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Struyf T, Deeks JJ, Dinnes J, Takwoingi Y, Davenport C, Leeflang MM, Spijker R, Hooft L, Emperador D, Domen J, Tans A, Janssens S, Wickramasinghe D, Lannoy V, Horn SRA, Van den Bruel A. Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19. Cochrane Database Syst Rev 2022; 5:CD013665. [PMID: 35593186 PMCID: PMC9121352 DOI: 10.1002/14651858.cd013665.pub3] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
BACKGROUND COVID-19 illness is highly variable, ranging from infection with no symptoms through to pneumonia and life-threatening consequences. Symptoms such as fever, cough, or loss of sense of smell (anosmia) or taste (ageusia), can help flag early on if the disease is present. Such information could be used either to rule out COVID-19 disease, or to identify people who need to go for COVID-19 diagnostic tests. This is the second update of this review, which was first published in 2020. OBJECTIVES To assess the diagnostic accuracy of signs and symptoms to determine if a person presenting in primary care or to hospital outpatient settings, such as the emergency department or dedicated COVID-19 clinics, has COVID-19. SEARCH METHODS We undertook electronic searches up to 10 June 2021 in the University of Bern living search database. In addition, we checked repositories of COVID-19 publications. We used artificial intelligence text analysis to conduct an initial classification of documents. We did not apply any language restrictions. SELECTION CRITERIA Studies were eligible if they included people with clinically suspected COVID-19, or recruited known cases with COVID-19 and also controls without COVID-19 from a single-gate cohort. Studies were eligible when they recruited people presenting to primary care or hospital outpatient settings. Studies that included people who contracted SARS-CoV-2 infection while admitted to hospital were not eligible. The minimum eligible sample size of studies was 10 participants. All signs and symptoms were eligible for this review, including individual signs and symptoms or combinations. We accepted a range of reference standards. DATA COLLECTION AND ANALYSIS Pairs of review authors independently selected all studies, at both title and abstract, and full-text stage. They resolved any disagreements by discussion with a third review author. Two review authors independently extracted data and assessed risk of bias using the QUADAS-2 checklist, and resolved disagreements by discussion with a third review author. Analyses were restricted to prospective studies only. We presented sensitivity and specificity in paired forest plots, in receiver operating characteristic (ROC) space and in dumbbell plots. We estimated summary parameters using a bivariate random-effects meta-analysis whenever five or more primary prospective studies were available, and whenever heterogeneity across studies was deemed acceptable. MAIN RESULTS We identified 90 studies; for this update we focused on the results of 42 prospective studies with 52,608 participants. Prevalence of COVID-19 disease varied from 3.7% to 60.6% with a median of 27.4%. Thirty-five studies were set in emergency departments or outpatient test centres (46,878 participants), three in primary care settings (1230 participants), two in a mixed population of in- and outpatients in a paediatric hospital setting (493 participants), and two overlapping studies in nursing homes (4007 participants). The studies did not clearly distinguish mild COVID-19 disease from COVID-19 pneumonia, so we present the results for both conditions together. Twelve studies had a high risk of bias for selection of participants because they used a high level of preselection to decide whether reverse transcription polymerase chain reaction (RT-PCR) testing was needed, or because they enrolled a non-consecutive sample, or because they excluded individuals while they were part of the study base. We rated 36 of the 42 studies as high risk of bias for the index tests because there was little or no detail on how, by whom and when, the symptoms were measured. For most studies, eligibility for testing was dependent on the local case definition and testing criteria that were in effect at the time of the study, meaning most people who were included in studies had already been referred to health services based on the symptoms that we are evaluating in this review. The applicability of the results of this review iteration improved in comparison with the previous reviews. This version has more studies of people presenting to ambulatory settings, which is where the majority of assessments for COVID-19 take place. Only three studies presented any data on children separately, and only one focused specifically on older adults. We found data on 96 symptoms or combinations of signs and symptoms. Evidence on individual signs as diagnostic tests was rarely reported, so this review reports mainly on the diagnostic value of symptoms. Results were highly variable across studies. Most had very low sensitivity and high specificity. RT-PCR was the most often used reference standard (40/42 studies). Only cough (11 studies) had a summary sensitivity above 50% (62.4%, 95% CI 50.6% to 72.9%)); its specificity was low (45.4%, 95% CI 33.5% to 57.9%)). Presence of fever had a sensitivity of 37.6% (95% CI 23.4% to 54.3%) and a specificity of 75.2% (95% CI 56.3% to 87.8%). The summary positive likelihood ratio of cough was 1.14 (95% CI 1.04 to 1.25) and that of fever 1.52 (95% CI 1.10 to 2.10). Sore throat had a summary positive likelihood ratio of 0.814 (95% CI 0.714 to 0.929), which means that its presence increases the probability of having an infectious disease other than COVID-19. Dyspnoea (12 studies) and fatigue (8 studies) had a sensitivity of 23.3% (95% CI 16.4% to 31.9%) and 40.2% (95% CI 19.4% to 65.1%) respectively. Their specificity was 75.7% (95% CI 65.2% to 83.9%) and 73.6% (95% CI 48.4% to 89.3%). The summary positive likelihood ratio of dyspnoea was 0.96 (95% CI 0.83 to 1.11) and that of fatigue 1.52 (95% CI 