Improved Utilization of Serial Testing Without Increased Admissions after Implementation of High-Sensitivity Troponin I: a Controlled Retrospective Cohort Study.

BACKGROUND: Guidelines recommend high-sensitivity cardiac troponin (hs-cTn) for diagnosis of myocardial infarction. Use of hs-cTn is increasing across the U.S., but questions remain regarding clinical and operational impact. Prior studies have had methodologic limitations and yielded conflicting results. OBJECTIVE: To evaluate the impact of transitioning from conventional cardiac troponin (cTn) to hs-cTn on test and resource utilization, operational efficiency, and patient safety. DESIGN: Retrospective cohort study in two New York City hospitals during the months before and after transition from conventional cTn to hs-cTn at Hospital 1. Hospital 2 served as a control. PARTICIPANTS: Consecutive emergency department (ED) patients with at least one cTn test resulted. INTERVENTION: Multifaceted hs-cTn intervention bundle, including a 0/2-h diagnostic algorithm for non-ST-elevation myocardial infarction, an educational bundle, enhancements to the electronic medical record, and nursing interventions to facilitate timed sample collection. MAIN MEASURES: Primary outcomes included serial cTn test utilization, probability of hospital admission, ED length of stay (LOS), and among discharged patients, probability of ED revisit within 72 h resulting in hospital admission. Multivariable regression models adjusted for age, sex, temporal trends, and interhospital differences. KEY RESULTS: The intervention was associated with increased use of serial cTn testing (adjusted risk difference: 48 percentage points, 95% CI: 45-50, P < 0.001) and ED LOS (adjusted geometric mean difference: 50 min, 95% CI: 50-51, P < 0.001). There was no significant association between the intervention and probability of admission (adjusted relative risk [aRR]: 0.99, 95% CI: 0.89-1.1, P = 0.81) or probability of ED revisit within 72 h resulting in admission (aRR: 1.1, 95% CI: 0.44-2.9, P = 0.81). CONCLUSIONS: Implementation of a hs-cTn intervention bundle was associated with an improvement in serial cTn testing, a neutral effect on probability of hospital admission, and a modest increase in ED LOS.

A Collaborative Emergency Medicine and Radiology Pulmonary Nodule Program: Identification of Associated Efficacy and Outcomes.

PURPOSE: Incidental radiologic findings are commonplace, but the episodic nature of emergency department (ED) care makes it challenging to ensure that patients obtain appropriate follow-up. Rates of follow-up range from 30% to 77%, with some studies demonstrating that more than 30% have no follow-up at all. The aim of this study is to describe and analyze the outcomes of a collaborative emergency medicine and radiology initiative to establish a formal workflow for the follow-up of pulmonary nodules identified during ED care. METHODS: A retrospective analysis was performed of patients referred to the pulmonary nodule program (PNP). Patients were divided into two categories: those with follow-up and those who do not have post-ED follow-up. The primary outcome was determining follow-up rates and outcomes, including patients referred for biopsy. The characteristics of patients who completed follow-up compared with those lost to follow-up were also examined. RESULTS: A total of 574 patients were referred to the PNP. Initial follow-up was established in 390 (69.1%); 30.8% were considered lost to follow-up, and more than half of these patients did not respond to initial contact. There were minimal differences in characteristics between patients in these two categories. Of the 259 patients who completed PNP follow-up, 26 were referred for biopsy (13%). CONCLUSIONS: The PNP provided effective transitions of care and potentially improved patient health care. Strategies to further enhance follow-up adherence will provide iterative improvement of the program. The PNP provides an implementation framework for post-ED pulmonary nodule follow-up in other health care systems and can be modified for use with other incidental diagnostic findings.

Laboratory Interventions to Eliminate Unnecessary Rapid COVID-19 Testing During a Reagent Shortage.

OBJECTIVES: In the fall of 2020, US medical centers were running out of rapid coronavirus disease 2019 (COVID-19) tests. The aim of this study is to evaluate the impact of an intervention to eliminate rapid test misutilization and to quantify the effect of the countermeasures to control rapid test ordering using a test utilization dashboard. METHODS: Interventions were made to preserve a severely limited supply of rapid diagnostic tests based on real-time analysis of a COVID-19 test utilization dashboard. This study is a retrospective observational study evaluating pre- and postintervention rates of appropriate rapid test use, reporting times, and cost/savings of resources used. RESULTS: This study included 14,462 severe acute respiratory syndrome coronavirus 2 reverse transcriptase polymerase chain reaction tests ordered during the study period. After the intervention, there was a 27.3% decrease in nonconforming rapid tests. Rapid test reporting time from laboratory receipt decreased by 1.47 hours. The number of days of rapid test inventory on hand increased by 39 days. CONCLUSIONS: Performing diagnostic test stewardship, informed by real-time review of a test utilization dashboard, was associated with significantly improved appropriate utilization of rapid diagnostic COVID-19 tests, improved reporting times, implied cost savings, and improved reagent inventory on hand, which facilitated the management of scarce resources during a pandemic.

