COVID-19 Registry Research Articles

Cardiovascular Factors Associated with COVID-19 from an International Registry of Primarily Japanese Patients.

Aims: We developed an international registry to examine cardiovascular complications of COVID-19. Methods: A REDCap form was created in March 2020 at Mayo Clinic in collaboration with the International Society of Cardiomyopathy, Myocarditis and Heart Failure (ISCMF) and data were entered from April 2020 through April 2021. Results: Of the 696 patients in the COVID-19 Registry, 411 (59.2%) were male and 283 (40.8%) were female, with a sex ratio of 1.5:1 male to female. In total, 95.5% of the patients were from Japan. The average age was 52 years with 31.5% being >65 years of age. COVID-19 patients with a history of cardiovascular disease (CVD) had more pre-existing conditions including type II diabetes (p < 0.0001), cancer (p = 0.0003), obesity (p = 0.001), and kidney disease (p = 0.001). They also had a greater mortality of 10.1% compared to 1.7% in those without a history of CVD (p < 0.0001). The most common cardiovascular conditions in patients with a history of CVD were hypertension (33.7%), stroke (5.7%) and arrhythmias (5.1%). We found that troponin T, troponin I, brain natriuretic peptide (BNP), N-terminal pro-BNP (NT-proBNP), C-reactive protein (CRP), IL-6 and lambda immunoglobulin free light chains (Ig FLC) were elevated above reference levels in patients with COVID-19. Myocarditis is known to occur mainly in adults under the age of 50, and when we examined biomarkers in patients that were ≤50 years of age and had no history of CVD we found that a majority of patients had elevated levels of troponin T (71.4%), IL-6 (59.5%), creatine kinase/CK-MB (57.1%), D-dimer (57.8%), kappa Ig FLC (75.0%), and lambda Ig FLC (71.4%) suggesting myocardial injury and possible myocarditis. Conclusions: We report the first findings to our knowledge of cardiovascular complications from COVID-19 in the first year of the pandemic in a predominantly Japanese population. Mortality was increased by a history of CVD and pre-existing conditions including type II diabetes, cancer, obesity, and kidney disease. Our findings indicate that even in cases where no abnormalities are found in ECG or ultrasound cardiography that myocardial damage may occur, and cardiovascular and inflammatory biomarkers may be useful for the diagnosis.

Functional and Clinical Characteristics of Individuals Attending Pulmonary Rehabilitation After Severe COVID-19.

A proportion of patients with COVID-19 need hospitalization due to severe respiratory symptoms. We sought to analyze characteristics of survivors of severe COVID-19 subsequently admitted to in-patient pulmonary rehabilitation and identify their rehabilitation needs. From the COVID-19 Registry of Fondazione Don Gnocchi, we extracted 203 subjects admitted for in-patient pulmonary rehabilitation after severe COVID-19 from April 2020-September 2021. Specific information on acute-hospital stay and clinical and functional characteristics on admission to rehabilitation units were collected. During the acute phase of disease, 168 subjects received mechanical ventilation for 26 d; 85 experienced delirium during their stay in ICU. On admission to rehabilitation units, 20 subjects were still on mechanical ventilation; 57 had tracheostomy; 142 were on oxygen therapy; 49 were diagnosed critical illness neuropathy; 162 showed modified Barthel Index < 75; only 51 were able to perform a 6-min walk test; 32 of 90 scored abnormal at Montreal Cognitive Assessment; 43 of 88 scored abnormal at Hospital Anxiety and Depression Scale; 65 scored ≥ 2 at Malnutrition Universal Screening Tool, and 95 showed dysphagia needing logopedic treatment. Our analysis shows that subjects admitted for in-patient pulmonary rehabilitation after severe COVID-19 represent an extraordinarily multifaceted and clinically complex patient population who need customized, comprehensive rehabilitation programs carried out by teams with different professional skills. The need for step-down facilities, such as sub-intensive rehabilitation units, is also highlighted.