1.21 to 1.91), which means that the presence of fatigue slightly increases the probability of having COVID-19. Anosmia alone (7 studies), ageusia alone (5 studies), and anosmia or ageusia (6 studies) had summary sensitivities below 50% but summary specificities over 90%. Anosmia had a summary sensitivity of 26.4% (95% CI 13.8% to 44.6%) and a specificity of 94.2% (95% CI 90.6% to 96.5%). Ageusia had a summary sensitivity of 23.2% (95% CI 10.6% to 43.3%) and a specificity of 92.6% (95% CI 83.1% to 97.0%). Anosmia or ageusia had a summary sensitivity of 39.2% (95% CI 26.5% to 53.6%) and a specificity of 92.1% (95% CI 84.5% to 96.2%). The summary positive likelihood ratios of anosmia alone and anosmia or ageusia were 4.55 (95% CI 3.46 to 5.97) and 4.99 (95% CI 3.22 to 7.75) respectively, which is just below our arbitrary definition of a 'red flag', that is, a positive likelihood ratio of at least 5. The summary positive likelihood ratio of ageusia alone was 3.14 (95% CI 1.79 to 5.51). Twenty-four studies assessed combinations of different signs and symptoms, mostly combining olfactory symptoms. By combining symptoms with other information such as contact or travel history, age, gender, and a local recent case detection rate, some multivariable prediction scores reached a sensitivity as high as 90%. AUTHORS' CONCLUSIONS Most individual symptoms included in this review have poor diagnostic accuracy. Neither absence nor presence of symptoms are accurate enough to rule in or rule out the disease. The presence of anosmia or ageusia may be useful as a red flag for the presence of COVID-19. The presence of cough also supports further testing. There is currently no evidence to support further testing with PCR in any individuals presenting only with upper respiratory symptoms such as sore throat, coryza or rhinorrhoea. Combinations of symptoms with other readily available information such as contact or travel history, or the local recent case detection rate may prove more useful and should be further investigated in an unselected population presenting to primary care or hospital outpatient settings. The diagnostic accuracy of symptoms for COVID-19 is moderate to low and any testing strategy using symptoms as selection mechanism will result in both large numbers of missed cases and large numbers of people requiring testing. Which one of these is minimised, is determined by the goal of COVID-19 testing strategies, that is, controlling the epidemic by isolating every possible case versus identifying those with clinically important disease so that they can be monitored or treated to optimise their prognosis. The former will require a testing strategy that uses very few symptoms as entry criterion for testing, the latter could focus on more specific symptoms such as fever and anosmia.
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Affiliation(s)
- Thomas Struyf
- Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
| | - Jonathan J Deeks
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Jacqueline Dinnes
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Yemisi Takwoingi
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Clare Davenport
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Mariska Mg Leeflang
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - René Spijker
- Medical Library, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Amsterdam, Netherlands
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Lotty Hooft
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | | | - Julie Domen
- Department of Primary Care, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Anouk Tans
- Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
| | | | | | | | - Sebastiaan R A Horn
- Department of Primary Care, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Ann Van den Bruel
- Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
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Dardenne N, Locquet M, Diep AN, Gilbert A, Delrez S, Beaudart C, Brabant C, Ghuysen A, Donneau AF, Bruyère O. Clinical prediction models for diagnosis of COVID-19 among adult patients: a validation and agreement study. BMC Infect Dis 2022; 22:464. [PMID: 35568825 PMCID: PMC9107295 DOI: 10.1186/s12879-022-07420-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 04/26/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Since the beginning of the pandemic, hospitals have been constantly overcrowded, with several observed waves of infected cases and hospitalisations. To avoid as much as possible this situation, efficient tools to facilitate the diagnosis of COVID-19 are needed. OBJECTIVE To evaluate and compare prediction models to diagnose COVID-19 identified in a systematic review published recently using performance indicators such as discrimination and calibration measures. METHODS A total of 1618 adult patients present at two Emergency Department triage centers and for whom qRT-PCR tests had been performed were included in this study. Six previously published models were reconstructed and assessed using diagnostic tests as sensitivity (Se) and negative predictive value (NPV), discrimination (Area Under the Roc Curve (AUROC)) and calibration measures. Agreement was also measured between them using Kappa's coefficient and IntraClass Correlation Coefficient (ICC). A sensitivity analysis has been conducted by waves of patients. RESULTS Among the 6 selected models, those based only on symptoms and/or risk exposure were found to be less efficient than those based on biological parameters and/or radiological examination with smallest AUROC values (< 0.80). However, all models showed good calibration and values above > 0.75 for Se and NPV but poor agreement (Kappa and ICC < 0.5) between them. The results of the first wave were similar to those of the second wave. CONCLUSION Although quite acceptable and similar results were found between all models, the importance of radiological examination was also emphasized, making it difficult to find an appropriate triage system to classify patients at risk for COVID-19.
