Return Hospital Admissions Among 1419 COVID-19 Patients Discharged from Five U.S. Emergency Departments

Characteristics and outcomes of patients with COVID-19 who return to the emergency department: a multicentre observational study by the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN)

OBJECTIVE

Unplanned return emergency department (ED) visits can reflect clinical deterioration or unmet need from the original visit. We determined the characteristics and outcomes of patients with COVID-19 who return to the ED for COVID-19-related revisits.

METHODS

This retrospective observational study used data for all adult patients visiting 47 Canadian EDs with COVID-19 between 1 March 2020 and 31 March 2022. Multivariable logistic regression assessed the characteristics associated with having a no return visit (SV=single visit group) versus at least one return visit (MV=return visit group) after being discharged alive at the first ED visit.

RESULTS

39 809 patients with COVID-19 had 44 862 COVID-19-related ED visits: 35 468 patients (89%) had one visit (SV group) and 4341 (11%) returned to the ED (MV group) within 30 days (mean 2.2, SD=0.5 ED visit). 40% of SV patients and 16% of MV patients were admitted at their first visit, and 41% of MV patients not admitted at their first ED visit were admitted on their second visit. In the MV group, the median time to return was 4 days, 49% returned within 72 hours. In multivariable modelling, a repeat visit was associated with a variety of factors including older age (OR=1.25 per 10 years, 95% CI (1.22 to 1.28)), pregnancy (1.86 (1.46 to 2.36)) and presence of comorbidities (eg, 1.72 (1.40 to 2.10) for cancer, 2.01 (1.52 to 2.66) for obesity, 2.18 (1.42 to 3.36) for organ transplant), current/prior substance use, higher temperature or WHO severe disease (1.41 (1.29 to 1.54)). Return was less likely for females (0.82 (0.77 to 0.88)) and those boosted or fully vaccinated (0.48 (0.34 to 0.70)).

CONCLUSIONS

Return ED visits by patients with COVID-19 within 30 days were common during the first two pandemic years and were associated with multiple factors, many of which reflect known risk for worse outcomes. Future studies should assess reasons for revisit and opportunities to improve ED care and reduce resource use.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov, NCT04702945.

Large-scale Implementation of a COVID-19 Remote Patient Monitoring Program

Introduction

We implemented a large-scale remote patient monitoring (RPM) program for patients diagnosed with coronavirus 2019 (COVID-19) at a not-for-profit regional healthcare system. In this retrospective observational study, patients from nine emergency department (ED) sites were provided a pulse oximeter and enrolled onto a monitoring platform upon discharge.

Methods

The RPM team captured oxygen saturation (SpO2), heart rate, temperature, and symptom progression data over a 16-day monitoring period, and the team engaged patients via video call, phone call, and chat within the platform. Abnormal vital signs were flagged by the RPM team, with escalation to in-person care and return to ED as appropriate. Our primary outcome was to describe study characteristics: patients enrolled in the COVID-19 RPM program; engagement metrics; and physiologic and symptomatic data trends. Our secondary outcomes were return-to-ED rate and subsequent readmission rate.

Results

Between December 2020-August 2021, a total of 3,457 patients were referred, and 1,779 successfully transmitted at least one point of data. Patients on COVID-19 RPM were associated with a lower 30-day return-to-ED rate (6.2%) than those not on RPM (14.9%), with capture of higher acuity patients (47.7% of RPM 30-day returnees were subsequently hospitalized vs 34.8% of non-RPM returnees).

Conclusion

Our program, one of the largest studies to date that captures both physiologic and symptomatic data, may inform others who look to implement a program of similar scope. We also share lessons learned regarding barriers and disparities in enrollment and discuss implications for RPM in other acute disease states.

Effectiveness, safety, and efficiency of a drive-through care model as a response to the COVID-19 testing demand in the United States

Objectives

Here we report the clinical performance of COVID-19 curbside screening with triage to a drive-through care pathway versus main emergency department (ED) care for ambulatory COVID-19 testing during a pandemic. Patients were evaluated from cars to prevent the demand for testing from spreading COVID-19 within the hospital.

Methods

We examined the effectiveness of curbside screening to identify patients who would be tested during evaluation, patient flow from screening to care team evaluation and testing, and safety of drive-through care as 7-day ED revisits and 14-day hospital admissions. We also compared main ED efficiency versus drive-through care using ED length of stay (EDLOS). Standardized mean differences (SMD) >0.20 identify statistical significance.

