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Shi S, Meng Y, Jiao J, Shi D, Feng X, Meng H. A nomogram-based predictive model for tooth survival in Chinese patients with periodontitis: An 11-year retrospective cohort study. J Clin Periodontol 2024. [PMID: 38986602 DOI: 10.1111/jcpe.14027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 05/02/2024] [Accepted: 05/27/2024] [Indexed: 07/12/2024]
Abstract
AIM To develop a nomogram-based predictive model of tooth survival by comprehensively analysing clinical and radiographic risk factors of tooth loss (TL). MATERIALS AND METHODS In this study, 3447 teeth of 131 subjects who underwent non-surgical periodontal treatment were examined retrospectively within a mean follow-up period of 11.6 years. The association of risk factors including clinical and radiographic parameters with TL was assessed using univariate and multivariate Cox regression analyses. A nomogram-based predictive model was developed, and its validation and discriminatory ability were analysed. RESULTS In all, 313 teeth were lost in 94 patients in this study (overall tooth loss [OTL] 9.08%; 0.21 teeth/patient/year). Male, heavy smoking, molar teeth, probing depth (PD), attachment loss (AL), tooth mobility and radiographic bone loss were significantly associated with TL (p < .05). A gradient effect of tooth mobility on TL increased from degree I to III versus none (p < .0001). The area under the curve (AUC) of the model was 0.865. Calibration curve and decision curve analysis demonstrated good performance and high net benefit, respectively. CONCLUSIONS Adopting a specific nomogram could facilitate the prediction of tooth survival and the development of tailored treatment plans in Chinese patients with advanced periodontitis.
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Affiliation(s)
- Shuwen Shi
- Department of Periodontology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Laboratory for Digital and Material Technology of Stomatology & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, Beijing, People's Republic of China
- Department of Stomatology, Beijing Friendship Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Yang Meng
- Department of Periodontology, Qingdao Stomatological Hospital Affiliated to Qingdao University, Qingdao, People's Republic of China
| | - Jian Jiao
- First Clinical Division & Department of Periodontology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Laboratory for Digital and Material Technology of Stomatology & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, Beijing, People's Republic of China
| | - Dong Shi
- Department of Periodontology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Laboratory for Digital and Material Technology of Stomatology & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, Beijing, People's Republic of China
| | - Xianghui Feng
- Department of Periodontology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Laboratory for Digital and Material Technology of Stomatology & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, Beijing, People's Republic of China
| | - Huanxin Meng
- Department of Periodontology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Laboratory for Digital and Material Technology of Stomatology & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, Beijing, People's Republic of China
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Chow DY, Tay JRH, Nascimento GG. Systematic Review of Prognosis Models in Predicting Tooth Loss in Periodontitis. J Dent Res 2024; 103:596-604. [PMID: 38726948 DOI: 10.1177/00220345241237448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/24/2024] Open
Abstract
This study reviews and appraises the methodological and reporting quality of prediction models for tooth loss in periodontitis patients, including the use of regression and machine learning models. Studies involving prediction modeling for tooth loss in periodontitis patients were screened. A search was performed in MEDLINE via PubMed, Embase, and CENTRAL up to 12 February 2022, with citation chasing. Studies exploring model development or external validation studies for models assessing tooth loss in periodontitis patients for clinical use at any time point, with all prediction horizons in English, were considered. Studies were excluded if models were not developed for use in periodontitis patients, were not developed or validated on any data set, predicted outcomes other than tooth loss, or were prognostic factor studies. The CHARMS checklist was used for data extraction, TRIPOD to assess reporting quality, and PROBAST to assess the risk of bias. In total, 4,661 records were screened, and 45 studies were included. Only 26 studies reported any kind of performance measure. The median C-statistic reported was 0.671 (range, 0.57-0.97). All studies were at a high risk of bias due to inappropriate handling of missing data (96%), inappropriate evaluation of model performance (92%), and lack of accounting for model overfitting in evaluating model performance (68%). Many models predicting tooth loss in periodontitis are available, but studies evaluating these models are at a high risk of bias. Model performance measures are likely to be overly optimistic and might not be replicated in clinical use. While this review is unable to recommend any model for clinical practice, it has collated the existing models and their model performance at external validation and their associated sample sizes, which would be helpful to identify promising models for future external validation studies.
