Clinical decision support tool for diagnosis of COVID-19 in hospitals

Abstract

Background The coronavirus infectious disease 19 (COVID-19) pandemic has resulted in significant morbidities, severe acute respiratory failures and subsequently emergency departments’ (EDs) overcrowding in a context of insufficient laboratory testing capacities. The development of decision support tools for real-time clinical diagnosis of COVID-19 is of prime importance to assist patients’ triage and allocate resources for patients at risk. Methods and principal findings From March 2 to June 15, 2020, clinical patterns of COVID-19 suspected patients at admission to the EDs of Liège University Hospital, consisting in the recording of eleven symptoms (i.e. dyspnoea, chest pain, rhinorrhoea, sore throat, dry cough, wet cough, diarrhoea, headache, myalgia, fever and anosmia) plus age and gender, were investigated during the first COVID-19 pandemic wave. Indeed, 573 SARS-CoV-2 cases confirmed by qRT-PCR before mid-June 2020, and 1579 suspected cases that were subsequently determined to be qRT-PCR negative for the detection of SARS-CoV-2 were enrolled in this study. Using multivariate binary logistic regression, two most relevant symptoms of COVID-19 were identified in addition of the age of the patient, i.e. fever (odds ratio [OR] = 3.66; 95% CI: 2.97–4.50), dry cough (OR = 1.71; 95% CI: 1.39–2.12), and patients older than 56.5 y (OR = 2.07; 95% CI: 1.67–2.58). Two additional symptoms (chest pain and sore throat) appeared significantly less associated to the confirmed COVID-19 cases with the same OR = 0.73 (95% CI: 0.56–0.94). An overall pondered (by OR) score (OPS) was calculated using all significant predictors. A receiver operating characteristic (ROC) curve was generated and the area under the ROC curve was 0.71 (95% CI: 0.68–0.73) rendering the use of the OPS to discriminate COVID-19 confirmed and unconfirmed patients. The main predictors were confirmed using both sensitivity analysis and classification tree analysis. Interestingly, a significant negative correlation was observed between the OPS and the cycle threshold (Ct values) of the qRT-PCR. Conclusion and main significance The proposed approach allows for the use of an interactive and adaptive clinical decision support tool. Using the clinical algorithm developed, a web-based user-interface was created to help nurses and clinicians from EDs with the triage of patients during the second COVID-19 wave.

Clinical prediction models for diagnosis of COVID-19 among adult patients: a validation and agreement study

“…Data in the present study have been extracted from the Medical and Economic Information Service (SIME) of the University Hospital Center of Liège (CHU Liège) and included patients present at the two ED triage centers [ 18 ] of the CHU (Sart Tilman and Notre-Dame des Bruyères) with suspicion of COVID-19. Data were collected during the period from March 2, 2020, to January 31, 2021.…”

“…The outcome was confirmed or unconfirmed COVID-19 case using a qRT-PCR. Two different qRT-PCR tests were used during these periods: one adapted from the protocol described by Corman et al [ 19 ]; and a second was a commercial assay using the cobas ® 6800 platform (Roche) [ 18 ]. Patients for whom no qRT-PCR test was realised, aged < 18 years and for whom no biological parameters were not included in the analysis, representing 80% of the original dataset.…”

University population-based prospective cohort study of SARS-CoV-2 infection and immunity (SARSSURV-ULiège): a study protocol

“…In case the participant does not agree for swab voluntary study, COVID-19 infection symptoms are still collected by phone. A total of 20 symptoms suggestive of SARS-CoV-2 infection are considered: dyspnoea effort, fatigue, dry cough, chest pain, headache, loss of appetite, myalgias, dyspnoea at rest, anosmia, agueusia, rhinorrhea, paresthesias/dysesthesias, memory loss, diarrhoea, wet cough, pharyngeal pain, confusional syndrome, nausea, vomiting, fever and other 24–26. For each symptom, the result (Likert scale ranging from 0 (no symptom) to 10 (severe symptom)) are encoded in the online platform by the nurses.…”

Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review

“…Assaf et al [46]; Chou et al [55] Neural network, random forest, classification and regression decision tree (CRT) Van Singer et al [96]; Möckel et al [79] Logistic regression and CRT Diep et al [56] Logistic regression, Mann-Whitey, chi-cuadrado Saegerman et al [88] Binary logistic regression and bootstrapped quantile regression, classification and regression tree analysis. Romero-Gameros et al [87] Logistic regression, Mantel-Haenszel Bolourani et al [50] Artificial intelligence, logistic regression, XGBoost combines a recursive gradient-boosting method called Newton boosting, with a decision-tree model, decision making Goodacre et al [65]; Feng et al [46] Multivariable regression with least absolute shrinkage and selection operator (LASSO) Gavelli et al [64] logistic regression and cox regression…”