An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department.
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ABSTRACT:
SUBMITTER: Shamout FE
PROVIDER: S-EPMC8115328 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundCOVID-19 may lead to severe disease, requiring intensive care treatment and challenging the capacity of health care systems. The aim of this study was to compare the ability of commonly used scoring systems for sepsis and pneumonia to predict severe COVID-19 in the emergency department.MethodsProspective, observational, single centre study in a secondary/tertiary care hospital in Oslo, Norway. Patients were assessed upon hospital admission using the following scoring systems; quick Sequential Failure Assessment (qSOFA), Systemic Inflammatory Response Syndrome criteria (SIRS), National Early Warning Score 2 (NEWS2), CURB-65 and Pneumonia Severity index (PSI). The ratio of arterial oxygen tension to inspiratory oxygen fraction (P/F-ratio) was also calculated. The area under the receiver operating characteristics curve (AUROC) for each scoring system was calculated, along with sensitivity and specificity for the most commonly used cut-offs. Severe disease was defined as death or treatment in ICU within 14 days.Results38 of 175 study participants developed severe disease, 13 (7%) died and 29 (17%) had a stay at an intensive care unit (ICU). NEWS2 displayed an AUROC of 0.80 (95% confidence interval 0.72-0.88), CURB-65 0.75 (0.65-0.84), PSI 0.75 (0.65-0.84), SIRS 0.70 (0.61-0.80) and qSOFA 0.70 (0.61-0.79). NEWS2 was significantly better than SIRS and qSOFA in predicating severe disease, and with a cut-off of5 points, had a sensitivity and specificity of 82% and 60%, respectively.ConclusionNEWS2 predicted severe COVID-19 disease more accurately than SIRS and qSOFA, but not significantly better than CURB65 and PSI. NEWS2 may be a useful screening tool in evaluating COVID-19 patients during hospital admission.Trial registration: ClinicalTrials.gov Identifier: NCT04345536. (https://clinicaltrials.gov/ct2/show/NCT04345536).
| S-EPMC7577659 | biostudies-literature
Project description:Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 .
| S-EPMC7547659 | biostudies-literature
Project description:Background: Little is known about the role of artificial intelligence (AI) as a decisive technology in the clinical management of COVID-19 patients. We aimed to systematically review and critically appraise the current evidence on AI applications for COVID-19 in intensive care and emergency settings. Methods: We systematically searched PubMed, Embase, Scopus, CINAHL, IEEE Xplore, and ACM Digital Library databases from inception to 1 October 2020, without language restrictions. We included peer-reviewed original studies that applied AI for COVID-19 patients, healthcare workers, or health systems in intensive care, emergency, or prehospital settings. We assessed predictive modelling studies and critically appraised the methodology and key findings of all other studies. Results: Of fourteen eligible studies, eleven developed prognostic or diagnostic AI predictive models, all of which were assessed to be at high risk of bias. Common pitfalls included inadequate sample sizes, poor handling of missing data, failure to account for censored participants, and weak validation of models. Conclusions: Current AI applications for COVID-19 are not ready for deployment in acute care settings, given their limited scope and poor quality. Our findings underscore the need for improvements to facilitate safe and effective clinical adoption of AI applications, for and beyond the COVID-19 pandemic.
| S-EPMC8125462 | biostudies-literature
Project description:Drug repurposing or repositioning is a technique whereby existing drugs are used to treat emerging and challenging diseases, including COVID-19. Drug repurposing has become a promising approach because of the opportunity for reduced development timelines and overall costs. In the big data era, artificial intelligence (AI) and network medicine offer cutting-edge application of information science to defining disease, medicine, therapeutics, and identifying targets with the least error. In this Review, we introduce guidelines on how to use AI for accelerating drug repurposing or repositioning, for which AI approaches are not just formidable but are also necessary. We discuss how to use AI models in precision medicine, and as an example, how AI models can accelerate COVID-19 drug repurposing. Rapidly developing, powerful, and innovative AI and network medicine technologies can expedite therapeutic development. This Review provides a strong rationale for using AI-based assistive tools for drug repurposing medications for human disease, including during the COVID-19 pandemic.
