Long-term Evaluation of Treatment Planning Decisions for Nonhealing Endodontic Cases by Different Groups of Practitioners

Why do dentists refrain from intervention in cases of persistent asymptomatic apical periodontitis in root canal filled teeth? An interview study among general dental practitioners

AIM

To explore the context in which general dental practitioners (GDPs) decide to refrain from further treatment, that is, ortho- or retrograde retreatment or extraction of a root canal filled tooth with persistent asymptomatic apical periodontitis (PAAP).

METHODOLOGY

Fifteen GDPs were strategically selected for in-depth interviews. The informants were encouraged to describe in their own words and in as much detail as possible, the three most recent patient cases of PAAP of a root canal filled tooth, in which they decided to refrain from further treatment. The interviews were recorded digitally and transcribed verbatim. The collected material was analysed according to Qualitative Content Analysis with an inductive approach.

RESULTS

A pattern of varying degrees of uncertainty associated with the decision process was identified. The motives to refrain intervention had great diversity. The result from analysis of the qualitative data was formulated in an overall theme 'Between doubt and certainty in a complex clinical context' covering the latent content. The first main category covering the manifest content was 'The continuum of confidence' with three subcategories 'Experienced uncertainty', 'Reluctant approval' and 'At ease with refraining' illustrating the feelings and attitudes experienced by the informants. The second category was 'In support of acceptance' with three subcategories 'Patient's autonomy, risks and cost-benefits', 'Emotional aspects' and 'Relieving measures' representing the reasons for refraining from intervention.

CONCLUSIONS

The decision to refrain from further treatment, that is, ortho- or retrograde retreatment or extraction of a root canal filled tooth with PAAP was made with some measure of confidence, combined with compensatory strategies to support the decision, taking into account not only values beneficial to the patient and awareness of limited external resources, but also factors related to the informants' personal preferences, convenience, concerns, ambition and emotions.

Machine learning models for prognosis prediction in endodontic microsurgery

OBJECTIVES

This study aimed to establish and validate machine learning models for prognosis prediction in endodontic microsurgery, avoiding treatment failure and supporting clinical decision-making.

METHODS

A total of 234 teeth from 178 patients were included in this study. We developed gradient boosting machine (GBM) and random forest (RF) models. For each model, 80% of the data were randomly selected for the training set and the remaining 20% were used as the test set. A stratified 5-fold cross-validation approach was used in model training and testing. Correlation analysis and importance ranking were conducted for feature selection. The predictive accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, and the area under the curve (AUC) of receiver operating characteristic (ROC) curves were calculated to evaluate the predictive performance.

RESULTS

There were eight important predictors, including tooth type, lesion size, type of bone defect, root filling density, root filling length, apical extension of post, age, and sex. For the GBM model, the predictive accuracy was 0.80, with a sensitivity of 0.92, specificity of 0.71, PPV of 0.71, NPV of 0.92, F1 of 0.80/0.80, and AUC of 0.88. For the RF model, the accuracy was 0.80, with a sensitivity of 0.85, specificity of 0.76, PPV of 0.73, NPV of 0.87, F1 of 0.79/0.81, and AUC of 0.83.

CONCLUSIONS

The trained models were developed by eight common variables, showing the potential ability to predict the prognosis of endodontic microsurgery. The GBM model outperformed the RF model slightly on our dataset.

CLINICAL SIGNIFICANCE

Clinicians can use machine learning models for preoperative analysis in endodontic microsurgery. The models are expected to improve the efficiency of clinical decision-making and assist in clinician-patient communication.