Machine learning model uses blood tests to predict COVID-19 survival
Levels of 14 proteins in the blood of critically ill COVID-19 patients
are associated with survival

Date:
January 18, 2022
Source:
PLOS
Summary:
A single blood sample from a critically ill COVID-19 patient can
be analyzed by a machine learning model which uses blood plasma
proteins to predict survival, weeks before the outcome, according
to a new study.



FULL STORY ==========================================================================
A single blood sample from a critically ill COVID-19 patient can be
analyzed by a machine learning model which uses blood plasma proteins
to predict survival, weeks before the outcome, according to a new study published this week in the open-access journal PLOS Digital Health by
Florian Kurth and Markus Ralser of the Charite' -- Universita"tsmedizin
Berlin, Germany, and colleagues.


========================================================================== Healthcare systems around the world 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.

In the new study, researchers 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 centers in Germany
and Austria. A machine learning approach was used to find associations
between the measured proteins and patient survival.

Fifteen of the patients in the cohort died; the average time from
admission to death was 28 days. For patients who survived, the median time
of hospitalization 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.

========================================================================== Story Source: Materials provided by PLOS. Note: Content may be edited
for style and length.


========================================================================== Journal Reference:
1. Vadim Demichev, Pinkus Tober-Lau, Tatiana Nazarenko, Oliver Lemke,
Simran
Kaur Aulakh, Harry J. Whitwell, Annika Ro"hl, Anja Freiwald, Mirja
Mittermaier, Lukasz Szyrwiel, Daniela Ludwig, Clara Correia-Melo,
Lena J.

Lippert, Elisa T. Helbig, Paula Stubbemann, Nadine Olk, Charlotte
Thibeault, Nana-Maria Gru"ning, Oleg Blyuss, Spyros Vernardis,
Matthew White, Christoph B. Messner, Michael Joannidis, Thomas
Sonnweber, Sebastian J. Klein, Alex Pizzini, Yvonne Wohlfarter,
Sabina Sahanic, Richard Hilbe, Benedikt Schaefer, Sonja Wagner,
Felix Machleidt, Carmen Garcia, Christoph Ruwwe-Glo"senkamp,
Tilman Lingscheid, Laure Bosquillon de Jarcy, Miriam S. Stegemann,
Moritz Pfeiffer, Linda Ju"rgens, Sophy Denker, Daniel Zickler,
Claudia Spies, Andreas Edel, Nils B. Mu"ller, Philipp Enghard,
Aleksej Zelezniak, Rosa Bellmann-Weiler, Gu"nter Weiss, Archie
Campbell, Caroline Hayward, David J. Porteous, Riccardo E.

Marioni, Alexander Uhrig, Heinz Zoller, Judith Lo"ffler-Ragg,
Markus A.

Keller, Ivan Tancevski, John F. Timms, Alexey Zaikin, Stefan
Hippenstiel, Michael Ramharter, Holger Mu"ller-Redetzky, Martin
Witzenrath, Norbert Suttorp, Kathryn Lilley, Michael Mu"lleder,
Leif Erik Sander, Florian Kurth, Markus Ralser. A proteomic survival
predictor for COVID-19 patients in intensive care. PLOS Digital
Health, 2022; 1 (1): e0000007 DOI: 10.1371/journal.pdig.0000007 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/01/220118145724.htm

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