Clinical outcomes in patients with COVID-19 and gynecologic cancer: A society of gynecologic oncology COVID-19 and gynecologic cancer registry study

ObjectivesPatients with gynecologic malignancies may have varied responses to COVID-19 infection. We aimed to describe clinical courses, treatment changes, and short-term clinical outcomes for gynecologic oncology patients with concurrent COVID-19 in the United States. MethodsThe Society of Gynecologic Oncology COVID-19 and Gynecologic Cancer Registry was created to capture clinical courses of gynecologic oncology patients with COVID-19. Logistic regression models were employed to evaluate factors for an association with hospitalization and death, respectively, within 30 days of COVID-19 diagnosis. ResultsData were available for 348 patients across 7 institutions. At COVID-19 diagnosis, 125 patients (36%) had active malignancy. Delay (n = 88) or discontinuation (n = 10) of treatment due to COVID-19 infection occurred in 28% with those on chemotherapy (53/88) or recently receiving surgery (32/88) most frequently delayed. In addition to age, performance status, diabetes, and specific COVID symptoms, both non-White race (adjusted odds ratio (aOR) = 3.93, 95% CI 2.06–7.50) and active malignancy (aOR = 2.34, 95% CI 1.30–4.20) were associated with an increased odds of hospitalization. Eight percent of hospitalized patients (8/101) died of COVID-19 complications and 5% (17/348) of the entire cohort died within 30 days after diagnosis. ConclusionsGynecologic oncology patients diagnosed with COVID-19 are at risk for hospitalization, delay of anti-cancer treatments, and death. One in 20 gynecologic oncology patients with COVID-19 died within 30 days after diagnosis. Racial disparities exist in patient hospitalizations for COVID-19, a surrogate of disease severity. Additional studies are needed to determine long-term outcomes and the impact of race.

Despite Vaccination: A Real-Life Experience of Severe and Life-Threatening COVID-19 in Vaccinated and Unvaccinated Patients.

Some vaccinated individuals still develop severe COVID-19, and the underlying causes are not entirely understood. We aimed at identifying demographic, clinical, and coinfection characteristics of vaccinated patients who were hospitalized. We also hypothesized that coinfections might play a role in disease severity and mortality. We retrospectively collected data from our COVID-19 registry for whom vaccination data were available. Patients were split into groups based on the number of administered doses (zero, one, two, or three). Data were assessed with Chi-square and Kruskal–Wallis tests and multiple logistic regression analysis. We collected data from 1686 patients and found that intra-hospital mortality was not associated to the vaccination status (e.g., p = 0.2 with three doses), while older age, sepsis, and non-viral pneumonia were (p < 0.001). Unvaccinated patients needed mechanical ventilation more often (8.5%) than vaccinated patients, in whom the probability of mechanical ventilation decreased with increasing doses (8.7%, 2.8%, 0%). We did not find more coinfections in vaccinated people. We concluded that there is a lack of real-life data to adequately characterize the pathophysiology and risk factors of patients who develop severe COVID-19, but coinfections do not appear to play a role in disease severity.

The American Academy of Dermatology and International League of Dermatological Societies Monkeypox Registry: Expanding the COVID-19 registry to emerging infections

The World Health Organization declared the global monkeypox outbreak a public health emergency of international concern in July 2022. In response, the American Academy of Dermatology and International League of Dermatological Societies expanded the existing COVID-19 Dermatology Registry to become the “AAD/ILDS Dermatology COVID-19, Monkeypox, and Emerging Infections Registry.” The goal of the registry is to rapidly collate cases of monkeypox and other emerging infections and enable prompt dissemination of findings to front-line healthcare workers and other members of the medical community. The registry is now accepting reports of monkeypox cases and cutaneous reactions to monkeypox/smallpox vaccines. The success of this collaborative effort will depend on active case entry by the global dermatology community.

Demographics, Outcomes, and Risk Factors for Patients with Sarcoma and COVID-19: A CCC19-Registry Based Retrospective Cohort Study.