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Affiliation(s)
- Nadia Dardenne
- Biostatistics Unit, University of Liège, Quartier Hôpital, Av. Hippocrate 13, CHU B23, 4000, Liège, Belgium.
| | - Médéa Locquet
- WHO Collaborating Centre for Public Health, Aspects of Musculo-Skeletal Health and Ageing, Research Unit in Public Health, Epidemiology and Health, Economics, University of Liège, Quartier Hôpital, Av. Hippocrate 13, CHU B23, 4000, Liège, Belgium
| | - Anh Nguyet Diep
- Biostatistics Unit, University of Liège, Quartier Hôpital, Av. Hippocrate 13, CHU B23, 4000, Liège, Belgium
| | - Allison Gilbert
- Emergency Department, University Hospital Center, Avenue de L'Hôpital 1, 4000, Liège, Belgium
| | - Sophie Delrez
- Emergency Department, University Hospital Center, Avenue de L'Hôpital 1, 4000, Liège, Belgium
| | - Charlotte Beaudart
- WHO Collaborating Centre for Public Health, Aspects of Musculo-Skeletal Health and Ageing, Research Unit in Public Health, Epidemiology and Health, Economics, University of Liège, Quartier Hôpital, Av. Hippocrate 13, CHU B23, 4000, Liège, Belgium
| | - Christian Brabant
- WHO Collaborating Centre for Public Health, Aspects of Musculo-Skeletal Health and Ageing, Research Unit in Public Health, Epidemiology and Health, Economics, University of Liège, Quartier Hôpital, Av. Hippocrate 13, CHU B23, 4000, Liège, Belgium
| | - Alexandre Ghuysen
- Emergency Department, University Hospital Center, Avenue de L'Hôpital 1, 4000, Liège, Belgium
| | - Anne-Françoise Donneau
- Biostatistics Unit, University of Liège, Quartier Hôpital, Av. Hippocrate 13, CHU B23, 4000, Liège, Belgium
| | - Olivier Bruyère
- WHO Collaborating Centre for Public Health, Aspects of Musculo-Skeletal Health and Ageing, Research Unit in Public Health, Epidemiology and Health, Economics, University of Liège, Quartier Hôpital, Av. Hippocrate 13, CHU B23, 4000, Liège, Belgium
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Tang W, Zhou H, Quan T, Chen X, Zhang H, Lin Y, Wu R. XGboost Prediction Model Based on 3.0T Diffusion Kurtosis Imaging Improves the Diagnostic Accuracy of MRI BiRADS 4 Masses. Front Oncol 2022; 12:833680. [PMID: 35372060 PMCID: PMC8968064 DOI: 10.3389/fonc.2022.833680] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 02/21/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The malignant probability of MRI BiRADS 4 breast lesions ranges from 2% to 95%, leading to unnecessary biopsies. The purpose of this study was to construct an optimal XGboost prediction model through a combination of DKI independently or jointly with other MR imaging features and clinical characterization, which was expected to reduce false positive rate of MRI BiRADS 4 masses and improve the diagnosis efficiency of breast cancer. METHODS 120 patients with 158 breast lesions were enrolled. DKI, Diffusion-weighted Imaging (DWI), Proton Magnetic Resonance Spectroscopy (1H-MRS) and Dynamic Contrast-Enhanced MRI (DCE-MRI) were performed on a 3.0-T scanner. Wilcoxon signed-rank test and χ2 test were used to compare patient's clinical characteristics, mean kurtosis (MK), mean diffusivity (MD), apparent diffusion coefficient (ADC), total choline (tCho) peak, extravascular extracellular volume fraction (Ve), flux rate constant (Kep) and volume transfer constant (Ktrans). ROC curve analysis was used to analyze the diagnostic performances of the imaging parameters. Spearman correlation analysis was performed to evaluate the associations of imaging parameters with prognostic factors and breast cancer molecular subtypes. The Least Absolute Shrinkage and Selectionator operator (lasso) and the area under the curve (AUC) of imaging parameters were used to select discriminative features for differentiating the breast benign lesions from malignant ones. Finally, an XGboost prediction model was constructed based on the discriminative features and its diagnostic efficiency was verified in BiRADS 4 masses. RESULTS MK derived from DKI performed better for differentiating between malignant and benign lesions than ADC, MD, tCho, Kep and Ktrans (p < 0.05). Also, MK was shown to be more strongly correlated with histological grade, Ki-67 expression and lymph node status. MD, MK, age, shape and menstrual status were selected to be the optimized feature subsets to construct an XGboost model, which exhibited superior diagnostic ability for breast cancer characterization and an improved evaluation of suspicious breast tumors in MRI BiRADS 4. CONCLUSIONS DKI is promising for breast cancer diagnosis and prognostic factor assessment. An optimized XGboost model that included DKI, age, shape and menstrual status is effective in improving the diagnostic accuracy of BiRADS 4 masses.
