Résumé

<p>ABSTRACT<br>Context: Alveolar osteitis (AO) is a clinical complication post-<br>dental extraction from inflammatory or infectious origin. Poorly&nbsp;<br>designed studies with different statistical biases and the poorly&nbsp;<br>conducted analysis make the identification of risk factors&nbsp;<br>contradictory. Present study was conducted to identify the risk&nbsp;<br>factors of the occurrence of alveolar osteitis (AO).<br>Settings and Design: Descriptive and Prospective study done&nbsp;<br>in Kinshasa University, Faculty of Medicine.<br>Methods and Materials: The Bayesian model, using a&nbsp;<br>qualitative method, had invited eight experts to reflect on the&nbsp;<br>alveolar osteitis (AO.).A subjective Bayesian model (SBM) was&nbsp;<br>constructed by the selection, interview of the experts and&nbsp;<br>training by Delphi method. Brainstorming technical has helped&nbsp;<br>an elaboration of 48 factors selected by expertsfrom the&nbsp;<br>literature.The nominal group technical, allowed to the experts&nbsp;<br>to regroup a list of seven independent factors from 48 factors&nbsp;<br>selected.&nbsp;<br>Results: The Pre-existent infections factor (F3) was most&nbsp;<br>occurrence with 614 cases of AO ( 98%), followed by the bad&nbsp;<br>oral and dental hygiene factor (F1) with 572 cases of AO&nbsp;<br>(84%), Inexperienced practitioner factor (F6) with 485 cases of&nbsp;<br>AO (78%. Preoperative infection factor (F4=454; 73%) and&nbsp;<br>followed by the post-operative infection factor (F7=370 cases&nbsp;<br>of AO; 59%). The lack of asepsis factor (F5) was the most less&nbsp;<br>implicated in the occurrence of AO with 57 cases (9%).<br>Conclusions: The identification of risk factors by the Bayesian&nbsp;<br>model can help practitioners to take primary preventive&nbsp;<br>measures in order to reduce the morbidity of alveolar osteitis in&nbsp;<br>our context.<br>Keywords: Alveolar Osteitis (AO.) Risk Factors, Occurrence,&nbsp;<br>Bayesian Mode</p>