have any relevant financial / non-financial relationships with any proprietary interests.
• 

  David Hines, MD

  Infectious Disease Physician
  Metro Infectious Disease Consultants
  Oak Park, Illinois

  Disclosure: I do not have any relevant financial / non-financial relationships with any proprietary interests.