Predictors of long‐term outcomes in patients undergoing periodontal maintenance

Martinez-Canut, Pedro; Andrés, L. Rodríguez; Romero, Antonio

doi:10.1111/jcpe.12730

Cited by 27 publications

(49 citation statements)

References 68 publications

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“…This probability clearly differed depending on the type of tooth, as it was previously shown (Martinez‐Canut, Alcaraz, Alcaraz Jr. et al, 2017). …”

Section: Resultssupporting

confidence: 58%

“…Treatment rendered and periodontal maintenance programme has been previously described (Martinez‐Canut, 2015; Martinez‐Canut, Llobell & Romero, 2017). In brief, these patients received similar treatment and complied with PM every 4 months.…”

Section: Methodsmentioning

confidence: 99%

“…This index was assessed by two independent examiners/the authors, utilizing the baseline clinical and radiological records of each patient, as has been previously described (Martinez‐Canut, Llobell & Romero, 2017). Inter‐examiner agreement was verified (Kappa statistics).…”

Section: Methodsmentioning

confidence: 99%

“…The first approach attempted to characterize the baseline status of patients that lose more teeth during PM (Fardal, Grytten, Martin, Houlihan, & Heasman, 2016; Martinez‐Canut, 2015; Martinez‐Canut, Llobell, & Romero, 2017; Tonetti, Muller‐Campanile, & Lang, 1998; Wasserman & Hirschfeld, 1988); the second approach focused on assessing the risk of disease progression and the resulting tooth loss (Lang & Tonetti, 2003; Lindskog et al., 2010; Page et al., 2002; Page, Krall, Martin, Mancl, & Garcia, 2002). The tools developed for this purpose have been assessed in longitudinal studies, their capability to identify patients with different levels of risk being demonstrated (Lang, Suvan, & Tonetti, 2015).…”

Section: Introductionmentioning

confidence: 99%

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A comprehensive approach to assigning periodontal prognosis

Martinez-Canut

Llobell

2018

J Clinic Periodontology

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AimThe purpose of this retrospective case series study was to evaluate the usefulness and performance of a comprehensive approach to assigning periodontal prognosis by assessing the risk of tooth loss due to periodontal disease (TLPD) and estimate the survival time (ST) of periodontally compromised teeth (PCT).Material and MethodsThe Long‐Term Outcome (LTO) index was utilized to assess the risk of TLPD, at baseline, in 100 patients following periodontal maintenance for a mean 24.7 year (±2.4). A TLPD prediction model was utilized to assign ST to PCT. The performance of the TLPD prediction based on both parameters was assessed.ResultsThe mean TLPD rate increased as the LTO index increased (Kruskal–Wallis p < .001). The percentage of TLPD increased as the ST decreased, with significantly differences between the LTO categories <4 and ≥4 for all ST intervals (Mann–Whitney p < .001 to p .021).Only 21% of PCT were lost. This percentage was 58% for teeth assigned the shortest ST and increased to 71% and 88% for these teeth in LTO ≥4 patients.ConclusionsThis approach might be useful to predict TLPD in patients with an initial higher risk of TLPD while it was not useful in patients with lower risk of TLPD.

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“…This probability clearly differed depending on the type of tooth, as it was previously shown (Martinez‐Canut, Alcaraz, Alcaraz Jr. et al, 2017). …”

Section: Resultssupporting

confidence: 58%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A comprehensive approach to assigning periodontal prognosis

Martinez-Canut

Llobell

2018

J Clinic Periodontology

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“…[1][2][3] Currently, several therapeutic approaches have been applied to reduce the frequency and/or intensity of SB. [1][2][3] Currently, several therapeutic approaches have been applied to reduce the frequency and/or intensity of SB.…”

Section: Introductionmentioning

confidence: 99%

Association of masticatory muscle activity with sleep arousal and other concomitant movements during sleep

Miki

Minakuchi

Miyagi

et al. 2019

J of Oral Rehabilitation

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Objective: This study aims to verify the associations among sleep bruxism (SB), sleep arousal (SA) and concurrent body movements. Material and Methods:Subjects underwent a standard overnight polysomnography test and audio-video recordings. Sleep quality was evaluated according to the Rechtschaffen and Kales criteria, while SA was determined as per the American Sleep Disorders Association criteria. Analyses were performed by an external institution after masking of the subjects' information. SB was assessed based on the presence/ absence of rhythmic masticatory muscle activity (RMMA) episodes, which were identified by using electromyography of the masseter muscle. The observed simultaneous movements included lower leg movement (LLM), swallowing, face scratching, head movement, body movement, eye blinking, coughing, licking, sighing, body scratching, lip sucking, somniloquy and yawning. The LLM was determined visually, as well as through an increase in the tibialis electromyogram signal. Other movements were visually assessed using audio-video recordings. The incidences of all the simultaneous movements were compared between RMMA with intercurrent SA (SAwRMMA; RMMA episode derived from a masseter electromyogram showing more than 10% of maximum voluntary contraction) and SA without RMMA (SAw/oRMMA).Results: Fourteen subjects were included in this study (females/males: 4/10, mean age: 31.5 ± 5.7 years). Among these, LLM, swallowing, body movement, licking, body scratching and lip sucking were frequently observed in SAwRMMA episodes than in SAw/oRMMA episodes, significantly. However, the non-specific simultaneous movements were higher observed in SAw/oRMMA episodes than that in SAwRMMA. Conclusion:Our results suggest that SB is concurrently activated with LLM in relation to arousal. K E Y W O R D Sconcomitant movement, non-specific simultaneous movements, polysomnography assessment, rhythmic masticatory muscle activity, sleep arousal, sleep bruxism

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Predicting peri‐implant disease: Chi‐square automatic interaction detection (CHAID) decision tree analysis of risk indicators

Atieh

Pang

Lian

et al. 2019

Journal of Periodontology

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Background Further validation of the risk indicators / predictors for peri‐implant diseases is required to allow clinicians and patients to make informed decisions and optimize dental implant treatment outcomes. The aim of this study was to build prediction models, using Chi‐square automatic interaction detection (CHAID) analysis, to determine which systemic‐, patient‐, implant‐, site‐, surgical‐ and prostheses‐related risk indicators had more impact on the onset of peri‐implant diseases. Methods A retrospective analysis of 200 patients who received implant‐supported prostheses between 1998 and 2011 was conducted to evaluate the prevalences and risk indicators for peri‐implant mucositis and peri‐implantitis. The data were further analyzed using CHAID to produce two predictive models. Results The prevalence of peri‐implant mucositis was 20.2% and 10.2% for patients and implants, respectively, while the prevalence of peri‐implantitis was 10.1% at the patient level and 5.4% at the implant level. CHAID decision tree analysis identified three predictors (history of treated periodontitis, absence of regular supportive peri‐implant maintenance, and use of bone graft) for peri‐implant mucositis and three predictors (smoking, absence of regular supportive peri‐implant maintenance, and placement of ≥2 implants) for peri‐implantitis. Conclusions Within the limitations of this study, CHAID decision tree analysis identified the most plausible risk indicators and provided two predictive models for use in a particular university setting that would allow early detection and ensure appropriate care and maintenance of patients at high risk of peri‐implant diseases.

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Predictors of long‐term outcomes in patients undergoing periodontal maintenance

Cited by 27 publications

References 68 publications

A comprehensive approach to assigning periodontal prognosis

A comprehensive approach to assigning periodontal prognosis

Association of masticatory muscle activity with sleep arousal and other concomitant movements during sleep

Predicting peri‐implant disease: Chi‐square automatic interaction detection (CHAID) decision tree analysis of risk indicators

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