In-Hospital Mortality in a Cohort of Hospitalized Pregnant and Nonpregnant Patients With COVID-19.
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ABSTRACT:
SUBMITTER: Pineles BL
PROVIDER: S-EPMC8251936 | biostudies-literature | 2021 Aug
REPOSITORIES: biostudies-literature
Publications
In-Hospital Mortality in a Cohort of Hospitalized Pregnant and Nonpregnant Patients With COVID-19.
Pineles Beth L BL Goodman Katherine E KE Pineles Lisa L O'Hara Lyndsay M LM Nadimpalli Gita G Magder Laurence S LS Baghdadi Jonathan D JD Parchem Jacqueline G JG Harris Anthony D AD
Annals of internal medicine 20210511 8
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Project description:Current medical guidelines consider pregnant women with COVID-19 to be a high-risk group. Since physiological gestation downregulates the immunological response to maintain "maternal-fetal tolerance", SARS-CoV-2 infection may constitute a potentially threatening condition to both the mother and the fetus. To establish the immune profile in pregnant COVID-19+ patients, a cross-sectional study was conducted. Pregnant women with COVID-19 (P-COVID-19+; n = 15) were analyzed and compared with nonpregnant women with COVID-19 (NP-COVID-19+; n = 15) or those with physiological pregnancy (P-COVID-19-; n = 13). Serological cytokine and chemokine concentrations, leucocyte immunophenotypes, and mononuclear leucocyte responses to polyclonal stimuli were analyzed in all groups. Higher concentrations of serological TNF-α, IL-6, MIP1b and IL-4 were observed within the P-COVID-19+ group, while cytokines and chemokines secreted by peripheral leucocytes in response to LPS, IL-6 or PMA-ionomicin were similar among the groups. Immunophenotype analysis showed a lower percentage of HLA-DR+ monocytes in P-COVID-19+ than in P-COVID-19- and a higher percentage of CD39+ monocytes in P-COVID-19+ than in NP-COVID-19+. After whole blood polyclonal stimulation, similar percentages of T cells and TNF+ monocytes between groups were observed. Our results suggest that P-COVID-19+ elicits a strong inflammatory response similar to NP-COVID19+ but also displays an anti-inflammatory response that controls the ATP/adenosine balance and prevents hyperinflammatory damage in COVID-19.
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Project description:ObjectiveTo explore the possible associations of serum 25-hydroxyvitamin D [25(OH)D] concentration with coronavirus disease 2019 (COVID-19) in-hospital mortality and need for invasive mechanical ventilation.Patients and methodsA retrospective, observational, cohort study was conducted at 2 tertiary academic medical centers in Boston and New York. Eligible participants were hospitalized adult patients with laboratory-confirmed COVID-19 between February 1, 2020, and May 15, 2020. Demographic and clinical characteristics, comorbidities, medications, and disease-related outcomes were extracted from electronic medical records.ResultsThe final analysis included 144 patients with confirmed COVID-19 (median age, 66 years; 64 [44.4%] male). Overall mortality was 18%, whereas patients with 25(OH)D levels of 30 ng/mL (to convert to nmol/L, multiply by 2.496) and higher had lower rates of mortality compared with those with 25(OH)D levels below 30 ng/mL (9.2% vs 25.3%; P=.02). In the adjusted multivariable analyses, 25(OH)D as a continuous variable was independently significantly associated with lower in-hospital mortality (odds ratio, 0.94; 95% CI, 0.90 to 0.98; P=.007) and need for invasive mechanical ventilation (odds ratio, 0.96; 95% CI, 0.93 to 0.99; P=.01). Similar data were obtained when 25(OH)D was studied as a continuous variable after logarithm transformation and as a dichotomous (<30 ng/mL vs ≥30 ng/mL) or ordinal variable (quintiles) in the multivariable analyses.ConclusionAmong patients admitted with laboratory-confirmed COVID-19, 25(OH)D levels were inversely associated with in-hospital mortality and the need for invasive mechanical ventilation. Further observational studies are needed to confirm these findings, and randomized clinical trials must be conducted to assess the role of vitamin D administration in improving the morbidity and mortality of COVID-19.
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Project description:BackgroundEarly prediction of COVID-19 patients' mortality risk may be beneficial in adequate triage and risk assessment. Therefore, we aimed to single out the independent morality predictors of hospitalized COVID-19 patients among parameters available on hospital admission.MethodsAn observational, retrospective-prospective cohort study was conducted on 703 consecutive COVID-19 patients hospitalized in the University Clinical Center Kragujevac between September and December 2021. Patients were followed during the hospitalization, and in-hospital mortality was observed as a primary end-point. Within 24 h of admission, patients were sampled for blood gas and laboratory analysis, including complete blood cell count, inflammation biomarkers and other biochemistry, coagulation parameters, and cardiac biomarkers. Socio-demographic and medical history data were obtained using patients' medical records.ResultsThe overall prevalence of mortality was 28.4% (n = 199). After performing multiple regression analysis on 20 parameters, according to the initial univariate analysis, only four independent variables gave statistically significant contributions to the model: SaO2 < 88.5 % (aOR 3.075), IL-6 > 74.6 pg/mL (aOR 2.389), LDH > 804.5 U/L (aOR 2.069) and age > 69.5 years (aOR 1.786). The C-index of the predicted probability calculated using this multivariate logistic model was 0.740 (p < 0.001).ConclusionsParameters available on hospital admission can be beneficial in predicting COVID-19 mortality.