Simple SummarySarcomas are a group of cancers with differing clinical features, some of which require long courses of cytotoxic chemotherapy. Therefore, patients with sarcoma may be at high risk of developing severe COVID-19. The aim of our study was to describe risk factors and clinical outcomes for patients with sarcoma and COVID-19. We show that patients with sarcoma have high rates of complications from COVID-19. Risk factors for more severe COVID-19 included older age, poor performance status, and lung metastases. We also compared 30 day mortality rates to a matched cohort of patients with sarcoma without COVID-19 and found that patients with bone sarcoma may be at higher risk of death from COVID-19 than patients with other sarcoma subtypes. Background: Patients with sarcoma often require individualized treatment strategies and are likely to receive aggressive immunosuppressive therapies, which may place them at higher risk for severe COVID-19. We aimed to describe demographics, risk factors, and outcomes for patients with sarcoma and COVID-19. Methods: We performed a retrospective cohort study of patients with sarcoma and COVID-19 reported to the COVID-19 and Cancer Consortium (CCC19) registry (NCT04354701) from 17 March 2020 to 30 September 2021. Demographics, sarcoma histologic type, treatments, and COVID-19 outcomes were analyzed. Results: of 281 patients, 49% (n = 139) were hospitalized, 33% (n = 93) received supplemental oxygen, 11% (n = 31) were admitted to the ICU, and 6% (n = 16) received mechanical ventilation. A total of 23 (8%) died within 30 days of COVID-19 diagnosis and 44 (16%) died overall at the time of analysis. When evaluated by sarcoma subtype, patients with bone sarcoma and COVID-19 had a higher mortality rate than patients from a matched SEER cohort (13.5% vs 4.4%). Older age, poor performance status, recent systemic anti-cancer therapy, and lung metastases all contributed to higher COVID-19 severity. Conclusions: Patients with sarcoma have high rates of severe COVID-19 and those with bone sarcoma may have the greatest risk of death.

Development and validation of the MMCD score to predict kidney replacement therapy in COVID-19 patients

BackgroundAcute kidney injury (AKI) is frequently associated with COVID-19, and the need for kidney replacement therapy (KRT) is considered an indicator of disease severity. This study aimed to develop a prognostic score for predicting the need for KRT in hospitalised COVID-19 patients, and to assess the incidence of AKI and KRT requirement.MethodsThis study is part of a multicentre cohort, the Brazilian COVID-19 Registry. A total of 5212 adult COVID-19 patients were included between March/2020 and September/2020. Variable selection was performed using generalised additive models (GAM), and least absolute shrinkage and selection operator (LASSO) regression was used for score derivation. Accuracy was assessed using the area under the receiver operating characteristic curve (AUC-ROC).ResultsThe median age of the model-derivation cohort was 59 (IQR 47–70) years, 54.5% were men, 34.3% required ICU admission, 20.9% evolved with AKI, 9.3% required KRT, and 15.1% died during hospitalisation. The temporal validation cohort had similar age, sex, ICU admission, AKI, required KRT distribution and in-hospital mortality. The geographic validation cohort had similar age and sex; however, this cohort had higher rates of ICU admission, AKI, need for KRT and in-hospital mortality. Four predictors of the need for KRT were identified using GAM: need for mechanical ventilation, male sex, higher creatinine at hospital presentation and diabetes. The MMCD score had excellent discrimination in derivation (AUROC 0.929, 95% CI 0.918–0.939) and validation (temporal AUROC 0.927, 95% CI 0.911–0.941; geographic AUROC 0.819, 95% CI 0.792–0.845) cohorts and good overall performance (Brier score: 0.057, 0.056 and 0.122, respectively). The score is implemented in a freely available online risk calculator (https://www.mmcdscore.com/).ConclusionsThe use of the MMCD score to predict the need for KRT may assist healthcare workers in identifying hospitalised COVID-19 patients who may require more intensive monitoring, and can be useful for resource allocation.

500P The impact of COVID-19 on the management and outcome of oncology patients: The results of the Middle East and North African (MENA) COVID-19 and Cancer Registry (MCCR)

Despite extensive studies of the impact of COVID-19 on patients with cancer, there is a dearth of information from the MENA region. Our study aims to report pertinent MCCR findings on patient management and outcomes.

The Association of Latitude and Altitude with COVID-19 Symptoms: A VIRUS: COVID-19 Registry Analysis.

