Patient Characteristics and Pre-Existing Chronic Diseases with COVID-19 Related Outcomes: A Real-World Experience Patient and Pre-Existing Chronic Diseases with COVID-19 Related Outcomes: A Real-World Experience.

Background: Significant variations in experience of the COVID-19 pandemic have been observed in the United States. However, there is currently no published study which comprehensively examines the relationship between patient characteristics and COVID-19 related health outcomes. Methods: In this study, using aggregated real-world data extracted from TriNetx electronic medical record data from 34 hospitals around United States, we intended to fill the gap. Results: A total of 12,555 patients aged 18-80 years old who contracted COVID-19 were identified from January 20th to April 20th, 2020. First, in the univariate analysis, we found that patients who were older (age 51-80), Black, male, and had pre-existing chronic diseases (e.g. obesity, diabetes, hypertension, and chronic kidney diseases (CKD)) had increased risk ratio (RR) of exhibiting severe outcomes, including increased C-reactive protein (CRP), decreased oxygen saturation, hospitalization, use of ventilator, and ultimately death. Next, we applied propensity score matching to match the patients based on their characteristics. We found that patients who were older, Black, male, and diagnosed with CKD had 3.69, 1.77, 1.75, and 1,61-fold increased RR of death. On the other hand, while obesity, diabetes, and hypertension had no direct relationship with death, they were associated with other severe outcomes. In further analysis by including CRP as a matching variable, death disparity by age group, race, gender, and CKD was reduced, particularly for race and CKD where a significant disparity was no longer observed. Conclusion: In summary, our data show significant disparities in COVID-19 related outcomes by patient characteristics and further suggest that acute inflammation plays an important role in the disparity in COVID-19 death. Characteristics Biomed Sci & Tech Res 31(3)-2020.

The disparity in COVID-19 mortality among Black Americans has been attributed to the confluence of chronic diseases that already disproportionately afflict Black Americans [1]. Compared to Whites, Black Americans experience a higher prevalence of chronic diseases including asthma, hypertension, diabetes, and obesity [6]. According to the Center for Disease Control, those with underlying medical conditions are at higher risk for severe illness and death from COVID-19 [1,7]. In Chicago, 94% of fatal COVID-19 cases have occurred in those with preexisting conditions [8]. As Black Americans disproportionately experience chronic underlying medical conditions, they are predisposed to the higher observed mortality rates from COVID-19 [5]. A recent study confirms this hypothesis using data from the 2018 Behavioral Risk Factor Surveillance System, reporting that Black Americans are more likely to have conditions associated with increased risk of illness from COVID-19 relative to white Americans [9].
However, there is no published study to thoroughly examine the relationship between chronic diseases and racial disparity in COVID-19 mortality. Additionally, no study has further assessed whether racial disparities exist in other COVID-19 related outcomes, such as disease severity, treatment, etc. Such knowledge is critically needed for not only examining the root causes of racial disparities in COVID-19 related death, but for also supporting disease surveillance and response activities. In order to thoroughly examine racial disparities in COVID-19 outcomes, we require a nationwide dataset, which has captured the events in near realtime and contains sufficient information on demographics and preexisting chronic diseases.
In this study, we plan to utilize real-world aggregated data extracted from TriNetx electronic medical record data from 34 hospitals around U.S. In the first three months of pandemic (January 20 th to April 20 th , 2020), we intend to assess the relationship between selected patient characteristics (e.g. age, gender, race) and pre-existing chronic diseases (e.g. obesity, hypertension, diabetes, and CKD) with COVID-19 related outcomes, including death, hospitalization, use of ventilator, oxygen saturation, and CRP.

Identification of COVID-19 Cases
To identify COVID-19 cases, we searched TriNetx electronic medical record data from 34 hospitals around U.S. As the first case in U.S. was recorded on January 20th, we set the time period as the first three months of the pandemic from January 20 th to April 20 th , 2020. We applied the searching criteria suggested by TriNetx. The inclusion criteria included 1) Population: 18 to 80 years, any sex; and 2) Event which occurred on or after Jan 20, 2020 characterized by one or more of the following ICD- 10

Outcomes
The main outcome variable was death within a month after the diagnosis. We also included hospitalization (yes or no, from 2 weeks before to one month after diagnosis), ventilation usage (yes or no, from 2 weeks before to one month after diagnosis), oxygen saturation (≥93 or <93, from 5 days before to 5 days after diagnosis), and CRP (≥10.0mg/dL or <10.0mg/dL, from 5 days before to 5 days after diagnosis). Though multiple measurements of oxygen saturation and CRP may have been recorded for the same patient, we chose to use the most recent measurement.

Covariates
Multiple covariates were included in the propensity score matching. In Table 1, age of COVID-19 diagnosis, gender, race, obesity (body mass index 30.0 or higher, yes or no, past 5 years), hypertension (I10, yes or no, past 5 years), diabetes (E11, yes or no, past 5 years), asthma (J45.9, yes or no, past 5 years), and CKD (N18.9 yes or no, past 5 years) were included as appropriate. ICD-10 code was used to define the chronic diseases. In Table 2, we further included CRP the latest measurement during 5 days before to 5 days after diagnosis).