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Affiliation(s)
- D Y Chow
- Department of Restorative Dentistry, National Dental Centre Singapore, Singapore
| | - J R H Tay
- Department of Restorative Dentistry, National Dental Centre Singapore, Singapore
| | - G G Nascimento
- National Dental Research Institute Singapore, National Dental Centre Singapore, Singapore
- ORH ACP, Duke-NUS Medical School Singapore, Singapore
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Holtfreter B, Kuhr K, Borof K, Tonetti MS, Sanz M, Kornman K, Jepsen S, Aarabi G, Völzke H, Kocher T, Krois J, Papapanou PN. ACES: A new framework for the application of the 2018 periodontal status classification scheme to epidemiological survey data. J Clin Periodontol 2024; 51:512-521. [PMID: 38385950 DOI: 10.1111/jcpe.13965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 01/30/2024] [Accepted: 02/03/2024] [Indexed: 02/23/2024]
Abstract
AIM To propose a framework for consistently applying the 2018 periodontal status classification scheme to epidemiological surveys (Application of the 2018 periodontal status Classification to Epidemiological Survey data, ACES). PROPOSED FRAMEWORK We specified data requirements and workflows for either completed or planned epidemiological surveys, utilizing commonly collected measures of periodontal status (clinical attachment levels [CAL], probing depths, bleeding on probing), as well as additional necessary variables for the implementation of the 2018 periodontal status classification (tooth loss due to periodontitis and complexity factors). Following detailed instructions and flowcharts, survey participants are classified as having periodontal health, gingivitis or periodontitis. Rates of edentulism must also be reported. In cases of periodontitis, instructions on how to compute the stage and extent are provided. Assessment of grade can be derived from CAL measurements (or from radiographic alveolar bone loss data) in relation to root length and the participant's age. CONCLUSIONS ACES is a framework to be used in epidemiological studies of periodontal status that (i) have been completed, and in which stage and grade according to the 2018 classification are inferred retroactively, or (ii) are being planned. Consistent use of the proposed comprehensive approach will facilitate the comparability of periodontitis prevalence estimates across studies.
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Affiliation(s)
- Birte Holtfreter
- Department of Restorative Dentistry, Periodontology Endodontology and Preventive and Pediatric Dentistry, University Medicine Greifswald, Greifswald, Germany
| | - Kathrin Kuhr
- Health Care Research and Epidemiology, Institute of German Dentists (IDZ), Cologne, Germany
| | - Katrin Borof
- Department of Periodontics, Preventive and Restorative Dentistry, Center for Dental and Oral Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Maurizio S Tonetti
- Shanghai PerioImplant Innovation Centre, Department of Oral and Maxillofacial Implantology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, National Centre for Stomatology, Shanghai, China
- National Clinical Research Centre for Oral Diseases, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Mariano Sanz
- Department of Dental Clinical Specialties, ETEP Research Group, Faculty of Odontology, University Complutense of Madrid, Madrid, Spain
| | - Kenneth Kornman
- Department of Periodontics and Oral Medicine, The University of Michigan-School of Dentistry, Ann Arbor, Michigan, USA
| | - Søren Jepsen
- Department of Periodontology, Operative and Preventive Dentistry, University of Bonn, Bonn, Germany
| | - Ghazal Aarabi
- Department of Periodontics, Preventive and Restorative Dentistry, Center for Dental and Oral Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Thomas Kocher
- Department of Restorative Dentistry, Periodontology Endodontology and Preventive and Pediatric Dentistry, University Medicine Greifswald, Greifswald, Germany
| | - Joachim Krois
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Panos N Papapanou
- Division of Periodontics, Section of Oral, Diagnostic and Rehabilitation Sciences, College of Dental Medicine, Columbia University, New York, New York, USA
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Gao J, Yang Y, Yin W, Zhao X, Qu Y, Yang X, Wu Y, Xiang L, Man Y. A nomogram prediction of implant apical non-coverage on bone-added transcrestal sinus floor elevation: A retrospective cohort study. Clin Oral Implants Res 2024; 35:282-293. [PMID: 38108637 DOI: 10.1111/clr.14225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 11/18/2023] [Accepted: 11/28/2023] [Indexed: 12/19/2023]
Abstract
OBJECTIVES To identify the risk indicators and develop and validate a nomogram prediction model of implant apical non-coverage by comprehensively analyzing clinical and radiographic factors in bone-added transcrestal sinus floor elevation (TSFE). MATERIAL AND METHODS A total of 260 implants in 195 patients receiving bone-added TSFE were included in the study. The population was divided into a development (180 implants) and a validation (80 implants) cohort. According to 6 months post-surgery radiographic images, implants were categorized as "apical non-coverage" or "apical covered." The association of risk factors including clinical and radiographic parameters with implant apical non-coverage was assessed using regression analyses. A nomogram prediction model was developed, and its validation and discriminatory ability were analyzed. RESULTS The nomogram predicting bone-added TSFE's simultaneously placed implant's apex non-coverage after 6 months. This study revealed that sinus angle, endo-sinus bone gain, implant protrusion length, graft contact walls, and distal angle were predictors of implant apical non-coverage. The generated nomogram showed a strong predictive capability (area under the curve [AUC] = 0.845), confirmed by internal validation using 10-fold cross-validation (Median AUC of 0.870) and temporal validation (AUC = 0.854). The calibration curve and decision curve analysis demonstrated good performance and high net benefit of the nomogram, respectively. CONCLUSIONS The clinical implementation of the present nomogram is suitable for predicting the apex non-coverage of implants placed simultaneously with bone-added TSFE after 6 months.
