Predicting thromboembolic complications in COVID-19 ICU patients using machine learning

J Clin Transl Res. 2020 Oct 14;6(4):179-186. eCollection 2020 Nov 15.


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.

Keywords: COVID-19; artificial intelligence; intensive care units; machine learning; pulmonary embolism.