Dataset Information

Predicting patients with false negative SARS-CoV-2 testing at hospital admission: A retrospective multi-center study.

ABSTRACT:

Importance

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.

Objective

To characterize and develop a model to predict true SARS-CoV-2 infection among patients who initially test negative for COVID by PCR.

Design

Retrospective cohort study.

Setting

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.

Exposure

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.

Main outcomes and measures

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.

Results

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.

Conclusion and relevance

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.

SUBMITTER: Ghazi L

PROVIDER: S-EPMC7724687 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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:IntroductionRecent data have suggested that patient admission during intensive care unit (ICU) morning bedside rounds is associated with less favorable outcome. We undertook the present study to explore the association between morning round-time ICU admissions and hospital mortality in a large Canadian health region.MethodsA multi-center retrospective cohort study was performed at five hospitals in Edmonton, Canada, between July 2002 and December 2009. Round-time ICU admission was defined as occurring between 8 and 11:59 a.m. Multivariable logistic regression analysis was used to explore the association between round-time admission and outcome.ResultsOf 18,857 unique ICU admissions, 2,055 (10.9%) occurred during round time. Round-time admissions were more frequent in community hospitals compared with tertiary hospitals (12.0% vs. 10.5%; odds ratio [OR] 1.16; 95% CI, 1.05-1.29, P < 0.004) and from the ward compared with the emergency department (ED) or operating theater (17.5% vs. 9.2%; OR 2.1; 95% CI, 1.9-2.3, P < 0.0001). Round-time admissions were more often medical than surgical (12.6% vs. 6.6%; OR 2.06; 95% CI, 1.83-2.31, P < 0.0001), had more comorbid illness (11.9% vs. 10.5%; OR 1.15; 95% CI, 1.04-1.27, P < 0.008) and higher APACHE II score (22.2 vs. 21.3, P < 0.001), and were more likely to have a primary diagnosis of respiratory failure (37.0% vs. 31.3%, P < 0.001) or sepsis (11.1% vs. 9.0%, P = 0.002). Crude ICU mortality (15.3% vs. 11.6%; OR 1.38; 95% CI, 1.21-1.57, P < 0.0001) and hospital mortality (23.9% vs. 20.6%; OR 1.21; 95% CI, 1.09-1.35, P < 0.001) were higher for round-time compared with non-round-time admissions. In multi-variable analysis, round-time admission was associated with increased ICU mortality (OR 1.19, 95% CI, 1.03-1.38, P = 0.017) but was not significantly associated with hospital mortality (OR 1.02; 95% CI, 0.90-1.16, P = 0.700). In the subgroup admitted from the ED, round-time admission showed significantly higher ICU mortality (OR 1.54; 95% CI, 1.21-1.95; P < 0.001) and a trend for higher hospital mortality (OR 1.22; 95% CI, 0.99-1.51, P = 0.057).ConclusionsApproximately 1 in 10 patients is admitted during morning rounds. These patients are more commonly admitted from the ward and are burdened by comorbidities, are non-operative, and have higher illness severity. These patients admitted during morning rounds have higher observed ICU mortality but no difference in hospital mortality.

Project description:BACKGROUND: An accurate medication list at hospital admission is essential for the evaluation and further treatment of patients. The objective of this study was to describe the frequency, type and predictors of errors in medication history, and to evaluate the extent to which standard care corrects these errors. METHODS: A descriptive study was carried out in two medical wards in a Swedish hospital using Lund Integrated Medicines Management (LIMM)-based medication reconciliation. A clinical pharmacist identified each patient's most accurate pre-admission medication list by conducting a medication reconciliation process shortly after admission. This list was then compared with the patient's medication list in the hospital medical records. Addition or withdrawal of a drug or changes to the dose or dosage form in the hospital medication list were considered medication discrepancies. Medication discrepancies for which no clinical reason could be identified (unintentional changes) were considered medication history errors. RESULTS: The final study population comprised 670 of 818 eligible patients. At least one medication history error was identified by pharmacists conducting medication reconciliations for 313 of these patients (47%; 95% CI 43-51%). The most common medication error was an omitted drug, followed by a wrong dose. Multivariate logistic regression analysis showed that a higher number of drugs at admission (odds ratio [OR] per 1 drug increase = 1.10; 95% CI 1.06-1.14; p < 0.0001) and the patient living in their own home without any care services (OR = 1.58; 95% CI 1.02-2.45; p = 0.042) were predictors for medication history errors at admission. The results further indicated that standard care by non-pharmacist ward staff had partly corrected the errors in affected patients by four days after admission, but a considerable proportion of the errors made in the initial medication history at admission remained undetected by standard care (OR for medication errors detected by pharmacists' medication reconciliation carried out on days 4-11 compared to days 0-1 = 0.52; 95% CI 0.30-0.91; p=0.021). CONCLUSIONS: Clinical pharmacists conducting LIMM-based medication reconciliations have a high potential for correcting errors in medication history for all patients. In an older Swedish population, those prescribed many drugs seem to benefit most from admission medication reconciliation.