Predicting Delirium Risk Using an Automated Mayo Delirium Prediction Tool: Development and Validation of a Risk-Stratification Model

ObjectiveTo develop a delirium risk-prediction tool that is applicable across different clinical patient populations and can predict the risk of delirium at admission to hospital.MethodsThis retrospective study included 120,764 patients admitted to Mayo Clinic between January 1, 2012, and December 31, 2017, with age 50 and greater. The study group was randomized into a derivation cohort (n=80,000) and a validation cohort (n=40,764). Different risk factors were extracted and analyzed using least absolute shrinkage and selection operator (LASSO) penalized logistic regression.ResultsThe area under the receiver operating characteristic curve (AUROC) for Mayo Delirium Prediction (MDP) tool using derivation cohort was 0.85 (95% confidence interval [CI], .846 to .855). Using the regression coefficients obtained from the derivation cohort, predicted probability of delirium was calculated for each patient in the validation cohort. For the validation cohort, AUROC was 0.84 (95% CI, .834 to .847). Patients were classified into 1 of the 3 risk groups, based on their predicted probability of delirium: low (≤5%), moderate (6% to 29%), and high (≥30%). In the derivation cohort, observed incidence of delirium was 1.7%, 12.8%, and 44.8% (low, moderate, and high risk, respectively), which is similar to the incidence rates in the validation cohort of 1.9%, 12.7%, and 46.3%.ConclusionThe Mayo Delirium Prediction tool was developed from a large heterogeneous patient population with good validation results and appears to be a reliable automated tool for delirium risk prediction with hospitalization. Further prospective validation studies are required.     

Geographic Inequalities in Cardiovascular Mortality in the United States: 1999 to 2018

ObjectiveTo evaluate the trends in cardiovascular, ischemic heart disease (IHD), stroke, and heart failure mortality in the stroke belt in comparison with the rest of the United States.Patients and MethodsWe evaluated the nationwide mortality data of all Americans from the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research database from 1999 to 2018. Cause-specific deaths were identified in the stroke belt and nonstroke belt populations using International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes. The relative percentage gap was estimated as the absolute difference computed relative to nonstroke belt mortality. Piecewise linear regression and age-period-cohort modeling were used to assess, respectively, the trends and to forecast mortality across the 2 regions.ResultsThe cardiovascular mortality rate (per 100,000 persons) was 288.3 (95% CI, 288.0 to 288.6; 3,684,273 deaths) in the stroke belt region and 251.2 (95% CI, 251.0 to 251.3; 13,296,164 deaths) in the nonstroke belt region. In the stroke belt region, age-adjusted mortality rates due to all cardiovascular causes (average annual percentage change [AAPC] in mortality rates, ?2.4; 95% CI, ?2.8 to ?2.0), IHD (AAPC, ?3.8; 95% CI, ?4.2 to ?3.5), and stroke (AAPC, ?2.8; 95% CI, ?3.4 to ?2.1) declined from 1999 to 2018. A similar decline in cardiovascular (AAPC, ?2.5; 95% CI, ?3.0 to ?2.0), IHD (AAPC, ?4.0; 95% CI, ?4.3 to ?3.7), and stroke (AAPC, ?2.9; 95% CI, ?3.2 to ?2.2) mortality was seen in the nonstroke belt region. There was no overall change in heart failure mortality in both regions (P_AAPC>.05). The cardiovascular mortality gap was 11.8% in 1999 and 15.9% in 2018, with a modest reduction in absolute mortality rate difference (~7 deaths per 100,000 persons). These patterns were consistent across subgroups of age, sex, race, and urbanization status. An estimated 101,953 additional cardiovascular deaths need to be prevented from 2020 to 2025 in the stroke belt to ameliorate the gap between the 2 regions.ConclusionDespite the overall decline, substantial geographic disparities in cardiovascular mortality persist. Novel approaches are needed to attenuate the long-standing geographic inequalities in cardiovascular mortality in the United States, which are projected to increase.     