In severe COVID-19, high-flow vs. conventional oxygen therapy reduced intubation and time to clinical recovery at 28 d.

SOURCE CITATION: Ospina-Tascón GA, Calderón-Tapia LE, García AF, et al. Effect of high-flow oxygen therapy vs conventional oxygen therapy on invasive mechanical ventilation and clinical recovery in patients with severe COVID-19: a randomized clinical trial. JAMA. 2021;326:2161-71. 34874419.

Emergency Department Patient Navigator Program Demonstrates Reduction in Emergency Department Return Visits and Increase in Follow-up Appointment Adherence.

BACKGROUND: An estimated 56% of emergency department (ED) visits are avoidable. One motivation for return visits is patients' perception of poor access to timely outpatient care. Efforts to facilitate access may help reduce preventable ED visits. We aimed to analyze whether an ED patient navigator (PN) program improved adherence with outpatient appointments and reduced ED return visits. METHODS: We performed a retrospective analysis of patients evaluated and discharged from two EDs from October 2016 to December 2019. Using propensity score matching, an intervention case group was matched against two control groups - patients similar to the case group who presented either (1) pre-PN intervention or (2) post-PN intervention and did not receive intervention. The four outcomes included 72-h return ED visits, 30-day return ED visits, overall ED utilization, as well as the intervention group's adherence rates to PN-scheduled outpatient appointments. From 482,896 charts, propensity matching led to a total of 14,295 patients in each group. RESULTS: PN intervention decreased both acute and subacute ED return visits. Compared to both pre-PN and post-PN controls, navigated patients had a decrease in 72-h and 30-day return visits from 2% to 1% and 7% to 4% (p < 0.001) respectively. Navigated patients also had outpatient appointment adherence rates of 74-80% compared to the estimated national average of 25-56%. While there was no difference in mean ED utilization between the intervention group and pre-PN control group, mean ED utilization was found to be higher in the intervention group compared to the post-PN control group with 0.62 visits compared to 0.38 mean visits (p < 0.001). CONCLUSIONS: By facilitating access to post-ED care, PNs may reduce avoidable ED utilization and improve outpatient follow-up adherence. While overall ED utilization did not change, this may be due to the overall vulnerability of the navigated group which is the goal PN intervention group.

Deep significance clustering: a novel approach for identifying risk-stratified and predictive patient subgroups.

OBJECTIVE: Deep significance clustering (DICE) is a self-supervised learning framework. DICE identifies clinically similar and risk-stratified subgroups that neither unsupervised clustering algorithms nor supervised risk prediction algorithms alone are guaranteed to generate. MATERIALS AND METHODS: Enabled by an optimization process that enforces statistical significance between the outcome and subgroup membership, DICE jointly trains 3 components, representation learning, clustering, and outcome prediction while providing interpretability to the deep representations. DICE also allows unseen patients to be predicted into trained subgroups for population-level risk stratification. We evaluated DICE using electronic health record datasets derived from 2 urban hospitals. Outcomes and patient cohorts used include discharge disposition to home among heart failure (HF) patients and acute kidney injury among COVID-19 (Cov-AKI) patients, respectively. RESULTS: Compared to baseline approaches including principal component analysis, DICE demonstrated superior performance in the cluster purity metrics: Silhouette score (0.48 for HF, 0.51 for Cov-AKI), Calinski-Harabasz index (212 for HF, 254 for Cov-AKI), and Davies-Bouldin index (0.86 for HF, 0.66 for Cov-AKI), and prediction metric: area under the Receiver operating characteristic (ROC) curve (0.83 for HF, 0.78 for Cov-AKI). Clinical evaluation of DICE-generated subgroups revealed more meaningful distributions of member characteristics across subgroups, and higher risk ratios between subgroups. Furthermore, DICE-generated subgroup membership alone was moderately predictive of outcomes. DISCUSSION: DICE addresses a gap in current machine learning approaches where predicted risk may not lead directly to actionable clinical steps. CONCLUSION: DICE demonstrated the potential to apply in heterogeneous populations, where having the same quantitative risk does not equate with having a similar clinical profile.

Calculated decisions: ACEP ED COVID-19 management tool.

