LC risk score - development and evaluation of a scale for assessing the risk of developing long COVID

Mateusz Babicki; Mateusz Lejawa; Tadeusz Osadnik; Joanna Kapusta; Maciej Banach; Piotr Jankowski; Agnieszka Mastalerz-Migas; Żaneta Kałuzińska-Kołat; Damian Kołat; Michal Chudzik

doi:10.5114/aoms/187781

LC risk score - development and evaluation of a scale for assessing the risk of developing long COVID

Arch Med Sci. 2024 Apr 21;21(1):121-130. doi: 10.5114/aoms/187781. eCollection 2025.

Authors

Mateusz Babicki¹, Mateusz Lejawa², Tadeusz Osadnik², Joanna Kapusta³, Maciej Banach^{4

5}, Piotr Jankowski⁶, Agnieszka Mastalerz-Migas¹, Żaneta Kałuzińska-Kołat^{7

8}, Damian Kołat^{7

8}, Michal Chudzik^{6

9}

Affiliations

¹ Department of Family Medicine, Wroclaw Medical University, Wroclaw, Poland.
² Department of Pharmacology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, Poland.
³ Department of Internal Diseases, Rehabilitation, and Physical Medicine, Medical University of Lodz, Lodz, Poland.
⁴ Department of Preventive Cardiology and Lipidology, Medical University of Lodz, Lodz, Poland.
⁵ Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, USA.
⁶ Department of Internal Medicine and Geriatric Cardiology, Medical Centre for Postgraduate Education, Warsaw, Poland.
⁷ Department of Biomedicine and Experimental Surgery, Medical University of Lodz, Lodz, Poland.
⁸ Department of Functional Genomics, Faculty of Medicine, Medical University of Lodz, Lodz, Poland.
⁹ Department of Nephrology, Hypertension and Family Medicine, Medical University of Lodz, Lodz, Poland.

Abstract

Introduction: The aim of the study was to create a valuable practical tool for evaluating the risk of developing long COVID.

Material and methods: 1150 patients from the Polish STOP-COVID registry (PoLoCOV study) were used to develop the risk score. The patients were ill between 03/2020 and 04/2022. To develop a clinically useful scoring model, the LC risk score was generated using the machine learning-based framework AutoScore. Patient data were first randomised into a training (70% of output) and a test (30% of output) cohort. Due to the relatively small study group, cross-validation was used. Model predictive ability was evaluated based on the ROC curve and the AUC value. The result of the risk score for a given patient was the total value of points assigned to selected variables.

Results: To create the LC risk score, eight variables were ultimately selected due to their significance and clinical value. Female gender significantly contributed to higher final outcome values, with age range 40-49, body mass index < 18.5 kg/m², hospitalisation during active disease, arthralgia, myalgia as well as loss of taste and smell during infection, COVID-19 symptoms lasting at least 14 days, and unvaccinated status. The final predictive value of the developed LC risk score for a cut-off of 58 points was AUC = 0.630 (95% CI: 0.571-0.688) with sensitivity 39.80%, specificity 85.1%, positive predictive value 80.8%, and negative predictive value 47.3%.

Conclusions: The LC risk score may be a practical and undemanding utility that employs basic sociodemographic data, vaccination status, and symptoms during COVID-19 to assess the risk of long COVID.

Keywords: AutoScore; long COVID; post-COVID; risk score; risk stratification.