Better delineation of COVID-19 presentations in different climatological conditions might assist with prompt diagnosis and isolation of patients. To study the association of latitude and altitude with COVID-19 symptomatology. This observational cohort study included 12267 adult COVID-19 patients hospitalized between 03/2020 and 01/2021 at 181 hospitals in 24 countries within the SCCM Discovery VIRUS: COVID-19 Registry. The outcome was symptoms at admission, categorized as respiratory, gastrointestinal, neurological, mucocutaneous, cardiovascular, and constitutional. Other symptoms were grouped as atypical. Multivariable regression modeling was performed, adjusting for baseline characteristics. Models were fitted using generalized estimating equations to account for the clustering. The median age was 62 years, with 57% males. The median age and percentage of patients with comorbidities increased with higher latitude. Conversely, patients with comorbidities decreased with elevated altitudes. The most common symptoms were respiratory (80%), followed by constitutional (75%). Presentation with respiratory symptoms was not associated with the location. After adjustment, at lower latitudes (<30º), patients presented less commonly with gastrointestinal symptoms (p<.001, odds ratios for 15º, 25º, and 30º: 0.32, 0.81, and 0.98, respectively). Atypical symptoms were present in 21% of the patients and showed an association with altitude (p=.026, odds ratios for 75, 125, 400, and 600 meters above sea level: 0.44, 0.60, 0.84, and 0.77, respectively). We observed geographic variability in symptoms of COVID-19 patients. Respiratory symptoms were most common but were not associated with the location. Gastrointestinal symptoms were less frequent in lower latitudes. Atypical symptoms were associated with higher altitude.

Evaluation of the representativeness of data in the COVID-19 Registry Japan during the first six waves of the epidemic.

The COVID-19 Registry Japan (COVIREGI-JP), a registry of patients hospitalized with coronavirus disease (COVID-19), contains the largest national COVID-19 inpatient population. Since COVIREGI-JP invites voluntary participation by facilities, selection bias is inevitable. The current study examined the representativeness of COVIREGI-JP data in comparison to open-source national data. The number of infections and deaths among hospitalized COVID-19 patients in COVIREGI-JP were compared to those in national data recorded during the six waves of the COVID-19 epidemic until March 6, 2022. During the period studied, patients in COVIREGI-JP represented 1% of the total COVID-19 cases according to national data; the proportion was high during the first wave (32.7%) and tended to decrease, especially after the fourth wave. The overall proportion of patients from each region varied from 0.8% to 2.5%, but case fatality rates in COVIREGI-JP tended to be higher than those in the national data, with the exception of a few waves, in several regions. The difference was smallest during the first wave. Although COVIREGI-JP consistently registered cases from all regions of the country, the proportion tended to decline after the beginning of the epidemic. Given the epidemiological persistence and the ever-changing epidemiology of COVID-19, continued case registration and data utilization in COVIREGI-JP is desirable, although selection bias in COVIREGI-JP registration of cases should be carefully interpreted.

Hyperglycaemia in COVID-19 Patients at Admission, a Study during the Beginning of the Pandemic in Sidi-Bel-Abbes, Algeria

Reportedly, SARS-CoV-2 infection impairs glucose homeostasis and metabolism. Moreover, hyperglycaemia has emerged as an important risk factor for COVID-19 mortality. A cross-sectional study involving hospitalized patients in Sidi-Bel-Abbes with COVID-19 critical infection regardless of diabetes status was conducted. Data was collected from COVID-19 Registry, including age, sex, and blood sugar level at admission. Out of 800 patients admitted for SARS-CoV-2 with, 332 patients (206 males, 126 females) exhibited hyperglycaemia, which equates to a prevalence of 41,5%. Patients had a median age of 64 years and a median blood sugar of 1,59. Admission hyperglycaemia was not statically associated with age and sex. Blood glucose status is paramount for ensuring safe and effective treatment of inpatients COVID-19. Additional studies are forthcoming to address the study's limitations and to better understand the impact of hyperglycaemia on COVID-19 patients.

Leveraging artificial intelligence and data science techniques in harmonizing, sharing, accessing and analyzing SARS-COV-2/COVID-19 data in Rwanda (LAISDAR Project): study design and rationale