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Affiliation(s)
- Wan Tang
- Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
- Institute of Health Monitoring, Inspection and Protection, Hubei Provincial Center for Disease Control and Prevention, Wuhan, China
| | - Han Zhou
- Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Tianhong Quan
- Department of Electronic and information Engineering, College of Engineering, Shantou University, Shantou, China
| | - Xiaoyan Chen
- Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Huanian Zhang
- Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Yan Lin
- Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
- Guangdong Provincial Key Laboratory for Breast Cancer Diagnosis and Treatment, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Renhua Wu
- Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
- Guangdong Provincial Key Laboratory for Breast Cancer Diagnosis and Treatment, Cancer Hospital of Shantou University Medical College, Shantou, China
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McRae AD, Hohl CM, Rosychuk R, Vatanpour S, Ghaderi G, Archambault PM, Brooks SC, Cheng I, Davis P, Hayward J, Lang E, Ohle R, Rowe B, Welsford M, Yadav K, Morrison LJ, Perry J. CCEDRRN COVID-19 Infection Score (CCIS): development and validation in a Canadian cohort of a clinical risk score to predict SARS-CoV-2 infection in patients presenting to the emergency department with suspected COVID-19. BMJ Open 2021; 11:e055832. [PMID: 34857584 PMCID: PMC8640195 DOI: 10.1136/bmjopen-2021-055832] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVES To develop and validate a clinical risk score that can accurately quantify the probability of SARS-CoV-2 infection in patients presenting to an emergency department without the need for laboratory testing. DESIGN Cohort study of participants in the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN) registry. Regression models were fitted to predict a positive SARS-CoV-2 test result using clinical and demographic predictors, as well as an indicator of local SARS-CoV-2 incidence. SETTING 32 emergency departments in eight Canadian provinces. PARTICIPANTS 27 665 consecutively enrolled patients who were tested for SARS-CoV-2 in participating emergency departments between 1 March and 30 October 2020. MAIN OUTCOME MEASURES Positive SARS-CoV-2 nucleic acid test result within 14 days of an index emergency department encounter for suspected COVID-19 disease. RESULTS We derived a 10-item CCEDRRN COVID-19 Infection Score using data from 21 743 patients. This score included variables from history and physical examination and an indicator of local disease incidence. The score had a c-statistic of 0.838 with excellent calibration. We externally validated the rule in 5295 patients. The score maintained excellent discrimination and calibration and had superior performance compared with another previously published risk score. Score cut-offs were identified that can rule-in or rule-out SARS-CoV-2 infection without the need for nucleic acid testing with 97.4% sensitivity (95% CI 96.4 to 98.3) and 95.9% specificity (95% CI 95.5 to 96.0). CONCLUSIONS The CCEDRRN COVID-19 Infection Score uses clinical characteristics and publicly available indicators of disease incidence to quantify a patient's probability of SARS-CoV-2 infection. The score can identify patients at sufficiently high risk of SARS-CoV-2 infection to warrant isolation and empirical therapy prior to test confirmation while also identifying patients at sufficiently low risk of infection that they may not need testing. TRIAL REGISTRATION NUMBER NCT04702945.
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Affiliation(s)
- Andrew D McRae
- Department of Emergency Medicine, University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
| | - Corinne M Hohl
- Department of Emergency Medicine, The University of British Columbia Faculty of Medicine, Vancouver, British Columbia, Canada
| | - Rhonda Rosychuk
- Department of Paediatrics, University of Alberta Faculty of Medicine & Dentistry, Edmonton, Alberta, Canada
| | - Shabnam Vatanpour
- Department of Emergency Medicine, University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
| | - Gelareh Ghaderi
- Department of Emergency Medicine, The University of British Columbia Faculty of Medicine, Vancouver, British Columbia, Canada
| | - Patrick M Archambault
- Department of Emergency Medicine, Universite Laval Faculte de medecine, Quebec, Quebec, Canada
| | - Steven C Brooks
- Department of Emergency Medicine, Queen's University School of Medicine, Kingston, Ontario, Canada
| | - Ivy Cheng
- Department of Emergency Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Philip Davis
- Department of Emergency Medicine, University of Saskatchewan College of Medicine, Saskatoon, Saskatchewan, Canada
| | - Jake Hayward
- Department of Emergency Medicine, University of Alberta Faculty of Medicine & Dentistry, Edmonton, Alberta, Canada
| | - Eddy Lang
- Department of Emergency Medicine, University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
| | - Robert Ohle
- Department of Emergency Medicine, Northern Ontario School of Medicine, Thunder Bay, Ontario, Canada
| | - Brian Rowe
- Department of Emergency Medicine, University of Alberta Faculty of Medicine & Dentistry, Edmonton, Alberta, Canada
| | - Michelle Welsford
- Department of Emergency Medicine, McMaster University Faculty of Health Sciences, Hamilton, Ontario, Canada
| | - Krishan Yadav
- Department of Emergency Medicine, University of Ottawa Faculty of Medicine, Ottawa, Ontario, Canada
| | - Laurie J Morrison
- Department of Emergency Medicine, St Michael's Hospital, Toronto, Ontario, Canada
| | - Jeffrey Perry
- Department of Emergency Medicine, University of Ottawa Faculty of Medicine, Ottawa, Ontario, Canada
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5
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Ortíz-Barrios MA, Coba-Blanco DM, Alfaro-Saíz JJ, Stand-González D. Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:8814. [PMID: 34444561 PMCID: PMC8392152 DOI: 10.3390/ijerph18168814] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 08/15/2021] [Accepted: 08/17/2021] [Indexed: 12/23/2022]
Abstract
