Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms
Abstract Background The coronavirus disease 2019 (COVID-19) pandemic demands reliable prognostic models for estimating the risk of long COVID. We developed and validated a prediction model to estimate the probability of known common long COVID symptoms at least 60 days after acute COVID-19. Methods...
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| Format: | Article |
| Language: | English |
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BMC
2022-11-01
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| Series: | Diagnostic and Prognostic Research |
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| Online Access: | https://doi.org/10.1186/s41512-022-00135-9 |
| _version_ | 1828094071718019072 |
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| author | Manja Deforth Caroline E. Gebhard Susan Bengs Philipp K. Buehler Reto A. Schuepbach Annelies S. Zinkernagel Silvio D. Brugger Claudio T. Acevedo Dimitri Patriki Benedikt Wiggli Raphael Twerenbold Gabriela M. Kuster Hans Pargger Joerg C. Schefold Thibaud Spinetti Pedro D. Wendel-Garcia Daniel A. Hofmaenner Bianca Gysi Martin Siegemund Georg Heinze Vera Regitz-Zagrosek Catherine Gebhard Ulrike Held |
| author_facet | Manja Deforth Caroline E. Gebhard Susan Bengs Philipp K. Buehler Reto A. Schuepbach Annelies S. Zinkernagel Silvio D. Brugger Claudio T. Acevedo Dimitri Patriki Benedikt Wiggli Raphael Twerenbold Gabriela M. Kuster Hans Pargger Joerg C. Schefold Thibaud Spinetti Pedro D. Wendel-Garcia Daniel A. Hofmaenner Bianca Gysi Martin Siegemund Georg Heinze Vera Regitz-Zagrosek Catherine Gebhard Ulrike Held |
| author_sort | Manja Deforth |
| collection | DOAJ |
| description | Abstract Background The coronavirus disease 2019 (COVID-19) pandemic demands reliable prognostic models for estimating the risk of long COVID. We developed and validated a prediction model to estimate the probability of known common long COVID symptoms at least 60 days after acute COVID-19. Methods The prognostic model was built based on data from a multicentre prospective Swiss cohort study. Included were adult patients diagnosed with COVID-19 between February and December 2020 and treated as outpatients, at ward or intensive/intermediate care unit. Perceived long-term health impairments, including reduced exercise tolerance/reduced resilience, shortness of breath and/or tiredness (REST), were assessed after a follow-up time between 60 and 425 days. The data set was split into a derivation and a geographical validation cohort. Predictors were selected out of twelve candidate predictors based on three methods, namely the augmented backward elimination (ABE) method, the adaptive best-subset selection (ABESS) method and model-based recursive partitioning (MBRP) approach. Model performance was assessed with the scaled Brier score, concordance c statistic and calibration plot. The final prognostic model was determined based on best model performance. Results In total, 2799 patients were included in the analysis, of which 1588 patients were in the derivation cohort and 1211 patients in the validation cohort. The REST prevalence was similar between the cohorts with 21.6% (n = 343) in the derivation cohort and 22.1% (n = 268) in the validation cohort. The same predictors were selected with the ABE and ABESS approach. The final prognostic model was based on the ABE and ABESS selected predictors. The corresponding scaled Brier score in the validation cohort was 18.74%, model discrimination was 0.78 (95% CI: 0.75 to 0.81), calibration slope was 0.92 (95% CI: 0.78 to 1.06) and calibration intercept was −0.06 (95% CI: −0.22 to 0.09). Conclusion The proposed model was validated to identify COVID-19-infected patients at high risk for REST symptoms. Before implementing the prognostic model in daily clinical practice, the conduct of an impact study is recommended. |
| first_indexed | 2024-04-11T06:53:22Z |
| format | Article |
| id | doaj.art-683c9ba713cb425b8c158e6f5bb2b80c |
| institution | Directory Open Access Journal |
| issn | 2397-7523 |
| language | English |
| last_indexed | 2024-04-11T06:53:22Z |
| publishDate | 2022-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Diagnostic and Prognostic Research |
| spelling | doaj.art-683c9ba713cb425b8c158e6f5bb2b80c2022-12-22T04:39:05ZengBMCDiagnostic and Prognostic Research2397-75232022-11-016111110.1186/s41512-022-00135-9Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptomsManja Deforth0Caroline E. Gebhard1Susan Bengs2Philipp K. Buehler3Reto A. Schuepbach4Annelies S. Zinkernagel5Silvio D. Brugger6Claudio T. Acevedo7Dimitri Patriki8Benedikt Wiggli9Raphael Twerenbold10Gabriela M. Kuster11Hans Pargger12Joerg C. Schefold13Thibaud Spinetti14Pedro D. Wendel-Garcia15Daniel A. Hofmaenner16Bianca Gysi17Martin Siegemund18Georg Heinze19Vera Regitz-Zagrosek20Catherine Gebhard21Ulrike Held22Department of Biostatistics at Epidemiology, Biostatistics and Prevention Institute, University of ZurichIntensive Care Unit, Department of Acute Medicine, University Hospital