Development and External Validation of Clinical Features-based Machine Learning Models for Predicting COVID-19 in the Emergency Department.

Introduction: Timely diagnosis of patients affected by an emerging infectious disease plays a crucial role in treating patients and avoiding disease spread. In prior research, we developed an approach by using machine learning (ML) algorithms to predict serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection based on clinical features of patients visiting an emergency department (ED) during the early coronavirus 2019 (COVID-19) pandemic. In this study, we aimed to externally validate this approach within a distinct ED population. Methods: To create our training/validation cohort (model development) we collected data retrospectively from suspected COVID-19 patients at a US ED from February 23-May 12, 2020. Another dataset was collected as an external validation (testing) cohort from an ED in another country from May 12-June 15, 2021. Clinical features including patient demographics and triage information were used to train and test the models. The primary outcome was the confirmed diagnosis of COVID-19, defined as a positive reverse transcription polymerase chain reaction test result for SARS-CoV-2. We employed three different ML algorithms, including gradient boosting, random forest, and extra trees classifiers, to construct the predictive model. The predictive performances were evaluated with the area under the receiver operating characteristic curve (AUC) in the testing cohort. Results: In total, 580 and 946 ED patients were included in the training and testing cohorts, respectively. Of them, 98 (16.9%) and 180 (19.0%) were diagnosed with COVID-19. All the constructed ML models showed acceptable discrimination, as indicated by the AUC. Among them, random forest (0.785, 95% confidence interval [CI] 0.747-0.822) performed better than gradient boosting (0.774, 95% CI 0.739-0.811) and extra trees classifier (0.72, 95% CI 0.677-0.762). There was no significant difference between the constructed models. Conclusion: Our study validates the use of ML for predicting COVID-19 in the ED and demonstrates its potential for predicting emerging infectious diseases based on models built by clinical features with temporal and spatial heterogeneity. This approach holds promise for scenarios where effective diagnostic tools for an emerging infectious disease may be lacking in the future.

Clinical Characteristics of Patients Returning to Emergency Department With Initial False-Negative Reverse Transcriptase Polymerase Chain Reaction (RT-PCR)-Based COVID-19 Test.

Background: The coronavirus disease 2019 (COVID-19) outbreak is an international public health emergency. Early identification of COVID-19 patients with false-negative RT-PCR tests is paramount in the ED to prevent both nosocomial and community transmission. This study aimed to compare clinical characteristics of repeat emergency department (ED) visits among coronavirus disease 2019 (COVID-19) patients with initial false-negative reverse transcriptase-polymerase chain reaction (RT-PCR)-based COVID-19 test. Methods: This is a retrospective, multi-center, cohort study conducted at 12 hospitals affiliated with Baylor Scott & White Health system. Patients visiting the EDs of these hospitals between June and August 2020 were screened. Patients tested negative for viral RNA by quantitative RT-PCR in the first ED visit and positive in the second ED visit were included. The primary outcome was the comparison of clinical characteristics between two consecutive ED visits including the clinical symptoms, triage vital signs, laboratory, and chest X-ray (CXR) results. Results: A total of 88 confirmed COVID-19 patients with initial false-negative RT-PCR COVID-19 test in the ED were included in the final analyses. The mean duration of symptoms in the second ED visit was significantly higher (3.6 ± 0.4 vs. 2.6 ± 0.3 days, p = 0.020). In the first ED visit, lymphocytopenia (35.2%), fever (32.6%), nausea (29.5%), and dyspnea (27.9%) are the most common signs of COVID-19 infection during the window period. There were significant increases in the rate of hypoxia (13.6% vs. 4.6%, p = 0.005), abnormal infiltrate on CXR (59.7% vs. 25.9%, p < 0.001), and aspartate aminotransferase (AST) elevation (26.1% vs. 9.1%, p < 0.001) in the second ED visit. Conclusions: Early COVID-19 testing (less than 3 days of symptom duration) could be associated with a false-negative result. In this window period, lymphocytopenia, fever, nausea, and dyspnea are the most common early signs that can potentially be clinical hints for COVID-19 diagnosis.

