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1.
Critical Care Medicine ; 51(1 Supplement):554, 2023.
Article in English | EMBASE | ID: covidwho-2190670

ABSTRACT

INTRODUCTION: Since the start of the COVID-19 pandemic there has been an evolution of variant strains that have spread throughout the world. As time has passed, clinicians have appreciated that these variants have different symptomology and clinical course. As our understanding of the disease process has progressed, medical management has evolved. Throughout, cancer patients have represented a uniquely at-risk population. We sought to compare the characteristics of critically ill cancer patients with Omicron variant to those infected with the ancestral strain. METHOD(S): Single-center retrospective cohort study analyzed all cancer patients >=18 years of age with current or past (< 2 years) diagnosis of cancer, who were admitted to ICU with COVID-19. The ancestral strain period was defined as March 1 to June 30, 2020, and the Omicron variant period was December 15, 2021 to April 1, 2022. Demographics, clinical and laboratory data of critically ill cancer patients were extracted from electronic health record and an ICU database. RESULT(S): A total of 127 patients were analyzed (38 Omicron and 89 ancestral strain). Median age was similar (67 years Omicron, 65 ancestral) and slightly higher male (47% Omicron, 58% ancestral). There was a higher number of hematologic malignancy (53% Omicron, 43% ancestral). Mechanical ventilation and vasopressors were less commonly used (58% and 53% Omicron, 67% and 71% ancestral), respectively. Prone positioning was utilized less frequently (47% Omicron, 56% ancestral) as was tracheostomy (11% omicron, 34% ancestral). ICU mortality was similar in both groups, (39% vs 37% however, hospital mortality was higher (55% Omicron group, 45% ancestral). CONCLUSION(S): Critically ill cancer patients infected with the Omicron variant may be less likely to undergo tracheostomy however, they are more likely to die during their hospitalization. Even with higher hospital mortality Omicron patients also seemed to be less acutely ill as their requirement for mechanical ventilation, vasopressors and prone positioning was lower. This should be considered as we counsel patients and set expectations about what might happen during a COVID admission to the ICU.

2.
Pediatric Diabetes ; 23(Supplement 31):48, 2022.
Article in English | EMBASE | ID: covidwho-2137177

ABSTRACT

Introduction: The use of continuous glucose monitors (CGM) and insulin pumps (PUMP) have been associated with improved outcomes in type 1 diabetes (T1D) care. Therefore, disengaging from these devices represents a risk for worsening health outcomes. Objective(s): We sought to evaluate the effect of the COVID-19 pandemic on device disengagement rates by race and ethnicity. Method(s): This retrospective cohort study Pre-COVID-19 [n = 15,838] + peri-COVID-19 ([n = 14,799]) used EMR data from 15 sites (i.e., 3 adult and 12 pediatric diabetes centers) within the T1D Exchange Quality Improvement Collaborative. We identified individuals using at least one Advanced Diabetes Technology (ADT [PUMP or CGM]) at their most recent visit. Individuals who continued to use that technology for at least two subsequent visits were classified as engaged. Those who reported not using ADT in two subsequent visits were classified as disengaged. Result(s): Comparing pre-COVID-19 (January 2017-March 2020) to peri-COVID-19 (April 2020-2021) time periods, we observed increases in disengagement among non-Hispanic White (NHW;42% to 45%, p = 0.03) and Hispanic (12% to 19%, p < 0.001) individuals. We found no difference among NH Black (NHB;61% to 62%, p = 0.7) individuals. Conclusion(s): The pandemic has presented self-care challenges for individuals with T1D, including continued use of ADT. NHB individuals exhibited the highest disengagement rates overall, while NHW/Hispanic individuals experienced significant pandemic-related increases in disengagement. Future research should evaluate the relative impact of intrinsic (i.e., patient-level) versus extrinsic (i.e., family-, environment-, and system-level) factors associated with race-/ethnicity- based differences in rate of disengagement.

