Furcation involvement and tooth loss: A registry-based retrospective cohort study

Effects of periodontal treatment on periodontal status in Finland: a register-based study

OBJECTIVE

This register-based follow-up study investigated periodontal status after periodontal treatment (PT) based on need following oral health examination (OHE).

MATERIALS AND METHODS

A total of 42,533 adults aged 18-89 years receiving OHE in the public oral health clinics of the City of Helsinki in 2009 were included. Dentists recorded periodontal status by the Community Periodontal Index (CPI), and determined the individual recall interval (IRI). Follow-up OHE between 2010 and 2015 was performed for 16,040 adults based on IRI or later. Outcome of interest was change of CPI during follow-up and was modelled with proportional odds model for each sextant separately. Results were reported as odds ratios (ORs).

RESULTS

Signs of periodontal disease were present in 95% of the study population. Symptoms of periodontitis (CPI score 3 or 4) were observed in 24% of patients. In models, PT indicated better outcome in all six sextants and in sextant 5 after one treatment (OR 5.05, 95% confidence interval [CI] 4.53-5.63). A poorer outcome was observed in patients with diabetes or severe mental disorders and in men.

CONCLUSIONS

The study population had a high prevalence of periodontal diseases. Men and patients with diabetes or severe mental disorders should be specifically targeted by dentists.

Microbiological and molecular profile of furcation defects in a population with untreated periodontitis

AIM

To describe the microbiological composition of subgingival dental plaque and molecular profile of gingival crevicular fluid (GCF) of periodontal furcation-involved defects.

MATERIALS AND METHODS

Fifty-seven participants with periodontitis contributed with a degree II-III furcation involvement (FI), a non-furcation (NF) periodontal defect and a periodontally healthy site (HS). Subgingival plaque was analysed by sequencing the V3-V4 region of the 16S rRNA gene, and a multiplex bead immunoassay was carried out to estimate the GCF levels of 18 GCF biomarkers. Aiming to explore inherent patterns and the intrinsic structure of data, an AI-clustering method was also applied.

RESULTS

In total, 171 subgingival plaque and 84 GCF samples were analysed. Four microbiome clusters were identified and associated with FI, NF and HS. A reduced aerobic microbiota (p = .01) was detected in FI compared with NF; IL-6, MMP-3, MMP-8, BMP-2, SOST, EGF and TIMP-1 levels were increased in the GCF of FI compared with NF.

CONCLUSIONS

This is the first study to profile periodontal furcation defects from a microbiological and inflammatory standpoint using conventional and AI-based analyses. A reduced aerobic microbial biofilm and an increase of several inflammatory, connective tissue degradation and repair markers were detected compared with other periodontal defects.

Periodontal conditions of teeth adjacent to dental implants with or without peri-implantitis after non-surgical therapy in patients treated for periodontitis: A retrospective study

OBJECTIVES

To retrospectively assess the periodontal conditions of teeth adjacent to and contralateral to implants presenting with or without peri-implantitis, following non-surgical periodontal and peri-implant mechanical therapy.

MATERIALS AND METHODS

One hundred and one patients with existing dental implants and chronic periodontitis, who underwent non-surgical periodontal and peri-implant mechanical therapy, were included. The periodontal clinical probing depth (PPD), gingival recession (GR), and bleeding on probing (BOP) were recorded at six sites around the adjacent (Adj-) teeth and the contralateral (CL-) teeth relative to the implant. The potential factors influencing the periodontal conditions of 316 teeth were analyzed by multivariate linear regression models with generalized estimating equation methods and α = .05.

RESULTS

The PPD of Adj-teeth was significantly different from that of CL-teeth before and after non-surgical therapy when the implant was diagnosed with peri-implantitis (PI) (p < .05). The PPD of teeth was shown to be affected by neighboring implants diagnosed with peri-implantitis (β = .825 mm, p < .001), teeth adjacent to implants (β = .245 mm, p = .004), a molar tooth type (β = .435 mm, p = .019), and non-surgical therapy (β = -.522 mm, p < .001).

CONCLUSIONS

Relatively compromised periodontal conditions at Adj-teeth after non-surgical PI therapy were detected. Therefore, clinicians should be aware that non-surgical therapy may be less successful at teeth adjacent to implants with PI.

Investigation of the furcation morphology of permanent mandibular first molars by using micro-computed tomography

BACKGROUND

To investigate the anatomic features of the root furcation of permanent mandibular first molars.

METHODS

A total of 50 extracted mandibular first molars (25 two-rooted and 25 three-rooted) were collected and scanned using micro-computed tomography. The digital models of teeth and root canal systems were reconstructed three-dimensionally. The tooth models were displayed in parallel projection mode from buccal and distal views. Screenshots were captured and subsequently analyzed using Image-Pro Plus 6.0 software after calibration. The furcation angle, root trunk length, maximum depth and level of distal root concaves of mesial roots, and length of enamel projections were measured, and the furcation types (classified into type V, type U and type W) were detected. Statistical analysis was performed using the Shapiro-Wilk's test, one-way analysis of variance, Student's t-test and Chi-square test.

RESULTS

The mean furcation angle between the distobuccal (DB) and distolingual (DL) roots (in distal view) was the greatest (59.2°), whereas the furcation angle between the mesial and DL roots (in buccal view) was the smallest (25.4°) among the four furcation angles (all p < 0.05). Regarding the furcation types, bucco-lingual root trunk length, maximum depth and site of the distal root concavities, and enamel projection length, no significant differences were detected between the three- and two-rooted molar groups (all p > 0.05). The frequency of type V was the highest (54.0%), followed by type U (26.0%), and type W had the lowest occurrence rate (20.0%). The mean length of distal root trunk in the three-rooted mandibular molars was significantly greater than that of the buccal/lingual one (3.7 mm vs. 3.0 mm, p < 0.01). The maximum depth of the distal concavities of the mesial roots was on average 0.66 ± 0.19 mm, and the site was located at an average of 2.8 ± 1.3 mm below furcation. The mean length of buccal enamel projections was significantly longer than that of lingual ones (3.1 mm vs. 0.7 mm, p < 0.01).