BackgroundSince the outbreak of COVID-19 pandemic in Rwanda, a vast amount of SARS-COV-2/COVID-19-related data have been collected including COVID-19 testing and hospital routine care data. Unfortunately, those data are fragmented in silos with different data structures or formats and cannot be used to improve understanding of the disease, monitor its progress, and generate evidence to guide prevention measures. The objective of this project is to leverage the artificial intelligence (AI) and data science techniques in harmonizing datasets to support Rwandan government needs in monitoring and predicting the COVID-19 burden, including the hospital admissions and overall infection rates.MethodsThe project will gather the existing data including hospital electronic health records (EHRs), the COVID-19 testing data and will link with longitudinal data from community surveys. The open-source tools from Observational Health Data Sciences and Informatics (OHDSI) will be used to harmonize hospital EHRs through the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The project will also leverage other OHDSI tools for data analytics and network integration, as well as R Studio and Python. The network will include up to 15 health facilities in Rwanda, whose EHR data will be harmonized to OMOP CDM.Expected resultsThis study will yield a technical infrastructure where the 15 participating hospitals and health centres will have EHR data in OMOP CDM format on a local Mac Mini (“data node”), together with a set of OHDSI open-source tools. A central server, or portal, will contain a data catalogue of participating sites, as well as the OHDSI tools that are used to define and manage distributed studies. The central server will also integrate the information from the national Covid-19 registry, as well as the results of the community surveys. The ultimate project outcome is the dynamic prediction modelling for COVID-19 pandemic in Rwanda.DiscussionThe project is the first on the African continent leveraging AI and implementation of an OMOP CDM based federated data network for data harmonization. Such infrastructure is scalable for other pandemics monitoring, outcomes predictions, and tailored response planning.

Economic and mental health impacts of multiple adverse events: Hurricane Harvey, other flooding events, and the COVID-19 pandemic

ObjectivesTo assess the economic and mental health impacts of COVID-19 in the presence of previous exposure to flooding events. MethodsStarting in April 2018, the Texas Flood Registry (TFR) invited residents to complete an online survey regarding their experiences with Hurricane Harvey and subsequent flooding events. Starting in April 2020, participants nationwide were invited to complete a brief online survey on their experiences during the pandemic. This study includes participants in the TFR (N = 20,754) and the COVID-19 Registry (N = 8568) through October 2020 (joint N = 2929).Logistic regression and generalized estimating equations were used to examine the relationship between exposure to flooding events and the economic and mental health impacts of COVID-19. ResultsAmong COVID-19 registrants, 21% experienced moderate to severe anxiety during the pandemic, and 7% and 12% of households had difficulty paying rent and bills, respectively. Approximately 17% of Black and 15% of Hispanic households had difficulty paying rent, compared to 5% of non-Hispanic white households. The odds of COVID-19 income loss are 1.20 (1.02, 1.40) times higher for those who previously had storm-related home damage compared to those who did not and 3.84 (3.25–4.55) times higher for those who experienced Harvey income loss compared to those who did not. For registrants for whom Harvey was a severe impact event, the odds of having more severe anxiety during the pandemic are 5.14 (4.02, 6.58) times higher than among registrants for whom Harvey was a no meaningful impact event. ConclusionsMultiple crises can jointly and cumulatively shape health and wellbeing outcomes. This knowledge can help craft emergency preparation and intervention programs.

Predictive model of risk factors of High Flow Nasal Cannula using machine learning in COVID-19.

With the rapid increase in the number of COVID-19 patients in Japan, the number of patients receiving oxygen at home has also increased rapidly, and some of these patients have died. An efficient approach to identify high-risk patients with slowly progressing and rapidly worsening COVID-19, and to avoid missing the timing of therapeutic intervention will improve patient prognosis and prevent medical complications. Patients admitted to medical institutions in Japan from November 14, 2020 to April 11, 2021 and registered in the COVID-19 Registry Japan were included. Risk factors for patients with High Flow Nasal Cannula invasive respiratory management or higher were comprehensively explored using machine learning. Age-specific cohorts were created, and severity prediction was performed for the patient surge period. We were able to obtain a model that was able to predict severe disease with a sensitivity of 57% when the specificity was set at 90% for those aged 40–59 years, and with a specificity of 50% and 43% when the sensitivity was set at 90% for those aged 60–79 years and 80 years and older, respectively. We were able to identify lactate dehydrogenase level (LDH) as an important factor in predicting the severity of illness in all age groups. Using machine learning, we were able to identify risk factors with high accuracy, and predict the severity of the disease. We plan to develop a tool that will be useful in determining the indications for hospitalisation for patients undergoing home care and early hospitalisation.

