Discharge in Pandemic: Suspected Covid-19 patients returning to the Emergency Department within 72 hours for admission

Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19

BACKGROUND

The COVID-19 outbreak has revealed a high demand for timely surveillance of pandemic developments. Google Trends (GT), which provides freely available search volume data, has been proven to be a reliable forecast and nowcast measure for public health issues. Previous studies have tended to use relative search volumes from GT directly to analyze associations and predict the progression of pandemic. However, GT's normalization of the search volumes data and data retrieval restrictions affect the data resolution in reflecting the actual search behaviors, thus limiting the potential for using GT data to predict disease outbreaks.

OBJECTIVE

This study aimed to introduce a merged algorithm that helps recover the resolution and accuracy of the search volume data extracted from GT over long observation periods. In addition, this study also aimed to demonstrate the extended application of merged search volumes (MSVs) in combination of network analysis, via tracking the COVID-19 pandemic risk.

METHODS

We collected relative search volumes from GT and transformed them into MSVs using our proposed merged algorithm. The MSVs of the selected coronavirus-related keywords were compiled using the rolling window method. The correlations between the MSVs were calculated to form a dynamic network. The network statistics, including network density and the global clustering coefficients between the MSVs, were also calculated.

RESULTS

Our research findings suggested that although GT restricts the search data retrieval into weekly data points over a long period, our proposed approach could recover the daily search volume over the same investigation period to facilitate subsequent research analyses. In addition, the dynamic time warping diagrams show that the dynamic networks were capable of predicting the COVID-19 pandemic trends, in terms of the number of COVID-19 confirmed cases and severity risk scores.

CONCLUSIONS

The innovative method for handling GT search data and the application of MSVs and network analysis to broaden the potential for GT data are useful for predicting the pandemic risk. Further investigation of the GT dynamic network can focus on noncommunicable diseases, health-related behaviors, and misinformation on the internet.

Derivation and validation of a clinical decision rule to risk-stratify COVID-19 patients discharged from the emergency department: The CCEDRRN COVID discharge score

Objective

To risk-stratify COVID-19 patients being considered for discharge from the emergency department (ED).

Methods

We conducted an observational study to derive and validate a clinical decision rule to identify COVID-19 patients at risk for hospital admission or death within 72 hours of ED discharge. We used data from 49 sites in the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN) between March 1, 2020, and September 8, 2021. We randomly assigned hospitals to derivation or validation and prespecified clinical variables as candidate predictors. We used logistic regression to develop the score in a derivation cohort and examined its performance in predicting short-term adverse outcomes in a validation cohort.

Results

Of 15,305 eligible patient visits, 535 (3.6%) experienced the outcome. The score included age, sex, pregnancy status, temperature, arrival mode, respiratory rate, and respiratory distress. The area under the curve was 0.70 (95% confidence interval [CI] 0.68-0.73) in derivation and 0.71 (95% CI 0.68-0.73) in combined derivation and validation cohorts. Among those with a score of 3 or less, the risk for the primary outcome was 1.9% or less, and the sensitivity of using 3 as a rule-out score was 89.3% (95% CI 82.7-94.0). Among those with a score of ≥9, the risk for the primary outcome was as high as 12.2% and the specificity of using 9 as a rule-in score was 95.6% (95% CI 94.9-96.2).

Conclusion

The CCEDRRN COVID discharge score can identify patients at risk of short-term adverse outcomes after ED discharge with variables that are readily available on patient arrival.

Roles of CRP and Neutrophil-to-Lymphocyte Ratio in the Prediction of Readmission of COVID-19 Patients Discharged From the ED

Background

Patient admissions beyond the capacity of emergency departments (EDs) have been reported since the coronavirus disease (COVID-19) pandemic. Thus, laboratory parameters to predict the readmission of patients discharged from the ED are needed. For this purpose, we investigated whether C-reactive protein (CRP) level and neutrophil-to-lymphocyte ratio (NLR) could predict the readmission of patients with COVID-19.

