DISCUSSION
This study, which included a very large cohort of COVID-19-positive
patients (380,089), recruited during almost two years of the pandemic,
identified predictors of three different outcomes. It allows us to see a
pattern of variables common to all three outcomes, including age, sex,
cardio-cerebrovascular diseases, diabetes, kidney and liver disease,
tumors, and some more serious specific lung diseases such as
interstitial lung disease. Additionally, we found two common treatments
to all three outcomes, namely the chronic systemic use of steroids and
diuretics.
Most of the above factors have been identified and summarized in
previous studies.21,22 Among the predictors of these
three outcomes, we find a number of chronic pathologies identified by
different studies such as cardiovascular disease (CVD) and
cerebrovascular disease (CVD), as well as diabetes, kidney and liver
disease. A history of tumors has also been identified as a predictor.
In the case of CVD, the exact pathophysiology underlying the
pre-existing role and poor outcome has yet to be determined. SARS-CoV-2
is believed to infect the heart, vascular tissues, and circulating cells
via ACE2 (angiotensin-converting enzyme 2), the host cell receptor for
the viral spike protein. However, these patients are at higher risk due
to concurrent underlying conditions such as advanced age, hypertension,
cardiovascular disorders such as arrhythmia, diabetes, etc. These
patients are also at risk of developing cardioembolic events, secondary
to viral and bacterial infections or new cerebrovascular events
secondary to thrombotic microangiopathy, hypercoagulability leading to
macro and microthrombus formation in the vessels, hypoxic injury and
blood-brain barrier disruption.Likewise, acute cardiac injury is a
common extrapulmonary manifestation of COVID-19 with possible chronic
consequences and is more prevalent amongst patients with advanced age, a
functionally impaired immune system or high levels of ACE2, or patients
with CVD predisposed to COVID-19.
Possible pathogenetic links between diabetes mellitus and COVID-19
include effects on glucose homeostasis, inflammation, altered immune
status, and activation of the renin-angiotensin-aldosterone system
(RAAS).
In the case of patients with renal disease, most cases of fatality were
related to end-stage renal disease (ESRD). This could be partly
explained by immune system dysfunction and high frequency of underlying
comorbidities such as hypertension, CVD, and diabetes in ESRD patients.
The results of two recent meta-analyses reveal a significant association
between preexisting CKD and severe COVID-19. CKD has been associated
with inflammatory status and impaired immune system, as well as a result
of over-expression of ACE2 receptor in the tubular cells of patients
with CKD.
Any explanations of the relationship between patients with liver disease
and adverse outcomes of COVID-19 infection remain controversial. Some
studies have shown that patients with a pre-existing hepatic disease
have an increased risk of severe COVID-19 infection and higher
mortality, which might be correlated with low platelets and lymphocytes
in those patients. This may be due to cirrhosis-associated immune
dysfunction. Additionally, it has been postulated that liver impairment
in COVID-19 patients could also be drug-related and induced when
treating COVID-19 infection.
With regard to cancer patients, some analyses of clinical outcomes in
different cancer types indicate that the case fatality rate is higher in
lung or hematological cancer than other solid cancers. In any case, the
occurrence of severe events and death in cancer patients with COVID-19
appears to be primarily accentuated by age, sex, and coexisting
comorbidities.
As for less prevalent diseases such as ILD and cystic fibrosis, fewer
studies have been conducted in this field. However, patients with ILD
are more susceptible to COVID-19 and experience more severe evolution as
compared to those without ILD .
With regard to treatment, chronic or recurrent use of systemic steroids
prior to SARS-CoV-2 infection may be linked to a greater alteration in
these patients’ immunity.
Dementia appears as a potential risk factor in many studies. Changes in
health care delivery may disproportionately affect older adults with
ADRD. Patients with dementia have higher vulnerability, which may be due
to living conditions in nursing homes, need for intensive caregiver
assistance, and to the inability to self-isolate and manage preventative
health measures. As hypotheses, the presence of chronic inflammatory
conditions or defective immune responses in patients with dementia may
increase their vulnerability to infection or reduce their ability to
mount effective responses to infection.
Most previous studies have also shown that age and sex (male) are
significant risk factors for adverse outcomes. Furthermore, it has been
hypothesized that age-related decline and dysregulation of the immune
function, i.e., immunosenescence and inflammation, may play an important
role in contributing to increased vulnerability to severe COVID-19
outcomes in older adults. Furthermore, circulating sex hormones in men
and women could influence susceptibility to COVID-19 infection, as
demonstrated in a previous study, since they modulate adaptive and
innate immunity responses.
Amongst the strengths of this study are the enormous sample size, which
includes all epidemics and patients in our region up to the beginning of
this year, and validation of the models in the wave of the more recent
and less severe Omicron variant. In developing all predictive models, we
followed the standards of the TRIPOD guidelines. The three models are
based on variables that are easy to obtain in any setting, easy to
calculate and provide a quick prediction of the patient’s risk. Though
different prediction models have been proposed, to the best of our
knowledge this is the first model that has been validated in
Omicron-infected patients. As a practical proposal, patients with low
scores (low or moderate classes for death or adverse evolution) can
safely stay at home, while those in very high classes should be seen at
a hospital level and more intensive care should be considered. In any
case, the clinical judgment for each individual patient should prevail.
Regarding the limitations, our data is limited to baseline diseases and
treatments plus sociodemographic data, without subsequent clinical
follow-up information on those admitted. It was decided to proceed in
this way in order to select the information we believed to be most
reliable and easiest to obtain in any setting. Nonetheless, the AUC of
all models is very high, even in the case of hospitalized patients, and
is replicated in the Omicron sample.
These analyses provide very useful practical tools both in the field of
primary care and in emergency and hospital settings for making decisions
on follow-up and treatment of these patients, including during the
current Omicron wave. This may allow better clinical follow-up and case
management.