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Routine Hospital-based SARS-CoV-2 Testing Outperforms State-based Data in Predicting Clinical Burden.

ABSTRACT: Throughout the coronavirus disease 2019 (COVID-19) pandemic, government policy and healthcare implementation responses have been guided by reported positivity rates and counts of positive cases in the community. The selection bias of these data calls into question their validity as measures of the actual viral incidence in the community and as predictors of clinical burden. In the absence of any successful public or academic campaign for comprehensive or random testing, we have developed a proxy method for synthetic random sampling, based on viral RNA testing of patients who present for elective procedures within a hospital system. We present here an approach under multilevel regression and poststratification to collecting and analyzing data on viral exposure among patients in a hospital system and performing statistical adjustment that has been made publicly available to estimate true viral incidence and trends in the community. We apply our approach to tracking viral behavior in a mixed urban-suburban-rural setting in Indiana. This method can be easily implemented in a wide variety of hospital settings. Finally, we provide evidence that this model predicts the clinical burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) earlier and more accurately than currently accepted metrics. See video abstract at, http://links.lww.com/EDE/B859.

SUBMITTER: Covello L

PROVIDER: S-EPMC8478110 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectivesRisk of hospital-acquired COVID-19 (HA-COVID-19) infection is increased by cohorting infected and non-infected patients together in assessment areas, whist awaiting laboratory PCR results. Molecular point-of-care tests (mPOCT) reduce time to results and improve patient flow but the impact on HA-COVID-19 is unknown.MethodsIn this pre and post implementation study patients were evaluated across two time periods: March 1st to August 13th 2020, prior to the introduction of mPOCT in medical admissions areas, and 14th August 2020 to 1st April 2021, after mPOCT introduction. The primary outcome was proportion of HA-COVID-19 infection among all COVID-19 positive patients. Secondary outcome measures included time to SARS-CoV-2 results, length of time spent in the medical assessment area and comparison of local, regional and national proportions of HA-COVID-19.Results1988 patients were admitted through the acute medicine admission cohorting area and tested for SARS-CoV-2 prior to introducing mPOCT and 4640 afterwards. Median (IQR) time to SARS-CoV-2 result was 6.5 (2.1-17.9) hours prior to introducing mPOCT and 1.0 (0.8-1.3) hours afterwards (p < 0.0001). Median (IQR) duration in the assessment cohort area was 12.0 (4.8-20.6) hours prior to introduction of POCT and 3.2 (2.0-5.6) hours afterwards (p < 0.0001). The proportion of hospital-acquired COVID-19 cases was 108 (16.5%) of 654 prior to introducing mPOCT compared with 168 (9.4%) of 1782 afterwards, (HR 0.55, 95%CI 0.43-0.70; p < 0.0001). In the period following the introduction of mPOCT up to 1st April 2021 the median proportion of HA-COVID-19 was 13.6% (95%CI 8.2-18.9%) locally, compared with 43.8% (95%CI 37.8-49.9%) for all acute NHS trusts regionally and 30.9% (95%CI 28.4-33.5%) for all NHS trusts nationally.ConclusionsRoutine mPOCT for SARS-CoV-2 was associated with reduced time to results, time spent in admission cohort areas, and hospital-acquired COVID-19, compared to laboratory PCR.

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.

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.

Project description:Background The ongoing global SARS-CoV-2 pandemic has caused over 4.7 million infections greatly challenging healthcare workers (HCW) and medical institutions worldwide. The SARS-CoV-2 pandemic has shown to significantly impact mental and physical health of HCW. Thus, implementation of testing facilities supporting HCW are urgently needed. Methods A low-threshold SARS-CoV-2 testing facility was introduced at the University Hospital Bonn, Germany, in March 2020. Irrespective of clinical symptoms employees were offered a voluntary and free SARS-CoV-2 test. Furthermore, employees returning from SARS-CoV-2 risk regions and employees after risk contact with SARS-CoV-2 infected patients or employees were tested for SARS-CoV-2 infection. Pharyngeal swabs were taken and reverse transcription polymerase chain reaction for detection of SARS-CoV-2 was performed, test results being available within 24 h. Profession, symptoms and reason for SARS-CoV-2 testing of employees were recorded. Results Between 9th March and April 30, 2020, a total of 1510 employees were tested for SARS-CoV-2 infection. 1185 employees took advantage of the low-threshold testing facility. One percent (n = 11) were tested positive for SARS-CoV-2 infection, 18% being asymptomatic, 36% showing mild and 36% moderate/severe symptoms (missing 10%). Furthermore, of 56 employees returning from SARS-CoV-2 risk regions, 18% (10/56) were tested SARS-CoV-2 positive. After risk contact tracking by the hospital hygiene 6 patient-to-employee transmissions were identified in 163 employees with contact to 55 SARS-CoV-2 positive patients. Conclusion In the absence of easily accessible public SARS-CoV-2 testing facilities low-threshold SARS-CoV-2 testing facilities in hospitals with rapid testing resources help to identify SARS-CoV-2 infected employees with absent or mild symptoms, thus stopping the spread of infection in vulnerable hospital environments. High levels of professional infection prevention training and implementation of specialized wards as well as a perfectly working hospital hygiene network identifying and tracking risk contacts are of great importance in a pandemic setting. Highlights • Incidence of SARS-CoV-2 infections are higher in employees of a hospital environment compared to the general population.• Low-threshold testing facility helps to identify SARS-CoV-2 positive employees with absent or mild clinical symptoms.• Tracking of risk contacts by the hospital hygiene prevent distribution of SARS-CoV-2 infection.

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Routine Hospital-based SARS-CoV-2 Testing Outperforms State-based Data in Predicting Clinical Burden.

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