Using Shapes of COVID-19 Positive Patient-Specific Trajectories for Mortality Prediction

Alaleh Azhir; Soheila Talebi; Louis-Henri Merino; Thomas Lukasiewicz; Edgar Argulian; Jagat Narula; Borislava Mihaylova

Using Shapes of COVID-19 Positive Patient-Specific Trajectories for Mortality Prediction

AMIA Jt Summits Transl Sci Proc. 2022 May 23:2022:130-139. eCollection 2022.

Authors

Alaleh Azhir¹, Soheila Talebi², Louis-Henri Merino³, Thomas Lukasiewicz¹, Edgar Argulian², Jagat Narula², Borislava Mihaylova^{1

4}

Affiliations

¹ University of Oxford, Oxford, UK.
² Icahn School of Medicine at Mount Sinai, New York, NY, USA.
³ Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland.
⁴ Queen Mary University of London, London, UK.

PMID: 35854727
PMCID: PMC9285142

Abstract

Machine learning can be used to identify relevant trajectory shape features for improved predictive risk modeling, which can help inform decisions for individualized patient management in intensive care during COVID-19 outbreaks. We present explainable random forests to dynamically predict next day mortality risk in COVID -19 positive and negative patients admitted to the Mount Sinai Health System between March 1st and June 8th, 2020 using patient time-series data of vitals, blood and other laboratory measurements from the previous 7 days. Three different models were assessed by using time series with: 1) most recent patient measurements, 2) summary statistics of trajectories (min/max/median/first/last/count), and 3) coefficients of fitted cubic splines to trajectories. AUROC and AUPRC with cross-validation were used to compare models. We found that the second and third models performed statistically significantly better than the first model. Model interpretations are provided at patient-specific level to inform resource allocation and patient care.