Discontinuation of Universal Admission Testing for SARS-CoV-2 and Hospital-Onset COVID-19 Infections in England and Scotland.
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ABSTRACT:
SUBMITTER: Pak TR
PROVIDER: S-EPMC10242507 | biostudies-literature | 2023 Jun
REPOSITORIES: biostudies-literature
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Discontinuation of Universal Admission Testing for SARS-CoV-2 and Hospital-Onset COVID-19 Infections in England and Scotland.
Pak Theodore R TR Rhee Chanu C Wang Rui R Klompas Michael M
JAMA internal medicine 20230801 8
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Project description:ObjectivesTo derive and validate risk prediction algorithms (QCOVID4) to estimate the risk of covid-19 related death and hospital admission in people with a positive SARS-CoV-2 test result during the period when the omicron variant of the virus was predominant in England, and to evaluate performance compared with a high risk cohort from NHS Digital.DesignCohort study.SettingQResearch database linked to English national data on covid-19 vaccinations, SARS-CoV-2 test results, hospital admissions, and cancer and mortality data, 11 December 2021 to 31 March 2022, with follow-up to 30 June 2022.Participants1.3 million adults in the derivation cohort and 0.15 million adults in the validation cohort, aged 18-100 years, with a positive test result for SARS-CoV-2 infection.Main outcome measuresPrimary outcome was covid-19 related death and secondary outcome was hospital admission for covid-19. Risk equations with predictor variables were derived from models fitted in the derivation cohort. Performance was evaluated in a separate validation cohort.ResultsOf 1 297 922 people with a positive test result for SARS-CoV-2 infection in the derivation cohort, 18 756 (1.5%) had a covid-19 related hospital admission and 3878 (0.3%) had a covid-19 related death during follow-up. The final QCOVID4 models included age, deprivation score and a range of health and sociodemographic factors, number of covid-19 vaccinations, and previous SARS-CoV-2 infection. The risk of death related to covid-19 was lower among those who had received a covid-19 vaccine, with evidence of a dose-response relation (42% risk reduction associated with one vaccine dose and 92% reduction with four or more doses in men). Previous SARS-CoV-2 infection was associated with a reduction in the risk of covid-19 related death (49% reduction in men). The QCOVID4 algorithm for covid-19 explained 76.0% (95% confidence interval 73.9% to 78.2%) of the variation in time to covid-19 related death in men with a D statistic of 3.65 (3.43 to 3.86) and Harrell's C statistic of 0.970 (0.962 to 0.979). Results were similar for women. QCOVID4 was well calibrated. QCOVID4 was substantially more efficient than the NHS Digital algorithm for correctly identifying patients at high risk of covid-19 related death. Of the 461 covid-19 related deaths in the validation cohort, 333 (72.2%) were in the QCOVID4 high risk group and 95 (20.6%) in the NHS Digital high risk group.ConclusionThe QCOVID4 risk algorithm, modelled from data during the period when the omicron variant of the SARS-CoV-2 virus was predominant in England, now includes vaccination dose and previous SARS-CoV-2 infection, and predicted covid-19 related death among people with a positive test result. QCOVID4 more accurately identified individuals at the highest levels of absolute risk for targeted interventions than the approach adopted by NHS Digital. QCOVID4 performed well and could be used for targeting treatments for covid-19 disease.
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Project description:Rapid identification and investigation of healthcare-associated infections (HCAIs) is important for suppression of SARS-CoV-2, but the infection source for hospital onset COVID-19 infections (HOCIs) cannot always be readily identified based only on epidemiological data. Viral sequencing data provides additional information regarding potential transmission clusters, but the low mutation rate of SARS-CoV-2 can make interpretation using standard phylogenetic methods difficult. We developed a novel statistical method and sequence reporting tool (SRT) that combines epidemiological and sequence data in order to provide a rapid assessment of the probability of HCAI among HOCI cases (defined as first positive test >48 hr following admission) and to identify infections that could plausibly constitute outbreak events. The method is designed for prospective use, but was validated using retrospective datasets from hospitals in Glasgow and Sheffield collected February-May 2020. We analysed data from 326 HOCIs. Among HOCIs with time from admission ≥8 days, the SRT algorithm identified close sequence matches from the same ward for 160/244 (65.6%) and in the remainder 68/84 (81.0%) had at least one similar sequence elsewhere in the hospital, resulting in high estimated probabilities of within-ward and within-hospital transmission. For HOCIs with time from admission 3-7 days, the SRT probability of healthcare acquisition was >0.5 in 33/82 (40.2%). The methodology developed can provide rapid feedback on HOCIs that could be useful for infection prevention and control teams, and warrants further prospective evaluation. The integration of epidemiological and sequence data is important given the low mutation rate of SARS-CoV-2 and its variable incubation period. COG-UK HOCI funded by COG-UK consortium, supported by funding from UK Research and Innovation, National Institute of Health Research and Wellcome Sanger Institute.
