New cardiovascular events in the convalescent period among survivors of SARS-CoV-2 infection.

BACKGROUND: Patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may have an increased risk of acute cardiovascular events in the convalescent period. AIMS: To determine whether patients with SARS-CoV-2 infection have an increased risk of cardiovascular events during the convalescent period. METHODS: We analyzed 10,691 hospitalized adult pneumonia patients with SARS-CoV-2 infection and contemporary matched controls of pneumonia patients without SARS-CoV-2 infection. The risk of new cardiovascular events following >30 days pneumonia admission (convalescent period) was ascertained using Cox proportional hazards regression analysis to adjust for potential confounders. RESULTS: Among 10,691 pneumonia patients with SARS-CoV-2 infection, 697 patients (5.8%; 95% CI, 5.4-6.2%) developed new cardiovascular events (median time interval of 218 days post pneumonia admission; interquartile range Q1 = 117 days, Q3 = 313 days). The risk of new cardiovascular events was not significantly higher among pneumonia patients with SARS-CoV-2 infection compared with those with pneumonia without SARS-CoV-2 infection (hazard ratio (HR), 0.90, 95% CI, 0.80-1.02) after adjustment for potential confounders. In addition, no significant difference in the rate of a new ischemic stroke (HR, 0.84; 95% CI, 0.70-1.02) or ischemic heart disease (HR, 1.00; 95% CI, 0.87-1.15) was observed between the pneumonia patients with and without SARS-CoV-2 infection. CONCLUSION: Our study suggests that new cardiovascular events rate in the convalescent period among pneumonia patients with SARS-CoV-2 infection was not significantly higher than the rate seen with other pneumonias.

Liver injury on admission linked to worse outcomes in COVID-19: an analysis of 14,138 patients

Background Infection with the SARS-CoV-2 virus, which can result in hepatic inflammation and injury that varies from mild to severe and potentially acute fulminant liver injury, may be associated with poor outcomes. Our aims were to: (I) assess baseline clinical and demographic characteristics in patients with coronavirus disease 2019 (COVID-19) who did and did not have abnormalities in liver chemistries [alkaline phosphatase (ALP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and total bilirubin (Tbili)] and (II) evaluate associations between abnormalities in liver chemistries and the primary outcomes of in-hospital death, intubation, and hospital length of stay (LOS). Methods In this nationwide retrospective cohort study of 14,138 patients, we analyzed associations between abnormalities in liver chemistries (ALT, AST, ALP, and Tbili) and mortality, intubation, and prolonged hospital LOS in patients with laboratory-confirmed COVID-19. We used Pearson's chi-squared tests to detect significant differences in categorical variables for patients with and without abnormal liver chemistries. Welch's two-sample t-tests were used to make comparisons of liver chemistry (ALT, AST, ALP, Tbili) and serum albumin results. All other continuous variables were analyzed using independent samples t-tests. A P value of <0.05 was considered significant. Results Propensity score matching demonstrated that abnormalities in liver chemistries at admission are significantly associated with increased risk for mortality (RR 1.70) and intubation (RR 1.44) in patients with COVID-19. Elevated AST is the liver chemistry abnormality associated with the highest risk for mortality (RR 2.27), intubation (RR 2.12), and prolonged hospitalization (RR 1.19). Male gender, pre-existing liver disease, and decreased serum albumin are also significantly associated with severe outcomes and death in COVID-19. Conclusions Routine liver chemistry testing should be implemented and used for risk stratification at the time of COVID-19 diagnosis.

COVID-Specific Long-term Sequelae in Comparison to Common Viral Respiratory Infections: An Analysis of 17 487 Infected Adult Patients.

Background: A better understanding of long-term health effects after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has become one of the health care priorities in the current pandemic. We analyzed a large and diverse patient cohort to study health effects related to SARS-CoV-2 infection occurring >1 month postinfection. Methods: We analyzed 17 487 patients who received diagnoses for SARS-CoV-2 infection in a total of 122 health care facilities in the United States before April 14, 2022. Patients were propensity score-matched with patients diagnosed with the common cold, influenza, or viral pneumonia from March 1, 2020, to April 1, 2021. For each outcome, SARS-CoV-2 was compared with a generic viral respiratory infection (VRI) by predicting diagnoses in the period between 30 and 365 days postinfection. Both coronavirus disease 2019 (COVID-19) and VRI patients were propensity score-matched with patients with no record of COVID-19 or VRI, and the same methodology was applied. Diagnoses where COVID-19 infection was a significant positive predictor in both COVID-19 vs VRI and COVID-19 vs control comparisons were considered COVID-19-specific effects. Results: Compared with common VRIs, SARS-CoV-2 was associated with diagnoses of palpitations, hair loss, fatigue, chest pain, dyspnea, joint pain, and obesity in the postinfectious period. Conclusions: We identify that some diagnoses commonly described as "long COVID" do not appear significantly more frequent post-COVID-19 infection compared with other common VRIs. We also identify sequelae that are specifically associated with a prior SARS-CoV-2 infection.