Results

Of 5931 ED patients seen, 2788 (47.0%) were walk-in patients. Of these patients, 1111 (39.8%) screened positive for potential COVID symptoms, of whom 708 (63.7%) were triaged to drive-through care (with 96.3% tested), and 403 (36.3%) triaged to the main ED (with 90.5% tested). The 1677 (60.2%) patients who screened negative were seen in the main ED, with 440 (26.2%) tested. Curbside screening sensitivity and specificity for predicting who ultimately received testing were 70.3% and 94.5%. Compared to the main ED, drive-through patients had fewer 7-day ED revisits (3.8% vs 12.5%, SMD = 0.321), fewer 14-day hospital readmissions (4.5% vs 15.6%, SMD = 0.37), and shorter EDLOS (0.56 vs 5.12 hours, SMD = 1.48).

Conclusion

Curbside screening had high sensitivity, permitting early respiratory isolation precautions for most patients tested. Low ED revisit, hospital readmissions, and EDLOS suggest drive-through care, with appropriate screening, is safe and efficient for future respiratory illness pandemics.

A 4C mortality score based dichotomic rule supports Emergency Department discharge of COVID-19 patients

BACKGROUND

For COVID-19 patients evaluated in the Emergency Department (ED), decision on hospital admission vs. home discharge is challenging. The 4C mortality score (4CMS) is a prognostication tool integrating key demographic/clinical/biochemical data validated for COVID-19 inpatients. We sought to derive and validate a dichotomic rule based on 4CMS identifying patients with mild outcomes, suitable for safe ED discharge.

METHODS

Derivation was performed in a prospective cohort of ED patients with suspected COVID-19 from two centers (April 2020). Validation was pursued in a prospective multicenter cohort of ED patients with confirmed COVID-19 from 6 centers (October 2020 to January 2021). Chest X-ray (CXR) images were independently scored. The primary composite outcome was all-cause 30-day mortality or hospital admission. Secondary outcomes were ED re-visit, oxygen therapy and ventilation.

RESULTS

In a derivation cohort of 838 ED patients with suspected COVID-19, 4CMS≤8 was associated with low outpatient mortality (0.4%) and was thus selected as a feasible discharge rule. In a validation cohort of 521 COVID-19 outpatients, the mean age was 51±17 years; 97 (18.6%) patients had ≥1 CXR infiltrate. The 4CMS had an AUC of 0.82 for the primary outcome and 0.93 for mortality, outperforming other scores (CURB-65, qCSI, qSOFA, NEWS) and CXR. In 474 (91%) patients with 4CMS≤8, the mortality rate was 0.2% and the hospital admission rate was 6.8%, versus 12.8% and 36.2% for 4CMS≥9 (P<0.001). CXR did not provide additional discrimination.

CONCLUSIONS

COVID-19 outpatients with 4CMS≤8 have mild outcomes and can be safely discharged from the ED. [NCT0462918].

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.

Hospital readmissions and emergency department re-presentation of COVID-19 patients: a systematic review

Objective.

To characterize the frequency, causes, and predictors of readmissions of COVID-19 patients after discharge from heath facilities or emergency departments, interventions used to reduce readmissions, and outcomes of COVID-19 patients discharged from such settings.

Methods.

We performed a systematic review for case series and observational studies published between January 2020 and April 2021 in PubMed, Embase, LILACS, and MedRxiv, reporting the frequency, causes, or risk factors for readmission of COVID-19 survivors/patients. We conducted a narrative synthesis and assessed the methodological quality using the JBI critical appraisal checklist.

Results.

We identified 44 studies including data from 10 countries. The overall 30-day median readmission rate was 7.1%. Readmissions varied with the length of follow-up, occurring <10.5%, <14.5%, <21.5%, and <30%, respectively, for 10, 30, 60, and 253 days following discharge. Among those followed up for 30 and 60 days, the median time from discharge to readmission was 3 days and 8–11 days, respectively. The significant risk factor associated with readmission was having shorter length of stay, and the important causes included respiratory or thromboembolic events and chronic illnesses. Emergency department re-presentation was >20% in four studies. Risk factors associated with mortality were male gender, advanced age, and comorbidities.

Conclusions.

Readmission of COVID-19 survivors is frequent, and post-discharge mortality is significant in specific populations. There is an urgent need to further examine underlying reasons for early readmission and to prevent additional readmissions and adverse outcomes in COVID-19 survivors.