Effectiveness of COVID-19 Booster on the Risk of Hospitalization Among Medicare Beneficiaries

ObjectiveTo determine the effectiveness of booster vaccinations on the risk of hospitalization with coronavirus disease 2019 (COVID-19) and how it varies by enrollee characteristics and interval from the initial vaccination to receipt of a booster.Patients and MethodsThis cohort study used 100% Medicare claims from January 1, 2020, through December 31, 2021, and matched 3,940,475 individuals who received boosters to 3,940,475 controls based on week and type of original COVID-19 vaccine and demographic and clinical characteristics. We compared the association of booster vs no booster with COVID-19 hospitalization using Cox proportional hazards regression models controlling for patient characteristics. We also determined the association of time from original vaccine to booster with COVID-19 hospitalization.ResultsOver a maximum of 130 days of follow-up, boosted enrollees had 8.20 (95% CI, 7.81 to 8.60) COVID-19 hospitalizations per million days vs 43.70 (95% CI, 42.79 to 44.64) for controls (81% effectiveness). Effectiveness varied by race, prior hospitalizations, and certain comorbidities, for example, leukemia/lymphoma (53% effectiveness), autoimmune disease (73%), and dementia (73%). Boosters received between 6 and 9 months after original vaccination varied between 81% and 85% effectiveness, while boosters received at 5 to 6 months (62%) or less than 5 months (58%) were less effective.ConclusionBoosters are highly effective in the Medicare population. Approximately 69,225 hospitalizations would be prevented by boosters in the 15 million individuals aged 65 years or older currently not boosted in a period similar to the September 2020 through January 2021 period studied. Boosters provided the greatest benefits if they were received between 6 and 9 months following original vaccinations. However, boosters were associated with substantial decreases in COVID-19 hospitalizations in all categories of enrollees.     

Obesity is a Major Risk Factor for Hospitalization in Community-Managed COVID-19 Pneumonia

ObjectiveWe aimed to investigate whether the stratification of outpatients with coronavirus disease 2019 (COVID-19) pneumonia by body mass index (BMI) can help predict hospitalization and other severe outcomes.Patients and MethodsWe prospectively collected consecutive cases of community-managed COVID-19 pneumonia from March 1 to April 20, 2020, in the province of Bergamo and evaluated the association of overweight (25 kg/m² ≤ BMI <30 kg/m²) and obesity (≥30 kg/m²) with time to hospitalization (primary end point), low-flow domiciliary oxygen need, noninvasive mechanical ventilation, intubation, and death due to COVID-19 (secondary end points) in this cohort. We analyzed the primary end point using multivariable Cox models.ResultsOf 338 patients included, 133 (39.4%) were overweight and 77 (22.8%) were obese. Age at diagnosis was younger in obese patients compared with those overweight or with normal weight (P<.001), whereas diabetes, dyslipidemia, and heart diseases were differently distributed among BMI categories. Azithromycin, hydroxychloroquine, and prednisolone use were similar between BMI categories (P>.05). Overall, 105 (31.1%) patients were hospitalized, and time to hospitalization was significantly shorter for obese vs over- or normal-weight patients (P<.001). In the final multivariable analysis, obese patients were more likely to require hospitalization than nonobese patients (hazard ratio, 5.83; 95% CI, 3.91 to 8.71). Results were similar in multiple sensitivity analyses. Low-flow domiciliary oxygen need, hospitalization with noninvasive mechanical ventilation, intubation, and death were significantly associated with obesity (P<.001).ConclusionIn patients with community-managed COVID-19 pneumonia, obesity is associated with a higher hospitalization risk and overall worse outcomes than for nonobese patients.     