Impact of diabetes status and related factors on COVID-19-associated hospitalization: A nationwide retrospective cohort study of 116,370 adults with SARS-CoV-2 infection.

AIMS: We examined diabetes status (no diabetes; type 1 diabetes [T1D]; type 2 diabetes [T2D]) and other demographic and clinical factors as correlates of coronavirus disease 2019 (COVID-19)-related hospitalization. Further, we evaluated predictors of COVID-19-related hospitalization in T1D and T2D. METHODS: We analyzed electronic health record data from the de-identified COVID-19 database (December 2019 through mid-September 2020; 87 US health systems). Logistic mixed models were used to examine predictors of hospitalization at index encounters associated with confirmed SARS-CoV-2 infection. RESULTS: In 116,370 adults (>=18 years old) with COVID-19 (93,098 no diabetes; 802 T1D; 22,470 T2D), factors that independently increased risk for hospitalization included diabetes, male sex, public health insurance, decreased body mass index (BMI; <25.0-29.9 kg/m2), increased BMI (>25.0-29.9 kg/m2), vitamin D deficiency/insufficiency, and Elixhauser comorbidity score. After further adjustment for concurrent hyperglycemia and acidosis in those with diabetes, hospitalization risk was substantially higher in T1D than T2D and in those with low vitamin D and elevated hemoglobin A1c (HbA1c). CONCLUSIONS: The higher hospitalization risk in T1D versus T2D warrants further investigation. Modifiable risk factors such as vitamin D deficiency/insufficiency, BMI, and elevated HbA1c may serve as prognostic indicators for COVID-19-related hospitalization in adults with diabetes.

Community Case Study: The Role of Live-Interactive Learning Network in Addressing Community Wide Information Needs Through Show-Me COVID-19 ECHO

After the COVID-19 pandemic reached Missouri, the Show-Me ECHO (Extension for Community Healthcare Outcomes) project initiated COVID-19 ECHO virtual knowledge-sharing networking sessions. These live-interactive weekly sessions inform participants about up-to-date evidence-based recommendations and guidelines through expert didactic lectures followed by real-life case discussions. We conducted a qualitative analysis of pre-session surveys and questions asked during sessions to learn about information needs of community members during first months of public health emergency. This was a pilot project using qualitative analysis of registration questions regarding anticipated COVID-19 community information needs, and participants' questions asked during sessions collected from March 23 until May 4, 2020. We also analyzed participants' satisfaction surveys collected in December 2020. A total of 761 unique participants attended COVID-19 ECHO during the study period. Survey was completed by 692 respondents. Participants asked 315 questions resulting in 797 identified community information needs. Five thematic categories were recognized: patient care, information seeking, minimizing exposure, financial themes, and general comments. Most attendees rated content quality, logistics, and technical operations as good or excellent on a five-point Likert scale. The COVID-19 ECHO model was responsive to the needs of participants by sharing and discussing up-to-date recommendations and guidelines regarding COVID-19. Sessions were well-attended, and the didactic presenters were invited to deliver same or similar presentations at Boone County Medical Society (BCMS) weekly seminars, suggesting the value of the project to healthcare providers and other community members caring for or working with the most vulnerable populations.

Investigating Health Context Using a Spatial Data Analytical Tool: Development of a Geospatial Big Data Ecosystem.

