H H Bruins1, R Koole, D E Jolly. 1. Department of Oral and Maxillofacial Surgery, University Medical Hospital, University of Utrecht. spec.dent@kmb.azu.nl
Abstract
OBJECTIVE: The proposed model was designed to function as a tool for the development and testing of evidence-based clinical guidelines for the pretherapy oral screening and dental management of patients with head and neck cancer. STUDY DESIGN: Methods of clinical decision analysis were used to analyze the decision dilemma and construct a decision algorithm and decision tree. The robustness of the model was tested by means of a probabilistic sensitivity analysis with second-order Monte Carlo simulations (n = 10.000). RESULTS: Clinical criteria for evaluating dental pathologic conditions and malignancy- and patient-related conditions were transformed in probability estimates. The tradeoffs between the benefits and drawbacks of the dental intervention were integrated into the model to identify the optimal option for dental intervention. The calculation process of "folding back and averaging out" the decision tree enabled the identification of the optimal options for dental intervention in four different pretherapy risk conditions. CONCLUSIONS: A priori testing of the proposed model with 95% confidence intervals suggests that it has a great potential for solving clinical dilemmas associated with pretherapy dental decision-making. In addition, it seems a useful tool for the development of evidence-based clinical guidelines. A posteriori clinical testing should further validate the model before its assimilation into clinical practice takes place.
OBJECTIVE: The proposed model was designed to function as a tool for the development and testing of evidence-based clinical guidelines for the pretherapy oral screening and dental management of patients with head and neck cancer. STUDY DESIGN: Methods of clinical decision analysis were used to analyze the decision dilemma and construct a decision algorithm and decision tree. The robustness of the model was tested by means of a probabilistic sensitivity analysis with second-order Monte Carlo simulations (n = 10.000). RESULTS: Clinical criteria for evaluating dental pathologic conditions and malignancy- and patient-related conditions were transformed in probability estimates. The tradeoffs between the benefits and drawbacks of the dental intervention were integrated into the model to identify the optimal option for dental intervention. The calculation process of "folding back and averaging out" the decision tree enabled the identification of the optimal options for dental intervention in four different pretherapy risk conditions. CONCLUSIONS: A priori testing of the proposed model with 95% confidence intervals suggests that it has a great potential for solving clinical dilemmas associated with pretherapy dental decision-making. In addition, it seems a useful tool for the development of evidence-based clinical guidelines. A posteriori clinical testing should further validate the model before its assimilation into clinical practice takes place.