Examining Disparities and Excess Cardiovascular Mortality Before and During the COVID-19 Pandemic

ObjectiveTo investigate the patterns and demographic features of cardiovascular disease (CVD) death and subtypes myocardial infarction (MI), stroke, and heart failure in the pre–COVID-19 era (2018-2019) vs during the COVID-19 pandemic (2020-2021) in the United States.MethodsIn this cross-sectional study, we used the US Multiple Cause of Death files for 2018 to 2021 to examine the trend of excess cause-specific deaths using International Classification of Diseases, Tenth Revision codes for CVD (I00 to I99), MI (I21 and I22), stroke (I60 to I69), and heart failure (I42 and I50). Our primary outcome was excess mortality from CVD and its 3 subtypes (MI, stroke, and heart failure) between prepandemic (2018-2019) and pandemic (2020-2021) years. We performed a subgroup analysis on race and month-to-month and year-to-year variation using χ² analysis to test statistical significance.ResultsOverall, 3,598,352 CVD deaths were analyzed during the study period. There was a 6.7% excess CVD mortality, 2.5% MI mortality, and 8.5% stroke mortality during the COVID-19 pandemic (2020-2021) compared with the prepandemic era (2018-2019). Black individuals had higher excess CVD mortality (13.8%) than White individuals (5.1%; P<.001). This remained consistent across subtypes of CVD, including MI (9.6% vs 1.0%; P<.001), stroke (14.5% vs 6.9%; P<.001), and heart failure (5.1% vs ?1.2%; P<.001).ConclusionThere has been a significant rise in CVD and subtype-specific mortality during the COVID-19 pandemic that has been persistent despite 2 years since the onset of the pandemic. Excess CVD mortality has disproportionately affected Black compared with White individuals. Further studies targeting and eliminating health care disparities are necessary.     

Readability of Participant Informed Consent Forms and Informational Documents: From Phase 3 COVID-19 Vaccine Clinical Trials in the United States

ObjectiveTo assess the readability of the informed consent forms from the phase 3 COVID-19 vaccine trials conducted in the United States.Patients and MethodsEnglish consent forms were used for patients in phase 3 COVID-19 vaccine clinical trials. Consent forms were obtained in October 2020. Using Microsoft Word tools, we analyzed the readability (ie, the ease of reading) of written consent forms and informational documents from phase 3 COVID-19 vaccine clinical trials in the United States from the following manufacturers: AstraZeneca, Moderna, Pfizer, Johnson & Johnson, and Novavax.ResultsOwing to low readability and several format factors, this study determined that none of the consent forms or informational documents from the recent phase 3 COVID-19 vaccine clinical trials conducted in the United States met readability standards at the recommended 7th grade readability level for the average vaccine research volunteer in any readability category. The average English-speaking vaccine trial volunteer would have great difficulty comprehending the information provided in the consent forms and informational documents. To ensure that study subjects receive and fully comprehend information regarding a clinical study and can provide reliable consent, greater attention should be given to the development and use of simplified consent forms, multimedia formatting, personal discussion, and comprehension assessments.     

Impact of a High-Risk,Ambulatory COVID-19 Remote Patient Monitoring Program on Utilization,Cost, and Mortality

ObjectiveTo evaluate care utilization, cost, and mortality among high-risk patients enrolled in a coronavirus disease 2019 (COVID-19) remote patient monitoring (RPM) program.MethodsThis retrospective analysis included patients diagnosed with COVID-19 at risk for severe disease who enrolled in the RPM program between March 2020 and October 2021. The program included in-home technology for symptom and physiologic data monitoring with centralized care management. Propensity score matching established matched cohorts of RPM-engaged (defined as ≥1 RPM technology interactions) and non-engaged patients using a logistic regression model of 59 baseline characteristics. Billing codes and the electronic death certificate system were used for data abstraction from the electronic health record and reporting of care utilization and mortality endpoints.ResultsAmong 5796 RPM-enrolled patients, 80.0% engaged with the technology. Following matching, 1128 pairs of RPM-engaged and non-engaged patients comprised the analysis cohorts. Mean patient age was 63.3 years, 50.9% of patients were female, and 81.9% were non-Hispanic White. Patients who were RPM-engaged experienced significantly lower rates of 30-day, all-cause hospitalization (13.7% vs 18.0%, P=.01), prolonged hospitalization (3.5% vs 6.7%, P=.001), intensive care unit admission (2.3% vs 4.2%, P=.01), and mortality (0.5% vs 1.7%; odds ratio, 0.31; 95% CI, 0.12 to 0.78; P=.01), as well as cost of care ($2306.33 USD vs $3565.97 USD, P=0.04), than those enrolled in RPM but non-engaged.ConclusionHigh-risk COVID-19 patients enrolled and engaged in an RPM program experienced lower rates of hospitalization, intensive care unit admission, mortality, and cost than those enrolled and non-engaged. These findings translate to improved hospital bed access and patient outcomes.     