Impact of COVID-19 on gynecologic oncology patients: an SGO COVID-19 and Gynecologic Cancer Registry study (050)

Objectives: The COVID-19 pandemic has disrupted timely medical care, including receipt of anticancer therapy. Gynecologic oncology (GO) patients (pts), who are often comorbid and require a range of therapies, may have varied responses to COVID-19 infection. Our study aimed to describe outcomes for GO pts with concurrent COVID-19 infection in the United States.Methods: The Society of Gynecologic Oncology COVID-19 and Gynecologic Cancer Registry was developed as a web-based data entry platform to capture the clinical courses and cancer treatment-related effects in GO pts with confirmed COVID-19 infection. Demographics, clinical manifestations, outcomes, treatment type, and disease severity were analyzed.Results: Data were available for 312 GO pts across eight institutions. The median age was 63 years, and most pts were identified as White (70%), Black/African American (17%), or Asian (2%). GO diagnosis included low-grade endometrial (31%), high-grade ovarian (22%), cervical (14%), high-grade endometrial (13%), vulvar (3%), and other (16%). At the time of COVID-19 diagnosis, 37% of pts had active malignancy, with 25% receiving anticancer therapy. The most common anticancer treatments included chemotherapy (51%, n=39), targeted agents (25%, n = 27), and surgery (27%, n = 21), whereas 12 pts (16%) received multi-modal therapy. Delay or discontinuation of treatment due to COVID-19 infection occurred in 28% of patients (median 3-4 weeks delay). Chemotherapy was most frequently delayed (43%), followed by surgery (27%). In total, 19% of GO pts required supplemental oxygen, and 29% were hospitalized (5% in the intensive care setting). Ten pts (3%) required ventilator support, and 30% of pts receiving chemotherapy were hospitalized. Older age, having more than two comorbid conditions, and non-White race were associated (p<0.05) with a higher likelihood of being hospitalized or admitted to the intensive care unit. Patients who required hospitalization were disproportionately Black/African American, Asian, or other non-Whites (p = 0.003). On multivariable analysis, pts of non-White race (aOR: 2.86, 95% CI: 1.64-5.00; p<0.001) and advancing age (aOR: 1.28, 95% CI: 1.16-1.42; p<0.001) had a higher risk of hospitalization. In terms of mortality, 8% of hospitalized pts died of COVID-19 complications, and 4% of the entire cohort was not alive 30 days after COVID-19 diagnosis. Patients who died were more likely to be older (OR: 1.25 per 5-year increase, 95% CI: 1.01-1.55), had active malignancy (OR: 3.86, 95% CI: 1.16-12.85), or be of the non-White race (death rate of 3% White, 8% Black/African American, 25% Asian).Conclusions: GO pts diagnosed with COVID-19 are at high risk of hospitalization, delay of anticancer treatments, and death. Approximately one in 23 GO pts who contracted COVID-19 were not alive 30 days after diagnosis. Racial disparities exist in pt hospitalizations and mortality rates. Additional studies are needed to determine the long-term oncologic and mortality outcomes and the impact of race in this pt cohort.

Smoking is associated with increased risk of cardiovascular events, disease severity, and mortality among patients hospitalized for SARS-CoV-2 infections.

The clinical sequalae of SARS-CoV-2 infection are in part dependent upon age and pre-existing health conditions. Although the use of tobacco products decreases cardiorespiratory fitness while increasing susceptibility to microbial infections, limited information is available on how smoking affects COVID-19 severity. Therefore, we examined whether smokers hospitalized for COVID-19 are at a greater risk for developing severe complications than non-smokers. Data were from all hospitalized adults with SARS-CoV-2 infection from the American Heart Association's Get-With-The-Guidelines COVID-19 Registry, from January 2020 to March 2021, which is a hospital-based voluntary national registry initiated in 2019 with 122 participating hospitals across the United States. Patients who reported smoking at the time of admission were classified as smokers. Severe outcome was defined as either death or the use of mechanical ventilation. Of the 31,545 patients in the cohort, 6,717 patients were 1:2 propensity matched (for age, sex, race, medical history, medications, and time-frame of hospital admission) and classified as current smokers or non-smokers according to admission data. In multivariable analyses, after adjusting for sociodemographic characteristics, medical history, medication use, and the time of hospital admission, patients self-identified as current smokers had higher adjusted odds of death (adjusted odds ratio [aOR], 1.41; 95% CI, 1.21-1.64), the use of mechanical ventilation (aOR 1.15; 95% CI 1.01-1.32), and increased risk of major adverse cardiovascular events (aOR, 1.27; 95% CI 1.05-1.52). Independent of sociodemographic characteristics and medical history, smoking was associated with a higher risk of severe COVID-19, including death.