The COVID-19 pandemic has strongly affected the dynamics of Emergency Departments (EDs) worldwide and has accentuated the need for tackling different operational inefficiencies that decrease the quality of care provided to infected patients. The EDs continue to struggle against this outbreak by implementing strategies maximizing their performance within an uncertain healthcare environment. The efforts, however, have remained insufficient in view of the growing number of admissions and increased severity of the coronavirus disease. Therefore, the primary aim of this paper is to review the literature on process improvement interventions focused on increasing the ED response to the current COVID-19 outbreak to delineate future research lines based on the gaps detected in the practical scenario. Therefore, we applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to perform a review containing the research papers published between December 2019 and April 2021 using ISI Web of Science, Scopus, PubMed, IEEE, Google Scholar, and Science Direct databases. The articles were further classified taking into account the research domain, primary aim, journal, and publication year. A total of 65 papers disseminated in 51 journals were concluded to satisfy the inclusion criteria. Our review found that most applications have been directed towards predicting the health outcomes in COVID-19 patients through machine learning and data analytics techniques. In the overarching pandemic, healthcare decision makers are strongly recommended to integrate artificial intelligence techniques with approaches from the operations research (OR) and quality management domains to upgrade the ED performance under social-economic restrictions.
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Affiliation(s)
- Miguel Angel Ortíz-Barrios
- Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 081001, Colombia; (D.M.C.-B.); (D.S.-G.)
| | - Dayana Milena Coba-Blanco
- Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 081001, Colombia; (D.M.C.-B.); (D.S.-G.)
| | - Juan-José Alfaro-Saíz
- Research Centre on Production Management and Engineering, Universitat Politècnica de València, 46022 Valencia, Spain;
| | - Daniela Stand-González
- Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 081001, Colombia; (D.M.C.-B.); (D.S.-G.)
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Abstract
INTRODUCTION The novel COVID-19 pandemic struck the world unprepared. This keynote outlines challenges and successes using data to inform providers, government officials, hospitals, and patients in a pandemic. METHODS The authors outline the data required to manage a novel pandemic including their potential uses by governments, public health organizations, and individuals. RESULTS An extensive discussion on data quality and on obstacles to collecting data is followed by examples of successes in clinical care, contact tracing, and forecasting. Generic local forecast model development is reviewed followed by ethical consideration around pandemic data. We leave the reader with thoughts on the next inevitable outbreak and lessons learned from the COVID-19 pandemic. CONCLUSION COVID-19 must be a lesson for the future to direct us to better planning and preparing to manage the next pandemic with health informatics.
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Affiliation(s)
- Mujeeb A. Basit
- Clinical Informatics Center, UT Southwestern, Medical Center, Dallas, TX, USA
- Department of Internal Medicine, UT Southwestern, Medical Center, Dallas, TX, USA
| | - Christoph U. Lehmann
- Clinical Informatics Center, UT Southwestern, Medical Center, Dallas, TX, USA
- Departments of Pediatrics, Population & Data Sciences, and Bioinformatics, UT Southwestern, Medical Center, Dallas, TX, USA
| | - Richard J. Medford
- Clinical Informatics Center, UT Southwestern, Medical Center, Dallas, TX, USA
- Department of Internal Medicine, UT Southwestern, Medical Center, Dallas, TX, USA
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Nevel AE, Kline JA. Inter-rater reliability and prospective validation of a clinical prediction rule for SARS-CoV-2 infection. Acad Emerg Med 2021; 28:761-767. [PMID: 34133794 PMCID: PMC8441807 DOI: 10.1111/acem.14309] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 05/19/2021] [Accepted: 05/25/2021] [Indexed: 12/20/2022]
Abstract
Objectives Accurate estimation of the risk of SARS‐CoV‐2 infection based on bedside data alone has importance to emergency department (ED) operations and throughput. The 13‐item CORC (COVID [or coronavirus] Rule‐out Criteria) rule had good overall diagnostic accuracy in retrospective derivation and validation. The objective of this study was to prospectively test the inter‐rater reliability and diagnostic accuracy of the CORC score and rule (score ≤ 0 negative, > 0 positive) and compare the CORC rule performance with physician gestalt. Methods This noninterventional study was conducted at an urban academic ED from February 2021 to March 2021. Two practitioners were approached by research coordinators and asked to independently complete a form capturing the CORC criteria for their shared patient and their gestalt binary prediction of the SARS‐CoV‐2 test result and confidence (0%–100%). The criterion standard for SARS‐CoV‐2 was from reverse transcriptase polymerase chain reaction performed on a nasopharyngeal swab. The primary analysis was from weighted Cohen's kappa and likelihood ratios (LRs). Results For 928 patients, agreement between observers was good for the total CORC score, κ = 0.613 (95% confidence interval [CI] = 0.579–0.646), and for the CORC rule, κ = 0.644 (95% CI = 0.591–0.697). The agreement for clinician gestalt binary determination of SARs‐CoV‐2 status was κ = 0.534 (95% CI = 0.437–0.632) with median confidence of 76% (first–third quartile = 66–88.5). For 425 patients who had the criterion standard, a negative CORC rule (both observers scored CORC < 0), the sensitivity was 88%, and specificity was 51%, with a negative LR (LR−) of 0.24 (95% CI = 0.10–0.50). Among patients with a mean CORC score of >4, the prevalence of a positive SARS‐CoV‐2 test was 58% (95% CI = 28%–85%) and positive LR was 13.1 (95% CI = 4.5–37.2). Clinician gestalt demonstrated a sensitivity of 51% and specificity of 86% with a LR− of 0.57 (95% CI = 0.39–0.74). Conclusion In this prospective study, the CORC score and rule demonstrated good inter‐rater reliability and reproducible diagnostic accuracy for estimating the pretest probability of SARs‐CoV‐2 infection.
