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 »  Home  »  Endodontic Articles 3  »  Relationship between number of proximal contacts and survival of root canal treated teeth
Relationship between number of proximal contacts and survival of root canal treated teeth
Introduction - Materials and methods.



D. J. Caplan, J. Kolker, E. M. Rivera & R. E. Walton
Department of Dental Ecology, University of North Carolina, Chapel Hill, NC, USA.
Departments of Operative Dentistry and Endodontics, University of Iowa, Iowa City, IA, USA.

Introduction.
As part of treatment planning, dentists frequently advise when endodontically involved teeth should be extracted rather than root canal treated (RCT) and restored. The decision depends on several factors, including periodontal status and structural integrity of the tooth, the desires of the patient, and the importance of the tooth in the overall treatment plan. Yet if a patient were to ask, ‘How long will I be able to keep the tooth if I elect to undergo root canal therapy?’ or ‘What factors affect my keeping the tooth in the long run, should I choose to save it?’, the provider’s response would be based heavily on anecdotal evidence. Because this treatment planning decision is common and consequences can be time-consuming and costly, there should be scientific evidence to support claims of longevity for RCT teeth. In addition, variables associated with tooth survival should be identified.
Few published articles address survival of RCT teeth (Meeuwissen & Eschen 1983, Sjögren et al. 1990, Jaoui et al. 1995). Variables related to loss of RCT teeth are even less understood; most studies have not employed multivariate regression analyses. One investigation (Eckerbom et al. 1992) reported that, amongst patients seen in a Swedish hospital dental clinic, RCT teeth with preoperative periapical periodontitis, a root filling >2 mm from the radiographic apex, or a screw-type post were more likely lost over a 5–7-year period than were RCT teeth without these features. Caplan & Weintraub (1997) reported that amongst patients enrolled continuously for 8 years in a single dental health maintenance organization in the Pacific North-West of the USA, five factors were predictive of RCT tooth loss over a 6–8-year period: older age, history of facial injury, missing non-third molars at the time of endodontic access, higher plaque level, and fewer than two proximal contacts (PCs) on the RCT tooth at access.
In the Caplan & Weintraub (1997) study, RCT teeth with two PCs at access were almost three times less likely to be extracted during follow-up than RCT teeth missing only a mesial and/or distal contact at access. In reaching their conclusion, the authors employed a case-control design and logistic regression analysis to develop predictive models of tooth loss; the number of PCs at access was the most predictive of all factors analyzed. The present investigation built upon that study by using a retrospective cohort design and proportional hazards regression to develop explanatory models of loss of RCT teeth. The objective was to test the hypothesis that the number of PCs at access is associated with improved survival of RCT teeth, controlling for important preaccess, endodontic and restorative factors.

Materials and methods.
The protocol was approved by the Committee for the Protection of Human Subjects at the University of Iowa College of Dentistry (COD), USA. Study data were collected in 1997 from existing records at the COD.
An existing treatment database identified all permanent teeth receiving root fillings between 1 July 1985 (the database’s first date of operation) and 31 December 1987. This interval allowed identification of an adequate number of cases with the longest potential follow-up. The list was restricted to patients with at least one visit to the COD in each two-year interval from 1985 to 1986 through 1995–96, resulting in 1089 teeth from 734 patients. Teeth were grouped by patient, and patients were listed in random order. A simple random sample of teeth was chosen by selecting the first 400 teeth on the list (corresponding to the first 280 patients).

Table 1. Collected variables and data sources

From computerized databases.
Age (years).
Sex.
a. Insurance/Medicaid billed for root canal therapy.
a. Provider type (for root canal therapy).
a. Clinic.

From treatment notes.
Tooth arch.
Tooth type.
Pulpal diagnosis.
a. Endodontic complication.
a. Time from access to obturation (days).
a. Time from obturation to foundation (days).
a. Intracoronal restoration.
a,b. Crown status (crowned initially after obturation versus crowned at access versus never crowned).
a. Endodontic retreatment/apicoectomy/root amputation.

