Clinical prediction models for diagnosis of COVID-19 among adult patients: a validation and agreement study
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 evalu...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-05-01
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| Series: | BMC Infectious Diseases |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12879-022-07420-4 |
| _version_ | 1818202179948773376 |
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| author | Nadia Dardenne Médéa Locquet Anh Nguyet Diep Allison Gilbert Sophie Delrez Charlotte Beaudart Christian Brabant Alexandre Ghuysen Anne-Françoise Donneau Olivier Bruyère |
| author_facet | Nadia Dardenne Médéa Locquet Anh Nguyet Diep Allison Gilbert Sophie Delrez Charlotte Beaudart Christian Brabant Alexandre Ghuysen Anne-Françoise Donneau Olivier Bruyère |
| author_sort | Nadia Dardenne |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T03:05:21Z |
| format | Article |
| id | doaj.art-d24f27766bd94371af09d3e785996d55 |
| institution | Directory Open Access Journal |
| issn | 1471-2334 |
| language | English |
| last_indexed | 2024-12-12T03:05:21Z |
| publishDate | 2022-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Infectious Diseases |
| spelling | doaj.art-d24f27766bd94371af09d3e785996d552022-12-22T00:40:32ZengBMCBMC Infectious Diseases1471-23342022-05-0122111310.1186/s12879-022-07420-4Clinical prediction models for diagnosis of COVID-19 among adult patients: a validation and agreement studyNadia Dardenne0Médéa Locquet1Anh Nguyet Diep2Allison Gilbert3Sophie Delrez4Charlotte Beaudart5Christian Brabant6Alexandre Ghuysen7Anne-Françoise Donneau8Olivier Bruyère9Biostatistics Unit, University of Liège, Quartier HôpitalWHO 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 HôpitalBiostatistics Unit, University of Liège, Quartier HôpitalEmergency Department, University Hospital CenterEmergency Department, University Hospital CenterWHO 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 HôpitalWHO 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 HôpitalEmergency Department, University Hospital CenterBiostatistics Unit, University of Liège, Quartier HôpitalWHO 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 HôpitalAbstract 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.https://doi.org/10.1186/s12879-022-07420-4COVID-19Patients’ triagePrediction modelsValidation studyAgreement |
| spellingShingle | Nadia Dardenne Médéa Locquet Anh Nguyet Diep Allison Gilbert Sophie Delrez Charlotte Beaudart Christian Brabant Alexandre Ghuysen Anne-Françoise Donneau Olivier Bruyère Clinical prediction models for diagnosis of COVID-19 among adult patients: a validation and agreement study BMC Infectious Diseases COVID-19 Patients’ triage Prediction models Validation study Agreement |
| title | Clinical prediction models for diagnosis of COVID-19 among adult patients: a validation and agreement study |
| title_full | Clinical prediction models for diagnosis of COVID-19 among adult patients: a validation and agreement study |
| title_fullStr | Clinical prediction models for diagnosis of COVID-19 among adult patients: a validation and agreement study |
| title_full_unstemmed | Clinical prediction models for diagnosis of COVID-19 among adult patients: a validation and agreement study |
| title_short | Clinical prediction models for diagnosis of COVID-19 among adult patients: a validation and agreement study |
| title_sort | clinical prediction models for diagnosis of covid 19 among adult patients a validation and agreement study |
| topic | COVID-19 Patients’ triage Prediction models Validation study Agreement |
| url | https://doi.org/10.1186/s12879-022-07420-4 |
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