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Affiliation(s)
- Jiayu Gao
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Yufei Yang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
- Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Wumeng Yin
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Xiangqi Zhao
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
- Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Yili Qu
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
- Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Xingmei Yang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Yingying Wu
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Lin Xiang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Yi Man
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
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5
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Troiano G, Nibali L, Petsos H, Eickholz P, Saleh MHA, Santamaria P, Jian J, Shi S, Meng H, Zhurakivska K, Wang HL, Ravidà A. Development and international validation of logistic regression and machine-learning models for the prediction of 10-year molar loss. J Clin Periodontol 2023; 50:348-357. [PMID: 36305042 DOI: 10.1111/jcpe.13739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/21/2022] [Accepted: 10/24/2022] [Indexed: 12/13/2022]
Abstract
AIM To develop and validate models based on logistic regression and artificial intelligence for prognostic prediction of molar survival in periodontally affected patients. MATERIALS AND METHODS Clinical and radiographic data from four different centres across four continents (two in Europe, one in the United States, and one in China) including 515 patients and 3157 molars were collected and used to train and test different types of machine-learning algorithms for their prognostic ability of molar loss over 10 years. The following models were trained: logistic regression, support vector machine, K-nearest neighbours, decision tree, random forest, artificial neural network, gradient boosting, and naive Bayes. In addition, different models were aggregated by means of the ensembled stacking method. The primary outcome of the study was related to the prediction of overall molar loss (MLO) in patients after active periodontal treatment. RESULTS The general performance in the external validation settings (aggregating three cohorts) revealed that the ensembled model, which combined neural network and logistic regression, showed the best performance among the different models for the prediction of MLO with an area under the curve (AUC) = 0.726. The neural network model showed the best AUC of 0.724 for the prediction of periodontitis-related molar loss. In addition, the ensembled model showed the best calibration performance. CONCLUSIONS Through a multi-centre collaboration, both prognostic models for the prediction of molar loss were developed and externally validated. The ensembled model showed the best performance in terms of both discrimination and validation, and it is made freely available to clinicians for widespread use in clinical practice.