| S-EPMC9605560 | biostudies-literature
Project description:BackgroundThe novel coronavirus SARS-CoV-2 and its associated disease, COVID-19, have caused worldwide disruption, leading countries to take drastic measures to address the progression of the disease. As SARS-CoV-2 continues to spread, hospitals are struggling to allocate resources to patients who are most at risk. In this context, it has become important to develop models that can accurately predict the severity of infection of hospitalized patients to help guide triage, planning, and resource allocation.ObjectiveThe aim of this study was to develop accurate models to predict the mortality of hospitalized patients with COVID-19 using basic demographics and easily obtainable laboratory data.MethodsWe performed a retrospective study of 375 hospitalized patients with COVID-19 in Wuhan, China. The patients were randomly split into derivation and validation cohorts. Regularized logistic regression and support vector machine classifiers were trained on the derivation cohort, and accuracy metrics (F1 scores) were computed on the validation cohort. Two types of models were developed: the first type used laboratory findings from the entire length of the patient's hospital stay, and the second type used laboratory findings that were obtained no later than 12 hours after admission. The models were further validated on a multicenter external cohort of 542 patients.ResultsOf the 375 patients with COVID-19, 174 (46.4%) died of the infection. The study cohort was composed of 224/375 men (59.7%) and 151/375 women (40.3%), with a mean age of 58.83 years (SD 16.46). The models developed using data from throughout the patients' length of stay demonstrated accuracies as high as 97%, whereas the models with admission laboratory variables possessed accuracies of up to 93%. The latter models predicted patient outcomes an average of 11.5 days in advance. Key variables such as lactate dehydrogenase, high-sensitivity C-reactive protein, and percentage of lymphocytes in the blood were indicated by the models. In line with previous studies, age was also found to be an important variable in predicting mortality. In particular, the mean age of patients who survived COVID-19 infection (50.23 years, SD 15.02) was significantly lower than the mean age of patients who died of the infection (68.75 years, SD 11.83; P<.001).ConclusionsMachine learning models can be successfully employed to accurately predict outcomes of patients with COVID-19. Our models achieved high accuracies and could predict outcomes more than one week in advance; this promising result suggests that these models can be highly useful for resource allocation in hospitals.
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Project description:BackgroundAlthough mass vaccination against COVID-19 may prove to be the most efficacious end to this deadly pandemic, there remain concern and indecision among the public toward vaccination. Because pregnant and reproductive-aged women account for a large proportion of the population with particular concerns regarding vaccination against COVID-19, this survey aimed at investigating their current attitudes and beliefs within our own institution.ObjectiveThis study aimed to understand vaccine acceptability among pregnant, nonpregnant, and breastfeeding respondents and elucidate factors associated with COVID-19 vaccine acceptance.Study designWe administered an anonymous online survey to all women (including patients, providers, and staff) at our institution assessing rates of acceptance of COVID-19 vaccination. Respondents were contacted in 1 of 3 ways: by email, advertisement flyers, and distribution of quick response codes at virtual town halls regarding the COVID-19 vaccine. Based on their responses, respondents were divided into 3 mutually exclusive groups: (1) nonpregnant respondents, (2) pregnant respondents, and (3) breastfeeding respondents. The primary outcome was acceptance of vaccination. Prevalence ratios were calculated to ascertain the independent effects of multiple patient-level factors on vaccine acceptability.ResultsThe survey was administered from January 7, 2021, to January 29, 2021, with 1012 respondents of whom 466 (46.9%) identified as non-Hispanic White, 108 (10.9%) as non-Hispanic Black, 286 (28.8%) as Hispanic, and 82 (8.2%) as non-Hispanic Asian. The median age was 36 years (interquartile range, 25-47 years). Of all the respondents, 656 respondents (64.8%) were nonpregnant, 216 (21.3%) were pregnant, and 122 (12.1%) were breastfeeding. There was no difference in chronic comorbidities when evaluated as a composite variable (Table 1). A total of 390 respondents (39.2%) reported working in healthcare. Nonpregnant respondents were most likely to accept vaccination (457 respondents, 76.2%; P<.001) with breastfeeding respondents the second most likely (55.2%). Pregnant respondents had the lowest rate of vaccine acceptance (44.3%; P<.001). Prevalence ratios revealed all non-White races except for non-Hispanic Asian respondents, and Spanish-speaking respondents were less likely to accept vaccination (Table 3). Working in healthcare was not found to be associated with vaccine acceptance among our cohort.ConclusionIn this survey study of only women at a single institution, pregnant respondents of non-White or non-Asian races were more likely to decline vaccination than nonpregnant and breastfeeding respondents. Working in healthcare was not associated with vaccine acceptance.
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