Association, prediction, generalizability: Cross-center validity of predicting tooth loss in periodontitis patients. (June 2021) - Schwendicke, F.; Arsiwala, L.T.; Krois, J.; Bäumer, A.; Pretzl, B.; Eickholz, P.; Petsos, H.; Kocher, T.; Holtfreter, B.; Graetz, C.
Abstract: Objectives: To predict patients' tooth loss during supportive periodontal therapy across four German university centers. Methods: Tooth loss in 897 patients in four centers (Kiel (KI) n = 391; Greifswald (GW) n = 282; Heidelberg (HD) n = 175; Frankfurt/Main (F) n = 49) during supportive periodontal therapy (SPT) was assessed. Our outcome was annualized tooth loss per patient. Multivariable linear regression models were built on data of 75 % of patients from one center and used for predictions on the remaining 25 % of this center and 100 % of data from the other three centers. The prediction error was assessed as root-mean-squared-error (RMSE), i.e., the deviation of predicted from actually lost teeth per patient and year. Results: Annualized tooth loss/patient differed significantly between centers (between median 0.00 (interquartile interval: 0.00, 0.17) in GW and 0.09 (0.00, 0.19) in F, p = 0.001). Age, smoking status and number of teeth before SPT were significantly associated with tooth loss (p < 0.03). Prediction within centers showed RMSE of 0.14−0.30, and cross-center RMSE was 0.15−0.31. Predictions were more accurate in F and KI than in HD and GW, while the center on which the model was trained had a less consistent impact. No model showed useful predictive values. Conclusion: While covariates were significantly associated with tooth loss in linear regression models, a clinically useful prediction was not possible with any of the models and generalizability was not given. Predictions were more accurate for certain centers. Clinical Relevance: Association should not be confused with predictive value: Despite significant associations of covariates with tooth loss, none of our models was useful for prediction. Usually, model accuracy was even lower when tested across centers, indicating low generalizability.