Impact of health insurance type on access to vaccination and mortality due to COVID-19 on patients with cancer in Colombia: a cohort study

BackgroundIn Colombia, all paid workers earning minimum wage or higher contribute part of their salary for access to the national health-care system through a type of insurance called contributive plan, which supports the remaining low-income population that is enrolled on the subsidised plan. During the COVID-19 pandemic, Colombia documented high mortality rate in patients with cancer, with higher mortality among low-income patients, according to data from our national registry of COVID-19 infection in patients with cancer. The aim of this research was to establish the differential access to COVID-19 vaccination depending on health insurance type, and its impact on mortality due to COVID-19 infection.MethodsA cohort study was conducted with data from the Colombian National Cancer and COVID-19 Registry. Data were collected between June 1, 2021, when COVID-19 vaccines became available for patients with cancer in Colombia, and Oct 31, 2021. Included patients were aged 18 years or older, had a diagnosis of a solid tumour, were receiving active treatment or on follow-up, and had a confirmed SARS-CoV-2 infection. The cumulative incidence of mortality in the vaccinated and unvaccinated cohorts was compared. The estimation of the effect was done through relative risk (RR), and a multivariate analysis (generalised linear model, binomial family) was then done to estimate the effect of the type of health insurance.Findings896 patients were included. 470 (52%) were older than 60 years; 530 (59%) were women and 366 (41%) were men. 172 (19%) patients were vaccinated and 724 (81%) patients were not vaccinated (resulting in a ratio of 1:4). According to the type of health insurance, the vaccine was administered to 26 (12%) of 211 patients in the subsidised plan and to 146 (21%) of 685 patients in the contributive plan. The rate of vaccination according with socioeconomic status was 69 (16%) of 430 patients in the low-income group, 75 (23%) of 329 in middle-income group, and 12 (29%) of 42 in high-income group. The cumulative incidence of mortality for all causes was 17% (n=123) in the non-vaccinated cohort and 5% (n=8) in vaccinated cohort. The cumulative incidence of mortality per health-care insurance was 24% (n=50) in the subsidised plan and 12% (n=81) in the contributive plan. The adjusted RR for mortality was 3·4 (95% CI 1·7–6·8) in unvaccinated versus vaccinated patients, and 1·8 (1·3–2·4) in patients on the subsidised versus the contributive plan.InterpretationPatients on the subsidised health plan in Colombia had higher mortality due to COVID-19 infection and less access to vaccines than patients with a contributive plan. Strategies to promote COVID-19 vaccination for all patients with cancer should be strengthened, with implementation of special measures to improve care for the low-income population.FundingNone.

Metabolic syndrome correlates with significantly adverse disease outcomes in COVID-19 patients during the second wave of pandemic

Background: This Retrospective Study was conducted by collecting patient records from the COVID-19 registry of Parul Sevasharm Hospital. The goal of this study was to investigate the risk and mortality of COVID-19 patients with Metabolic Syndrome especially during the second wave of pandemic. Methods: This Retrospective Study was conducted by collecting patient records from the COVID-19 registry of Parul Sevasharm Hospital. The record of COVID-19 patients admitted during the second wave of pandemic (April – June, 2021) were included and analyzed for Metabolic syndrome, Demographic characteristics and Clinical Outcomes. Comorbidities were classified into the following Groups: DM (having DM with or without comorbidities), only DM (having DM without other comorbidities); HT (having HT with or without other comorbidities), only HT (having HT without other comorbidities); CVD (having CVD with or without comorbidities), only CVD (having CVD without other comorbidities). Correlation analysis was also done for sub-groups of DM+CVD, CVD+HT, HT+DM, (with or without other comorbidities). Results: Among 994 Patients with COVID-19, 748/994 (75.25%) were discharged as cured and 246 (24.74%) succumbed to death. The mean age of the study population was 52.64±14.72, in which 366 (36.82%) were Females and 628/994 (63.57%) were Males.

Machine Learning Models to Predict In-Hospital Mortality among Inpatients with COVID-19: Underestimation and Overestimation Bias Analysis in Subgroup Populations.