The return visit, outcome and predicting factors of return visit among suspected COVID-19 outpatients

Rate of return visit, predicting factors of return visit and occurrence of adverse events in suspected to be or likely cases of COVID-19 patients who received outpatient treatment. This is a retrospective observational cohort study on patients (> 16 years), suspected to be or likely cases of COVID-19 who were visited in a respiratory emergency department and subsequently discharged home. Patients' baseline characteristics were extracted from medical charts. All patients were followed-up for 7 days after their first visit. Patients' outcomes during the7-day follow-up, as well as the severity of pulmonary involvement based on imaging were recorded. A total number of 601 patients (350 men and 251 women) were recruited. The rate of return visit was 27.74% (144 patients) with 6.74% (34 patients) experiencing a poor outcome. Six factors with a significant odds ratio were predictors of poor outcome in patients who received outpatient treatment, namely, older age [odds ratio = 3.278, 95% confidence interval: 1.115-9.632], days from onset of symptoms [1.068, 1.003-1.137], and history of diabetes [6.373, 2.271-17.883]). Predictors of favorable outcome were female gender [0.376, 0.158-0.894], oxygen saturation > 93% [0.862, 0.733-1.014], smoking habit [0.204, 0.045-0.934]. The findings of this study demonstrate that the rate of return visit with poor outcome in patients who received outpatient treatment was reasonably low. Age, male sex, diabetes mellitus and pulmonary disease are predicting factors of poor outcome in these COVID-19 patients who received outpatient management.

The MedConnect Program: Symptomatology, Return Visits, and Hospitalization of COVID-19 Outpatients Following Discharge From the Emergency Department

Background and objective

Although hospitalization is required for only a minority of those infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the high rates of morbidity and mortality among these patients have led researchers to focus on the predictors of admission and adverse outcomes in the inpatient population. However, there is scarce data on the clinical trajectory of individuals symptomatic enough to present for emergency care, but not sick enough to be admitted. In light of this, we aimed to examine the symptomatology, emergency department (ED) revisits, and hospitalization of coronavirus disease 2019 (COVID-19) outpatients after discharge from the ED.

Methods

Adult patients with COVID-19 infection were prospectively enrolled after discharge from the ED between May and December 2020. Patients were followed up longitudinally for 14 days via phone interviews designed to provide support and information and to track symptomatology, ED revisits, and hospitalization.

Results

A volunteer, medical student-run program enrolled 199 COVID-19 patients discharged from the ED during the first nine months of the pandemic. Of the 176 patients (88.4%) who completed the 14-day protocol, 29 (16.5%) had a second ED visit and 17 (9.6%) were admitted, 16 (9%) for worsening COVID-19 symptoms. Age, male sex, comorbid illnesses, and self-reported dyspnea, diarrhea, chills, and fever were associated with hospital admission for patients with a subsequent ED visit. For those who did not require admission, symptoms generally improved following ED discharge. Age >65 years and a history of cardiovascular disease (CVD) were associated with a longer duration of cough, but generally, patient characteristics and comorbidities did not significantly affect the overall number or duration of symptoms.

Conclusions

Nearly one in five patients discharged from the ED with COVID-19 infection had a second ED evaluation during a 14-day follow-up period, despite regular phone interactions aimed at providing support and information. More than half of them required admission for worsening COVID-19 symptoms. Established risk factors for severe disease and self-reported persistence of certain symptoms were associated with hospital admission, while those who did not require hospitalization had a steady improvement in symptoms over the 14-day period.

Lung Ultrasound Improves Outcome Prediction over Clinical Judgment in COVID-19 Patients Evaluated in the Emergency Department

In the Emergency Department (ED), the decision to hospitalize or discharge COVID-19 patients is challenging. We assessed the utility of lung ultrasound (LUS), alone or in association with a clinical rule/score. This was a multicenter observational prospective study involving six EDs (NCT046291831). From October 2020 to January 2021, COVID-19 outpatients discharged from the ED based on clinical judgment were subjected to LUS and followed-up at 30 days. The primary clinical outcome was a composite of hospitalization or death. Within 393 COVID-19 patients, 35 (8.9%) reached the primary outcome. For outcome prognostication, LUS had a C-index of 0.76 (95%CI 0.68−0.84) and showed good performance and calibration. LUS-based classification provided significant differences in Kaplan−Meier curves, with a positive LUS leading to a hazard ratio of 4.33 (95%CI 1.95−9.61) for the primary outcome. The sensitivity and specificity of LUS for primary outcome occurrence were 74.3% (95%CI 59.8−88.8) and 74% (95%CI 69.5−78.6), respectively. The integration of LUS with a clinical score further increased sensitivity. In patients with a negative LUS, the primary outcome occurred in nine (3.3%) patients (p < 0.001 vs. unselected). The efficiency for rule-out was 69.7%. In unvaccinated ED patients with COVID-19, LUS improves prognostic stratification over clinical judgment alone and may support standardized disposition decisions.

Healthcare Resource Utilization of Patients With COVID-19 Visiting US Hospitals

OBJECTIVES

Severe cases of COVID-19 have overwhelmed hospital systems across the nation. This study aimed to describe the healthcare resource utilization of patients with COVID-19 from hospital visit to 30 days after discharge for inpatients and hospital-based outpatients in the United States.

