A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study

Chen, Xiaofeng; Tang, Ying‐Mei; Mo, Yongkang; Li, Shengkai; Lin, Daiying; Yang, Zhijian; Yang, Zhiqi; Sun, Hongfu; Qiu, Jinming; Liao, Yuting; Xiao, Jianning; Chen, Xiangguang; Wu, Xi; Wu, Renhua; Dai, Zhuozhi

doi:10.1007/s00330-020-06829-2

Cited by 116 publications

(135 citation statements)

References 26 publications

(31 reference statements)

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“…In our previous study, we demonstrated that based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished (11). Harrison et al…”

Section: Introductionmentioning

confidence: 92%

Distinguishing COVID-19 from influenza pneumonia in the early stage through CT imaging and clinical features

Yang

Lin

Chen

et al. 2020

Preprint

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AbbreviationsCOVID-19 = coronavirus disease 2019, GGO = ground-glass opacity, RT-PCR = reverse transcription polymerase chain reaction, WBC = white blood cell, CRP = C-reactive protein, AUC = area under the curve, SARS = severe acute respiratory syndrome, MERS = middle east respiratory syndrome. AbstractPurpose: To identify differences in CT imaging and clinical features between COVID-19 and influenza pneumonia in the early stage, and to identify the most valuable features in the differential diagnosis. Materials and Method:A consecutive cohort of 73 COVID-19 and 48 influenza pneumonia patients were retrospectively recruited from five independent institutions. The courses of both diseases were confirmed to be in the early stages (2.66 ± 2.62 days for COVID-19 and 2.19 ± 2.10 days for influenza pneumonia after onset). The chi-square test, student's t-test, and Kruskal-Wallis H-test were performed to compare CT imaging and clinical features between the two groups. Spearman or Kendall correlation tests between feature metrics and diagnosis outcomes were also assessed. The diagnostic performance of each feature in differentiating COVID-19 from influenza pneumonia was evaluated with univariate analysis. The corresponding area under the curve (AUC), accuracy, specificity, sensitivity and threshold were reported.

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Section: Introductionmentioning

confidence: 92%

Distinguishing COVID-19 from influenza pneumonia in the early stage through CT imaging and clinical features

Yang

Lin

Chen

et al. 2020

Preprint

Self Cite

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“…[84][85][86][87] When the COVID-19 epidemic erupted in China, clinicians lacked access to rRT-PCR kits and then as rRT-PCR became available, low rRT-PCR sensitivity reinforced belief in the additive value of CT for many. 88 These observations and scenarios prompted some to advocate for CT as a first-line supplement to the diagnostic evaluation for COVID-19, combining rRT-PCR with CT. 89,90 If CT alone or in combination with rRT-PCR reduced false-negative rates, the positive public health implications for case identification and control of disease transmission could be substantial. However, these benefits must be balanced against the cost of CT, medical radiation dangers, or practical limitations in busy hospitals with hourly trauma and stroke arrivals and potentially time-dependent emergencies juxtaposed against advised CT shutdowns for COVID-19 cleaning requiring 30 or more minutes.…”

Section: Antigen Testsmentioning

confidence: 99%

Diagnosing COVID‐19 in the Emergency Department: A Scoping Review of Clinical Examinations, Laboratory Tests, Imaging Accuracy, and Biases

