Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms

TY - JOUR

T1 - Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms

AU - Deforth, Manja

AU - Gebhard, Caroline E

AU - Bengs, Susan

AU - Buehler, Philipp K

AU - Schuepbach, Reto A

AU - Zinkernagel, Annelies S

AU - Brugger, Silvio D

AU - Acevedo, Claudio T

AU - Patriki, Dimitri

AU - Wiggli, Benedikt

AU - Twerenbold, Raphael

AU - Kuster, Gabriela M

AU - Pargger, Hans

AU - Schefold, Joerg C

AU - Spinetti, Thibaud

AU - Wendel-Garcia, Pedro D

AU - Hofmaenner, Daniel A

AU - Gysi, Bianca

AU - Siegemund, Martin

AU - Heinze, Georg

AU - Regitz-Zagrosek, Vera

AU - Gebhard, Catherine

AU - Held, Ulrike

N1 - © 2022. The Author(s).

PY - 2022/11/17

Y1 - 2022/11/17

N2 - 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.

AB - 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.

U2 - 10.1186/s41512-022-00135-9

DO - 10.1186/s41512-022-00135-9

M3 - SCORING: Journal article

C2 - 36384641

VL - 6

SP - 22

JO - Diagnostic and prognostic research

JF - Diagnostic and prognostic research

SN - 2397-7523

IS - 1

ER -