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Affiliation(s)
- Adam E. Nevel
- Department of Emergency Medicine Indiana University School of Medicine Indianapolis Indiana USA
| | - Jeffrey A. Kline
- Department of Emergency Medicine Indiana University School of Medicine Indianapolis Indiana USA
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Locquet M, Diep AN, Beaudart C, Dardenne N, Brabant C, Bruyère O, Donneau AF. A systematic review of prediction models to diagnose COVID-19 in adults admitted to healthcare centers. Arch Public Health 2021; 79:105. [PMID: 34144711 PMCID: PMC8211973 DOI: 10.1186/s13690-021-00630-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 06/06/2021] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic is putting significant pressure on the hospital system. To help clinicians in the rapid triage of patients at high risk of COVID-19 while waiting for RT-PCR results, different diagnostic prediction models have been developed. Our objective is to identify, compare, and evaluate performances of prediction models for the diagnosis of COVID-19 in adult patients in a health care setting. METHODS A search for relevant references has been conducted on the MEDLINE and Scopus databases. Rigorous eligibility criteria have been established (e.g., adult participants, suspicion of COVID-19, medical setting) and applied by two independent investigators to identify suitable studies at 2 different stages: (1) titles and abstracts screening and (2) full-texts screening. Risk of bias (RoB) has been assessed using the Prediction model study Risk of Bias Assessment Tool (PROBAST). Data synthesis has been presented according to a narrative report of findings. RESULTS Out of the 2334 references identified by the literature search, 13 articles have been included in our systematic review. The studies, carried out all over the world, were performed in 2020. The included articles proposed a model developed using different methods, namely, logistic regression, score, machine learning, XGBoost. All the included models performed well to discriminate adults at high risks of presenting COVID-19 (all area under the ROC curve (AUROC) > 0.500). The best AUROC was observed for the model of Kurstjens et al (AUROC = 0.940 (0.910-0.960), which was also the model that achieved the highest sensitivity (98%). RoB was evaluated as low in general. CONCLUSION Thirteen models have been developed since the start of the pandemic in order to diagnose COVID-19 in suspected patients from health care centers. All these models are effective, to varying degrees, in identifying whether patients were at high risk of having COVID-19.