BaselDepartment of Nuclear Medicine, University Hospital ZurichInstitute of Intensive Care Medicine, University Hospital ZurichInstitute of Intensive Care Medicine, University Hospital ZurichDepartment of Infectious Diseases and Hospital Epidemiology, University Hospital ZurichDepartment of Infectious Diseases and Hospital Epidemiology, University Hospital ZurichDepartment of Infectious Diseases and Hospital Epidemiology, University Hospital ZurichDepartment of Internal Medicine, Cantonal Hospital BadenDepartment of Infectiology and Infection Control, Cantonal Hospital BadenDepartment of Cardiology, University Hospital BaselDepartment of Cardiology, University Hospital BaselIntensive Care Unit, Department of Acute Medicine, University Hospital BaselDepartment of Intensive Care Medicine, University Hospital BernDepartment of Intensive Care Medicine, University Hospital BernInstitute of Intensive Care Medicine, University Hospital ZurichInstitute of Intensive Care Medicine, University Hospital ZurichIntensive Care Unit, Department of Acute Medicine, University Hospital BaselIntensive Care Unit, Department of Acute Medicine, University Hospital BaselCenter for Medical Statistics, Informatics and Intelligent Systems, Section for Clinical Biometrics, Medical University of ViennaUniversity of ZurichDepartment of Nuclear Medicine, University Hospital ZurichDepartment of Biostatistics at Epidemiology, Biostatistics and Prevention Institute, University of ZurichAbstract Background The coronavirus disease 2019 (COVID-19) pandemic demands reliable prognostic models for estimating the risk of long COVID. We developed and validated a prediction model to estimate the probability of known common long COVID symptoms at least 60 days after acute COVID-19. Methods The prognostic model was built based on data from a multicentre prospective Swiss cohort study. Included were adult patients diagnosed with COVID-19 between February and December 2020 and treated as outpatients, at ward or intensive/intermediate care unit. Perceived long-term health impairments, including reduced exercise tolerance/reduced resilience, shortness of breath and/or tiredness (REST), were assessed after a follow-up time between 60 and 425 days. The data set was split into a derivation and a geographical validation cohort. Predictors were selected out of twelve candidate predictors based on three methods, namely the augmented backward elimination (ABE) method, the adaptive best-subset selection (ABESS) method and model-based recursive partitioning (MBRP) approach. Model performance was assessed with the scaled Brier score, concordance c statistic and calibration plot. The final prognostic model was determined based on best model performance. Results In total, 2799 patients were included in the analysis, of which 1588 patients were in the derivation cohort and 1211 patients in the validation cohort. The REST prevalence was similar between the cohorts with 21.6% (n = 343) in the derivation cohort and 22.1% (n = 268) in the validation cohort. The same predictors were selected with the ABE and ABESS approach. The final prognostic model was based on the ABE and ABESS selected predictors. The corresponding scaled Brier score in the validation cohort was 18.74%, model discrimination was 0.78 (95% CI: 0.75 to 0.81), calibration slope was 0.92 (95% CI: 0.78 to 1.06) and calibration intercept was −0.06 (95% CI: −0.22 to 0.09). Conclusion The proposed model was validated to identify COVID-19-infected patients at high risk for REST symptoms. Before implementing the prognostic model in daily clinical practice, the conduct of an impact study is recommended.https://doi.org/10.1186/s41512-022-00135-9Clinical prediction modelLong COVIDPrognostic factorsStratified medicine |
| spellingShingle | Manja Deforth Caroline E. Gebhard Susan Bengs Philipp K. Buehler Reto A. Schuepbach Annelies S. Zinkernagel Silvio D. Brugger Claudio T. Acevedo Dimitri Patriki Benedikt Wiggli Raphael Twerenbold Gabriela M. Kuster Hans Pargger Joerg C. Schefold Thibaud Spinetti Pedro D. Wendel-Garcia Daniel A. Hofmaenner Bianca Gysi Martin Siegemund Georg Heinze Vera Regitz-Zagrosek Catherine Gebhard Ulrike Held Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms Diagnostic and Prognostic Research Clinical prediction model Long COVID Prognostic factors Stratified medicine |
| title | Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms |
| title_full | Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms |
| title_fullStr | Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms |
| title_full_unstemmed | Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms |
| title_short | Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms |
| title_sort | development and validation of a prognostic model for the early identification of covid 19 patients at risk of developing common long covid symptoms |
| topic | Clinical prediction model Long COVID Prognostic factors Stratified medicine |
| url | https://doi.org/10.1186/s41512-022-00135-9 |
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