Quality of logistic regression reporting in studies of tooth survival after periodontal treatment

Faggion, Clóvis Mariano; Tu, Yu–Kang

doi:10.1111/jcpe.12318

Cited by 8 publications

(6 citation statements)

References 45 publications

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“…Recent literature has shown that loss of molar teeth is related to smoking status, lack of compliance and furcation involvement or baseline bone loss in patients treated for periodontitis and under supportive periodontal therapy (19,20). A recent systematic review showed that the reported logistic regression models for studies assessing risk factors for tooth loss in patients who have received periodontal treatment are not optimal (21). The authors also suggest that survival analysis should be considered in future research.…”

Section: Discussionmentioning

confidence: 99%

Tooth Loss in Patients under Periodontal Maintenance in a Private Practice: A Retrospective Study

et al. 2017

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The aim of the present study was to evaluate the incidence and causes of tooth loss in periodontal subjects from a private practice in Brazil. Two trained examiners extracted data from the records of subjects who sought periodontal treatment from 1980 to 2013. Only records of patients who completed the non-surgical periodontal treatment and had at least one visit for maintenance were included. Data were analyzed by chi-square test, Student's t-test, Kaplan-Meier survival curve and Cox regression. A total of 3,319 records were reviewed and 737 records included (58.6% women, mean age of 46.6±13.0 years at the beginning of the treatment). Maintenance period ranged from 1 to 33 years (7.4±6 years). During this period, 202 individuals (27.4%) lost 360 teeth, 47.5% of losses within the first five years (n=171). Non-compliers lost more teeth than compliers (p<0.001), respectively 211 and 149 teeth. Regarding reasons of tooth loss, 84 individuals lost 38% of the teeth from periodontal disease progression (n=137). Survival analysis showed that most patients lost only one tooth from periodontal disease, and differences in the survival rates between compliers and non-compliers were observed following the second tooth loss. Approximately one-third of tooth losses was related to periodontal disease progression, and there was stability in time of the proportion of losses from disease progression and other reasons. Therefore, it is possible to conclude that compliant patients in a private practice lose fewer teeth than do non-compliers. Among compliers, periodontal disease progression was not the main cause of tooth loss.

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Section: Discussionmentioning

confidence: 99%

Tooth Loss in Patients under Periodontal Maintenance in a Private Practice: A Retrospective Study

et al. 2017

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“…, Faggion et al. ). Our criteria to select the publications were sample size ≥30 patients, at least 5 years of follow‐up and reliable data on either TL or TLPD.…”

Section: Methodsmentioning

confidence: 98%

Predictors of tooth loss due to periodontal disease in patients following long‐term periodontal maintenance

Martinez-Canut

2015

J Clinic Periodontology

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AimTo analyse patient‐related factors (PRFs) and tooth‐related factors (TRFs) associated with tooth loss due to periodontal disease (TLPD) in patients undergoing periodontal maintenance (PM).Material and MethodsThe sample consisted of 500 patients (mean follow‐up of 20 years). The impact of PRFs on TLPD was analysed with Poisson regression and multivariate logistic regression. The simultaneous impact of PRFs and TRFs was analysed with multilevel logistic regression and Cox regression.ResultsTooth loss due to periodontal disease was 515 (mean 0.05 patient/year). The significant PRFs were severe periodontitis (p < 0.001), aggressive periodontitis (p < 0.001), smoking (p = 0.018), bruxism (p = 0.022) and baseline number of teeth (p = 0.001). These PRFs allowed characterizing patients losing more teeth. The whole TRFs analysed were significant, depending on the type of tooth and the category of each factor (e.g. mobility 0, 1, 2, and 3). The significant PRFs increased the risk of TLPD by 2 to 3 times while TRFs increased the risk to a higher extent. Mobility was the main TRF.ConclusionsSevere periodontitis, aggressive periodontitis, smoking, bruxism and baseline number of teeth, as well as the whole TRFs analysed, were associated with TLPD.

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“…This analysis made it possible to select those variables more clearly associated with TLPD, which are also the ones that are most consistently found to be associated with TLPD in the literature, with fairly homogeneous relative risks. Thirty‐two studies of predictors of TLPD in patients following PM for more than 5 years were selected according to previously defined selection criteria (Chambrone et al., ; Faggion, Chambrone, & Tu, ). These are presented in Supporting Information (Table and Appendix ).…”

Section: Methodsmentioning

confidence: 99%

Introduction of a prediction model to assigning periodontal prognosis based on survival time

Martinez-Canut¹,

Alcaraz

Álvarez-Nóvoa

et al. 2017

J Clinic Periodontology

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AimsTo develop a prediction model for tooth loss due to periodontal disease (TLPD) in patients following periodontal maintenance (PM), and assess its performance using a multicentre approach.Material and methodsA multilevel analysis of eleven predictors of TLPD in 500 patients following PM was carried out to calculate the probability of TLPD. This algorithm was applied to three different TLPD samples (369 teeth) gathered retrospectively by nine periodontist, associating several intervals of probability with the corresponding survival time, based on significant differences in the mean survival time. The reproducibility of these associations was assessed in each sample (One‐way ANOVA and pairwise comparison with Bonferroni corrections).ResultsThe model presented high specificity and moderate sensitivity, with optimal calibration and discrimination measurements. Seven intervals of probability were associated with seven survival time and these associations contained close to 80% of the cases: the probability predicted the survival time at this percentage. The model performed well in the three samples, as the mean survival time of each association were significantly different within each sample, while no significant differences between the samples were found in pairwise comparisons of means.ConclusionsThis model might be useful for predicting survival time in different TLPD samples.

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Quality of logistic regression reporting in studies of tooth survival after periodontal treatment

Abstract: The reporting of logistic regression models in studies assessing risk factors for tooth loss in patients who have received periodontal treatment is not optimal.

Cited by 8 publications

References 45 publications

Tooth Loss in Patients under Periodontal Maintenance in a Private Practice: A Retrospective Study

Tooth Loss in Patients under Periodontal Maintenance in a Private Practice: A Retrospective Study

Predictors of tooth loss due to periodontal disease in patients following long‐term periodontal maintenance

Introduction of a prediction model to assigning periodontal prognosis based on survival time

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