Development and validation of a prediction model for estimating one-month mortality of adult COVID-19 patients presenting at emergency department with suspected pneumonia: a multicenter analysis.

There are only a few models developed for risk-stratifying COVID-19 patients with suspected pneumonia in the emergency department (ED). We aimed to develop and validate a model, the COVID-19 ED pneumonia mortality index (CoV-ED-PMI), for predicting mortality in this population. We retrospectively included adult COVID-19 patients who visited EDs of five study hospitals in Texas and who were diagnosed with suspected pneumonia between March and November 2020. The primary outcome was 1-month mortality after the index ED visit. In the derivation cohort, multivariable logistic regression was used to develop the CoV-ED-PMI model. In the chronologically split validation cohort, the discriminative performance of the CoV-ED-PMI was assessed by the area under the receiver operating characteristic curve (AUC) and compared with other existing models. A total of 1678 adult ED records were included for analysis. Of them, 180 patients sustained 1-month mortality. There were 1174 and 504 patients in the derivation and validation cohorts, respectively. Age, body mass index, chronic kidney disease, congestive heart failure, hepatitis, history of transplant, neutrophil-to-lymphocyte ratio, lactate dehydrogenase, and national early warning score were included in the CoV-ED-PMI. The model was validated with good discriminative performance (AUC: 0.83, 95% confidence interval [CI]: 0.79-0.87), which was significantly better than the CURB-65 (AUC: 0.74, 95% CI: 0.69-0.79, p-value: < 0.001). The CoV-ED-PMI had a good predictive performance for 1-month mortality in COVID-19 patients with suspected pneumonia presenting at ED. This free tool is accessible online, and could be useful for clinical decision-making in the ED.

Mortality Variations of COVID-19 from Different Hospital Settings During Different Pandemic Phases: A Multicenter Retrospective Study.

INTRODUCTION: Diverse coronavirus disease 2019 (COVID-19) mortalities have been reported but focused on identifying susceptible patients at risk of more severe disease or death. This study aims to investigate the mortality variations of COVID-19 from different hospital settings during different pandemic phases. METHODS: We retrospectively included adult (≥18 years) patients who visited emergency departments (ED) of five hospitals in the state of Texas and who were diagnosed with COVID-19 between March-November 2020. The included hospitals were dichotomized into urban and suburban based on their geographic location. The primary outcome was mortality that occurred either during hospital admission or within 30 days after the index ED visit. We used multivariable logistic regression to investigate the associations between independent variables and outcome. Generalized additive models were employed to explore the mortality variation during different pandemic phases. RESULTS: A total of 1,788 adult patients who tested positive for COVID-19 were included in the study. The median patient age was 54.6 years, and 897 (50%) patients were male. Urban hospitals saw approximately 59.5% of the total patients. A total of 197 patients died after the index ED visit. The analysis indicated visits to the urban hospitals (odds ratio [OR] 2.14, 95% confidence interval [CI], 1.41, 3.23), from March to April (OR 2.04, 95% CI, 1.08, 3.86), and from August to November (OR 2.15, 95% CI, 1.37, 3.38) were positively associated with mortality. CONCLUSION: Visits to the urban hospitals were associated with a higher risk of mortality in patients with COVID-19 when compared to visits to the suburban hospitals. The mortality risk rebounded and showed significant difference between urban and suburban hospitals since August 2020. Optimal allocation of medical resources may be necessary to bridge this gap in the foreseeable future.

Clinical Features of Emergency Department Patients from Early COVID-19 Pandemic that Predict SARS-CoV-2 Infection: Machine-learning Approach.

