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Fine-tuning multilevel modeling of risk factors associated with nonsurgical periodontal treatment outcome

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This retrospective study evaluated the influence of known risk factors on nonsurgical periodontal treatment (NSPT) response using a pocket depth fine-tuning multilevel linear model (MLM). Overall, 37 patients (24 males and 13 females) with moderate-to-severe chronic periodontitis underwent NSPT. Follow-up visits at 3, 6, and 12 months included measurements of several clinical periodontal parameters. Data were sourced from a previously reported database. In a total of 1416 initially affected sites (baseline PD ≥ 4 mm) on 536 teeth, probing depth (PD) and clinical attachment loss (CAL) reductions after NSPT were evaluated against known risk factors at 3 hierarchical levels (patient, tooth, and site). For each post-treatment follow-up, the variance component models fitted to evaluate the 3-level variance of PD and CAL decrease revealed that all levels contributed significantly to the overall variance (p < 0.001). Patients who underwent NSPT and were continually monitored had curative results. All 3 hierarchical levels included risk factors influencing the degree of PD and CAL reduction. Specifically, the type of tooth, surfaces involved, and tooth mobility site-level risk factors had the strongest impact on these reductions and were highly relevant for the success of NSPT.

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This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Multilevel analysis Periodontal disease Risk Factors

Citation

Botelho, João, Machado, Vanessa, Mascarenhas, Paulo, Alves, Ricardo, Cavacas, Maria Alzira, Mendes, José João. (2019). Fine-tuning multilevel modeling of risk factors associated with nonsurgical periodontal treatment outcome. Brazilian Oral Research, 33, e081. https://doi.org/10.1590/1807-3107bor-2019.vol33.0081

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Sociedade Brasileira de Pesquisa Odontológica

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