BACKGROUND: Enabling the use of spatial context is vital to understanding today's digital health problems. Any given location is associated with many different contexts. The strategic transformation of population health, epidemiology, and eHealth studies requires vast amounts of integrated digital data. Needed is a novel analytical framework designed to leverage location to create new contextual knowledge. The Geospatial Analytical Research Knowledgebase (GeoARK), a web-based research resource has robust, locationally integrated, social, environmental, and infrastructural information to address today's complex questions, investigate context, and spatially enable health investigations. GeoARK is different from other Geographic Information System (GIS) resources in that it has taken the layered world of the GIS and flattened it into a big data table that ties all the data and information together using location and developing its context. OBJECTIVE: It is paramount to build a robust spatial data analytics framework that integrates social, environmental, and infrastructural knowledge to empower health researchers' use of geospatial context to timely answer population health issues. The goal is twofold in that it embodies an innovative technological approach and serves to ease the educational burden for health researchers to think spatially about their problems. METHODS: A unique analytical tool using location as the key was developed. It allows integration across source, geography, and time to create a geospatial big table with over 162 million individual locations (X-Y points that serve as rows) and 5549 attributes (represented as columns). The concept of context (adjacency, proximity, distance, etc) is quantified through geoanalytics and captured as new distance, density, or neighbor attributes within the system. Development of geospatial analytics permits contextual extraction and investigator-initiated eHealth and mobile health (mHealth) analysis across multiple attributes. RESULTS: We built a unique geospatial big data ecosystem called GeoARK. Analytics on this big table occur across resolution groups, sources, and geographies for extraction and analysis of information to gain new insights. Case studies, including telehealth assessment in North Carolina, national income inequality and health outcome disparity, and a Missouri COVID-19 risk assessment, demonstrate the capability to support robust and efficient geospatial understanding of a wide spectrum of population health questions. CONCLUSIONS: This research identified, compiled, transformed, standardized, and integrated multifaceted data required to better understand the context of health events within a large location-enabled database. The GeoARK system empowers health professionals to engage more complex research where the synergisms of health and geospatial information will be robustly studied beyond what could be accomplished today. No longer is the need to know how to perform geospatial processing an impediment to the health researcher, but rather the development of how to think spatially becomes the greater challenge.

New-Onset Dementia Among Survivors of Pneumonia Associated With Severe Acute Respiratory Syndrome Coronavirus 2 Infection.

Background: Case series without control groups suggest that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may result in cognitive deficits and dementia in the postinfectious period. Methods: Adult pneumonia patients with SARS-CoV-2 infection (index hospitalization) and age-, gender-, and race/ethnicity-matched contemporary control pneumonia patients without SARS-CoV-2 infection were identified from 110 healthcare facilities in United States. The risk of new diagnosis of dementia following >30 days after the index hospitalization event without any previous history of dementia was identified using logistic regression analysis to adjust for potential confounders. Results: Among 10 403 patients with pneumonia associated with SARS-CoV-2 infection, 312 patients (3% [95% confidence interval {CI}, 2.7%-3.4%]) developed new-onset dementia over a median period of 182 days (quartile 1 = 113 days, quartile 3 = 277 days). After adjustment for age, gender, race/ethnicity, hypertension, diabetes mellitus, hyperlipidemia, nicotine dependence/tobacco use, alcohol use/abuse, atrial fibrillation, previous stroke, and congestive heart failure, the risk of new-onset dementia was significantly higher with pneumonia associated with SARS-CoV-2 infection compared with pneumonia unrelated to SARS-CoV-2 infection (odds ratio [OR], 1.3 [95% CI, 1.1-1.5]). The association remained significant after further adjustment for occurrence of stroke, septic shock, and intubation/mechanical ventilation during index hospitalization (OR, 1.3 [95% CI, 1.1-1.5]). Conclusions: Approximately 3% of patients with pneumonia associated with SARS-CoV-2 infection developed new-onset dementia, which was significantly higher than the rate seen with other pneumonias.

Reinfection With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Patients Undergoing Serial Laboratory Testing.

BACKGROUND: A better understanding of reinfection after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has become one of the healthcare priorities in the current pandemic. We determined the rate of reinfection, associated factors, and mortality during follow-up in a cohort of patients with SARS-CoV-2 infection. METHODS: We analyzed 9119 patients with SARS-CoV-2 infection who received serial tests in total of 62 healthcare facilities in the United States between 1 December 2019 and 13 November 2020. Reinfection was defined by 2 positive tests separated by interval of >90 days and resolution of first infection was confirmed by 2 or more consecutive negative tests. We performed logistic regression analysis to identify demographic and clinical characteristics associated with reinfection. RESULTS: Reinfection was identified in 0.7% (n = 63, 95% confidence interval [CI]: .5%-.9%) during follow-up of 9119 patients with SARS-CoV-2 infection. The mean period (±standard deviation [SD]) between 2 positive tests was 116 ± 21 days. A logistic regression analysis identified that asthma (odds ratio [OR] 1.9, 95% CI: 1.1-3.2) and nicotine dependence/tobacco use (OR 2.7, 95% CI: 1.6-4.5) were associated with reinfection. There was a significantly lower rate of pneumonia, heart failure, and acute kidney injury observed with reinfection compared with primary infection among the 63 patients with reinfection There were 2 deaths (3.2%) associated with reinfection. CONCLUSIONS: We identified a low rate of reinfection confirmed by laboratory tests in a large cohort of patients with SARS-CoV-2 infection. Although reinfection appeared to be milder than primary infection, there was associated mortality.

