A single blood sample from a critically ill COVID-19 patient can be analysed by a machine learning model, using blood plasma proteins to predict survival, weeks before the outcome, according to the study published in PLOS Digital Health by Florian Kurth and Markus Ralser of the Charité-Universitätsmedizin Berlin, Germany, and colleagues.
Healthcare systems worldwide are struggling to accommodate high numbers of severely ill COVID-19 patients who need special medical attention, especially if they are identified as being at high risk. Clinically established risk assessments in intensive care medicine, such as the SOFA or APACHE II, show only limited reliability in predicting future disease outcomes for COVID-19.
The team studied the levels of 321 proteins in blood samples taken at 349 timepoints from 50 critically ill COVID-19 patients being treated in two independent health care centres in Germany and Austria. A machine learning approach was used to find associations between the measured proteins and patient survival.
Of the patients in the cohort, 15 died; the average time from admission to death was 28 days. For patients who survived, the median time of hospitalisation was 63 days. The researchers pinpointed 14 proteins which, over time, changed in opposite directions for patients who survive, compared to patients who do not survive on intensive care. The team then developed a machine learning model to predict survival based on a single time-point measurement of relevant proteins and tested the model on an independent validation cohort of 24 critically ill COVID-10 patients. The model demonstrated high predictive power on this cohort, correctly predicting the outcome for 18 of 19 patients who survived and 5 out of 5 patients who died (AUROC = 1.0, P = 0.000047).
The researchers conclude that blood protein tests, if validated in larger cohorts, may be useful in both identifying patients with the highest mortality risk, as well as for testing whether a given treatment changes the projected trajectory of an individual patient.
Study details
A proteomic survival predictor for COVID-19 patients in intensive care
Florian Kurth, Vadim Demichev, Pinkus Tober-Lau ,Tatiana Nazarenko, Oliver Lemke, Simran Kaur Aulakh, Harry J. Whitwell, Annika Röhl, Anja Freiwald, Mirja Mittermaier, Lukasz Szyrwiel, Daniela Ludwig, Clara Correia-Melo, Lena Lippert, Markus Ralse et al.
Published in PLOS One Digital Health on 18 January 2022
Abstract
Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimise allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators.
We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0).
The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care.
See more from MedicalBrief archives:
Blood biomarkers offer early indicator of severe COVID-19 — Yale
Convalescent plasma may improve survival with severe COVID-19 — US/Brazil trial
Characterisation of in-hospital complications associated with COVID-19 — UK multicentre study