| S-EPMC8285103 | biostudies-literature
Project description:False negative SARS-CoV-2 tests can lead to spread of infection in the inpatient setting to other patients and healthcare workers. However, the population of patients with COVID who are admitted with false negative testing is unstudied. To characterize and develop a model to predict true SARS-CoV-2 infection among patients who initially test negative for COVID by PCR. Retrospective cohort study. Five hospitals within the Yale New Haven Health System between 3/10/2020 and 9/1/2020. Participants: Adult patients who received diagnostic testing for SARS-CoV-2 virus within the first 96 hours of hospitalization. We developed a logistic regression model from readily available electronic health record data to predict SARS-CoV-2 positivity in patients who were positive for COVID and those who were negative and never retested. This model was applied to patients testing negative for SARS-CoV-2 who were retested within the first 96 hours of hospitalization. We evaluated the ability of the model to discriminate between patients who would subsequently retest negative and those who would subsequently retest positive. We included 31,459 hospitalized adult patients; 2,666 of these patients tested positive for COVID and 3,511 initially tested negative for COVID and were retested. Of the patients who were retested, 61 (1.7%) had a subsequent positive COVID test. The model showed that higher age, vital sign abnormalities, and lower white blood cell count served as strong predictors for COVID positivity in these patients. The model had moderate performance to predict which patients would retest positive with a test set area under the receiver-operator characteristic (ROC) of 0.76 (95% CI 0.70 - 0.83). Using a cutpoint for our risk prediction model at the 90 th percentile for probability, we were able to capture 35/61 (57%) of the patients who would retest positive. This cutpoint amounts to a number-needed-to-retest range between 15 and 77 patients. We show that a pragmatic model can predict which patients should be retested for COVID. Further research is required to determine if this risk model can be applied prospectively in hospitalized patients to prevent the spread of SARS-CoV-2 infections.
| S-EPMC7724687 | biostudies-literature
Project description:ImportanceFalse negative SARS-CoV-2 tests can lead to spread of infection in the inpatient setting to other patients and healthcare workers. However, the population of patients with COVID who are admitted with false negative testing is unstudied.ObjectiveTo characterize and develop a model to predict true SARS-CoV-2 infection among patients who initially test negative for COVID by PCR.DesignRetrospective cohort study.SettingFive hospitals within the Yale New Haven Health System between 3/10/2020 and 9/1/2020.ParticipantsAdult patients who received diagnostic testing for SARS-CoV-2 virus within the first 96 hours of hospitalization.ExposureWe developed a logistic regression model from readily available electronic health record data to predict SARS-CoV-2 positivity in patients who were positive for COVID and those who were negative and never retested.Main outcomes and measuresThis model was applied to patients testing negative for SARS-CoV-2 who were retested within the first 96 hours of hospitalization. We evaluated the ability of the model to discriminate between patients who would subsequently retest negative and those who would subsequently retest positive.ResultsWe included 31,459 hospitalized adult patients; 2,666 of these patients tested positive for COVID and 3,511 initially tested negative for COVID and were retested. Of the patients who were retested, 61 (1.7%) had a subsequent positive COVID test. The model showed that higher age, vital sign abnormalities, and lower white blood cell count served as strong predictors for COVID positivity in these patients. The model had moderate performance to predict which patients would retest positive with a test set area under the receiver-operator characteristic (ROC) of 0.76 (95% CI 0.70-0.83). Using a cutpoint for our risk prediction model at the 90th percentile for probability, we were able to capture 35/61 (57%) of the patients who would retest positive. This cutpoint amounts to a number-needed-to-retest range between 15 and 77 patients.Conclusion and relevanceWe show that a pragmatic model can predict which patients should be retested for COVID. Further research is required to determine if this risk model can be applied prospectively in hospitalized patients to prevent the spread of SARS-CoV-2 infections.
