Machine learning uncovers blood test patterns subphenotypes at hospital
admission discerning increased 30-day ICU mortality rates in COVID-19 elderly
patients
Authors: Zhou Lexin , Romero-García Nekane , Badenes Rafael , Morales Teresa García , Lora David , Gómez de la Cámara Agustín , García Ruiz Francisco T , García-Gómez Juan M , Sáez Carlos 
Published: October 2022

Background
Elderly patients with COVID-19 are among the most numerous populations
being admitted in the ICU due to its high mortality rate and high
comorbidity incidence. An early severity risk stratification at hospital
admission could help optimize ICU usage towards those more vulnerable and
critically ill patients.

Methods
Of 503 Spanish patients aged>64 years admitted in the ICU between 26
Feb and 02 Nov 2020 in two Spanish hospitals, we included 193
quality-controlled patients. The subphenotyping combined PCA and t-SNE
dimensionality reduction methods to maximize non-linear correlation and
reduce noise among age and full blood count tests (FBC) at hospital
admission, followed by hierarchical clustering.

Findings
We identified five subphenotypes (Eld-ICU-COV19 clusters) with
heterogeneous FBC patterns associated to significantly disparate 30-day ICU
mortality rates ranging from 2% in a healthy cluster to 44% in a severe
cluster, along three moderate clusters.

Interpretations
To our knowledge, this is the first study using age and FBC at hospital
admission to early stratify the risk of death in ICU at 30 days in elderly
patients. Our results provide guidance to comprehend the phenotypic
classification and disparate severity patterns among elderly ICU patients
with COVID-19, based only on age and FBC, that have the potential to
establish target groups for early risk stratification or early triage
systems to provide personalized treatments or aid the decision-making during
resource allocation process for each target Eld-ICU-COV19 cluster,
especially in those circumstances with resource scarcity problem.

Funding
FONDO SUPERA COVID-19 by CRUE-Santander Bank grant
SUBCOVERWD-19.

Research in context

Evidence before this study
We searched on PubMed and Google Scholar using the search
terms “COVID-19”, “SARS-CoV2”, “phenotypes” for research
published between 2020 to 2022, with no language restriction, to
detect any published study identifying and characterizing
phenotypes among ICU COVID-19 patients. A previous COVID-19
phenotyping study found three phenotypes from hospitalized
patients associated with significantly disparate 30-day
mortality rates (ranging from 2·5 to 60·7%). However, it seems
to become harder to find phenotypes with discriminative
mortality rates among ICU COVID-19 patients. For example, we
found one study that uncovered two phenotypes from 39 ICU
COVID-19 patients based on biomarkers with 39% and 63% mortality
rates, but such difference was not statistically significant. We
also found another study with more success that uncovered two
ICU COVID-19 phenotypes using two different trajectories with
somehow disparate 28-day mortality rates of 27% versus 37%
(Ventilatory ratio trajectories) and of 25% versus 39%
(mechanical power trajectories).

Added value of this study
To our knowledge, this is the first study that uses age and
laboratory results at hospital admission (i.e., before ICU
admission) in elderly patients to early stratify, prior ICU
admission, the risk of death in ICU at 30 days. We classified
193 patients with COVID-19, based on age and ten Full Blood
Count (FBC) tests, into five subphenotypes (one healthy, three
moderate, and one severe) that showed significantly disparate
30-day ICU mortality rates from 2% to 44%.

Implications of all the available
evidence
Identifying, from elderly ICU patients with COVID-19
(Eld-ICU-COV19), subphenotypes could spur further investigation
to analyze the potential differences in their underlying disease
mechanisms, acquire better phenotypical understanding among
Eld-ICU-COV19 toward better decision-making in distributing the
limited resources (including both logistic and medical) as well
as shedding light on tailoring personalized treatment for each
specific target subgroup in future medical research and clinical
trial.