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A nomogram prediction for mandibular molar survival in Chinese patients with periodontitis: A 10-year retrospective cohort study.
J Clin Periodontol. 2020 Jul 09 [Online ahead of print]JC

Abstract

AIMS

To develop a nomogram prediction model of mandibular molar survival by comprehensively analysing clinical and radiographic risk factors of mandibular molar loss.

MATERIALS AND METHODS

Four hundred and seventy-eight mandibular molars of 139 subjects who underwent non-surgical periodontal treatment were examined retrospectively within a mean follow-up period of 11.1 years. The association of risk factors including clinical and radiographic parameters with mandibular molar loss was assessed using univariate and multivariate Cox regression analyses. A nomogram prediction model was developed, and the validation and discriminatory ability of it were analysed.

RESULTS

Hundred and four molars were lost in this study. Probing depth (PD), attachment loss (AL), furcation involvement (FI), bleeding on probing (BOP), tooth mobility and radiographic bone loss were significantly associated with tooth loss (p < .01). A gradient effect of degree of FI on mandibular molar loss existed increasing from degree Ⅱ (HR = 2.37, 95% CI: 1.48, 3.79) to Ⅲ (HR = 5.61, 95% CI: 3.01, 10.45) versus none & degree Ⅰ. The area under the curve (AUC) of the model was 0.891. The calibration curve and decision curve analysis demonstrated good performance and high net benefit of nomogram, respectively.

CONCLUSIONS

A specific nomogram could be adopted to predict the mandibular molar survival and formulate tailored treatment plans in Chinese.

Authors+Show Affiliations

Department of Periodontology, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing, China.Department of Periodontology, Qingdao Municipal Hospital of Stomatology, Qingdao, China.Department of Periodontology, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing, China.Department of Periodontology, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing, China.Department of Periodontology, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing, China.Department of Periodontology, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing, China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32645225

Citation

Shi, Shuwen, et al. "A Nomogram Prediction for Mandibular Molar Survival in Chinese Patients With Periodontitis: a 10-year Retrospective Cohort Study." Journal of Clinical Periodontology, 2020.
Shi S, Meng Y, Li W, et al. A nomogram prediction for mandibular molar survival in Chinese patients with periodontitis: A 10-year retrospective cohort study. J Clin Periodontol. 2020.
Shi, S., Meng, Y., Li, W., Jiao, J., Meng, H., & Feng, X. (2020). A nomogram prediction for mandibular molar survival in Chinese patients with periodontitis: A 10-year retrospective cohort study. Journal of Clinical Periodontology. https://doi.org/10.1111/jcpe.13343
Shi S, et al. A Nomogram Prediction for Mandibular Molar Survival in Chinese Patients With Periodontitis: a 10-year Retrospective Cohort Study. J Clin Periodontol. 2020 Jul 9; PubMed PMID: 32645225.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A nomogram prediction for mandibular molar survival in Chinese patients with periodontitis: A 10-year retrospective cohort study. AU - Shi,Shuwen, AU - Meng,Yang, AU - Li,Wenjing, AU - Jiao,Jian, AU - Meng,Huanxin, AU - Feng,Xianghui, Y1 - 2020/07/09/ PY - 2019/12/30/received PY - 2020/06/30/revised PY - 2020/07/02/accepted PY - 2020/7/10/pubmed PY - 2020/7/10/medline PY - 2020/7/10/entrez KW - mandibular molar KW - nomogram KW - periodontitis KW - survival JF - Journal of clinical periodontology JO - J. Clin. Periodontol. N2 - AIMS: To develop a nomogram prediction model of mandibular molar survival by comprehensively analysing clinical and radiographic risk factors of mandibular molar loss. MATERIALS AND METHODS: Four hundred and seventy-eight mandibular molars of 139 subjects who underwent non-surgical periodontal treatment were examined retrospectively within a mean follow-up period of 11.1 years. The association of risk factors including clinical and radiographic parameters with mandibular molar loss was assessed using univariate and multivariate Cox regression analyses. A nomogram prediction model was developed, and the validation and discriminatory ability of it were analysed. RESULTS: Hundred and four molars were lost in this study. Probing depth (PD), attachment loss (AL), furcation involvement (FI), bleeding on probing (BOP), tooth mobility and radiographic bone loss were significantly associated with tooth loss (p < .01). A gradient effect of degree of FI on mandibular molar loss existed increasing from degree Ⅱ (HR = 2.37, 95% CI: 1.48, 3.79) to Ⅲ (HR = 5.61, 95% CI: 3.01, 10.45) versus none & degree Ⅰ. The area under the curve (AUC) of the model was 0.891. The calibration curve and decision curve analysis demonstrated good performance and high net benefit of nomogram, respectively. CONCLUSIONS: A specific nomogram could be adopted to predict the mandibular molar survival and formulate tailored treatment plans in Chinese. SN - 1600-051X UR - https://www.unboundmedicine.com/medline/citation/32645225/A_nomogram_prediction_for_mandibular_molar_survival_in_Chinese_patients_with_periodontitis:_a_10-year_retrospective_cohort_study L2 - https://doi.org/10.1111/jcpe.13343 DB - PRIME DP - Unbound Medicine ER -
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