Predicting thromboembolic complications in COVID-19 ICU patients using machine learning
Davy van de Sande1, Michel E van Genderen1, Babette Rosman1
1Department of Adult Intensive Care, Erasmus University Medical Center, Rotterdam, the Netherlands.
Insights
A decision tree model can predict thromboembolic complications (TCs) in COVID-19 patients admitted to the ICU. Early identification of TCs aids clinical decision-making and management of critically ill patients.
Area of Science:
- Critical Care Medicine
- Hematology
- Infectious Diseases
Background:
- The COVID-19 pandemic presents significant challenges for intensive care units (ICUs).
- Hypercoagulability is strongly associated with poor outcomes in COVID-19 patients.
- Effective management of COVID-19 requires early risk identification and accurate prognosis.
Purpose of the Study:
- To develop a predictive model for early identification of COVID-19 patients at risk of thromboembolic complications (TCs).
Main Methods:
- Analysis of electronic health records from 108 COVID-19 ICU patients.
- Development of a decision tree classifier using 66% of the data to predict TCs.
- Validation of the model on a test dataset.
Main Results:
- 40% of patients developed TCs, with higher mortality in the TC group (26% vs. 8%).
- Key predictors identified by the model include lactate dehydrogenase, standardized bicarbonate, albumin, and leukocytes.
- The model achieved 73% sensitivity and a positive likelihood ratio of 2.7 for predicting TCs 2 days prior.
Conclusions:
- Clinically relevant TCs are frequent in critically ill COVID-19 patients.
- A decision tree model can successfully predict TCs, aiding clinical decision-making.
- Further validation in larger populations is needed to determine generalizability and clinical impact.
Background:
The coronavirus disease 2019 (COVID-19) pandemic is a challenge for intensive care units (ICU) in part due to the failure to identify risks for patients early and the inability to render an accurate prognosis. Previous reports suggest a strong association between hypercoagulability and poor outcome. Factors related to hemostasis may, therefore, serve as tools to improve the management of COVID-19 patients.
Aim:
The purpose of this report is to develop a model to determine whether it is possible to early identify COVID-19 patients at risk for thromboembolic complications (TCs).
Methods:
We analyzed electronic health record data of 108 consecutive COVID-19 patients admitted to the adult ICU of the Erasmus University Medical Center between February 27 and May 20, 2020. By training a decision tree classifier on 66% of the available data, a model for the prediction of TCs was developed.
Results:
The median (interquartile range) age was 62 (53-70) years and 73% were male. Forty-three patients (40%) developed a TC during their ICU stay. Mortality was higher for patients in the TCs group compared to the control group (26% vs. 8%, P=0.03). Lactate dehydrogenase, standardized bicarbonate, albumin, and leukocytes were identified by the Decision Tree classifier as the most powerful predictors for TCs 2 days before the onset of the TC, with a sensitivity of 73% and a positive likelihood ratio of 2.7 on the test dataset.
Conclusions:
Clinically relevant TCs frequently occur in critically ill COVID-19 patients. These can successfully be predicted using a decision tree model. Although this model could be of special importance to aid clinical decision making, its generalizability and clinical impact should be determined in a larger population.
Relevance For Patients:
Recently, severe TCs were observed in COVID-19 patients with progressive respiratory failure warranting ICU treatment. Timely identification of patients at risk of developing TCs is critical inasmuch as it would enable clinicians to initiate potentially salvaging therapeutic anticoagulation.
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