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Affiliation(s)
- Médéa Locquet
- WHO Collaborating Centre for Public Health, Aspects of Musculo-Skeletal Health and Ageing, Research Unit in Public Health, Epidemiology and Health, Economics, University of Liège, Quartier Hôpital, Av. Hippocrate 13, CHU B23, 4000 Liège, Belgium
| | - Anh Nguyet Diep
- Biostatistics Unit, University of Liège, Quartier Hôpital, Av. Hippocrate 13, CHU B23, 4000 Liège, Belgium
| | - Charlotte Beaudart
- WHO Collaborating Centre for Public Health, Aspects of Musculo-Skeletal Health and Ageing, Research Unit in Public Health, Epidemiology and Health, Economics, University of Liège, Quartier Hôpital, Av. Hippocrate 13, CHU B23, 4000 Liège, Belgium
| | - Nadia Dardenne
- Biostatistics Unit, University of Liège, Quartier Hôpital, Av. Hippocrate 13, CHU B23, 4000 Liège, Belgium
| | - Christian Brabant
- WHO Collaborating Centre for Public Health, Aspects of Musculo-Skeletal Health and Ageing, Research Unit in Public Health, Epidemiology and Health, Economics, University of Liège, Quartier Hôpital, Av. Hippocrate 13, CHU B23, 4000 Liège, Belgium
| | - Olivier Bruyère
- WHO Collaborating Centre for Public Health, Aspects of Musculo-Skeletal Health and Ageing, Research Unit in Public Health, Epidemiology and Health, Economics, University of Liège, Quartier Hôpital, Av. Hippocrate 13, CHU B23, 4000 Liège, Belgium
| | - Anne-Françoise Donneau
- Biostatistics Unit, University of Liège, Quartier Hôpital, Av. Hippocrate 13, CHU B23, 4000 Liège, Belgium
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9
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Kline JA, Camargo CA, Courtney DM, Kabrhel C, Nordenholz KE, Aufderheide T, Baugh JJ, Beiser DG, Bennett CL, Bledsoe J, Castillo E, Chisolm-Straker M, Goldberg EM, House H, House S, Jang T, Lim SC, Madsen TE, McCarthy DM, Meltzer A, Moore S, Newgard C, Pagenhardt J, Pettit KL, Pulia MS, Puskarich MA, Southerland LT, Sparks S, Turner-Lawrence D, Vrablik M, Wang A, Weekes AJ, Westafer L, Wilburn J. Clinical prediction rule for SARS-CoV-2 infection from 116 U.S. emergency departments 2-22-2021. PLoS One 2021; 16:e0248438. [PMID: 33690722 PMCID: PMC7946184 DOI: 10.1371/journal.pone.0248438] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 02/25/2021] [Indexed: 12/11/2022] Open
Abstract
Objectives Accurate and reliable criteria to rapidly estimate the probability of infection with the novel coronavirus-2 that causes the severe acute respiratory syndrome (SARS-CoV-2) and associated disease (COVID-19) remain an urgent unmet need, especially in emergency care. The objective was to derive and validate a clinical prediction score for SARS-CoV-2 infection that uses simple criteria widely available at the point of care. Methods Data came from the registry data from the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER network) comprising 116 hospitals from 25 states in the US. Clinical variables and 30-day outcomes were abstracted from medical records of 19,850 emergency department (ED) patients tested for SARS-CoV-2. The criterion standard for diagnosis of SARS-CoV-2 required a positive molecular test from a swabbed sample or positive antibody testing within 30 days. The prediction score was derived from a 50% random sample (n = 9,925) using unadjusted analysis of 107 candidate variables as a screening step, followed by stepwise forward logistic regression on 72 variables. Results Multivariable regression yielded a 13-variable score, which was simplified to a 13-point score: +1 point each for age>50 years, measured temperature>37.5°C, oxygen saturation<95%, Black race, Hispanic or Latino ethnicity, household contact with known or suspected COVID-19, patient reported history of dry cough, anosmia/dysgeusia, myalgias or fever; and -1 point each for White race, no direct contact with infected person, or smoking. In the validation sample (n = 9,975), the probability from logistic regression score produced an area under the receiver operating characteristic curve of 0.80 (95% CI: 0.79–0.81), and this level of accuracy was retained across patients enrolled from the early spring to summer of 2020. In the simplified score, a score of zero produced a sensitivity of 95.6% (94.8–96.3%), specificity of 20.0% (19.0–21.0%), negative likelihood ratio of 0.22 (0.19–0.26). Increasing points on the simplified score predicted higher probability of infection (e.g., >75% probability with +5 or more points). Conclusion Criteria that are available at the point of care can accurately predict the probability of SARS-CoV-2 infection. These criteria could assist with decisions about isolation and testing at high throughput checkpoints.
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Affiliation(s)
- Jeffrey A. Kline
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
- * E-mail:
| | - Carlos A. Camargo
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - D. Mark Courtney
- Department of Emergency Medicine, University of Texas Southwestern, Dallas, Texas, United States of America
| | - Christopher Kabrhel
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Kristen E. Nordenholz
- Department of Emergency Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Thomas Aufderheide
- Department of Emergency Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Joshua J. Baugh
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - David G. Beiser
- Section of Emergency Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Christopher L. Bennett
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, California, United States of America
| | - Joseph Bledsoe
- Department of Emergency Medicine, Healthcare Delivery Institute, Intermountain Healthcare, Salt Lake City, Utah, United States of America
| | - Edward Castillo
- Department of Emergency Medicine, University of California, San Diego, California, United States of America
| | - Makini Chisolm-Straker
- Department of Emergency Medicine, Mt. Sinai School of Medicine, New York, New York, United States of America
| | - Elizabeth M. Goldberg
- Department of Emergency Medicine, Warren Alpert Medical School of Brown University, Providence, Rhode Island, United States of America
| | - Hans House
- Department of Emergency Medicine, University of Iowa School of Medicine, Iowa City, Iowa, United States of America
| | - Stacey House
- Department of Emergency Medicine, Washington University School of Medicine, St. Louise, Missouri, United States of America