METHODS

A retrospective cohort study was conducted using Premier Healthcare Database COVID-19 Special Release, a large geographically diverse all-payer hospital administrative database. Adult patients (age ≥ 18 years) were identified by their first, or "index," visit between April 1, 2020, and February 28, 2021, with a principal or secondary discharge diagnosis of COVID-19.

RESULTS

Of 1 454 780 adult patients with COVID-19, 33% (n = 481 216) were inpatients and 67% (n = 973 564) were outpatients. Among inpatients, mean age was 64.4 years and comorbidities were common. Most patients (80%) originated from home, 10% from another acute care facility, and 95% were admitted through the emergency department. Of these patients, 23% (n = 108 120) were admitted to intensive care unit and 14% (n = 66 706) died during index hospitalization; 44% were discharged home, 15% to nursing or rehabilitation facility, and 12% to home health. Among outpatients, mean age was 48.8 years, 44% were male, and 60% were emergency department outpatients (n = 586 537). During index outpatient visit, 79% were sent home but 10% had another outpatient visit and 4% were hospitalized within 30 days.

CONCLUSIONS

COVID-19 is associated with high level of healthcare resource utilization and in-hospital mortality. More than one-third of inpatients required post hospital healthcare services. Such information may help healthcare providers better allocate resources for patients with COVID-19 during the pandemic.

Clinical and imaging characteristics of patients with COVID-19 predicting hospital readmission after emergency department discharge: a single-centre cohort study in Italy

OBJECTIVE

We aimed at identifying baseline predictive factors for emergency department (ED) readmission, with hospitalisation/death, in patients with COVID-19 previously discharged from the ED. We also developed a disease progression velocity index.

DESIGN AND SETTING

Retrospective cohort study of prospectively collected data. The charts of consecutive patients with COVID-19 discharged from the Reggio Emilia (Italy) ED (2 March 2 to 31 March 2020) were retrospectively examined. Clinical, laboratory and CT findings at first ED admission were tested as predictive factors using multivariable logistic models. We divided CT extension by days from symptom onset to build a synthetic velocity index.

PARTICIPANTS

450 patients discharged from the ED with diagnosis of COVID-19.

MAIN OUTCOME MEASURE

ED readmission within 14 days, followed by hospitalisation/death.

RESULTS

Of the discharged patients, 84 (18.7%) were readmitted to the ED, 61 (13.6%) were hospitalised and 10 (2.2%) died. Age (OR=1.05; 95% CI 1.03 to 1.08), Charlson Comorbidity Index 3 versus 0 (OR=11.61; 95% CI 1.76 to 76.58), days from symptom onset (OR for 1-day increase=0.81; 95% CI 0.73 to 0.90) and CT extension (OR for 1% increase=1.03; 95% CI 1.01 to 1.06) were associated in a multivariable model for readmission with hospitalisation/death. A 2-day lag velocity index was a strong predictor (OR for unit increase=1.21, 95% CI 1.08 to 1.36); the model including this index resulted in less information loss.

CONCLUSIONS

A velocity index combining CT extension and days from symptom onset predicts disease progression in patients with COVID-19. For example, a 20% CT extension 3 days after symptom onset has the same risk as does 50% after 10 days.

Return visit characteristics of SARS-CoV-2 PCR-positive cases in a pediatric emergency department

BACKGROUND

The aim of this study was to evaluate return visits to the pediatric emergency department (ED) for children who were detected to be positive for SARS-CoV-2 by polymerase chain reaction (PCR).

METHODS

Between April 2, 2020, and January 20, 2021, children aged 0 to 18 years who were detected to be SARS-CoV-2 PCR-positive and discharged from the ED were evaluated. Among them, patients who returned to the ED within 14 days of quarantine were included in the study. For the first presentation and return visit, demographics, clinical findings, laboratory and radiologic investigations, and ward/pediatric intensive care unit (PICU) admissions were recorded. Patients were divided into 5 groups according to clinical severity.

RESULTS

Among 575 children who were confirmed to be SARS-CoV-2 PCR-positive, 50 (8.6%) of them [median age: 10.4 years (IQR: 4.8-15.2); 26 females] had returned. There was no difference for age, sex, underlying diseases, or symptoms for patients who returned or did not for the first presentation, but the percentage of those from whom laboratory tests were obtained was higher in cases of return visits. For symptomatic cases on the first presentation, the most common reason for return was having additional symptoms. The most common symptoms at the return visit were fever, cough, and sore throat. There was no severe/critical case in terms of clinical severity. Among all cases, 36 (72.0%) patients were discharged from the ED, 13 (26.0%) were observed for 6-8 h and then discharged, and 1 (2.0%) was admitted to the ward; there was no PICU admission or death, respectively.

Comparative Effectiveness of an Automated Text Messaging Service for Monitoring COVID-19 at Home

BACKGROUND

Although most patients with SARS-CoV-2 infection can be safely managed at home, the need for hospitalization can arise suddenly.

