| Autor: |
Deforth, Manja, Gebhard, Caroline E, Bengs, Susan, Buehler, Philipp K, Schuepbach, Reto A, Zinkernagel, Annelies S, Brugger, Silvio D, Acevedo, Claudio T, Patriki, Dimitri, Wiggli, Benedikt, Twerenbold, Raphael, Kuster, Gabriela M, Pargger, Hans, Schefold, Joerg C, Spinetti, Thibaud, Wendel-Garcia, Pedro D, Hofmaenner, Daniel A, Gysi, Bianca, Siegemund, Martin, Heinze, Georg, Regitz-Zagrosek, Vera, Gebhard, Catherine, Held, Ulrike |
| Přispěvatelé: |
University of Zurich |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Diagnostic and prognostic research. 6(1) |
| ISSN: |
2397-7523 |
| Popis: |
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. Predictors were selected out of twelve candidate predictors based on three reliable 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 data. The same predictors were selected with the ABE and ABESS variable selection method. The final prognostic model was based on the ABE and ABESS selected predictors. The corresponding model discrimination in the validation cohort 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). A patient’s probability of developing REST symptoms \hat{y} = exp(S) / (1 + exp(S)) can be calculated with S = −4.947 + 0.349 × number of acute COVID-19 symptoms + 0.339 × severity of acute COVID-19 ward + 1.737 × severity of acute COVID-19 intensive or intermediate care + 0.128 × feeling of stress at home + 0.013 × age at presentation + 0.352 × female sex + 0.346 × presence of at least one cardiovascular risk factor − 0.097 × responsible for childcare/family member + 0.022 × body mass index, with feeling of stress at home ranges from 1 (no stress) to 10 (maximum stress) and responsibility for childcare/family member ranges from 1 (no responsibility/not applicable) to 6 (full responsibility). Conclusion: The proposed model is reliable to identify COVID-19 infected patients at risk for REST symptoms. Before implementing the prognostic model in daily clinical practice, the conduct of an impact study is recommended. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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