3.
Blood ; 138:3891, 2021.
Article in English | EMBASE | ID: covidwho-1582255

ABSTRACT

BACKGROUND Cellular therapies (allogeneic hematopoietic cell transplantation, allo-HCT, autologous hematopoietic cell transplantation, auto-HCT, and chimeric antigen receptor T cell therapy, CAR T) render patients severely immunocompromised for extended periods post-therapy. Emerging data suggest reduced immune responses to COVID-19 vaccines among patients with hematologic malignancies, but data for cellular therapy recipients are sparse. We therefore assessed immune responses to mRNA COVID-19 vaccines among patients who underwent cellular therapies at our center to identify predictors of response. PATIENT AND METHODS In this observational prospective study, anti-SARS-CoV-2 spike IgG antibody titers and circulating neutralizing antibodies were measured at 1 and 3 months after the 1 st dose of vaccination. CD4, CD19, mitogen, and IgG levels from patient samples collected prior to initiation of vaccination in a subset of patients were used to assess immune recovery and association with response. A concurrent healthy donor (HD) cohort provided control response rates. RESULTS Allo-HCT (N=149), auto HCT (N=61), and CAR T (N=7) patients vaccinated between 12/22/2020- 2/28/2021 with mRNA vaccines and 69 HD participated in this study. At 3 months, 188 pts (87%) had a positive anti-SARS-CoV-2 spike IgG levels (median 5,379 AU/mL, IQR 451-15,750), and 139 (77%) had a positive neutralization Ab assay (median 93%, IQR 36-96%). All HD (100%) had a positive anti-SARS-CoV-2 spike IgG and a positive neutralization Ab assay with median levels of 8,011 AU/mL (IQR 4573-11,159) and 96% (IQR 78- 96%), respectively. Time from vaccination to cellular therapy was associated with response;67% of patients vaccinated in the first 12 months post-cellular therapy (N=42) mounted a serologic response, compared with patients vaccinated between 12-24 (89%) (N=45), 24-36 (91%) (N=32) and >36 (93%) (N=98) months post-treatment, p= 0.001 (figure 1). Patients with immune parameters below the recommended threshold for vaccinations post-cellular therapies were also less likely to mount a response (figure 2): CD4+ T-cell count < 200 vs >200 cells/μL, 66% vs 87% (p=0.012);CD19+ B-cell count <50 vs >50 cells/μL;33% vs 95% (p<0.001), phytohemagglutinin mitogen response <40% vs >40%, 42% vs 89% (p<0.001), and IgG <500 vs >500 mg/dl, 71% vs 91% (p=0.003). Patient age, gender, prior COVID-19 infection, treatment with IVIG, and type of mRNA COVID-19 vaccine were not associated with the likelihood of serologic response. CONCLUSION This largest cohort to date, demonstrates that COVID-19 vaccine responses of cellular therapy recipients are reduced compared to healthy control and response varies based on time interval from cellular therapy and immune function at the time of vaccination, underscoring the importance of monitoring immune status parameters, as well as qualitative measures (neutralizing Ab) of vaccine response, in informing clinical decisions, including the indication for booster vaccines. [Formula presented] Disclosures: Politikos: Merck: Research Funding;ExcellThera, Inc: Other: Member of DSMB - Uncompensated. Vardhana: Immunai: Membership on an entity's Board of Directors or advisory committees. Perales: Equilium: Honoraria;Cidara: Honoraria;Sellas Life Sciences: Honoraria;Miltenyi Biotec: Honoraria, Other;Celgene: Honoraria;MorphoSys: Honoraria;Takeda: Honoraria;Incyte: Honoraria, Other;Karyopharm: Honoraria;Kite/Gilead: Honoraria, Other;Merck: Honoraria;NexImmune: Honoraria;Novartis: Honoraria, Other;Medigene: Honoraria;Omeros: Honoraria;Servier: Honoraria;Bristol-Myers Squibb: Honoraria;Nektar Therapeutics: Honoraria, Other. Shah: Amgen: Research Funding;Janssen Pharmaceutica: Research Funding.

4.
Pediatric Diabetes ; 22(SUPPL 30):33, 2021.
Article in English | EMBASE | ID: covidwho-1571042

ABSTRACT

Introduction: An increase in newly diagnosed type 1 diabetes (T1D) has been posited during the COVID-19 pandemic, but data have been conflicting. Objectives: We aimed to determine trends in newly diagnosed T1D and severity of presentation at diagnosis for pediatric and adolescent patients during COVID-19 year (2020) as compared to the previous year (2019) in a multi-center data analysis across the United States. Methods: This retrospective multi-center study included data from seven large U.S. clinical centers recruited from the T1D Exchange Quality Improvement Collaborative (T1DX-QI). Data on diagnosis, diabetic ketoacidosis (DKA), and clinical characteristics were collected from January 1 to December 31, 2020, compared to the prior year. Chi-square tests were used to compare differences in patient characteristics during the pandemic compared to the pre-pandemic comparison group. Results: Across seven member sites, there were 1399 newly diagnosed patients with T1D in 2020, compared to 1277 in 2019 (p=0.007). Of the newly diagnosed patients, a greater number, presented in DKA in 2020 compared to 2019 (599/1399 (42.8%) v. 493/1277 (38.6%), p<0.001), and a higher proportion of these patients presented with severe DKA (p=0.01) as characterized by a pH<7.1 or bicarbonate of <5mmol/L. The mean age at diagnosis was not different, but there were fewer females (p=0.004), and fewer NH White youth diagnosed in 2020 (p<0.001). Newly diagnosed T1D patients in 2020 were less likely to have private insurance (p=0.001). Monthly data trends demonstrated a higher number of new diagnoses of T1D over the spring and summer months (April to September) of 2020 compared to 2019 (p=0.007). Conclusions: We found an increase in newly diagnosed T1D and a greater proportion of newly diagnosed T1D patients presenting in DKA at diagnosis during the COVID-19 pandemic compared to the prior year. Future longitudinal studies are needed to confirm these findings with population level data and determine the long-term impact of COVID-19 on diabetes trends.

5.
Journal of Clinical Outcomes Management ; 27(6):256-259, 2020.
Article in English | Scopus | ID: covidwho-1143829
7.
Journal of Molecular Diagnostics ; 22(11):S39-S39, 2020.
Article in English | Web of Science | ID: covidwho-1070293
8.
Journal of Molecular Diagnostics ; 22(11):S37-S38, 2020.
Article in English | Web of Science | ID: covidwho-1070292
9.
Journal of Molecular Diagnostics ; 22(11):S37-S37, 2020.
Article in English | Web of Science | ID: covidwho-1070291
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