OBJECTIVE

To determine whether enrollment in an automated remote monitoring service for community-dwelling adults with COVID-19 at home ("COVID Watch") was associated with improved mortality.

DESIGN

Retrospective cohort analysis.

SETTING

Mid-Atlantic academic health system in the United States.

PARTICIPANTS

Outpatients who tested positive for SARS-CoV-2 between 23 March and 30 November 2020.

INTERVENTION

The COVID Watch service consists of twice-daily, automated text message check-ins with an option to report worsening symptoms at any time. All escalations were managed 24 hours a day, 7 days a week by dedicated telemedicine clinicians.

MEASUREMENTS

Thirty- and 60-day outcomes of patients enrolled in COVID Watch were compared with those of patients who were eligible to enroll but received usual care. The primary outcome was death at 30 days. Secondary outcomes included emergency department (ED) visits and hospitalizations. Treatment effects were estimated with propensity score-weighted risk adjustment models.

RESULTS

A total of 3488 patients enrolled in COVID Watch and 4377 usual care control participants were compared with propensity score weighted models. At 30 days, COVID Watch patients had an odds ratio for death of 0.32 (95% CI, 0.12 to 0.72), with 1.8 fewer deaths per 1000 patients (CI, 0.5 to 3.1) (P = 0.005); at 60 days, the difference was 2.5 fewer deaths per 1000 patients (CI, 0.9 to 4.0) (P = 0.002). Patients in COVID Watch had more telemedicine encounters, ED visits, and hospitalizations and presented to the ED sooner (mean, 1.9 days sooner [CI, 0.9 to 2.9 days]; all P < 0.001).

LIMITATION

Observational study with the potential for unobserved confounding.

CONCLUSION

Enrollment of outpatients with COVID-19 in an automated remote monitoring service was associated with reduced mortality, potentially explained by more frequent telemedicine encounters and more frequent and earlier presentation to the ED.

PRIMARY FUNDING SOURCE

Patient-Centered Outcomes Research Institute.

Evaluation of a COVID-19 emergency department observation protocol

Objectives

Caring for patients with COVID-19 has resulted in a considerable strain on hospital capacity. One strategy to mitigate crowding is the use of ED-based observation units to care for patients who may have otherwise required hospitalization. We sought to create a COVID-19 Observation Protocol for our ED Observation Unit (EDOU) for patients with mild to moderate COVID-19 to allow emergency physicians (EP) to gather more data for or against admission and intervene in a timely manner to prevent clinical deterioration.

Methods

This was a retrospective cohort study which included all patients who were positive for SARS-CoV-2 at the time of EDOU placement for the primary purpose of monitoring COVID-19 disease. Our institution updated the ED Observation protocol partway into the study period. Descriptive statistics were used to characterize demographics. We assessed for differences in demographics, clinical characteristics, and outcomes between admitted and discharged patients. Multivariate logistic regression models were used to assess whether meeting criteria for the ED observation protocols predicted disposition.

Results

During the time period studied, 120 patients positive for SARS-CoV-2 were placed in the EDOU for the primary purpose of monitoring COVID-19 disease. The admission rate for patients in the EDOU during the study period was 35%. When limited to patients who met criteria for version 1 or version 2 of the protocol, this dropped to 21% and 25% respectively. Adherence to the observation protocol was 62% and 60% during the time of version 1 and version 2 implementation, respectively. Using a multivariate logistic regression, meeting criteria for either version 1 (OR = 3.17, 95% CI 1.34–7.53, p < 0.01) or version 2 (OR = 3.18, 95% CI 1.39–7.30, p < 0.01) of the protocol resulted in a higher likelihood of discharge. There was no difference in EDOU LOS between admitted and discharged patients.

Conclusion

An ED observation protocol can be successfully created and implemented for COVID-19 which allows the EP to determine which patients warrant hospitalization. Meeting protocol criteria results in an acceptable admission rate.

Characteristics, risk factors, and outcomes associated with readmission in COVID-19 patients: A systematic review and meta-analysis

BACKGROUND

We aimed to determine the characteristics, risk factors, and outcomes associated with readmission in COVID-19 patients.

METHODS

PubMed, Embase, Web of Science, and Scopus databases were searched to retrieve articles on readmitted COVID-19 patients, available up to September 25, 2021. All studies comparing characteristics of readmitted and non-readmitted COVID-19 patients were included. We also included articles reporting the reasons for readmission in COVID-19 patients. Data were pooled and meta-analyzed using random or fixed-effect models, as appropriate. Subgroup analyses were conducted based on the place and duration of readmission.