| S-EPMC7500917 | biostudies-literature
Project description:BackgroundCoronavirus disease 2019 (Covid-19) might present neurological symptoms. We aimed to evaluate the frequency of them at the moment of emergency department (ED) visit and their impact in the prognosis.MethodsRetrospective cohort study including all consecutive hospitalized cases between March 8th and April 11th, 2020. Covid-19 diagnosis was confirmed by polymerase chain reaction test and/or serology. We compared, in patients with and without neurological symptoms on admission, demographic, clinical presentation, and frequency and type of abnormal laboratory values. We analyzed the variables that were associated with in-hospital all-cause mortality by Cox-regression log-rank test.ResultsWe included 576 hospitalized patients, 250 (43.3%) female, aged 67.2 years. At the moment of ED visit, 320 (55.6%) described neurological symptoms, including anosmia (146, 25.3%), myalgia (139, 24.1%), headache (137, 23.8%), and altered mental status (98, 17.0%). Neurological symptoms started the first symptomatic day in 198 (54.2%) cases. Patients with neurological symptoms presented later to the ED (7.9 versus. 6.6 days, p = .019). Only four (0.6%) cases had no typical Covid-19 general symptoms, and only six (1.9%) had a normal laboratory results, for a sensitivity of 98.7% (95% confidence interval (CI): 96.6%-99.6%) and 98.1% (95% CI: 95.7%-99.2%), respectively. In the multivariate Cox-regression of mortality predictors, anosmia (HR: 0.358, 95%CI: 0.140-0.916) and altered mental status (HR: 1.867, 95%CI: 1.162-3.001) were significant.ConclusionNeurological symptoms were the most frequent extrapulmonary symptoms. They were present in half of the Covid-19 patients at the time of the ED visit. Anosmia on admission was an independent predictor of lower in-hospital mortality and altered mental status on admission predicted in-hospital mortality.
| S-EPMC7994975 | biostudies-literature
Project description:ObjectiveProning has been shown to improve oxygenation and mortality in certain populations of intubated patients with acute respiratory distress syndrome. Small observational analyses of COVID-19 patients suggest awake proning may lead to clinical improvement. Data on safety and efficacy is lacking. We sought to describe the effect of proning on oxygenation in nonintubated COVID-19 patients. We also evaluated feasibility, safety, and other physiological and clinical outcomes associated with this intervention.MethodsWe conducted a prospective, observational cohort study of nonintubated patients with COVID-19 who underwent proning per an Emergency Department (ED) clinical protocol. Patients with mild to moderate respiratory distress were included. We calculated change in oxygenation by comparing the oxygen saturation to fraction of inspired oxygen ratio (SpO2:FiO2) during the five minutes prior to proning and first 30 min of proning. We also captured data on respiratory rate, duration of proning, need for intubation, intensive care unit admission, survival to discharge.ResultsFifty-two patients were enrolled. Thirty were excluded for not meeting protocol inclusion criteria or missing baseline oxygenation data, leaving 22 for analysis. The SpO2:FiO2 ratio increased by a median of 5 (IQR: 0-15) in the post-proning period compared to the pre-proning period (median: 298 (IQR: 263-352) vs 295 (IQR: 276-350), p = 0.01). Respiratory rate did not change significantly between time periods. No immediate adverse events occurred during proning. Five patients (23%) were intubated within 48 h of admission.ConclusionEarly, awake proning may be feasible in select COVID-19 patients and was associated with improved oxygenation.