INTRODUCTION: Within a few months coronavirus disease 2019 (COVID-19) evolved into a pandemic causing millions of cases worldwide, but it remains challenging to diagnose the disease in a timely fashion in the emergency department (ED). In this study we aimed to construct machine-learning (ML) models to predict severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection based on the clinical features of patients visiting an ED during the early COVID-19 pandemic. METHODS: We retrospectively collected the data of all patients who received reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2 at the ED of Baylor Scott & White All Saints Medical Center, Fort Worth, from February 23-May 12, 2020. The variables collected included patient demographics, ED triage data, clinical symptoms, and past medical history. The primary outcome was the confirmed diagnosis of COVID-19 (or SARS-CoV-2 infection) by a positive RT-PCR test result for SARS-CoV-2, and was used as the label for ML tasks. We used univariate analyses for feature selection, and variables with P<0.1 were selected for model construction. Samples were split into training and testing cohorts on a 60:40 ratio chronologically. We tried various ML algorithms to construct the best predictive model, and we evaluated performances with the area under the receiver operating characteristic curve (AUC) in the testing cohort. RESULTS: A total of 580 ED patients were tested for SARS-CoV-2 during the study periods, and 98 (16.9%) were identified as having the SARS-CoV-2 infection based on the RT-PCR results. Univariate analyses selected 21 features for model construction. We assessed three ML methods for performance: of the three methods, random forest outperformed the others with the best AUC result (0.86), followed by gradient boosting (0.83) and extra trees classifier (0.82). CONCLUSION: This study shows that it is feasible to use ML models as an initial screening tool for identifying patients with SARS-CoV-2 infection. Further validation will be necessary to determine how effectively this prediction model can be used prospectively in clinical practice.

Onsite telemedicine strategy for coronavirus (COVID-19) screening to limit exposure in ED.

Coronavirus (severe acute respiratory syndrome coronavirus 2) outbreak is a public health emergency and a global pandemic. During the present coronavirus disease (COVID-19) crisis, telemedicine has been recommended to screen suspected patients to limit risk of exposure and maximise medical staff protection. We constructed the protective physical barrier with telemedicine technology to limit COVID-19 exposure in ED. Our hospital is an urban community hospital with annual ED volume of approximately 50 000 patients. We equipped our patient exam room with intercom and iPad for telecommunication. Based on our telemedicine screening protocol, physician can conduct a visual physical examination on stable patients via intercom or videoconference. Telemedicine was initially used to overcome the physical barrier between patients and physicians. However, our protocol is designed to create a protective physical barrier to protect healthcare workers and enhance efficiency in ED. The implementation can be a promising protocol in making ED care more cost-effective and efficient during the COVID-19 pandemic and beyond.

Incidence, trends, and outcomes of infection sites among hospitalizations of sepsis: A nationwide study.

PURPOSE: To determine the trends of infection sites and outcome of sepsis using a national population-based database. MATERIALS AND METHODS: Using the Nationwide Inpatient Sample database of the US, adult sepsis hospitalizations and infection sites were identified using a validated approach that selects admissions with explicit ICD-9-CM codes for sepsis and diagnosis/procedure codes for acute organ dysfunctions. The primary outcome was the trend of incidence and in-hospital mortality of specific infection sites in sepsis patients. The secondary outcome was the impact of specific infection sites on in-hospital mortality. RESULTS: During the 9-year period, we identified 7,860,687 admissions of adult sepsis. Genitourinary tract infection (36.7%), lower respiratory tract infection (36.6%), and systemic fungal infection (9.2%) were the leading three sites of infection in patients with sepsis. Intra-abdominal infection (30.7%), lower respiratory tract infection (27.7%), and biliary tract infection (25.5%) were associated with highest mortality rate. The incidences of all sites of infections were trending upward. Musculoskeletal infection (annual increase: 34.2%) and skin and skin structure infection (annual increase: 23.0%) had the steepest increase. Mortality from all sites of infection has decreased significantly (trend p<0.001). Skin and skin structure infection had the fastest declining rate (annual decrease: 5.5%) followed by primary bacteremia (annual decrease: 5.3%) and catheter related bloodstream infection (annual decrease: 4.8%). CONCLUSIONS: The anatomic site of infection does have a differential impact on the mortality of septic patients. Intra-abdominal infection, lower respiratory tract infection, and biliary tract infection are associated with higher mortality in septic patients.