Prediction of the death among COVID-19 patients can help healthcare providers manage the patients better. We aimed to develop machine learning models to predict in-hospital death among these patients. We developed different models using different feature sets and datasets developed using the data balancing method. We used demographic and clinical data from a multicenter COVID-19 registry. We extracted 10,657 records for confirmed patients with PCR or CT scans, who were hospitalized at least for 24 hours at the end of March 2021. The death rate was 16.06%. Generally, models with 60 and 40 features performed better. Among the 240 models, the C5 models with 60 and 40 features performed well. The C5 model with 60 features outperformed the rest based on all evaluation metrics; however, in external validation, C5 with 32 features performed better. This model had high accuracy (91.18%), F-score (0.916), Area under the Curve (0.96), sensitivity (94.2%), and specificity (88%). The model suggested in this study uses simple and available data and can be applied to predict death among COVID-19 patients. Furthermore, we concluded that machine learning models may perform differently in different subpopulations in terms of gender and age groups.

Abstract 3585: Disparities in cancer treatment delays or discontinuation among cancer patients diagnosed with SARS-CoV-2 infection: An analysis of the U.S. ASCO COVID-19 and cancer registry

Abstract Background: Due to societal factors in the US, racial/ethnic minority adults are disproportionately impacted by the COVID-19 pandemic, particularly those with existing comorbid conditions such as cancer. It is currently unknown whether disparities exist in cancer treatment delivery among racial/ethnic minority patients with cancer and SARS-CoV-2. Methods: Data were obtained from the ASCO COVID-19 and Cancer Registry (March 2020-July 2021), including data from cancer patients diagnosed with SARS-CoV-2 during their care (n=3193) at 60 practices across the US. Data included patient demographics, SARS-CoV-2 diagnosis and treatment, cancer clinical characteristics, and modifications to cancer treatment plans. Cancer treatment delay or discontinuation (TDD) was defined as any treatment postponed more than two weeks from the original scheduled date. We descriptively evaluated demographic and clinical characteristics, compared disparities in TDD by race/ethnicity and urban/rural residency, and evaluated reasons for TDD as reported by the clinics. We computed adjusted odds ratios (aOR) using multivariable logistic regression, accounting for non-independence of patients within hospitals to evaluate racial/ethnic disparities of TDD. Multivariable models were adjusted for age, sex, body mass index, number of comorbidities, cancer type, cancer extent, cancer status at SARS-CoV-2 diagnosis (progressing/stable) and SARS-CoV-2 severity (death/hospitalization/ICU admission/mechanical ventilation). Results: Cancer patients with SARS-CoV-2were mostly female (57%), urban residents (84%), and NH-White (66%); 49% were 65+ years old. Most patients had solid tumors (75%). At SARS-CoV-2 diagnosis, 2403 patients (76%) were scheduled to receive drug-based therapy (69%), radiation therapy (7%), surgery (4%), or transplant (0.7%), of whom 49% experienced TDD. The most reported TDD reason from the clinic perspective was the patient’s COVID-19 disease (90%). Overall, NH-Black (64%) and Hispanic (57%) with SARS-CoV-2 were more likely to experience TDD versus NH-White adults (46%) (p&amp;lt;0.001). This disparity was also observed across urban residing adults (p&amp;lt;0.001). Among rural adults, NH-AI/AN (75%) and NH-Black (61%) were more likely to experience TDD versus NH-White patients (39%). In multivariable analyses, disparities persisted, by NH-Black cancer patients with 92% (aOR:1.92, 95% CI:1.24-2.96) and Hispanic patients with 41% (aOR:1.41, 95% CI:1.03-1.91) higher odds of experiencing TDD. We observed consistent results among urban and rural subgroups. Conclusion: Racial/ethnic disparities exist in TDD among cancer patients with SARS-CoV-2 in urban and rural care settings. Future studies should evaluate the impacts of delays to cancer treatment delivery on cancer outcomes among minoritized communities in the US. Citation Format: Jessica Yasmine Islam, Cassandra A. Hathaway, Emma Hume, Kea Turner, Julie Hallanger-Johnson, Shelley S. Tworoger, Marlene Camacho-Rivera. Disparities in cancer treatment delays or discontinuation among cancer patients diagnosed with SARS-CoV-2 infection: An analysis of the U.S. ASCO COVID-19 and cancer registry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3585.