The prevalence, predictive factors, and classification of intrapulpal cracks in maxillary premolars requiring endodontic treatment

• Main Author: Krygowski, Sarah
• Format: Others
• Published: VCU Scholars Compass 2015
• Subjects: Crack; probing; transillumination; intrapulpal; premolars; restorations; Endodontics and Endodontology
• Online Access: http://scholarscompass.vcu.edu/etd/3869; http://scholarscompass.vcu.edu/cgi/viewcontent.cgi?article=4736&context=etd

Cracked teeth may be difficult to diagnose. Craze lines rarely become symptomatic or require treatment. Cracks in the enamel and dentin alone may or may not become symptomatic and require restorative treatment. However, cracks extending into the enamel, dentin, and pulp chamber provide an avenue for bacteria to establish infection and this commonly results in symptoms and the need for endodontic and restorative treatment. The published endodontic literature has limited information regarding the prevalence or predictive factors for cracks extending into the pulp chamber of teeth. The purpose of this study was to determine the prevalence and classification of intrapulpal cracks in maxillary premolars and to identify factors that may aid in diagnosing the existence and extent of a crack. The cracks were classified according to the Intrapulpal Crack Classification System proposed by Detar in 2014. All maxillary premolar teeth treatment planned for non-surgical root canal therapy (NSRCT) or retreatment (RETX) at Virginia Commonwealth University (VCU) Graduate Endodontic Practice from January 2014 through February 2015 were included in the study after obtaining patient consent. Teeth were examined visually, stained, and examined microscopically for the presence of an intrapulpal crack. Demographic information, subjective data associated with the chief complaint, objective results of diagnostic testing (percussion, palpation, bite stick test, transillumination, probing depths), existing restorations, pulpal diagnosis, and periapical diagnosis were analyzed using chi-square and multiple logistic regression (P