CONCLUSIONS

The furcation anatomy of the mandibular first molar is complex, and the presence of the DL root may further complicate its topography. A thorough understanding of these anatomic features is essential for successful periodontal treatment.

Tooth- and implant-related prognostic factors in treatment planning

Following a comprehensive patient examination, including the assessment of periodontal and peri-implant diseases as well as considering the patient's needs, a pretherapeutic prognosis for each tooth and implant is given. Teeth and implants with a secure pretherapeutic prognosis require simple procedures and may be regarded as secure abutments for function and with a doubtful pretherapeutic prognosis usually need a comprehensive therapy. Such teeth and implants must be brought into the category with a secure prognosis by means of additional therapy such as endodontic, restorative, and surgical procedures. Teeth and implants with a hopeless pretherapeutic prognosis should be extracted/explanted during the initial phase of cause-related therapy (i.e., infection control). For example, teeth with vertical root fracture or unrestorable caries and implants with mobility or unrestorable malposition fall into the category of hopeless units. The primary goal of periodontal and peri-implant therapy should be to arrest disease progression. The latest consensus statement highlights that periodontitis can be successfully controlled and treated teeth can be retained for life. Nevertheless, for patients with uncontrolled contributing factors, the endpoints might not always be achievable, and low disease activity may be an acceptable therapeutic goal. Similarly, the management of peri-implantitis frequently requires surgical intervention following nonsurgical therapy due to incomplete treatment outcomes. Different surgical modalities can be effective and lead to significant improvement; however, achieving complete resolution of peri-implantitis is challenging, not always predictable, and can depend on multiple baseline factors. Therefore, this review aims at summarising available evidence on the rationale for incorporating systemic, lifestyle-related, clinical, and radiographic prognostic factors into treatment planning of patients diagnosed with periodontal and peri-implant diseases.

Periodontal pockets: Predictors for site-related worsening after non-surgical therapy-A long-term retrospective cohort study

AIM

To evaluate site-related changes in periodontal pocket depth (PPD) after non-surgical periodontal therapy and to identify predictors for PPD changes in a retrospective patient data analysis.

MATERIALS AND METHODS

PPD, clinical attachment level, bleeding on probing, tooth mobility (TM), furcation involvement (FI), abutment status, adherence to supportive periodontal care (SPC) and SPC follow-ups were obtained from fully documented patient data before periodontal therapy (baseline, T0), after active periodontal therapy (APT, T1) and during SPC (T2). PPD changes were classified into deteriorated or unchanged/improved at the site level. Multi-level logistic regression analysis was performed to identify factors influencing PPD changes during SPC.

RESULTS

This retrospective study included 51 females and 65 males (mean T0 age: 54.8 ± 10.1 years, 25 smokers, 12 diabetics) suffering from Stage III/IV periodontitis. Evaluation outcome: T0/16,044 sampling sites/2674 teeth; T1/15,636/2606; T2/14,754/2459. During 9.0 ± 2.3 years SPC, PPD decreased (-1.33 ± 0.70 mm) by 21.8% of the sites, remained unchanged by 41.4% and increased (1.40 ± 0.78 mm) by 36.8%. Distopalatal FI (p < .001, odds ratio [OR]: 0.252, 95% confidence interval [CI] for OR: 0.118-0.540), residual pockets (p < .001, OR: 0.503, 95% CI: 0.429-0.590) and TM Degrees I-III (Degree I: p = .002, OR: 0.765, 95% CI: 0.646-0.905; Degree II: p = .006, OR: 0.658, 95% CI: 0.489-0.886; Degree III: p = .023, OR: 0.398, 95% CI: 0.180-0.879) correlated significantly with increasing PPD.

CONCLUSIONS

Over 75% of PPD remained unchanged or increased during SPC. Distopalatal FI, TM Degrees I-III and residual pockets after APT lead to worsening of periodontal pockets.

Identifying predictors of the tooth loss phenotype in a large periodontitis patient cohort using a machine learning approach

OBJECTIVES

This study aimed to identify predictors associated with the tooth loss phenotype in a large periodontitis patient cohort in the university setting.

METHODS

Information on periodontitis patients and nineteen factors identified at the initial visit was extracted from electronic health records. The primary outcome is tooth loss phenotype (presence or absence of tooth loss). Prediction models were built on significant factors (single or combinatory) selected by the RuleFit algorithm, and these factors were further adopted by regression models. Model performance was evaluated by Area Under the Receiver Operating Characteristic Curve (AUROC) and Area Under the Precision-Recall Curve (AUPRC). Associations between predictors and the tooth loss phenotype were also evaluated by classical statistical approaches to validate the performance of machine learning models.

RESULTS

In total, 7840 patients were included. The machine learning model predicting the tooth loss phenotype achieved AUROC of 0.71 and AUPRC of 0.66. Age, periodontal diagnosis, number of missing teeth at baseline, furcation involvement, and tooth mobility were associated with the tooth loss phenotype in both machine learning and classical statistical models.

CONCLUSIONS

The rule-based machine learning approach improves model explainability compared to classical statistical methods. However, the model's generalizability needs to be further validated by external datasets.

CLINICAL SIGNIFICANCE

Predictors identified by the current machine learning approach using the RuleFit algorithm had clinically relevant thresholds in predicting the tooth loss phenotype in a large and diverse periodontitis patient cohort. The results of this study will assist clinicians in performing risk assessment for periodontitis at the initial visit.