This supplement reviews the ACEP ED COVID-19 Management Tool, an emergency department classification and management tool for adult patients (aged ≥18 years) with suspected or confirmed SARS-CoV-2.

A predictive model of clinical deterioration among hospitalized COVID-19 patients by harnessing hospital course trajectories.

From early March through mid-May 2020, the COVID-19 pandemic overwhelmed hospitals in New York City. In anticipation of ventilator shortages and limited ICU bed capacity, hospital operations prioritized the development of prognostic tools to predict clinical deterioration. However, early experience from frontline physicians observed that some patients developed unanticipated deterioration after having relatively stable periods, attesting to the uncertainty of clinical trajectories among hospitalized patients with COVID-19. Prediction tools that incorporate clinical variables at one time-point, usually on hospital presentation, are suboptimal for patients with dynamic changes and evolving clinical trajectories. Therefore, our study team developed a machine-learning algorithm to predict clinical deterioration among hospitalized COVID-19 patients by extracting clinically meaningful features from complex longitudinal laboratory and vital sign values during the early period of hospitalization with an emphasis on informative missing-ness. To incorporate the evolution of the disease and clinical practice over the course of the pandemic, we utilized a time-dependent cross-validation strategy for model development. Finally, we validated our prediction model on an external validation cohort of COVID-19 patients served in a demographically distinct population from the training cohort. The main finding of our study is the identification of risk profiles of early, late and no clinical deterioration during the course of hospitalization. While risk prediction models that include simple predictors at ED presentation and clinical judgement are able to identify any deterioration vs. no deterioration, our methodology is able to isolate a particular risk group that remain stable initially but deteriorate at a later stage of the course of hospitalization. We demonstrate the superior predictive performance with the utilization of laboratory and vital sign data during the early period of hospitalization compared to the utilization of data at presentation alone. Our results will allow efficient hospital resource allocation and will motivate research in understanding the late deterioration risk group.

MyEDCare: Evaluation of a Smartphone-Based Emergency Department Discharge Process.

BACKGROUND: Poor comprehension and low compliance with post-ED (emergency department) care plans increase the risk of unscheduled ED return visits and adverse outcomes. Despite the growth of personal health records to support transitions of care, technological innovation's focus on the ED discharge process has been limited. Recent literature suggests that digital communication incorporated into post-ED care can improve patient satisfaction and care quality. OBJECTIVES: We evaluated the feasibility of utilizing MyEDCare, a text message and smartphone-based electronic ED discharge process at two urban EDs. METHODS: MyEDCare sends text messages to patients' smartphones at the time of discharge, containing a hyperlink to a Health Insurance Portability and Accountability Act (HIPAA)-compliant website, to deliver patient-specific ED discharge instructions. Content includes information on therapeutics, new medications, outpatient care scheduling, return precautions, as well as results of laboratory and radiological diagnostic testing performed in the ED. Three text messages are sent to patients: at the time of ED discharge with the nurse assistance for initial access of content, as well as 2 and 29 days after ED discharge. MyEDCare was piloted in a 9-month pilot period in 2019 at two urban EDs in an academic medical center. We evaluated ED return visits, ED staff satisfaction, and patient satisfaction using ED Consumer Assessment of Healthcare Providers and Systems (ED-CAHPS) patient satisfaction scores. RESULTS: MyEDCare enrolled 27,713 patients discharged from the two EDs, accounting for 43% of treat-and-release ED patients. Of the treat-and-release patients, 27% completed MyEDCare discharge process, accessing the online content at the time of ED discharge. Patients discharged via MyEDCare had fewer 72-hour, 9-day, and 30-day unscheduled return ED visits and reported higher satisfaction related to nursing care. CONCLUSION: EDs and urgent care facilities may consider developing a HIPAA-compliant, text message, and smartphone-based discharge process, including the transmission of test results, to improve patient-centered outcomes.

Shotgun transcriptome, spatial omics, and isothermal profiling of SARS-CoV-2 infection reveals unique host responses, viral diversification, and drug interactions.

In less than nine months, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) killed over a million people, including >25,000 in New York City (NYC) alone. The COVID-19 pandemic caused by SARS-CoV-2 highlights clinical needs to detect infection, track strain evolution, and identify biomarkers of disease course. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs and a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, viral, and microbial profiling. We applied these methods to clinical specimens gathered from 669 patients in New York City during the first two months of the outbreak, yielding a broad molecular portrait of the emerging COVID-19 disease. We find significant enrichment of a NYC-distinctive clade of the virus (20C), as well as host responses in interferon, ACE, hematological, and olfaction pathways. In addition, we use 50,821 patient records to find that renin-angiotensin-aldosterone system inhibitors have a protective effect for severe COVID-19 outcomes, unlike similar drugs. Finally, spatial transcriptomic data from COVID-19 patient autopsy tissues reveal distinct ACE2 expression loci, with macrophage and neutrophil infiltration in the lungs. These findings can inform public health and may help develop and drive SARS-CoV-2 diagnostic, prevention, and treatment strategies.