Liver injury on admission linked to worse outcomes in COVID-19: an analysis of 14,138 patients.

Background: Infection with the SARS-CoV-2 virus, which can result in hepatic inflammation and injury that varies from mild to severe and potentially acute fulminant liver injury, may be associated with poor outcomes. Our aims were to: (I) assess baseline clinical and demographic characteristics in patients with coronavirus disease 2019 (COVID-19) who did and did not have abnormalities in liver chemistries [alkaline phosphatase (ALP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and total bilirubin (Tbili)] and (II) evaluate associations between abnormalities in liver chemistries and the primary outcomes of in-hospital death, intubation, and hospital length of stay (LOS). Methods: In this nationwide retrospective cohort study of 14,138 patients, we analyzed associations between abnormalities in liver chemistries (ALT, AST, ALP, and Tbili) and mortality, intubation, and prolonged hospital LOS in patients with laboratory-confirmed COVID-19. We used Pearson's chi-squared tests to detect significant differences in categorical variables for patients with and without abnormal liver chemistries. Welch's two-sample t-tests were used to make comparisons of liver chemistry (ALT, AST, ALP, Tbili) and serum albumin results. All other continuous variables were analyzed using independent samples t-tests. A P value of <0.05 was considered significant. Results: Propensity score matching demonstrated that abnormalities in liver chemistries at admission are significantly associated with increased risk for mortality (RR 1.70) and intubation (RR 1.44) in patients with COVID-19. Elevated AST is the liver chemistry abnormality associated with the highest risk for mortality (RR 2.27), intubation (RR 2.12), and prolonged hospitalization (RR 1.19). Male gender, pre-existing liver disease, and decreased serum albumin are also significantly associated with severe outcomes and death in COVID-19. Conclusions: Routine liver chemistry testing should be implemented and used for risk stratification at the time of COVID-19 diagnosis.

Effect of Race and Ethnicity on In-Hospital Mortality in Patients with COVID-19.

Objective: To identify differences in short-term outcomes of patients with coronavirus disease 2019 (COVID-19) according to various racial/ethnic groups. Design: Analysis of Cerner de-identified COVID-19 dataset. Setting: A total of 62 health care facilities. Participants: The cohort included 49,277 adult COVID-19 patients who were hospitalized from December 1, 2019 to November 13, 2020. Main Outcome Measures: The primary outcome of interest was in-hospital mortality. The secondary outcome was non-routine discharge (discharge to destinations other than home, such as short-term hospitals or other facilities including intermediate care and skilled nursing homes). Methods: We compared patients' age, gender, individual components of Charlson and Elixhauser comorbidities, medical complications, use of do-not-resuscitate, use of palliative care, and socioeconomic status between various racial and/or ethnic groups. We further compared the rates of in-hospital mortality and non-routine discharges between various racial and/or ethnic groups. Results: Compared with White patients, in-hospital mortality was significantly higher among African American (OR 1.5; 95%CI:1.3-1.6, P<.001), Hispanic (OR1.4; 95%CI:1.3-1.6, P<.001), and Asian or Pacific Islander (OR 1.5; 95%CI: 1.1-1.9, P=.002) patients after adjustment for age and gender, Elixhauser comorbidities, do-not-resuscitate status, palliative care use, and socioeconomic status. Conclusions: Our study found that, among hospitalized patients with COVID-2019, African American, Hispanic, and Asian or Pacific Islander patients had increased mortality compared with White patients after adjusting for sociodemographic factors, comorbidities, and do-not-resuscitate/palliative care status. Our findings add additional perspective to other recent studies.

Intracerebral Hemorrhage and Coronavirus Disease 2019 in a Cohort of 282,718 Hospitalized Patients.