Methods

Patients aged >18 years who visited the ED in October 2020 and had positive polymerase chain reaction test results were evaluated. Among these patients, those who were not hospitalized and were discharged from the ED on the same day were included in the study. The patients' readmission status within 14 days after discharge, age, sex, complaint on admission, comorbidity, systolic blood pressure, diastolic blood pressure, fever, pulse, oxygen saturation level, CRP level, blood urea nitrogen level, creatinine level, neutrophil count, lymphocyte count, and NLR were recorded. Data were compared between the groups.

Results

Of the 779 patients who were included in the study, 359 (46.1%) were male. The median age was 41 years (range, 31-53 years). Among these patients, those who were not hospitalized and were discharged from the ED on logistic regression analysis, age, CRP level, NLR, loss of smell and taste, and hypertension had odds ratios of 2.494, 2.207, 1.803, 0.341, and 1.879, respectively.

Conclusions

The strongest independent predictor of readmission within 14 days after same-day ED discharge was age > 50 years. In addition, CRP level and NLR were the laboratory parameters identified as independent predictors of ED readmission.

Radiography-based triage for COVID-19 in the Emergency Department in a Spanish cohort of patients

BACKGROUND

Strategies to determine who could be safely discharged home from the Emergency Department (ED) in COVID-19 are needed to decongestion healthcare systems.

OBJECTIVES

To describe the outcomes of an ED triage system for non-severe patients with suspected COVID-19 and possible pneumonia based on chest X-ray (CXR) upon admission.

MATERIAL AND METHODS

Retrospective, single-center study performed in Barcelona (Spain) during the COVID-19 peak in March-April 2020. Patients with COVID-19 symptoms and potential pneumonia, without respiratory insufficiency, with priority class IV-V (Andorran triage model) had a CXR upon admission. This approach tried to optimize resource use and to facilitate discharges. The results after adopting this organizational approach are reported.

RESULTS

We included 834 patients, 53% were female. Most patients were white (66%) or Hispanic (27%). CXR showed pneumonia in 523 (62.7%). Compared to those without pneumonia, patients with pneumonia were older (55 vs 46.6 years old) and had a higher Charlson comorbidity index (1.9 vs 1.3). Patients with pneumonia were at a higher risk for a combined outcome of admission and/or death (91 vs 12%). Death rates tended to be numerically higher in the pneumonia group (10 vs 1). Among patients without pneumonia in the initial CXR, 10% reconsulted (40% of them with new pneumonia).

CONCLUSION

CXR identified pneumonia in a significant number of patients. Those without pneumonia were mostly discharged. Mortality among patients with an initially negative CXR was low. CXR triage for pneumonia in non-severe COVID-19 patients in the ED can be an effective strategy to optimize resource use.

Discharge from the emergency department and early hospital revaluation in patients with COVID-19 pneumonia: a prospective study

Objective The national health systems are currently facing the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. We assessed the efficacy of outpatient management for patients with SARS-CoV-2 related pneumonia at risk of progression after discharge from the emergency department.Methods This was a single-center prospective study. We enrolled patients with confirmed SARS-CoV-2 pneumonia, without hypoxemic respiratory failure, and at least one of the following: age ≥ 65 years or the presence of relevant comorbidities or pneumonia extension > 25% on high resolution computed tomography. Patients with pneumonia extension > 50% were excluded. An ambulatory visit was performed after at least 48 hours, when patients were either discharged, admitted, or deferred for a further visit. As a control, we evaluated a comparable historical cohort of hospitalized patients.Results A total of 84 patients were enrolled (51 male patients; mean age, 62.8 years). Two-thirds of the patients had at least one comorbidity and 41.6% had a lung involvement > 25% on high resolution computed tomography; the mean duration of symptoms was 8.0 ± 3.0 days, and the mean PaO2/FiO2 ratio was 357.5 ± 38.6. At the end of the follow-up period, 69 patients had been discharged, and 15 were hospitalized (mean stay of 6 days). Older age and higher National Early Warning Score 2 were significant predictors of hospitalization at the first follow-up visit. One hospitalized patient died of septic shock. In the control group, the mean hospital stay was 8 days.Conclusion Adopting a “discharge and early revaluation” strategy appears to be safe, feasible, and may optimize hospital resources during the SARS-CoV-2 pandemic.

