A Dispatch Screening Tool to Identify Patients at High Risk for COVID-19 in the Prehospital Setting.

INTRODUCTION: Emergency medical services (EMS) dispatchers have made efforts to determine whether patients are high risk for coronavirus disease 2019 (COVID-19) so that appropriate personal protective equipment (PPE) can be donned. A screening tool is valuable as the healthcare community balances protection of medical personnel and conservation of PPE. There is little existing literature on the efficacy of prehospital COVID-19 screening tools. The objective of this study was to determine the positive and negative predictive value of an emergency infectious disease surveillance tool for detecting COVID-19 patients and the impact of positive screening on PPE usage. METHODS: This study was a retrospective chart review of prehospital care reports and hospital electronic health records. We abstracted records for all 911 calls to an urban EMS from March 1-July 31, 2020 that had a documented positive screen for COVID-19 and/or had a positive COVID-19 test. The dispatch screen solicited information regarding travel, sick contacts, and high-risk symptoms. We reviewed charts to determine dispatch-screening results, the outcome of patients' COVID-19 testing, and documentation of crew fidelity to PPE guidelines. RESULTS: The sample size was 263. The rate of positive COVID-19 tests for all-comers in the state of Massachusetts was 2.0%. The dispatch screen had a sensitivity of 74.9% (confidence interval [CI], 69.21-80.03) and a specificity of 67.7% (CI, 66.91-68.50). The positive predictive value was 4.5% (CI, 4.17-4.80), and the negative predictive value was 99.3% (CI, 99.09-99.40). The most common symptom that triggered a positive screen was shortness of breath (51.5% of calls). The most common high-risk population identified was skilled nursing facility patients (19.5%), but most positive tests did not belong to a high-risk population (58.1%). The EMS personnel were documented as wearing full PPE for the patient in 55.7% of encounters, not wearing PPE in 8.0% of encounters, and not documented in 27.9% of encounters. CONCLUSION: This dispatch-screening questionnaire has a high negative predictive value but moderate sensitivity and therefore should be used with some caution to guide EMS crews in their PPE usage. Clinical judgment is still essential and may supersede screening status.

Detection of COVID-19 in smartphone-based breathing recordings: A pre-screening deep learning tool.

This study was sought to investigate the feasibility of using smartphone-based breathing sounds within a deep learning framework to discriminate between COVID-19, including asymptomatic, and healthy subjects. A total of 480 breathing sounds (240 shallow and 240 deep) were obtained from a publicly available database named Coswara. These sounds were recorded by 120 COVID-19 and 120 healthy subjects via a smartphone microphone through a website application. A deep learning framework was proposed herein that relies on hand-crafted features extracted from the original recordings and from the mel-frequency cepstral coefficients (MFCC) as well as deep-activated features learned by a combination of convolutional neural network and bi-directional long short-term memory units (CNN-BiLSTM). The statistical analysis of patient profiles has shown a significant difference (p-value: 0.041) for ischemic heart disease between COVID-19 and healthy subjects. The Analysis of the normal distribution of the combined MFCC values showed that COVID-19 subjects tended to have a distribution that is skewed more towards the right side of the zero mean (shallow: 0.59±1.74, deep: 0.65±4.35, p-value: <0.001). In addition, the proposed deep learning approach had an overall discrimination accuracy of 94.58% and 92.08% using shallow and deep recordings, respectively. Furthermore, it detected COVID-19 subjects successfully with a maximum sensitivity of 94.21%, specificity of 94.96%, and area under the receiver operating characteristic (AUROC) curves of 0.90. Among the 120 COVID-19 participants, asymptomatic subjects (18 subjects) were successfully detected with 100.00% accuracy using shallow recordings and 88.89% using deep recordings. This study paves the way towards utilizing smartphone-based breathing sounds for the purpose of COVID-19 detection. The observations found in this study were promising to suggest deep learning and smartphone-based breathing sounds as an effective pre-screening tool for COVID-19 alongside the current reverse-transcription polymerase chain reaction (RT-PCR) assay. It can be considered as an early, rapid, easily distributed, time-efficient, and almost no-cost diagnosis technique complying with social distancing restrictions during COVID-19 pandemic.