BACKGROUND: To identify whether the risk of intracerebral hemorrhage is higher in patients with coronavirus disease 2019 (COVID-19), we compared the risk factors, comorbidities, and outcomes in patients intracerebral hemorrhage and COVID-19 and those without COVID-19. METHODS: We analyzed the data from the Cerner deidentified COVID-19 data set derived from 62 health care facilities. The data set included patients with an emergency department or inpatient encounter with discharge diagnoses codes that could be associated with suspicion of or exposure to COVID-19 or confirmed COVID-19. RESULTS: There were a total of 154 (0.2%) and 667 (0.3%) patients with intracerebral hemorrhage among 85,645 patients with COVID-19 and 197,073 patients without COVID-19, respectively. In the multivariate model, there was a lower risk of intracerebral hemorrhage in patients with COVID-19 (odds ratio 0.5; 95% confidence interval 0.5-0.6; p < .0001) after adjustment for sex, age strata, race/ethnicity, hypertension, diabetes mellitus, nicotine dependence/tobacco use, hyperlipidemia, atrial fibrillation, congestive heart failure, long-term anticoagulant use, and alcohol abuse. The proportions of patients who developed pneumonia (58.4% versus 22.5%; p < .0001), acute kidney injury (48.7% versus 31.0%; p < .0001), acute myocardial infarction (11% versus 6.4%; p = .048), sepsis (41.6% versus 22.5%; p < .0001), and respiratory failure (61.7% versus 42.3%; p < .0001) were significantly higher among patients with intracerebral hemorrhage and COVID-19 compared with those without COVID-19. The in-hospital mortality among patients with intracerebral hemorrhage and COVID-19 was significantly higher compared with that among those without COVID-19 (40.3% versus 19.0%; p < .0001). CONCLUSIONS: Our analysis does not suggest that rates of intracerebral hemorrhage are higher in patients with COVID-19. The higher mortality in patients with intracerebral hemorrhage and COVID-19 compared with those without COVID-19 is likely mediated by higher frequency of comorbidities and adverse in-hospital events.

Subarachnoid Hemorrhage and COVID-19: An Analysis of 282,718 Patients.

BACKGROUND: Intracranial hemorrhage (including subarachnoid hemorrhage [SAH]) has been reported in 0.3%-1.2% of patients with coronavirus disease 2019 (COVID-19). However, no study has evaluated the risk of SAH in patients with COVID-19. METHODS: We analyzed data from 62 health care facilities using the Cerner de-identified COVID-19 dataset. RESULTS: There were 86 (0.1%) and 376 (0.2%) patients with SAH among 85,645 patients with COVID-19 and 197,073 patients without COVID-19, respectively. In the multivariate model, there was a lower risk of SAH in patients with COVID-19 (odds ratio 0.5, 95% confidence interval 0.4-0.7, P < 0.0001) after adjusting for sex, age strata, race/ethnicity, hypertension, and nicotine dependence/tobacco use. The proportions of patients who developed pneumonia (58.1% vs. 21.3%, P < 0.0001), acute kidney injury (43% vs. 27.7%, P = 0.0005), septic shock (44.2% vs. 20.7%, P < 0.0001), and respiratory failure (64.0% vs. 39.1%, P < 0.0001) were significantly higher among patients with SAH and COVID-19 compared with patients without COVID-19. The in-hospital mortality among patients with SAH and COVID-19 was significantly higher compared with patients without COVID-19 (31.4% vs. 12.2%, P < 0.0001). CONCLUSIONS: The risk of SAH was not increased in patients with COVID-19. The higher mortality in patients with SAH and COVID-19 compared with patients without COVID-19 is likely mediated by higher frequency of systemic comorbidities.

Acute Ischemic Stroke and COVID-19: An Analysis of 27 676 Patients.

BACKGROUND AND PURPOSE: Acute ischemic stroke may occur in patients with coronavirus disease 2019 (COVID-19), but risk factors, in-hospital events, and outcomes are not well studied in large cohorts. We identified risk factors, comorbidities, and outcomes in patients with COVID-19 with or without acute ischemic stroke and compared with patients without COVID-19 and acute ischemic stroke. METHODS: We analyzed the data from 54 health care facilities using the Cerner deidentified COVID-19 dataset. The dataset included patients with an emergency department or inpatient encounter with discharge diagnoses codes that could be associated to suspicion of or exposure to COVID-19 or confirmed COVID-19. RESULTS: A total of 103 (1.3%) patients developed acute ischemic stroke among 8163 patients with COVID-19. Among all patients with COVID-19, the proportion of patients with hypertension, diabetes, hyperlipidemia, atrial fibrillation, and congestive heart failure was significantly higher among those with acute ischemic stroke. Acute ischemic stroke was associated with discharge to destination other than home or death (relative risk, 2.1 [95% CI, 1.6-2.4]; P<0.0001) after adjusting for potential confounders. A total of 199 (1.0%) patients developed acute ischemic stroke among 19 513 patients without COVID-19. Among all ischemic stroke patients, COVID-19 was associated with discharge to destination other than home or death (relative risk, 1.2 [95% CI, 1.0-1.3]; P=0.03) after adjusting for potential confounders. CONCLUSIONS: Acute ischemic stroke was infrequent in patients with COVID-19 and usually occurs in the presence of other cardiovascular risk factors. The risk of discharge to destination other than home or death increased 2-fold with occurrence of acute ischemic stroke in patients with COVID-19.