Introduction:
Objective: To create a valuable practical tool for evaluating the risk of LC development.

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) cohorts. Due to 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 long COVID 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, BMI <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:
Conclusions: The LC risk score might 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.