PMID- 33803753
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240331
IS  - 2036-7430 (Print)
IS  - 2036-7449 (Electronic)
IS  - 2036-7430 (Linking)
VI  - 13
IP  - 1
DP  - 2021 Mar 18
TI  - A Dynamic Bayesian Model for Identifying High-Mortality Risk in Hospitalized 
      COVID-19 Patients.
PG  - 239-250
LID - 10.3390/idr13010027 [doi]
AB  - As Coronavirus Disease 2019 (COVID-19) hospitalization rates remain high, there 
      is an urgent need to identify prognostic factors to improve patient outcomes. 
      Existing prognostic models mostly consider the impact of biomarkers at 
      presentation on the risk of a single patient outcome at a single follow up time. 
      We collected data for 553 Polymerase Chain Reaction (PCR)-positive COVID-19 
      patients admitted to hospital whose eventual outcomes were known. The data 
      collected for the patients included demographics, comorbidities and laboratory 
      values taken at admission and throughout the course of hospitalization. We 
      trained multivariate Markov prognostic models to identify high-risk patients at 
      admission along with a dynamic measure of risk incorporating time-dependent 
      changes in patients' laboratory values. From the set of factors available upon 
      admission, the Markov model determined that age >80 years, history of coronary 
      artery disease and chronic obstructive pulmonary disease increased mortality 
      risk. The lab values upon admission most associated with mortality included 
      neutrophil percentage, red blood cells (RBC), red cell distribution width (RDW), 
      protein levels, platelets count, albumin levels and mean corpuscular hemoglobin 
      concentration (MCHC). Incorporating dynamic changes in lab values throughout 
      hospitalization lead to dramatic gains in the predictive accuracy of the model 
      and indicated a catalogue of variables for determining high-risk patients 
      including eosinophil percentage, white blood cells (WBC), platelets, pCO2, RDW, 
      large unstained cells (LUC) count, alkaline phosphatase and albumin. Our 
      prognostic model highlights the nuance of determining risk for COVID-19 patients 
      and indicates that, rather than a single variable, a range of factors (at 
      different points in hospitalization) are needed for effective risk 
      stratification.
FAU - Momeni-Boroujeni, Amir
AU  - Momeni-Boroujeni A
AUID- ORCID: 0000-0002-7714-370X
AD  - Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA.
FAU - Mendoza, Rachelle
AU  - Mendoza R
AD  - Downstate Medical Center, Department of Pathology, State University of New York, 
      Brooklyn, NY 11203, USA.
FAU - Stopard, Isaac J
AU  - Stopard IJ
AUID- ORCID: 0000-0003-4612-5004
AD  - MRC Centre for Global Infectious Disease Analysis, School of Public Health, 
      Faculty of Medicine, Imperial College London, London W2 1PG, UK.
FAU - Lambert, Ben
AU  - Lambert B
AD  - MRC Centre for Global Infectious Disease Analysis, School of Public Health, 
      Faculty of Medicine, Imperial College London, London W2 1PG, UK.
FAU - Zuretti, Alejandro
AU  - Zuretti A
AD  - Downstate Medical Center, Department of Pathology, State University of New York, 
      Brooklyn, NY 11203, USA.
LA  - eng
PT  - Journal Article
DEP - 20210318
PL  - Switzerland
TA  - Infect Dis Rep
JT  - Infectious disease reports
JID - 101537203
PMC - PMC8006025
OTO - NOTNLM
OT  - Markov model
OT  - SARS-CoV-2
OT  - prognostication
OT  - time trends
OT  - triage
COIS- The authors declare no conflict of interest.
EDAT- 2021/04/04 06:00
MHDA- 2021/04/04 06:01
PMCR- 2021/03/18
CRDT- 2021/04/03 01:15
PHST- 2021/02/11 00:00 [received]
PHST- 2021/03/11 00:00 [revised]
PHST- 2021/03/12 00:00 [accepted]
PHST- 2021/04/03 01:15 [entrez]
PHST- 2021/04/04 06:00 [pubmed]
PHST- 2021/04/04 06:01 [medline]
PHST- 2021/03/18 00:00 [pmc-release]
AID - idr13010027 [pii]
AID - idr-13-00027 [pii]
AID - 10.3390/idr13010027 [doi]
PST - epublish
SO  - Infect Dis Rep. 2021 Mar 18;13(1):239-250. doi: 10.3390/idr13010027.