RESULTS

Our meta-analysis included 4823 readmitted and 63,413 non-readmitted COVID-19 patients. The re-hospitalization rate was calculated at 9.3% with 95% Confidence Interval (CI) [5.5%-15.4%], mostly associated with respiratory or cardiac complications (48% and 14%, respectively). Comorbidities including cerebrovascular disease (Odds Ratio (OR) = 1.812; 95% CI [1.547-2.121]), cardiovascular (2.173 [1.545-3.057]), hypertension (1.608 [1.319-1.960]), ischemic heart disease (1.998 [1.495-2.670]), heart failure (2.556 [1.980-3.300]), diabetes (1.588 [1.443-1.747]), cancer (1.817 [1.526-2.162]), kidney disease (2.083 [1.498-2.897]), chronic pulmonary disease (1.601 [1.438-1.783]), as well as older age (1.525 [1.175-1.978]), male sex (1.155 [1.041-1.282]), and white race (1.263 [1.044-1.528]) were significantly associated with higher readmission rates (P < 0.05 for all instances). The mortality rate was significantly lower in readmitted patients (OR = 0.530 [0.329-0.855], P = 0.009).

CONCLUSIONS

Male sex, white race, comorbidities, and older age were associated with a higher risk of readmission among previously admitted COVID-19 patients. These factors can help clinicians and policy-makers predict, and conceivably reduce the risk of readmission in COVID-19 patients.

Predicting 30-day return hospital admissions in patients with COVID-19 discharged from the emergency department: A national retrospective cohort study

OBJECTIVES

Identification of patients with coronavirus disease 2019 (COVID-19) at risk for deterioration after discharge from the emergency department (ED) remains a clinical challenge. Our objective was to develop a prediction model that identifies patients with COVID-19 at risk for return and hospital admission within 30 days of ED discharge.

METHODS

We performed a retrospective cohort study of discharged adult ED patients (n = 7529) with SARS-CoV-2 infection from 116 unique hospitals contributing to the National Registry of Suspected COVID-19 in Emergency Care. The primary outcome was return hospital admission within 30 days. Models were developed using classification and regression tree (CART), gradient boosted machine (GBM), random forest (RF), and least absolute shrinkage and selection (LASSO) approaches.

RESULTS

Among patients with COVID-19 discharged from the ED on their index encounter, 571 (7.6%) returned for hospital admission within 30 days. The machine-learning (ML) models (GBM, RF, and LASSO) performed similarly. The RF model yielded a test area under the receiver operating characteristic curve of 0.74 (95% confidence interval [CI], 0.71-0.78), with a sensitivity of 0.46 (95% CI, 0.39-0.54) and a specificity of 0.84 (95% CI, 0.82-0.85). Predictive variables, including lowest oxygen saturation, temperature, or history of hypertension, diabetes, hyperlipidemia, or obesity, were common to all ML models.

CONCLUSIONS

A predictive model identifying adult ED patients with COVID-19 at risk for return for return hospital admission within 30 days is feasible. Ensemble/boot-strapped classification methods (eg, GBM, RF, and LASSO) outperform the single-tree CART method. Future efforts may focus on the application of ML models in the hospital setting to optimize the allocation of follow-up resources.

Race, Ethnicity, and 60-Day Outcomes After Hospitalization With COVID-19

OBJECTIVE

To examine racial and ethnic disparities in clinical, financial, and mental health outcomes within a diverse sample of hospitalized COVID-19-positive patients in the 60 days postdischarge.

DESIGN

A cross-sectional study.

SETTING AND PARTICIPANTS

A total of 2217 adult patients who were hospitalized with a COVID-19-positive diagnosis as evidenced by test (reverse-transcriptase polymerase chain reaction), a discharge diagnosis of COVID-19 (ICD-10 code U07.1), or strong documented clinical suspicion of COVID-19 but no testing completed or recorded owing to logistical constraints (n=24).

METHODS

Patient records were abstracted for the Mi-COVID19 data registry, including the hospital and insurer data of patients discharged from one of 38 participating hospitals in Michigan between March 16, 2020, and July 1, 2020. Registry data also included patient responses to a brief telephone survey on postdischarge employment, mental and emotional health, persistence of COVID-19-related symptoms, and medical follow-up. Descriptive statistics were used to summarize data; analysis of variance and Pearson chi-squared test were used to evaluate racial and ethnic variances among patient outcomes and survey responses.

RESULTS

Black patients experienced the lowest physician follow-up postdischarge (n = 65, 60.2%) and the longest delays in returning to work (average 35.5 days). More than half of hospital readmissions within the 60 days following discharge were among nonwhite patients (n = 144, 55%). The majority of postdischarge deaths were among white patients (n = 153, 21.5%), most of whom were discharged on palliative care (n = 103). Less than a quarter of patients discharged back to assisted living, skilled nursing facilities, or subacute rehabilitation facilities remained at those locations in the 60 days following discharge (n = 48).

