Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 3 de 3
Filter
1.
Journal of Patient-Centered Research and Reviews ; 9(3):149-157, 2022.
Article in English | EuropePMC | ID: covidwho-1981208

ABSTRACT

Purpose This study sought to describe the changes in immune response to a third dose of either Pfizer’s or Moderna’s COVID-19 mRNA vaccine (3V) among patients with hematologic malignancies, as well as associated characteristics. Methods This retrospective cohort study analyzed pre-3V and post-3V data on 493 patients diagnosed with hematologic malignancies across a large Midwestern health system between August 28, 2021, and November 1, 2021. For antibody testing, S1 spike antigen of the SARS-CoV-2 virus titer was used to determine serostatus. Results Among 493 participants, 274 (55.6%) were seropositive both pre- and post-3V (+/+) while 115 (23.3%) seroconverted to positive from prior negative following the third dose (−/+). The remaining 104 (21.1%) were seronegative both before and after 3V (−/−). No participant was seropositive pre-3V and seronegative post-3V (+/−). Results showed a statistically significant increase in the proportion of seropositivity after receiving a third COVID-19 vaccine (P<0.00001). Response to 3V was significantly associated with the 3V vaccine type (P=0.0006), previous COVID-19 infection (P=0.0453), and malignancy diagnosis (P<0.0001). Likelihood of seroconversion (−/+) after 3V was higher in the group of patients with multiple myeloma or related disorders compared to patients with lymphoid leukemias (odds ratio: 8.22, 95% CI: 2.12–31.79;P=0.0008). Conclusions A third COVID-19 vaccination is effective in producing measurable seroconversion in many patients with hematologic malignancies. Oncologists should actively encourage all their patients, especially those with multiple myeloma, to receive a 3V, given the high likelihood of seroconversion.

2.
Cancer Rep (Hoboken) ; 4(5): e1388, 2021 10.
Article in English | MEDLINE | ID: covidwho-1235659

ABSTRACT

BACKGROUND: The understanding of the impact of COVID-19 in patients with cancer is evolving, with need for rapid analysis. AIMS: This study aims to compare the clinical and demographic characteristics of patients with cancer (with and without COVID-19) and characterize the clinical outcomes of patients with COVID-19 and cancer. METHODS AND RESULTS: Real-world data (RWD) from two health systems were used to identify 146 702 adults diagnosed with cancer between 2015 and 2020; 1267 COVID-19 cases were identified between February 1 and July 30, 2020. Demographic, clinical, and socioeconomic characteristics were extracted. Incidence of all-cause mortality, hospitalizations, and invasive respiratory support was assessed between February 1 and August 14, 2020. Among patients with cancer, patients with COVID-19 were more likely to be Non-Hispanic black (NHB), have active cancer, have comorbidities, and/or live in zip codes with median household income <$30 000. Patients with COVID-19 living in lower-income areas and NHB patients were at greatest risk for hospitalization from pneumonia, fluid and electrolyte disorders, cough, respiratory failure, and acute renal failure and were more likely to receive hydroxychloroquine. All-cause mortality, hospital admission, and invasive respiratory support were more frequent among patients with cancer and COVID-19. Male sex, increasing age, living in zip codes with median household income <$30 000, history of pulmonary circulation disorders, and recent treatment with immune checkpoint inhibitors or chemotherapy were associated with greater odds of all-cause mortality in multivariable logistic regression models. CONCLUSION: RWD can be rapidly leveraged to understand urgent healthcare challenges. Patients with cancer are more vulnerable to COVID-19 effects, especially in the setting of active cancer and comorbidities, with additional risk observed in NHB patients and those living in zip codes with median household income <$30 000.


Subject(s)
COVID-19/epidemiology , Neoplasms/epidemiology , Social Determinants of Health/statistics & numerical data , Socioeconomic Factors , Aged , COVID-19/diagnosis , COVID-19/therapy , COVID-19/virology , Comorbidity , Data Analysis , Female , Hospital Mortality , Humans , Male , Middle Aged , Neoplasms/complications , Neoplasms/immunology , Patient Admission/statistics & numerical data , Respiration, Artificial , Retrospective Studies , Risk Factors , SARS-CoV-2/immunology , Severity of Illness Index , United States/epidemiology
SELECTION OF CITATIONS
SEARCH DETAIL