From patient health questionnaire.
Heart/hypertension medication.
1 medication of any kind.
History of excessive bleeding.
Diabetes.
Interested in keeping teeth.
Ever had orthodontic treatment.
History of injury to mouth or jaws.
Grind or clench teeth.
Gums bleed when brushing teeth.

From preaccess periapical radiograph.
Tooth arch.
Tooth type.
Number of proximal contacts.
Caries.
Periapical lesion on any root.

From immediate postobturation periapical radiograph.
a. Root filling past apex of any root.
a. Root filling >2 mm short of apex in any root.
a. Root filling with lateral/apical voids in any root.
a. Value determined after endodontic access.
(other variables ascertainable prior to access).
b. Crown status at access from preaccess periapical radiograph; crown status after obturation from treatment notes.

Patient records, radiographs, and computerized databases were examined to ascertain variables with potential relationships to tooth loss (Table 1) and to verify that study inclusion criteria were satisfied. A total of 146 teeth did not satisfy study inclusion criteria, including those undergoing endodontic retreatment ( n = 50), those with no documentation of a permanent restoration after obturation ( n = 17), those with no recorded date of access ( n = 11), and those with a miscoded procedure or access date in the database ( n = 4). Teeth that were bridge abutments at access ( n = 28) were excluded because assignment of a value for the main exposure variable was not straightforward. Teeth that became bridge or overdenture abutments after obturation ( n = 30) were also excluded because the number of PCs at access did not represent their postobturation status with respect to stability or occlusion. Finally, third molars ( n = 6) were excluded, since they could not have two PCs. An additional 33 teeth could not be assigned a value for the main exposure variable due to missing radiographs ( n = 28) or charts ( n = 5). Data from the remaining 221 teeth (180 patients) were analyzed.
Proximal contacts were considered absent if the adjacent tooth was a root tip, missing or impacted. The outcome was ‘time to tooth loss’, with follow-up beginning on the date of access. For teeth that subsequently were extracted, follow-up ended on the extraction date. For teeth that were not extracted, follow-up ended on the data collection date. The most recent radiograph of the tooth space was examined to verify extraction (or lack thereof) as documented in the treatment notes.
Range checks were performed for each variable and data sources were re-examined to confirm values of potential outliers. Kaplan–Meier survival estimates for the RCT teeth were generated for the explanatory variables (Kleinbaum 1996), and crude associations between explanatory variables and tooth survival were evaluated via the Log-Rank test (Kleinbaum 1996) using SAS Version 6.12 for Windows (Cary, NC, USA). Multivariate Cox proportional hazards (PH) models (Kleinbaum 1996) then were developed to generate estimates of the association of interest whilst controlling for confounding factors. Because patients could contribute multiple teeth to the dataset, SUDAAN Release 7.11 for Windows (Research Triangle Institute, RTP, NC, USA) was used to obtain appropriate variance estimates (Caplan et al. 1999).
To be eligible for inclusion in multivariate models, covariates were required to have a moderately strong bivariate relationship with tooth survival (Log-Rank P < 0.20), no greater than a 90/10 split in their univariate frequency distributions, and no more than 5% missing values. Mean values were imputed for missing values of eligible covariates. Interaction terms were not tested, and inclusion of time-dependent covariates was not necessary, based on visual verification of PH assumptions using SAS log (–log survival) curves.
In developing the PH models, the first step was to obtain an unadjusted hazard ratio (HR) from a model containing only the main exposure variable. Next, the single covariate that most affected the parameter estimate of interest ( ) was added to the previous model, but only if it changed the main exposure by at least 10%. This process was repeated until the addition of no single covariate elicited a change in the main exposure of at least 10%, controlling for other variables in the model. At this point, included covariates were given the opportunity to be removed from the model, one at a time, if their removal did not elicit a change in the main exposure from the previous model by at least 10%. Allowing covariates to be removed from regression models permitted different combinations of covariates to be included. The goal was to generate the most parsimonious model for which the main exposure approximated that obtained from a model containing all eligible covariates.