Application of a portable instrument for rapid and reliable detection of SARS-CoV-2 infection in any environment.

The ongoing outbreak of the novel coronavirus (SARS-CoV-2) infection is creating serious challenges for health laboratories that seek to identify viral infections as early as possible, optimally at the earliest appearance of symptom. Indeed, there is urgent need to develop and deploy robust diagnostic methodologies not only to use in health laboratory environments but also directly in places where humans circulate and spread the virus such as airports, trains, boats, and any public aggregation places. The success of a reliable and sensitive asymptomatic diagnosis relies on the identification and measurement of informative biomarkers from human host and virus in a rapid, sensitive, and inexpensive manner. The objective of this article is to describe an innovative multidisciplinary approach to develop an efficient, inexpensive, and easy-to-use portable instrument (bCUBE® by Hyris Ltd) that can be employed as a surveillance system for the emergency caused by SARS-CoV-2. A solution for Coronavirus testing, compliant with CDC guidelines, is scheduled to be released in the next weeks. In addition, we will describe a workflow and path of an integrated multi-omic approach that will lead to host and pathogen biomarker discovery in order to train the instrument to provide reliable results based on a specific biomarker's fingerprint of SARS-CoV-2 infection.

Diabetic retinopathy screening in urban primary care setting with a handheld smartphone-based retinal camera.

AIMS: To evaluate diabetic retinopathy (DR) screening with a portable handheld smartphone-based retinal camera and telemedicine in an urban primary healthcare setting and to evaluate the learning curve for image acquisition, performed by healthcare personnel without previous experience in retinal imaging. METHODS: This was a prospective study that enrolled patients with type 2 diabetes mellitus (T2DM) followed at a primary healthcare unit in São Paulo, Brazil. After a brief training in image acquisition, there was further continuous feedback given by a retina specialist during the remote image reading process. Each patient underwent two fundus and one anterior ocular segment images per eye, after mydriasis. Patients were classified according to the need of referral. RESULTS: A total of 627 adult individuals with T2DM underwent retinal evaluation. The population was composed by 63.2% female individuals, age median of 66 years, diabetes duration 10.7 ± 8.2 years and HbA1c 7.7 ± 1.9% (61 + 20.8 mmol/mol). The most prevalent associated comorbidities were arterial hypertension (80.3%) and dyslipidemia (50.2%). Referral decision was possible in 81.2% patients. Most patients had absent or non-referable DR; the main ocular media opacity detected was cataract. After the 7th day of image acquisition, the daily rate of patients whose images allowed clinical decision was maintained above 80%. A higher HbA1c was associated with referable DR. CONCLUSIONS: A low-cost DR screening strategy with a handheld device and telemedicine is feasible and has the potential to increase coverage of DR screening in underserved areas; the possibility of mobile units is relevant for DR screening in the context of COVID-19 pandemic. Daily rate of patients whose examinations allowed clinical decision. X-axis: day of examination; Y-axis: rate (%) of patients whose examinations allowed a clinical decision.

Electronic personal protective equipment: A strategy to protect emergency department providers in the age of COVID-19.

Emergent policy changes related to telemedicine and the Emergency Medical Treatment and Labor Act during the novel coronavirus disease 2019 (COVID-19) pandemic have created opportunities for technology-based clinical evaluation, which serves to conserve personal protective equipment (PPE) and protect emergency providers. We define electronic PPE as an approach using telemedicine tools to perform electronic medical screening exams while satisfying the Emergency Medical Treatment and Labor Act. We discuss the safety, legal, and technical factors necessary for implementing such a pathway. This approach has the potential to conserve PPE and protect providers while maintaining safe standards for medical screening exams in the emergency department for low-risk patients in whom COVID-19 is suspected.