| S-EPMC8115794 | biostudies-literature
Project description:The implementation of rapid diagnostic tests (RDTs) may enhance the efficiency of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing, as RDTs are widely accessible and easy to use. The aim of this study was to evaluate the performance of a diagnosis strategy based on a combination of antigen and immunoglobulin M (IgM) or immunoglobulin G (IgG) serological RDTs. Plasma and nasopharyngeal samples were collected between 14 March and 11 April 2020 at hospital admission from 45 patients with reverse transcription polymerase chain reaction (RT-PCR) confirmed COVID-19 and 20 negative controls. SARS-CoV-2 antigen (Ag) was assessed in nasopharyngeal swabs using the Coris Respi-Strip. For IgM/IgG detection, SureScreen Diagnostics and Szybio Biotech RDTs were used in addition to laboratory assays (Abbott Alinity i SARS-CoV-2 IgG and Theradiag COVID-19 IgM enzyme-linked immunosorbent assay). Using the Ag RDT, 13 out of 45 (29.0%) specimens tested positive, the sensitivity was 87.0% for cycle threshold (Ct ) values ≤25% and 0% for Ct values greater than 25. IgG detection was associated with high Ct values and the amount of time after the onset of symptoms. The profile of isolated IgM on RDTs was more frequently observed during the first and second week after the onset of symptoms. The combination of Ag and IgM/IgG RDTs enabled the detection of up to 84.0% of COVID-19 confirmed cases at hospital admission. Antigen and antibody-based RDTs showed suboptimal performances when used alone. However when used in combination, they are able to identify most COVID-19 patients admitted in an emergency department.
| S-EPMC8013599 | biostudies-literature
Project description: Not available
| S-EPMC9201738 | biostudies-literature
Project description:BackgroundCarbapenemase-producing Enterobacteriaceae (CPE) are an emerging threat for healthcare providers worldwide.ObjectivesTo determine CPE carriage rates and risk factors in an unselected hospital cohort at the time of admission.MethodsWe approached 4567 patients within 72 h of admission to provide a rectal swab and answer a questionnaire on risk factors for carriage. Rectal swabs were cultured for carbapenem-resistant organisms on chromogenic and non-chromogenic agar, and tested for carbapenemase production by PCR (Check-Direct CPE). The study was approved by the NHS Research Ethics Committee.ResultsOnly 6 CPE were cultured from 5 (0.1%) of 4006 patients who provided a rectal swab; only 1 was cultured using non-chromogenic media. An additional 76 culture-negative rectal swabs were initially PCR positive, but none grew a carbapenem-resistant organism despite enrichment culture and only two were positive when retested several months later by Check-Direct and a second PCR assay (Cepheid GeneXpert® Carba-R). A modified Ct cut-off of <35 would have resolved these apparent false-positives. 40% of patients had a risk factor that should prompt screening and pre-emptive isolation as defined by UK CPE guidelines but only 8.1% and 20.2% of these patients had been screened and pre-emptively isolated by clinical teams, respectively. Overseas hospitalization was the only significant risk factor for CPE carriage (P < 0.001, OR 64.3, 95% CI 7.3-488.5).ConclusionsThis study highlights a very low carriage rate of CPE. Hospitalization abroad is the most important risk factor to guide admission screening in this low-prevalence setting.
| S-EPMC5890656 | biostudies-literature
Project description:BackgroundHead injury in England is common. Evidence suggests that socio-economic factors may cause variation in incidence, and this variation may affect planning for services to meet the needs of those who have sustained a head injury.MethodsSocio-economic data were obtained from the UK Office for National Statistics and merged with Hospital Episodes Statistics obtained from the Department of Health. All patients admitted for head injury with ICD-10 codes S00.0-S09.9 during 2001-2 and 2002-3 were included and collated at the level of the extant Health Authorities (HA) for 2002, and Primary Care Trust (PCT) for 2003. Incidence was determined, and cluster analysis and multiple regression analysis were used to look at patterns and associations.Results112,718 patients were admitted during 2001-2 giving a hospitalised incidence rate for England of 229 per 100,000. This rate varied across the English HA's ranging from 91-419 per 100,000. The rate remained unchanged for 2002-3 with a similar magnitude of variation across PCT's. Three clusters of HA's were identified from the 2001-2 data; those typical of London, those of the Shire counties, and those of Other Urban authorities. Socio-economic factors were found to account for a high proportion of the variance in incidence for 2001-2. The same pattern emerged for 2002-3 at the PCT level. The use of public transport for travel to work is associated with a decreased incidence and lifestyle indicators, such as the numbers of young unemployed, increase the incidence.ConclusionHead injury incidence in England varies by a factor of 4.6 across HA's and PCT's. Planning head injury related services at the local level thus needs to be based on local incidence figures rather than regional or national estimates. Socio-economic factors are shown to be associated with admission, including travel to work patterns and lifestyle indicators, which suggests that incidence is amenable to policy initiatives at the macro level as well as preventive programmes targeted at key groups.
| S-EPMC554988 | biostudies-literature