| S-EPMC7713606 | biostudies-literature
Project description:BackgroundThe global healthcare burden of COVID-19 continues to rise. There is currently limited information regarding the disease progression and the need for hospitalizations in patients who present to the Emergency Department (ED) with minimal or no symptoms.ObjectivesThis study identifies bounceback rates and timeframes for patients who return to the ED due to COVID-19 after initial discharge on the date of testing.MethodsUsing the NorthShore University Health System's (NSUHS) Enterprise Data Warehouse (EDW), we conducted a retrospective cohort analysis of patients who were tested positive for COVID-19 and were discharged home on the date of testing. A one-month follow-up period was included to ensure the capture of disease progression.ResultsOf 1883 positive cases with initially mild symptoms, 14.6% returned to the ED for complaints related to COVID-19. 56.9% of the mildly symptomatic bounceback patients were discharged on the return visit while 39.5% were admitted to the floor and 3.6% to the ICU. Of the 1120 positive cases with no initial symptoms, only four returned to the ED (0.26%) and only one patient was admitted. Median initial testing occurred on day 3 (2-5.6) of illness, and median ED bounceback occurred on day 9 (6.3-12.7). Our statistical model was unable to identify risk factors for ED bouncebacks.ConclusionCOVID-19 patients diagnosed with mild symptoms on initial presentation have a 14.6% rate of bounceback due to progression of illness.
| S-EPMC8054547 | biostudies-literature
Project description:Baricitinib is an oral Janus kinase (JAK)1/JAK2 inhibitor approved for the treatment of rheumatoid arthritis (RA) that was independently predicted, using artificial intelligence (AI) algorithms, to be useful for COVID-19 infection via proposed anti-cytokine effects and as an inhibitor of host cell viral propagation. We evaluated the in vitro pharmacology of baricitinib across relevant leukocyte subpopulations coupled to its in vivo pharmacokinetics and showed it inhibited signaling of cytokines implicated in COVID-19 infection. We validated the AI-predicted biochemical inhibitory effects of baricitinib on human numb-associated kinase (hNAK) members measuring nanomolar affinities for AAK1, BIKE, and GAK. Inhibition of NAKs led to reduced viral infectivity with baricitinib using human primary liver spheroids. These effects occurred at exposure levels seen clinically. In a case series of patients with bilateral COVID-19 pneumonia, baricitinib treatment was associated with clinical and radiologic recovery, a rapid decline in SARS-CoV-2 viral load, inflammatory markers, and IL-6 levels. Collectively, these data support further evaluation of the anti-cytokine and anti-viral activity of baricitinib and support its assessment in randomized trials in hospitalized COVID-19 patients.
| S-EPMC7300657 | biostudies-literature
Project description:Background: Recently, Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has affected more than 200 countries and lead to enormous losses. This study systematically reviews the application of Artificial Intelligence (AI) techniques in COVID-19, especially for diagnosis, estimation of epidemic trends, prognosis, and exploration of effective and safe drugs and vaccines; and discusses the potential limitations. Methods: We report this systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched PubMed, Embase and the Cochrane Library from inception to 19 September 2020 for published studies of AI applications in COVID-19. We used PROBAST (prediction model risk of bias assessment tool) to assess the quality of literature related to the diagnosis and prognosis of COVID-19. We registered the protocol (PROSPERO CRD42020211555). Results: We included 78 studies: 46 articles discussed AI-assisted diagnosis for COVID-19 with total accuracy of 70.00 to 99.92%, sensitivity of 73.00 to 100.00%, specificity of 25 to 100.00%, and area under the curve of 0.732 to 1.000. Fourteen articles evaluated prognosis based on clinical characteristics at hospital admission, such as clinical, laboratory and radiological characteristics, reaching accuracy of 74.4 to 95.20%, sensitivity of 72.8 to 98.00%, specificity of 55 to 96.87% and AUC of 0.66 to 0.997 in predicting critical COVID-19. Nine articles used AI models to predict the epidemic of the COVID-19, such as epidemic peak, infection rate, number of infected cases, transmission laws, and development trend. Eight articles used AI to explore potential effective drugs, primarily through drug repurposing and drug development. Finally, 1 article predicted vaccine targets that have the potential to develop COVID-19 vaccines. Conclusions: In this review, we have shown that AI achieved high performance in diagnosis, prognosis evaluation, epidemic prediction and drug discovery for COVID-19. AI has the potential to enhance significantly existing medical and healthcare system efficiency during the COVID-19 pandemic.
| S-EPMC8514781 | biostudies-literature
Project description:The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.
| S-EPMC8659534 | biostudies-literature