The Effect of Convalescent Plasma Therapy on Mortality Among Patients With COVID-19: Systematic Review and Meta-analysis

To determine the effect of COVID-19 convalescent plasma on mortality, we aggregated patient outcome data from 10 randomized clinical trials, 20 matched control studies, 2 dose-response studies, and 96 case reports or case series. Studies published between January 1, 2020, and January 16, 2021, were identified through a systematic search of online PubMed and MEDLINE databases. Random effects analyses of randomized clinical trials and matched control data demonstrated that patients with COVID-19 transfused with convalescent plasma exhibited a lower mortality rate compared with patients receiving standard treatments. Additional analyses showed that early transfusion (within 3 days of hospital admission) of higher titer plasma is associated with lower patient mortality. These data provide evidence favoring the efficacy of human convalescent plasma as a therapeutic agent in hospitalized patients with COVID-19.     

Outcomes of COVID-19 With the Mayo Clinic Model of Care and Research

ObjectiveTo report the Mayo Clinic experience with coronavirus disease 2019 (COVID-19) related to patient outcomes.MethodsWe conducted a retrospective chart review of patients with COVID-19 diagnosed between March 1, 2020, and July 31, 2020, at any of the Mayo Clinic sites. We abstracted pertinent comorbid conditions such as age, sex, body mass index, Charlson Comorbidity Index variables, and treatments received. Factors associated with hospitalization and mortality were assessed in univariate and multivariate models.ResultsA total of 7891 patients with confirmed COVID-19 infection with research authorization on file received care across the Mayo Clinic sites during the study period. Of these, 7217 patients were adults 18 years or older who were analyzed further. A total of 897 (11.4%) patients required hospitalization, and 354 (4.9%) received care in the intensive care unit (ICU). All hospitalized patients were reviewed by a COVID-19 Treatment Review Panel, and 77.5% (695 of 897) of inpatients received a COVID-19–directed therapy. Overall mortality was 1.2% (94 of 7891), with 7.1% (64 of 897) mortality in hospitalized patients and 11.3% (40 of 354) in patients requiring ICU care.ConclusionMayo Clinic outcomes of patients with COVID-19 infection in the ICU, hospital, and community compare favorably with those reported nationally. This likely reflects the impact of interprofessional multidisciplinary team evaluation, effective leveraging of clinical trials and available treatments, deployment of remote monitoring tools, and maintenance of adequate operating capacity to not require surge adjustments. These best practices can help guide other health care systems with the continuing response to the COVID-19 pandemic.     

Evidence-Based Strategies for Clinical Organizations to Address COVID-19 Vaccine Hesitancy

The success of vaccination programs is contingent upon irrefutable scientific safety data combined with high rates of public acceptance and population coverage. Vaccine hesitancy, characterized by lack of confidence in vaccination and/or complacency about vaccination that may lead to delay or refusal of vaccination despite the availability of services, threatens to undermine the success of coronavirus disease 2019 (COVID-19) vaccination programs. The rapid pace of vaccine development, misinformation in popular and social media, the polarized sociopolitical environment, and the inherent complexities of large-scale vaccination efforts may undermine vaccination confidence and increase complacency about COVID-19 vaccination. Although the experience of recent lethal surges of COVID-19 infections has underscored the value of COVID-19 vaccines, ensuring population uptake of COVID-19 vaccination will require application of multilevel, evidence-based strategies to influence behavior change and address vaccine hesitancy. Recent survey research evaluating public attitudes in the United States toward the COVID-19 vaccine reveals substantial vaccine hesitancy. Building upon efforts at the policy and community level to ensure population access to COVID-19 vaccination, a strong health care system response is critical to address vaccine hesitancy. Drawing on the evidence base in social, behavioral, communication, and implementation science, we review, summarize, and encourage use of interpersonal, individual-level, and organizational interventions within clinical organizations to address this critical gap and improve population adoption of COVID-19 vaccination.     