CONCLUSIONS AND IMPLICATIONS

Increased attention to postdischarge care coordination is critical to reducing negative health outcomes following a COVID-19-related hospitalization.

Federated learning for predicting clinical outcomes in patients with COVID-19

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare.

The COVID-19 Menace

Coronavirus disease 2019 (COVID-19) is caused by the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which binds to ectoenzyme angiotensin-converting enzyme 2. It is very contagious and is spreading rapidly around the world. Until now, coronaviruses have mainly been associated with the aerodigestive tract due to the presence of a monobasic cleavage site for the resident transmembrane serine protease 2. Notably, SARS-CoV-2 is equipped with a second, polybasic cleavage site for the ubiquitous furin protease, which may determine the widespread tissue tropism. Furthermore, the terminal sequence of the furin-cleaved spike protein also binds to neuropilin receptors. Clinically, there is enormous variability in the severity of the disease. Severe consequences are seen in a relatively small number of patients, most show moderate symptoms, but asymptomatic cases, especially among young people, drive disease spread. Unfortunately, the number of local infections can quickly build up, causing disease outbreaks suddenly exhausting health services' capacity. Therefore, COVID-19 is dangerous and unpredictable and has become the most serious threat for generations. Here, the latest research on COVID-19 is summarized, including its spread, testing methods, organ-specific complications, the role of comorbidities, long-term consequences, mortality, as well as a new hope for immunity, drugs, and vaccines.

Risk of readmission to the emergency department in mild COVID-19 outpatients with telehealth follow-up

Introduction

To describe patients´ characteristics of confirmed COVID-19 with mild symptoms discharged home from the Emergency Department (ED) and followed using telemedicine, to estimate ED-readmission rates and hospitalization, and to explore associated factors with these clinical outcomes.

Methods

We performed a retrospective cohort study in Hospital Italiano de Buenos Aires from June to August 2020, which included patients with mild COVID-19 symptoms, diagnosed with a positive result. Follow-up occurred from discharged until ED-readmission or 14 days. We estimate cumulative incidence using the Kaplan-Meier model and associated factors using logistic regression.

Results

We included 1,239 patients, with a median of 41 years and 53.82% male. A total of 167 patients were readmitted to the ED within 14 days, with a global incidence rate of 13.08% (95%CI 11.32-15.08). Of these, 83 required hospitalization (median time from diagnosis 4.98 days), 5.98% was not related to any COVID-19 complication, and five patients died. After adjustment by confounders (age ≥65, sex, diabetes, hypertension, former smoking, active smoking, fever, diarrhea, and oxygen saturation), we found significant associations: former smoking (adjusted OR 2.09, 95% CI 1.31-3.34, p0 .002), fever (aOR 1.56, 95% CI 1.07-2.28, p0.002) and oxygen saturation (aOR 0.82, 95% CI 0.71-0.95, p0.009).

Conclusion

The 13% rate of ED-readmission during 14 days of follow-up of mild symptomatic COVID-19 patients initially managed as outpatients with telehealth is highly significant in hospital management, quality performance, and patient safety.

Risk of readmission to the emergency department in mild COVID-19 outpatients with telehealth follow-up

Introduction

To describe patients´ characteristics of confirmed COVID-19 with mild symptoms discharged home from the Emergency Department (ED) and followed using telemedicine, to estimate ED-readmission rates and hospitalization, and to explore associated factors with these clinical outcomes.

Methods

We performed a retrospective cohort study in Hospital Italiano de Buenos Aires from June to August 2020, which included patients with mild COVID-19 symptoms, diagnosed with a positive result. Follow-up occurred from discharged until ED-readmission or 14 days. We estimate cumulative incidence using the Kaplan-Meier model and associated factors using logistic regression.

Results

We included 1,239 patients, with a median of 41 years and 53.82% male. A total of 167 patients were readmitted to the ED within 14 days, with a global incidence rate of 13.08% (95%CI 11.32-15.08). Of these, 83 required hospitalization (median time from diagnosis 4.98 days), 5.98% was not related to any COVID-19 complication, and five patients died. After adjustment by confounders (age ≥65, sex, diabetes, hypertension, former smoking, active smoking, fever, diarrhea, and oxygen saturation), we found significant associations: former smoking (adjusted OR 2.09, 95% CI 1.31-3.34, p0 .002), fever (aOR 1.56, 95% CI 1.07-2.28, p0.002) and oxygen saturation (aOR 0.82, 95% CI 0.71-0.95, p0.009).

Conclusion

The 13% rate of ED-readmission during 14 days of follow-up of mild symptomatic COVID-19 patients initially managed as outpatients with telehealth is highly significant in hospital management, quality performance, and patient safety.