Testing-on-a-probe biosensors reveal association of early SARS-CoV-2 total antibodies and surrogate neutralizing antibodies with mortality in COVID-19 patients.

The association of mortality with the early humoral response to SARS-CoV-2 infection within the first few days after onset of symptoms (DAOS) has not been thoroughly investigated partly due to a lack of sufficiently sensitive antibody testing methods. Here we report two sensitive and automated testing-on-a-probe (TOP) biosensor assays for SARS-CoV-2 viral specific total antibodies (TAb) and surrogate neutralizing antibodies (SNAb), which are suitable for clinical use. The TOP assays employ an RBD-coated quartz probe using a Cy5-Streptavidin-polysacharide conjugate to improve sensitivity and minimize interference. Disposable cartridges containing pre-dispensed reagents require no liquid manipulation or fluidics during testing. The TOP-TAb assay exhibited higher sensitivity in the 0-7 DAOS window than a widely used FDA-EUA assay. The rapid and automated TOP-SNAb correlated well with two well-established SARS-CoV-2 virus neutralization tests. The clinical utility of the TOP assays was demonstrated by evaluating early antibody responses in 120 SARS-CoV-2 RT-PCR positive adult hospitalized patients. Higher TAb and SNAb positivity rates and more robust antibody responses at patient's initial hospital presentation were seen in inpatients who survived COVID-19 than those who died in the hospital. Survival analysis using the Cox Proportional Hazards Model showed that patients who had negative TAb and/or SNAb at initial hospital presentation were at a higher risk of in-hospital mortality. Furthermore, TAb and SNAb levels at presentation were inversely associated with SARS-CoV-2 viral load based on concurrent RT-PCR testing. Overall, the sensitive and automated TAb and SNAb assays allow the detection of early SARS-CoV-2 antibodies which associate with mortality.

Machine Learning Highlights Downtrending of COVID-19 Patients with a Distinct Laboratory Profile.

Background: New York City (NYC) experienced an initial surge and gradual decline in the number of SARS-CoV-2-confirmed cases in 2020. A change in the pattern of laboratory test results in COVID-19 patients over this time has not been reported or correlated with patient outcome. Methods: We performed a retrospective study of routine laboratory and SARS-CoV-2 RT-PCR test results from 5,785 patients evaluated in a NYC hospital emergency department from March to June employing machine learning analysis. Results: A COVID-19 high-risk laboratory test result profile (COVID19-HRP), consisting of 21 routine blood tests, was identified to characterize the SARS-CoV-2 patients. Approximately half of the SARS-CoV-2 positive patients had the distinct COVID19-HRP that separated them from SARS-CoV-2 negative patients. SARS-CoV-2 patients with the COVID19-HRP had higher SARS-CoV-2 viral loads, determined by cycle threshold values from the RT-PCR, and poorer clinical outcome compared to other positive patients without the COVID12-HRP. Furthermore, the percentage of SARS-CoV-2 patients with the COVID19-HRP has significantly decreased from March/April to May/June. Notably, viral load in the SARS-CoV-2 patients declined, and their laboratory profile became less distinguishable from SARS-CoV-2 negative patients in the later phase. Conclusions: Our longitudinal analysis illustrates the temporal change of laboratory test result profile in SARS-CoV-2 patients and the COVID-19 evolvement in a US epicenter. This analysis could become an important tool in COVID-19 population disease severity tracking and prediction. In addition, this analysis may play an important role in prioritizing high-risk patients, assisting in patient triaging and optimizing the usage of resources.

No increase in the incidence of cardiac troponin I concentration above the 99th percentile by Siemens Centaur high-sensitivity compared to the contemporary assay.