Pain Manifestations of COVID-19 and Their Association With Mortality: A Multicenter Prospective Observational Study

ObjectivesTo determine the prevalence and breakdown of pain symptoms among patients with coronavirus disease 2019 (COVID-19) infection admitted for nonpain symptoms and the association between the presence of pain and intensive care unit (ICU) admission and death.Patients and MethodsIn this multicenter prospective study, data on the intensity and type of pain were collected on 169 patients with active severe acute respiratory syndrome coronavirus 2 infection at 2 teaching hospitals in the United States and Korea and on 8 patients with acute pain at another large teaching hospital between February 1, 2020, and June 15, 2020.ResultsSixty-five of 169 patients (38.5%) reported an active pain condition. Among the 73 patients with pain, the most common pain symptoms were headache (n=22; 30.1%), chest pain (n=17; 23.3%), spinal pain (n=18; 24.7%), myalgia (n=13; 17.8%), abdominal or pelvic pain (n=13; 17.8%), arthralgia (n=11; 15.1%), and generalized pain (n=9; 12.3%). Those reporting headache as their main symptom were less likely to require ICU admission (P=.003). Acetaminophen or nonsteroidal anti-inflammatory drugs were prescribed to 80.8% (n=59), opioids to 17.8% (n=13), adjuvants to 8.2% (n=6), and ketamine to 5.5% (n=4) of patients with pain. When age 65 years and older and sex were controlled for in multivariable analysis, the absence of pain was associated with ICU admission (odds ratio, 2.92; 95% CI, 1.42 to 6.28; P=.004) and death (odds ratio, 3.49; 95% CI, 1.40 to 9.76; P=.01).ConclusionAcute pain is common during active COVID-19 infection with the most common manifestations being headache, chest pain and spine pain. Individuals without pain were more likely to require intensive care and expire than those with pain. Reasons why pain may be associated with reduced mortality include that an intense systemic stimulus (eg, respiratory distress) might distract pain perception or that the catecholamine surge associated with severe respiratory distress might attenuate nociceptive signaling.     

Socioeconomic Disparities in Community Mobility Reduction and COVID-19 Growth

ObjectiveTo examine differences in community mobility reduction and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outcomes across counties with differing levels of socioeconomic disadvantage.MethodsThe sample included counties in the United States with at least one SARS-CoV-2 case between April 1 and May 15, 2020. Outcomes were growth in SARS-CoV-2 cases, SARS-CoV-2–related deaths, and mobility reduction across three settings: retail/recreation, grocery/pharmacy, and workplace. The main explanatory variable was the social deprivation index (SDI), a composite socioeconomic disadvantage measure.ResultsAdjusted differences in outcomes between low-, medium-, and high-SDI counties (defined by tertile) were calculated using linear regression with state-fixed effects. Workplace mobility reduction was 1.75 (95% CI, -2.36 to -1.14; P<.001) and 3.48 percentage points (95% CI, -4.21 to -2.75; P<.001) lower for medium- and high-SDI counties relative to low-SDI counties, respectively. Mobility reductions in the other settings were also significantly lower for higher-SDI counties. In analyses adjusted for SARS-CoV-2 prevalence on April 1, medium- and high-SDI counties had 1.39 (95% CI, 0.85 to 1.93; P<.001) and 2.56 (95% CI, 1.77 to 3.34; P<.001) more SARS-CoV-2 cases/1000 population on May 15 compared with low-SDI counties, respectively. Deaths per capita were also significantly higher for higher-SDI counties.ConclusionCounties with higher social deprivation scores experienced greater growth in SARS-CoV-2 cases and deaths, but reduced mobility at lower rates. These findings are consistent with evidence demonstrating that economically disadvantaged communities have been disproportionately impacted by the coronavirus disease 2019 pandemic. Efforts to socially distance may be more burdensome for these communities, potentially exacerbating disparities in SARS-CoV-2–related outcomes.     