Statistical Analysis
The statistical tool embedded in TriNetx was used. Each patient characteristic variable, including age of COVID-19 diagnosis (18-51 vs 51-80 years old), gender, race (Black vs White), and pre-existing chronic diseases, including obesity (yes or no), hypertension (yes or no), diabetes (yes or no), asthma (yes or no), and CKD (yes or no), was treated as the categorical variable. The outcome variables were also treated as categorical variables, including hospitalization (yes or no), ventilation usage (yes or no), oxygen saturation (≥93 vs <93), CRP (≥10.0mg/dL vs <10.0mg/dL), and death (yes or no). First, in the univariate analysis, we calculated risk ratio (RR) to assess the risk of each outcome variable between categories of each patient characteristic and pre-existing chronic disease variable. To adjust the potential co-variates, we applied propensity score matching for each comparison. The matching variables include age of COVID-19 diagnosis, gender, race, obesity, hypertension, diabetes, asthma, and CKD for Table 1, and further CRP in Table 2, as appropriate. We applied Bonferroni method to adjust for the multiple comparisons.
Since the study had 74 comparisons, we set a P value of 6.76x10 -4 (0.05/74) as the cutoff point for statistical significance.

Discussion
This is the first study which incorporates nationwide realworld data to assess patient characteristics and pre-existing chronic diseases among COVID-19 patients and their associations with COVID-19 related health outcomes. The results from our study further confirm that those who were older, Black, and had chronic diseases were at increased risk of dying or experiencing severe disease outcomes, such as increased CRP, decreased oxygen saturation, hospitalization, and ventilator usage. In addition, we found that men had a greater likelihood of experiencing severe disease outcomes compared to women. We also found that a certain proportion of disparity in mortality by age group, race, gender, and chronic kidney disease could be attributed to the difference in CRP level, an indicator of acute inflammation.
The gender difference in COVID-19 related health outcomes were particularly interesting. Similar results were also reported previously [10,11]. As we did not have data on cigarette smoking history and other lifestyle factors, we could not assess whether the observed gender difference is simply a reflection of the difference in lifestyle factors. Given that COVID-19 is a respiratory disease, it is possible that men consume more cigarettes and thereby have worse lung function than women, leading to worse COVID-19 related health outcomes, including death, increased CRP, decreased oxygen saturation, and hospitalization.
Compared to non-obese patients, it has been reported previously that obese patients are more likely to die from COVID-19 contraction [12]. However, we only observed a significant association in unmatched samples, but not in the matched samples, suggesting that obesity may not be directly related to COVID-19 death. Intriguingly, although obesity was not directly associated COVID-19 death, obese patients were more likely to have other COVID-19 related outcomes, including increased CRP, decreased oxygen saturation, increased hospitalization, and ventilator usage, all of which may eventually contribute to death. Thus, our data support an indirect association between obesity and COVID-19 related death.
The COVID-19 virus is a novel virus and is therefore able to circumvent recognition by the host. It is hypothesized that the innate immune response of the host is likely to be fully activated and release various types of cytokines and other factors (REF).
However, when the immune system becomes unchecked, it will begin to destroy its own tissue and consequently lead to unfavorable health consequences and death [13]. CRP is one of main cytokines released during this surge. In this study, we found that patients who were older, Black, male, and obese had increased risk of increased CRP around COVID-19 diagnosis, suggesting the occurrence of acute inflammation. We also found that increased CRP was not only associated with an increased risk of death, but may also modify the significant associations between age, gender, race, and CKD with death.
The relationship is particularly interesting for race. After adjusting CRP, the significant association between race and death disappeared. Our data may suggest that Blacks have higher innate immune response to COVID-19 contraction and may be likely to release more cytokines (e.g. CRP, IL-6, interferon) than Whites. Such elevated innate immune response may lead to higher mortality than Whites. Though we don't have adequate statistical tools available to clarify whether elevated CRP may mediate the relationship between race and COVID-19 death, our observation is actually supported by lines of evidences. Racial difference in stress and immune function has been reported previously [14].
Racism and other social-structural factors (e.g. social economic status, housing, and neighborhood environment) may heighten the immune reactivity of Black population. The racial difference could also be due to the genetic difference. In 2016, two studies published in Cell [15,16] showed that upon exposure to pathogens (e.g. bacteria and virus), the transcriptional response was higher in monocytes and macrophages obtained from Africans than in those from Europeans, indicating higher innate immune response in Africans. These studies suggested that the increased innate immune response is mainly due to differences at the genetic level directing the immune systems of people with European and African ancestry.
Interestingly, recent studies have suggested the possible role of Vitamin D in suppressing cytokine storm and associated mortality in COVID-19 Patients [17,18]. Vitamin D can suppress cytokine production by simultaneously boosting the innate immune system (thus reducing the viral load) and decreasing the overactivation of the adaptive immune system to immediately respond to the viral load [19]. Some researchers have suggested the potential role of Vitamin D in suppressing cytokine storm during the 1918-1919 viral influenza pandemic [20]. Moreover, the role of Vitamin D in enhancing immune response in flu and previous coronaviruses has been suggested [19,21]. Blacks are known to have lower levels of circulating vitamin D than their White counterparts [22]. Such Our study maintains several strengths and limitations. For one, the sample size is large with over 12,500 cases. As real-world samples are extracted from a nationwide network of 34 hospitals, they may have less selection bias and thereby provide better representation and generalization. A second major strength is the utilization of real-time data. The first reported case in the United States occurred on January 20 th , 2020. In just three months, we already have aggregated data on over 12,500 cases. The major weakness of this study is the lack of individual data, as only aggregated data were available. Thus, the scope of data is limited. Additionally, data on healthy behavior are missing. Embedded statistical tool in TriNetx is also limited, inhibiting the performance of advanced analysis.
Nevertheless, our study is the first to use real-world data to assess the relationship between personal characteristics and pre-existing chronic diseases with COVID-19 related health outcomes. The results from this study will help us better understand the disparity of COVID-19 related health outcomes among the population and identify means to reduce the disparities and improve the outcomes.