Emergency Physician Twitter Use in the COVID-19 Pandemic as a Potential Predictor of Impending Surge: Retrospective Observational Study

Background

The early conversations on social media by emergency physicians offer a window into the ongoing response to the COVID-19 pandemic.

Objective

This retrospective observational study of emergency physician Twitter use details how the health care crisis has influenced emergency physician discourse online and how this discourse may have use as a harbinger of ensuing surge.

Methods

Followers of the three main emergency physician professional organizations were identified using Twitter’s application programming interface. They and their followers were included in the study if they identified explicitly as US-based emergency physicians. Statuses, or tweets, were obtained between January 4, 2020, when the new disease was first reported, and December 14, 2020, when vaccination first began. Original tweets underwent sentiment analysis using the previously validated Valence Aware Dictionary and Sentiment Reasoner (VADER) tool as well as topic modeling using latent Dirichlet allocation unsupervised machine learning. Sentiment and topic trends were then correlated with daily change in new COVID-19 cases and inpatient bed utilization.

Results

A total of 3463 emergency physicians produced 334,747 unique English-language tweets during the study period. Out of 3463 participants, 910 (26.3%) stated that they were in training, and 466 of 902 (51.7%) participants who provided their gender identified as men. Overall tweet volume went from a pre-March 2020 mean of 481.9 (SD 72.7) daily tweets to a mean of 1065.5 (SD 257.3) daily tweets thereafter. Parameter and topic number tuning led to 20 tweet topics, with a topic coherence of 0.49. Except for a week in June and 4 days in November, discourse was dominated by the health care system (45,570/334,747, 13.6%). Discussion of pandemic response, epidemiology, and clinical care were jointly found to moderately correlate with COVID-19 hospital bed utilization (Pearson r=0.41), as was the occurrence of “covid,” “coronavirus,” or “pandemic” in tweet texts (r=0.47). Momentum in COVID-19 tweets, as demonstrated by a sustained crossing of 7- and 28-day moving averages, was found to have occurred on an average of 45.0 (SD 12.7) days before peak COVID-19 hospital bed utilization across the country and in the four most contributory states.

Conclusions

COVID-19 Twitter discussion among emergency physicians correlates with and may precede the rising of hospital burden. This study, therefore, begins to depict the extent to which the ongoing pandemic has affected the field of emergency medicine discourse online and suggests a potential avenue for understanding predictors of surge.

Predictors of readmission requiring hospitalization after discharge from emergency departments in patients with COVID-19

Introduction

Little is known on prevalence of early return hospital admission of subjects with COVID-19 previously evaluated and discharged from emergency departments (EDs). This study aims to describe readmission rate within 14 days of patients with COVID-19 discharged from ED and to identify predictors of return hospital admission.

Methods

We performed a retrospective cohort study of adult patients with COVID-19 discharged from two EDs. Return hospital admission was defined as an unscheduled return ED visit within 14 days after initial ED evaluation and discharge. We compared the group of patients who had a return hospital admission to those who did not. We also evaluated selected clinical characteristics (age, neutrophilia, SOFA, lactate dehydrogenase, C-reactive protein and D-dimer) associated with return hospital admission.

Results

Of 283 patients included in the study, 65 (22.9%) had a return ED visit within 14 days. 32 of those patients (11%) were then hospitalized, while the remaining 33 were again discharged. Patients requiring a return hospital admission was significantly older, had higher pro-calcitonin and D-dimer levels. Major predictors of return hospital admission were cognitive impairment (OR 17.3 [CI 4.7–63.2]), P/F < 300 mmHg (OR 8.6 [CI 1.6–44.3]), being resident in geriatric care facility (OR 7.6 [CI 2.1–26.4]) and neutrophilia (OR 5.8 [CI 1.6–22.0]).

Conclusion

Several factors are associated with 14-day return hospital admission in COVID-19 subjects. These should be considered when assessing discharge risk in ED clinical practice.