IADR Abstract Archives

Cross-Center Validity and Generalizability in Predicting Tooth Loss in Periodontitis

Objectives: An increasing number of teeth are being retained in a growing population of older adults, resulting in expected increases in number of periodontally-affected teeth. The knowledge of an individual’s predicted probability of losing periodontally-affected teeth would be instrumental in formulating prophylactic treatment plans. Hence, we aimed to predict tooth loss during supportive periodontal therapy (SPT) across four German university centers.
Methods: In our entire cohort (n=897), mean (standard deviation) age was 45 (11) years, with 59% females. Tooth loss in four centers (Kiel(KI) n=391; Greifswald(GW) n=282; Heidelberg(HD) n=175; Frankfurt/Main(F) n=49) during SPT was assessed. Our outcome was annual tooth loss per patient. Multivariable linear regression models were trained on data from 75% patients from one center and used for predictions on the remaining 25% patients of this center and 100% patients from the remaining centers. This procedure was performed for each center. The models’ prediction error was assessed via root-mean-squared-error (RMSE) i.e., deviation of predicted estimates from the observed data.
Results: Annual tooth loss per patient differed across centers (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 present before SPT were associated with tooth loss (p<0.03) (Table 1). Predictions within centers showed RMSE from 0.14 to 0.30, and cross-center RMSE ranged from 0.15 to 0.31, indicating low generalizability (Table 2). Predictions had higher accuracy in F and KI than in HD and GW, while the center on which the model was trained had a less consistent impact on prediction estimates. None of the models evaluated showed useful predictive value.
Conclusions: Associations should be distinguished from predictions. Despite significant associations of covariates with annual tooth loss, a clinically useful prediction was not possible, highlighting the need for further research to identify predictors of periodontally-affected tooth loss in adults receiving periodontal treatment.

2021 Continental European and Scandinavian Divisions Meeting (Brussels, Belgium, Hybrid)
Brussels, Belgium, Hybrid
2021
0043
Periodontal Research-Diagnosis/Epidemiology
  • Arsiwala, Lubaina  ( Charité – Universitätsmedizin Berlin , Berlin , Berlin , Germany )
  • Graetz, Christian  ( University of Kiel , Kiel , Germany )
  • Schwendicke, Falk  ( Charité – Universitätsmedizin Berlin , Berlin , Berlin , Germany )
  • Krois, Joachim  ( Charité – Universitätsmedizin Berlin , Berlin , Berlin , Germany )
  • Bäumer-könig, Amelie  ( University Hospital Heidelberg , Heidelberg , Germany )
  • Pretzl, Bernadette  ( University Hospital Heidelberg , Heidelberg , Germany )
  • Eickholz, Peter  ( Johann Wolfgang Goethe-University Frankfurt/Main , Frankfurt/Main , Germany )
  • Petsos, Hari  ( Johann Wolfgang Goethe-University Frankfurt/Main , Frankfurt/Main , Germany )
  • Kocher, Thomas  ( University Medicine Greifswald , Greifswald , Germany )
  • Holtfreter, Birte  ( University Medicine Greifswald , Greifswald , Germany )
  • NONE
    Oral Session IN PERSON
    Periodontology
    Friday, 09/17/2021 , 01:30PM - 03:30PM
    Table 1. Linear regression estimates (95% confidence interval) for the association of covariates with annual tooth loss per patient, in the whole cohort (n=897)
    CovariateEstimate95% LCI95% UCIp-value
    Male (ref.: female)-0.02-0.050.010.28
    Age (years)<0.01<0.001<0.01<0.01
    Former smoker (ref.: non-smoker)-0.01-0.040.030.64
    Current smoker (ref.: non-smoker)0.080.030.12<0.001
    Diabetes (ref.: no)0.07-0.010.150.07
    Aggressive periodontitis (ref.: CP)0.02-0.030.070.39
    Number of teeth before SPT0.00-0.010.000.02
    Abbreviations: LCI= lower confidence interval, UCI=upper confidence interval, ref=reference group, CP=chronic periodontitis, SPT= supportive periodontal therapy Significant associations (p<0.05) are written in bold.
    Table 2. Linear regression prediction models’ results
     Models built on data from 
    Estimates (95% CI)KielGreifswaldHeidelbergFrankfurt/Main 
    Male (ref.: female)-0.02 (-0.06-0.02)0.03 (-0.05-0.10)-0.11 (-0.23-0.01)-0.01 (-0.11-0.09)
    Age (years)0.00 (0.00-0.01)0.00 (0.00-0.00)0.00 (0.00-0.01)0.00 (-0.01-0.00)
    Ever smoker (ref.: never)0.04 (0.01-0.06)-0.03 (-0.08-0.02)0.09 (0.03-0.16)0.04 (-0.04-0.12)
    Diabetes (ref.: no)-0.03 (-0.17-0.11)0.18 (0.03-0.32)-0.01 (-0.38-0.35)0.11 (-0.03-0.26)
    Aggressive periodontitis (ref.: CP)0.03 (-0.03-0.09)Not applicable0.02 (-0.14-0.18)-0.09 (-0.28-0.10)
    Number of teeth before SPT0.00 (-0.01-0.00)0.00 (-0.01-0.01)0.00 (-0.02-0.01)0.00 (-0.02-0.02)
     
    Prediction error (RMSE (95% CI)) for models trained in…Kiel
    0.17 (0.16-0.18)
    Greifswald
    0.25 (0.23-0.27)
    Heidelberg
    0.25 (0.22-0.29)
    Frankfurt/Main
    0.14 (0.13-0.16)
    Mean RMSE for models tested on…..
    Kiel0.140.170.170.170.16
    Greifswald0.270.300.290.280.29
    Heidelberg0.290.310.240.300.29
    Frankfurt/Main0.160.150.150.160.16
    Mean RMSE for models trained in….0.220.230.210.23 
    Models were developed on 75% of the data available from one center and tested on 25% of the same center and 100% of data from the other centers. Abbreviations: ref=reference level, CP=chronic periodontitis, SPT= supportive periodontal therapy, RMSE=Root Mean Squared Error; lower RMSE values represent better model performance Mean RMSE on data from each center (right column) as well as for each model across centers (bottom line) are shown