Viral Endothelial Dysfunction: A Unifying Mechanism for COVID-19

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly transmissible virus with significant global impact, morbidity, and mortality. The SARS-CoV-2 virus may result in widespread organ manifestations including acute respiratory distress syndrome, acute renal failure, thromboembolism, and myocarditis. Virus-induced endothelial injury may cause endothelial activation, increased permeability, inflammation, and immune response and cytokine storm. Endothelial dysfunction is a systemic disorder that is a precursor of atherosclerotic vascular disease that is associated with cardiovascular risk factors and is highly prevalent in patients with atherosclerotic cardiovascular and peripheral disease. Several studies have associated various viral infections including SARS-CoV-2 infection with inflammation, endothelial dysfunction, and subsequent innate immune response and cytokine storm. Noninvasive monitoring of endothelial function and identification of high-risk patients who may require specific therapies may have the potential to improve morbidity and mortality associated with subsequent inflammation, cytokine storm, and multiorgan involvement.     

Leveraging the Electronic Health Record to Address the COVID-19 Pandemic

The coronavirus disease 2019 (COVID-19) pandemic continues its global spread. Coordinated effort on a vast scale is required to halt its progression and to save lives. Electronic health record (EHR) data are a valuable resource to mitigate the COVID-19 pandemic. We review how the EHR could be used for disease surveillance and contact tracing. When linked to “omics” data, the EHR could facilitate identification of genetic susceptibility variants, leading to insights into risk factors, disease complications, and drug repurposing. Real-time monitoring of patients could enable early detection of potential complications, informing appropriate interventions and therapy. We reviewed relevant articles from PubMed, MEDLINE, and Google Scholar searches as well as preprint servers, given the rapidly evolving understanding of the COVID-19 pandemic.     

Long-Term Effects of COVID-19

Coronavirus disease 2019 (COVID-19) is the third deadly coronavirus infection of the 21st century that has proven to be significantly more lethal than its predecessors, with the number of infected patients and deaths still increasing daily. From December 2019 to July 2021, this virus has infected nearly 200 million people and led to more than 4 million deaths. Our understanding of COVID-19 is constantly progressing, giving better insight into the heterogeneous nature of its acute and long-term effects. Recent literature on the long-term health consequences of COVID-19 discusses the need for a comprehensive understanding of the multisystemic pathophysiology, clinical predictors, and epidemiology to develop and inform an evidence-based, multidisciplinary management approach. A PubMed search was completed using variations on the term post-acute COVID-19. Only peer-reviewed studies in English published by July 17, 2021 were considered for inclusion. All studies discussed in this text are from adult populations unless specified (as with multisystem inflammatory syndrome in children). The preliminary evidence on the pulmonary, cardiovascular, neurological, hematological, multisystem inflammatory, renal, endocrine, gastrointestinal, and integumentary sequelae show that COVID-19 continues after acute infection. Interdisciplinary monitoring with holistic management that considers nutrition, physical therapy, psychological management, meditation, and mindfulness in addition to medication will allow for the early detection of post-acute COVID-19 sequelae symptoms and prevent long-term systemic damage. This review serves as a guideline for effective management based on current evidence, but clinicians should modify recommendations to reflect each patient's unique needs and the most up-to-date evidence. The presence of long-term effects presents another reason for vaccination against COVID-19.     

