The impact of coronavirus disease 2019 (COVID-19) on alternatives to opioids protocol for opioid reduction in emergency department

The United States (US) is in the midst of both an opioid epidemic and COVID-19 pandemic. The Alternatives to Opioids (ALTO) approach is a useful strategy of utilizing non-opioid options as the first-line pain therapy in the emergency department (ED). Since the COVID-19 pandemic began, more than 40 states have reported a rise in opioid-related deaths. Since there is a potential increasing need for pain management due to limited outpatient resources during the COVID-19 pandemic, it is unclear whether the COVID-19 has affected the effectiveness of the ALTO protocol in reducing opioid administration in the ED. To investigate the impact of COVID-19 on the usage of the ALTO protocol for opioid reduction, this retrospective cohort study was performed to compare patients receiving pain medication in an urban ED during the COVID-19 pandemic (March to August 2020) and patients during the same period from one year prior. The primary outcome was the change in ED opioid administration and outpatient opioid prescriptions. All opioid dosages were converted to morphine milligram equivalents (MME) for data analysis. Secondary outcomes included changes in ALTO medication use, patient satisfaction with pain control, ED length of stay, and rate of left without being seen (LWBS). The mean prescribed MME per discharged patient visit was significantly lower in the COVID-19 pandemic group (3.16 ± 0.31 versus 7.72 ± 0.31, p < 0.001). There was no significant difference in ED opioid administration, patient satisfaction with pain control, ED length of stay, and rate of LWBS between both groups. In conclusion, during the COVID-19 pandemic, the ALTO protocol can reduce out-patient opioid usage without changing opioid administration in the ED. [ FROM AUTHOR] Copyright of Signa Vitae is the property of Pharmamed Mado Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

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.

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.