BACKGROUND: This study aimed to compare the incidence of cardiac troponin I (cTnI) concentrations above the limit of quantification (LOQ) and the sex-specific 99th percentile upper reference limits (URLs) between the Ultra contemporary cTnI assay and the high-sensitivity (hs-cTnI) assay on Siemens Centaur in patients evaluated in the emergency department (ED) and inpatient at a U.S. urban academic hospital. METHODS: A retrospective study was performed in an unselected patient cohort who presented to the hospital with symptoms suggestive of myocardial injury. All clinically ordered samples for cTnI assay (n = 1,056, LOQ 0.03 µg/L, URL 0.04 µg/L) were simultaneously tested on the hs-cTnI assay (LOQ 2.5 ng/L; URL 58 ng/L and 39 ng/L for male and female, respectively). RESULTS: The incidence of elevated cTnI above the 99th percentile URL in males measured by the hs-cTnI assay was significantly lower compared to the cTnI assay (31.4% vs. 38.7%, p = 0.016), whereas there was no difference in females (27.4% vs. 30.2%, p = 0.35) in all the patient samples. In ED patient samples (n = 718), the incidence of elevated cTnI above the sex-specific 99th percentile URL was not significantly different between the hs-cTnI and contemporary cTnI assays in either sex (male: hs-cTnI 16.6% vs. cTnI 21.5%, p = 0.13; female: hs-cTnI 19.6% vs. cTnI 21.1%, p = 0.66). The agreement between the two assays was 93.5% (kappa = 0.798). Results were confirmed in an independent patient cohort measured by the same instruments at another hospital. CONCLUSION: Our study suggests that implementation of the hs-cTnI assay would not lead to an increase in the proportion of elevated cTnI above the 99th percentile in the emergency department and other inpatient units.

Testing-on-a-probe biosensors reveal association of early SARS-CoV-2 total antibodies and surrogate neutralizing antibodies with mortality in COVID-19 patients.

The association of mortality with early humoral response to SARS-CoV-2 infection within the first few days after onset of symptoms (DAOS) has not been thoroughly investigated partly due to a lack of sufficiently sensitive antibody testing methods. Here we report two sensitive and automated testing-on-a-probe (TOP) biosensor assays for SARS-CoV-2 viral specific total antibodies (TAb) and surrogate neutralizing antibodies (SNAb), which are suitable for clinical use. The TOP assays employ an RBD-coated quartz probe using a Cy5-Streptavidin-polysacharide conjugate to improved sensitivity and minimize interference. Disposable cartridge containing pre-dispensed reagents requires no liquid manipulation or fluidics during testing. The TOP-TAb assay exhibited higher sensitivity in the 0-7 DAOS window than a widely used FDA-EUA assay. The rapid (18 min) and automated TOP-SNAb correlated well with two well-established SARS-CoV-2 virus neutralization tests. The clinical utility of the TOP assays was demonstrated by evaluating early antibody responses in 120 SARS-CoV-2 RT-PCR positive adult hospitalized patients. Higher baseline TAb and SNAb positivity rates and more robust antibody responses were seen in patients who survived COVID-19 than those who died in the hospital. Survival analysis using the Cox Proportional Hazards Model showed that patients who were TAb and SNAb negative at initial hospital presentation were at a higher risk of in-hospital mortality. Furthermore, TAb and SNAb levels at presentation were inversely associated with SARS-CoV-2 viral load based on concurrent RT-PCR testing. Overall, the sensitive and automated TAb and SNAb assays allow detection of early SARS-CoV-2 antibodies which associate with mortality.

Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning.

BACKGROUND: Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours. METHOD: We developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual's SARS-CoV-2 infection status. Laboratory testing results obtained within 2 days before the release of SARS-CoV-2 RT-PCR result were used to train a gradient boosting decision tree (GBDT) model from 3,356 SARS-CoV-2 RT-PCR tested patients (1,402 positive and 1,954 negative) evaluated at a metropolitan hospital. RESULTS: The model achieved an area under the receiver operating characteristic curve (AUC) of 0.854 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within 2 days. CONCLUSION: This model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-CoV-2 infected patients before their RT-PCR results are available. It may play an important role in assisting the identification of SARS-CoV-2 infected patients in areas where RT-PCR testing is not accessible due to financial or supply constraints.

Shotgun Transcriptome and Isothermal Profiling of SARS-CoV-2 Infection Reveals Unique Host Responses, Viral Diversification, and Drug Interactions.

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused thousands of deaths worldwide, including >18,000 in New York City (NYC) alone. The sudden emergence of this pandemic has highlighted a pressing clinical need for rapid, scalable diagnostics that can detect infection, interrogate strain evolution, and identify novel patient biomarkers. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs, plus a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, bacterial, and viral profiling. We applied both technologies across 857 SARS-CoV-2 clinical specimens and 86 NYC subway samples, providing a broad molecular portrait of the COVID-19 NYC outbreak. Our results define new features of SARS-CoV-2 evolution, nominate a novel, NYC-enriched viral subclade, reveal specific host responses in interferon, ACE, hematological, and olfaction pathways, and examine risks associated with use of ACE inhibitors and angiotensin receptor blockers. Together, these findings have immediate applications to SARS-CoV-2 diagnostics, public health, and new therapeutic targets.