Carpenter

Mudd

West

et al. 2020

Academic Emergency Medicine

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Objective Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) emerged as a global pandemic in early 2020 with rapidly evolving approaches to diagnosing the clinical illness called coronavirus disease (COVID‐19). The primary objective of this scoping review is to synthesize current research of the diagnostic accuracy of history, physical examination, routine laboratory tests, real‐time reverse transcription–polymerase chain reaction (rRT‐PCR), immunology tests, and computed tomography (CT) for the emergency department (ED) diagnosis of COVID‐19. Secondary objectives included a synopsis of diagnostic biases likely with current COVID‐19 research as well as corresponding implications of false‐negative and false‐positive results for clinicians and investigators. Methods A Preferred Reporting Items for Systematic Reviews and Meta‐Analyses–Scoping Review (PRISMA‐ScR)–adherent synthesis of COVID‐19 diagnostic accuracy through May 5, 2020, was conducted. The search strategy was designed by a medical librarian and included studies indexed by PubMed and Embase since January 2020. Results A total of 1,907 citations were screened for relevance. Patients without COVID‐19 are rarely reported, so specificity and likelihood ratios were generally unavailable. Fever is the most common finding, while hyposmia and hypogeusia appear useful to rule in COVID‐19. Cough is not consistently present. Lymphopenia is the mostly commonly reported laboratory abnormality and occurs in over 50% of COVID‐19 patients. rRT‐PCR is currently considered the COVID‐19 criterion standard for most diagnostic studies, but a single test sensitivity ranges from 60% to 78%. Multiple reasons for false‐negatives rRT‐PCR exist, including sample site tested and disease stage during which sample was obtained. CT may increase COVID‐19 sensitivity in conjunction with rRT‐PCR, but guidelines for imaging patients most likely to benefit are emerging. IgM and IgG serology levels are undetectable in the first week of COVID‐19, but sensitivity (range = 82% to 100%) and specificity (range = 87% to 100%) are promising. Whether detectable COVID‐19 antibodies correspond to immunity remains unanswered. Current studies do not adhere to accepted diagnostic accuracy reporting standards and likely report significantly biased results if the same tests were to be applied to general ED populations with suspected COVID‐19. Conclusions With the exception of fever and disorders of smell/taste, history and physical examination findings are unhelpful to distinguish COVID‐19 from other infectious conditions that mimic SARS‐CoV‐2 like influenza. Routine laboratory tests are also nondiagnostic, although lymphopenia is a common finding and other abnormalities may predict severe disease. Although rRT‐PCR is the current criterion standard, more inclusive consensus‐based criteria will likely emerge because of the high false‐negative rate of PCR tests. The role of serology and CT in ED assessments remains undefined.

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“…Previous studies have reported prediction models for diagnosis and prognosis of COVID-19 and for detecting the risk of being admitted to a hospital for COVID-19. Chen et al constructed a diagnosis prediction model with 10 clinical factors based on 136 participants [22]. Wang et al enrolled 296 in-hospital patients with COVID-19 and developed a clinical model to predict the mortality of such patients [20].…”

Section: Discussionmentioning

confidence: 99%

Exploiting an early warning nomogram for predicting the risk of ICU admission in COVID-19 patients: A multi-center study in China

Zhou

Yang

et al. 2020

Preprint

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Background Novel coronavirus disease 2019 (COVID-19) is a global public health emergency. Here, we developed and validated a practical model based on the data from a multi-center cohort in China for early identification and prediction of which patients will be admitted to the intensive care unit (ICU).Methods Data of 1087 patients with laboratory-confirmed COVID-19 were collected from 49 sites between January 2 and February 28, 2020, in Sichuan and Wuhan. Patients were randomly categorized into the training and validation cohorts (7:3). The least absolute shrinkage and selection operator and logistic regression analyses were used to develop the nomogram. The performance of the nomogram was evaluated for the C-index, calibration, discrimination, and clinical usefulness. Further, the nomogram was externally validated in a different cohort.Results The individualized prediction nomogram included 6 predictors: age, respiratory rate, systolic blood pressure, smoking status, fever, and chronic kidney disease. The model demonstrated a high discriminative ability in the training cohort (C-index = 0.829), which was confirmed in the external validation cohort (C-index = 0.776). In addition, the calibration plots confirmed good concordance for predicting the risk of ICU admission. Decision curve analysis revealed that the prediction nomogram was clinically useful.Conclusion We established an early prediction model incorporating clinical characteristics that could be quickly obtained on hospital admission, even in community health centers. This model can be conveniently used to predict the individual risk for ICU admission of patients with COVID-19 and optimize the use of limited resources.

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A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study

Cited by 116 publications

References 26 publications

Distinguishing COVID-19 from influenza pneumonia in the early stage through CT imaging and clinical features

Distinguishing COVID-19 from influenza pneumonia in the early stage through CT imaging and clinical features

Diagnosing COVID‐19 in the Emergency Department: A Scoping Review of Clinical Examinations, Laboratory Tests, Imaging Accuracy, and Biases

Exploiting an early warning nomogram for predicting the risk of ICU admission in COVID-19 patients: A multi-center study in China

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