| | - Timothy Jang
- Department of Emergency Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America
| | - Stephen C. Lim
- University Medical Center New Orleans, Louisiana State University School of Medicine, New Orleans, Louisiana, United States of America
| | - Troy E. Madsen
- Division of Emergency Medicine, Department Surgery, University of Utah School of Medicine, Salt Lake City, Utah, United States of America
| | - Danielle M. McCarthy
- Department of Emergency Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Andrew Meltzer
- Department of Emergency Medicine, George Washington University School of Medicine, Washington D.C., DC, United States of America
| | - Stephen Moore
- Department of Emergency Medicine, Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania, United States of America
| | - Craig Newgard
- Department of Emergency Medicine, Oregon Health and Science University, Portland, Oregon, United States of America
| | - Justine Pagenhardt
- Department of Emergency Medicine, West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
| | - Katherine L. Pettit
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
| | - Michael S. Pulia
- Department of Emergency Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States of America
| | - Michael A. Puskarich
- Department of Emergency Medicine, Hennepin County Medical Center and the University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Lauren T. Southerland
- Department of Emergency Medicine, Ohio State University Medical Center, Columbus, Ohio, United States of America
| | - Scott Sparks
- Department of Emergency Medicine, Riverside Regional Medical Center, Newport News, Virginia, United States of America
| | - Danielle Turner-Lawrence
- Department of Emergency Medicine, Beaumont Health, Royal Oak, Michigan, United States of America
| | - Marie Vrablik
- Department of Emergency Medicine, University of Washington School of Medicine, Seattle, Washington, United States of America
| | - Alfred Wang
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
| | - Anthony J. Weekes
- Department of Emergency Medicine, Carolinas Medical Center at Atrium Health, Charlotte, North Carolina, United States of America
| | - Lauren Westafer
- Department of Emergency Medicine, Baystate Health, Springfield, Massachusetts, United States of America
| | - John Wilburn
- Department of Emergency Medicine, Wayne State University School of Medicine, Detroit, Michigan, United States of America
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10
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Dai W, Ke PF, Li ZZ, Zhuang QZ, Huang W, Wang Y, Xiong Y, Huang XZ. Establishing Classifiers With Clinical Laboratory Indicators to Distinguish COVID-19 From Community-Acquired Pneumonia: Retrospective Cohort Study. J Med Internet Res 2021; 23:e23390. [PMID: 33534722 PMCID: PMC7901596 DOI: 10.2196/23390] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 12/29/2020] [Accepted: 02/01/2021] [Indexed: 01/10/2023] Open
Abstract
Background The initial symptoms of patients with COVID-19 are very much like those of patients with community-acquired pneumonia (CAP); it is difficult to distinguish COVID-19 from CAP with clinical symptoms and imaging examination. Objective The objective of our study was to construct an effective model for the early identification of COVID-19 that would also distinguish it from CAP. Methods The clinical laboratory indicators (CLIs) of 61 COVID-19 patients and 60 CAP patients were analyzed retrospectively. Random combinations of various CLIs (ie, CLI combinations) were utilized to establish COVID-19 versus CAP classifiers with machine learning algorithms, including random forest classifier (RFC), logistic regression classifier, and gradient boosting classifier (GBC). The performance of the classifiers was assessed by calculating the area under the receiver operating characteristic curve (AUROC) and recall rate in COVID-19 prediction using the test data set. Results The classifiers that were constructed with three algorithms from 43 CLI combinations showed high performance (recall rate >0.9 and AUROC >0.85) in COVID-19 prediction for the test data set. Among the high-performance classifiers, several CLIs showed a high usage rate; these included procalcitonin (PCT), mean corpuscular hemoglobin concentration (MCHC), uric acid, albumin, albumin to globulin ratio (AGR), neutrophil count, red blood cell (RBC) count, monocyte count, basophil count, and white blood cell (WBC) count. They also had high feature importance except for basophil count. The feature combination (FC) of PCT, AGR, uric acid, WBC count, neutrophil count, basophil count, RBC count, and MCHC was the representative one among the nine FCs used to construct the classifiers with an AUROC equal to 1.0 when using the RFC or GBC algorithms. Replacing any CLI in these FCs would lead to a significant reduction in the performance of the classifiers that were built with them. Conclusions The classifiers constructed with only a few specific CLIs could efficiently distinguish COVID-19 from CAP, which could help clinicians perform early isolation and centralized management of COVID-19 patients.
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Affiliation(s)
- Wanfa Dai
- Department of Respiration, Gong An County People's Hospital, Jingzhou, China
| | - Pei-Feng Ke
- Department of Laboratory Medicine, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.,Guangdong Provincial Key Laboratory of Research on Emergency in Traditional Chinese Medicine, Guangzhou, China
| | - Zhen-Zhen Li
- Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Qi-Zhen Zhuang
- Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Wei Huang
- Department of Respiration, Gong An County People's Hospital, Jingzhou, China
| | - Yi Wang
- Department of Laboratory Medicine, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.,Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yujuan Xiong
- Department of Laboratory Medicine, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.,Guangdong Provincial Key Laboratory of Research on Emergency in Traditional Chinese Medicine, Guangzhou, China
| | - Xian-Zhang Huang
- Department of Laboratory Medicine, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.,Guangdong Provincial Key Laboratory of Research on Emergency in Traditional Chinese Medicine, Guangzhou, China
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