Lean-ing Method in an Emergency Department of the Italian Epicenter of the COVID-19 Outbreak: When the Algorithm Makes Difference

The Lean method entails a set of standardized processes intending to optimize resources, reduce waste, and improve results. Lean has been proposed as an operative model for the COVID-19 outbreak. Herein, we summarized data resulted from the Lean model adoption in an Emergency Department of the Lombardy region, the Italian epicenter of the pandemic, to critically appraise its effectiveness and feasibility. The Lean algorithm was applied in the Humanitas Clinical and Research Hospital, Milan, north of Italy. At admission, patients underwent outdoor pre-triage for fever, respiratory, and gastrointestinal symptoms, with a focus on SpO2. Based on these data, they were directed to the most appropriate area for the COVID-19 first-level screening. High-risk patients were assisted by trained staff for second-level screening and planning of treatment. Out of 7.778 patients, 21.9% were suspected of SARS-CoV-2 infection. Mortality was 21.9% and the infection rate in health workers was 4.8%. The lean model has proved to be effective in optimizing the overall management of COVID-19 patients in an emergency setting. It allowed for screening of a large volume of patients, while also limiting the health workers’ infection rate. Further studies are necessary to validate the suggested approach.

Short-Stay Hospitalizations for Patients with COVID-19: A Retrospective Cohort Study

OBJECTIVE

Patients requiring hospital care for COVID-19 may be stable for discharge soon after admission. This study sought to describe patient characteristics associated with short-stay hospitalization for COVID-19.

METHODS

We performed a retrospective cohort study of patients with COVID-19 admitted to five United States hospitals from March to December 2020. We used multivariable logistic regression to identify patient characteristics associated with short hospital length-of-stay.

RESULTS

Of 3103 patients, 648 (20.9%) were hospitalized for less than 48 h. These patients were significantly less likely to have an age greater than 60, diabetes, chronic kidney disease; emergency department vital sign abnormalities, or abnormal initial diagnostic testing. For patients with no significant risk factors, the adjusted probability of short-stay hospitalization was 62.4% (95% CI 58.9-69.6).

CONCLUSION

Identification of candidates for early hospital discharge may allow hospitals to streamline throughput using protocols that optimize the efficiency of hospital care and coordinate post-discharge monitoring.

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.

The ROX index can be a useful tool for the triage evaluation of COVID-19 patients with dyspnoea

Aim

To assess whether the application of a non‐invasive tool, such as ratio of oxygen saturation (ROX) index, during triage can identify patients with COVID‐19 at high risk of developing acute respiratory distress syndrome (ARDS).

Design

A multi‐centre, observational, retrospective study.

Methods

Only COVID‐19 positive patients who required an emergency department evaluation for dyspnoea were considered. The primary objective of the study was to compare the ROX value obtained during triage with the medical diagnosis of ARDS and intubation in 72 h of the triage evaluation. The ROX index value was also compared with objective outcomes, such as the pressure of arterial O₂ (PaO₂)/fraction of inspired oxygen (FiO₂) ratio and the lung parenchyma volume involved in COVID‐19‐related inflammatory processes, based on 3D reconstructions of chest computed tomography (CT).

Results

During the study period, from 20 March 2020 until 31 May 2020, a total of 273 patients with confirmed SARS‐CoV‐2 infection were enrolled. The predictive ability of ROX for the risk of developing ARDS in 72 h after triage evaluation was associated with an area under the receiver operating characteristic (AUROC) of 0.845 (0.797–0.892, p < 0.001), whereas the AUROC value was 0.727 (0.634–0.821, p < 0.001) for the risk of intubation. ROX values were strongly correlated with PaO₂/FiO₂ values (r = 0.650, p < 0.001), decreased ROX values were associated with increased percentages of lung involvement based on 3D CT reconstruction (r = −0.371, p < 0.001).

Conclusion

The ROX index showed a good ability to identify triage patients at high evolutionary risk. Correlations with objective but more invasive indicators (PaO₂/FiO₂ and CT) confirmed the important role of ROX in identifying COVID‐19 patients with extensive pathological processes.

Impact

During the difficult triage evaluation of COVID‐19 patients, the ROX index can help the nurse to identify the real severity of the patient. The triage systems could integrate the ROX in the rapid patient assessment to stratify patients more accurately.

Federated Learning used for predicting outcomes in SARS-COV-2 patients

'Federated Learning' (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a healthcare FL study to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the "EXAM" (EMR CXR AI Model) model. EXAM achieved an average Area Under the Curve (AUC) of over 0.92, an average improvement of 16%, and a 38% increase in generalisability over local models. The FL paradigm was successfully applied to facilitate a rapid data science collaboration without data exchange, resulting in a model that generalised across heterogeneous, unharmonized datasets. This provided the broader healthcare community with a validated model to respond to COVID-19 challenges, as well as set the stage for broader use of FL in healthcare.