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.