Dataset Information

Mortality in COVID-19 among women on hormone replacement therapy: a retrospective cohort study.

ABSTRACT:

Background

Limited recent observational data have suggested that there may be a protective effect of oestrogen on the severity of COVID-19 disease. Our aim was to investigate the association between hormone replacement therapy (HRT) or combined oral contraceptive pill (COCP) use and the likelihood of death in women with COVID-19.

Methods

We undertook a retrospective cohort study using routinely collected computerized medical records from the Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) primary care database. We identified a cohort of 1,863,478 women over 18 years of age from 465 general practices in England. Mixed-effects logistic regression models were used to quantify the association between HRT or COCP use and all-cause mortality among women diagnosed with confirmed or suspected COVID-19 in unadjusted and adjusted models.

Results

There were 5,451 COVID-19 cases within the cohort. HRT was associated with a reduction in all-cause mortality in COVID-19 (adjusted OR 0.22, 95% CI 0.05 to 0.94). There were no reported events for all-cause mortality in women prescribed COCPs. This prevented further examination of the impact of COCP.

Conclusions

We found that HRT prescription within 6 months of a recorded diagnosis of COVID-19 infection was associated with a reduction in all-cause mortality. Further work is needed in larger cohorts to examine the association of COCP in COVID-19, and to further investigate the hypothesis that oestrogens may contribute a protective effect against COVID-19 severity.

SUBMITTER: Dambha-Miller H

PROVIDER: S-EPMC9129218 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Publications

Mortality in COVID-19 among women on hormone replacement therapy: a retrospective cohort study.

Dambha-Miller Hajira H Hinton William W Wilcox Christopher R CR Joy Mark M Feher Michael M de Lusignan Simon S

Family practice 20221101 6

<h4>Background</h4>Limited recent observational data have suggested that there may be a protective effect of oestrogen on the severity of COVID-19 disease. Our aim was to investigate the association between hormone replacement therapy (HRT) or combined oral contraceptive pill (COCP) use and the likelihood of death in women with COVID-19.<h4>Methods</h4>We undertook a retrospective cohort study using routinely collected computerized medical records from the Oxford-Royal College of General Practit ...[more]

PMID: 35577349

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Project description:BackgroundCoronavirus disease 2019 (COVID-19) may predispose patients to thrombotic events. The best anticoagulation strategy for continuous renal replacement therapy (CRRT) in such patients is still under debate. The purpose of this study was to evaluate the impact that different anticoagulation protocols have on filter clotting risk.MethodsThis was a retrospective observational study comparing two different anticoagulation strategies (citrate only and citrate plus intravenous infusion of unfractionated heparin) in patients with acute kidney injury (AKI), associated or not with COVID-19 (COV + AKI and COV - AKI, respectively), who were submitted to CRRT. Filter clotting risks were compared among groups.ResultsBetween January 2019 and July 2020, 238 patients were evaluated: 188 in the COV + AKI group and 50 in the COV - AKI group. Filter clotting during the first filter use occurred in 111 patients (46.6%). Heparin use conferred protection against filter clotting (HR = 0.37, 95% CI 0.25-0.55), resulting in longer filter survival. Bleeding events and the need for blood transfusion were similar between the citrate only and citrate plus unfractionated heparin strategies. In-hospital mortality was higher among the COV + AKI patients than among the COV - AKI patients, although it was similar between the COV + AKI patients who received heparin and those who did not. Filter clotting was more common in patients with D-dimer levels above the median (5990 ng/ml). In the multivariate analysis, heparin was associated with a lower risk of filter clotting (HR = 0.28, 95% CI 0.18-0.43), whereas an elevated D-dimer level and high hemoglobin were found to be risk factors for circuit clotting. A diagnosis of COVID-19 was marginally associated with an increased risk of circuit clotting (HR = 2.15, 95% CI 0.99-4.68).ConclusionsIn COV + AKI patients, adding systemic heparin to standard regional citrate anticoagulation may prolong CRRT filter patency by reducing clotting risk with a low risk of complications.

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.

Project description:IntroductionHormone replacement therapy (HRT) can help women experiencing menopausal symptoms, but usage has declined due to uncertainty around risks of cancer and some cardiovascular diseases (CVD). Moreover, improved cancer survival rates mean that more women who survive cancer go on to experience menopausal symptoms. Understanding these relationships is important so that women and their clinicians can make informed decisions around the risks and benefits of HRT. This study's primary aim is to determine the association between HRT use after cancer diagnosis and the risk of cancer-specific mortality. The secondary aims are to investigate the risks of HRT on subsequent cancer, all-cause mortality and CVD.Methods and analysisWe will conduct a population-based longitudinal cohort study of 18-79 year-old women diagnosed with cancer between 1998 and 2020, using the QResearch database. The main exposure is HRT use, categorised based on compound, dose and route of administration, and modelled as a time-varying covariate. Analysis of HRT use precancer and postcancer diagnosis will be conducted separately. The primary outcome is cancer-specific mortality, which will be stratified by cancer site. Secondary outcomes include subsequent cancer diagnosis, CVD (including venous thrombo-embolism) and all-cause mortality. Adjustment will be made for key confounders such as age, body mass index, ethnicity, deprivation index, comorbidities, and cancer grade, stage and treatment. Statistical analysis will include descriptive statistics and Cox proportional hazards models to calculate HRs and 95% CIs.Ethics and disseminationEthical approval for this project was obtained from the QResearch Scientific Committee (Ref: OX24, project title 'Use of hormone replacement therapy and survival from cancer'). This project has been, and will continue to be, supported by patient and public involvement panels. We intend to the submit the findings for peer-reviewed publication in an academic journal and disseminate them to the public through Cancer Research UK.

Dataset Information

Mortality in COVID-19 among women on hormone replacement therapy: a retrospective cohort study.

Background

Methods

Results

Conclusions

Publications

Mortality in COVID-19 among women on hormone replacement therapy: a retrospective cohort study.

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