Host Factors and Outcomes in Hospitalizations for Pneumocystis Jirovecii Pneumonia in the United States

ObjectiveTo assess host factors in pneumocystis jirovecii pneumonia (PCP)–related hospitalizations and compare outcomes between HIV and non-HIV patients.MethodsUsing the National Inpatient Sample database, we identified 3384 hospitalizations with PCP (International Classification of Diseases, Ninth Revision, Clinical Modification code: 136.3) as the primary discharge diagnosis from 2005 to 2014. We evaluated hospitalizations for the following host factors: HIV, malignancies, organ transplantation, rheumatologic diseases, and vasculitides. We compared the prevalence of individual host factors among PCP hospitalizations over time, and compared intervention rates and outcomes between HIV and non-HIV patients with PCP.ResultsAmong all hospitalizations for PCP, malignancy was the most prevalent host factor (46.0%, n=1559), followed by HIV (17.8%, n=604); 60.7% (n=946) of malignancies were hematologic. The prevalence of HIV among hospitalizations for PCP decreased from 25.1% in 2005 to 9.2% in 2014 (P<.001), whereas the prevalence of non-HIV immunocompromising conditions increased. Compared with HIV patients, PCP patients without HIV had higher rates of bronchoscopy (52.3% vs 26.7%, P<.001) and endotracheal intubation (17.0% vs 7.9%, P<.001), prolonged hospitalizations (11.5 vs 8.7 days, P<.001), higher hospitalization costs (86.8 vs 48.2×10³ USD, P<.001) and increased in-hospital mortality (16.0% vs 5.0%, P<.001). After adjusting for age, sex, and smoking status, there was no difference in mortality between non-HIV and HIV patients with PCP (adjusted odds ratio, 1.4; 95% CI, 0.9 to 2.3).ConclusionThe epidemiology of PCP has shifted with an increase in the prevalence of non-HIV patients who have higher intubation rates and prolonged hospitalizations compared with matched HIV patients.     

Macrovascular Thrombotic Events in a Mayo Clinic Enterprise-Wide Sample of Hospitalized COVID-19–Positive Compared With COVID-19–Negative Patients

ObjectiveTo determine the difference in the rate of thromboembolic complications between hospitalized coronavirus disease 2019 (COVID-19)–positive compared with COVID-19–negative patients.Patients and MethodsAdult patients hospitalized from January 1, 2020, through May 8, 2020, who had COVID-19 testing by polymerase chain reaction assay were identified through electronic health records across multiple hospitals in the Mayo Clinic enterprise. Thrombotic outcomes (venous and arterial) were identified from the hospital problem list.ResultsWe identified 3790 hospitalized patients with COVID-19 testing across 19 hospitals, 102 of whom had positive test results. The median age was lower in the COVID-positive patients (62 vs 67 years; P=.03). The median duration of hospitalization was longer in COVID-positive patients (8.5 vs 4 days; P<.001) and more required intensive care unit care (56.9% [58 of 102] vs 26.8% [987 of 3688]; P<.001). Comorbidities, including atrial fibrillation/flutter, heart failure, chronic kidney disease, and malignancy, were observed less frequently with COVID-positive admissions. Any venous thromboembolism was identified in 2.9% of COVID-positive patients (3 of 102) and 4.6% of COVID-negative patients (168 of 3688). The frequency of venous and arterial events was not different between the groups. The unadjusted odds ratio (OR) for COVID-positive–patients for any venous thromboembolism was 0.63 (95% CI, 0.19 to 2.02). A multivariable logistic regression model evaluated death within 30 days of hospital discharge; neither COVID positivity (adjusted OR, 1.12; 95% CI, 0.54 to 2.34) nor thromboembolism (adjusted OR, 0.90; 95% CI, 0.60 to 1.32) was associated with death.ConclusionEarly experience in patients with COVID-19 across multiple academic and regional hospitals representing different US regions demonstrates a lower than previously reported incidence of thrombotic events. This incidence was not higher than a contemporary COVID-negative hospitalized comparator.