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Affiliation(s)
- Giuseppe Troiano
- Department of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy
| | - Luigi Nibali
- Periodontology Unit, Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK
| | - Hari Petsos
- Department of Periodontology, Center for Dentistry and Oral Medicine (Carolinum), Johann Wolfgang Goethe-University Frankfurt/Main, Frankfurt am Main, Germany
| | - Peter Eickholz
- Department of Periodontology, Center for Dentistry and Oral Medicine (Carolinum), Johann Wolfgang Goethe-University Frankfurt/Main, Frankfurt am Main, Germany
| | - Muhammad H A Saleh
- Department of Periodontics and Oral Medicine, University of Michigan School of Dentistry, Ann Arbor, Michigan, USA
| | - Pasquale Santamaria
- Periodontology Unit, Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK
| | - Jao Jian
- 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
| | - Shuwen Shi
- 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
| | - Huanxin Meng
- 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
| | - Khrystyna Zhurakivska
- Department of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy
| | - Hom-Lay Wang
- Department of Periodontics and Oral Medicine, University of Michigan School of Dentistry, Ann Arbor, Michigan, USA
| | - Andrea Ravidà
- Department of Periodontics and Oral Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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Evaluation of periodontitis-related tooth loss according to the new 2018 classification of periodontitis. Sci Rep 2022; 12:11893. [PMID: 35831375 PMCID: PMC9279363 DOI: 10.1038/s41598-022-15462-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 06/23/2022] [Indexed: 11/21/2022] Open
Abstract
The new 2018 classification of periodontal diseases is reported to be related to tooth loss due to periodontal disease (TLPD) during supportive periodontal therapy (SPT). However, few reports have evaluated this relationship for Asians or have analyzed the association of the new classification and TLPD by distinguishing between active periodontal therapy (APT) and SPT. In this study, we retrospectively applied the new classification to 607 Japanese periodontitis patients and examined the relationship between the new classification and annual TLPD rates per patient during the respective periods. TLPD rates were higher in patients in stage IV and/or grade C during both APT and SPT. TLPD during SPT was not associated with the presence or absence of TLPD during APT. Multivariate analysis revealed that stage IV and grade C as independent variables were significantly associated with the number of instances of TLPD not only during the total treatment period, but also during APT or SPT. Our results suggest that the new classification has a significantly strong association with TLPD during both APT and SPT, and that patients diagnosed with stage IV and/or grade C periodontitis had a higher risk of TLPD during both periods.
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7
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Sim CPC, Li H, Peres MA. Who to Be Treated: Nomogram Using Self-Reported Periodontal Screening Instrument among English-Speaking Adults in Multi-Ethnic Singapore. J Pers Med 2022; 12:jpm12060931. [PMID: 35743716 PMCID: PMC9225178 DOI: 10.3390/jpm12060931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 06/01/2022] [Accepted: 06/02/2022] [Indexed: 02/04/2023] Open
Abstract
Periodontal disease is a major public health problem. This study aimed to develop a nomogram using a self-reported periodontitis screening instrument in predicting severe periodontitis (SP), defined by the World Workshop on Classification of Periodontal and Peri-Implant Diseases and Conditions, and evaluate its utility in clinical setting. An Akaike information criterion selected multivariable model was developed to predict SP using a self-reported questionnaire, with a nomogram developed based on its regression coefficients. Discriminatory capability was evaluated by Receiver-operating characteristic curve. Ability to predict SP of individual patients was evaluated with bootstrapping. Decision curve analysis (DCA) was performed to evaluate its potential clinical utility by evaluating clinical net benefit at different thresholds. 58.1% of 155 participants were classified with SP. Older males without tertiary education, with ‘loose teeth’, ‘bone loss’ and ‘mouth rinse use’ had higher SP risk. The nomogram showed excellent discriminatory capability with Area under Curve of 0.83 (95% CI = (0.76, 0.89)), good calibration (intercept = 0.026) and slight overestimation of high risk and underestimation of low risk (slope = 0.834). DCA showed consistent clinical net benefit across the range of thresholds relative to assumption of ‘no patient’ or ‘all patient’ with SP. Our nomogram using a self-reported periodontitis instrument is useful in SP screening in English-speaking Singaporean adults.
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Affiliation(s)
- Christina P. C. Sim
- Department of Restorative Dentistry, National Dental Centre Singapore, 5 Second Hospital Avenue, Singapore 168938, Singapore
- Oral Health Academic Clinical Programme, Duke-NUS Medical School, National University of Singapore, 8 College Road, Singapore 169857, Singapore
- Correspondence: ; Tel.: +65-6324-8928; Fax: +65-6324-8900
| | - Huihua Li
- ACP Research Office, National Dental Centre Singapore, 5 Second Hospital Avenue, Singapore 168938, Singapore;
- Centre for Quantitative Medicine, Duke-NUS Medical School, National University of Singapore, 8 College Road, Singapore 169857, Singapore
| | - Marco A. Peres
- National Dental Centre Singapore, National Dental Research Institute Singapore, 5 Second Hospital Avenue, Singapore 168938, Singapore;
- Oral Health Academic Clinical Programme, Health Services and Systems Research Institute Singapore, Duke-NUS Medical School, National University of Singapore, 8 College Road, Singapore 169857, Singapore
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Cooray U, Watt RG, Tsakos G, Heilmann A, Hariyama M, Yamamoto T, Kuruppuarachchige I, Kondo K, Osaka K, Aida J. Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a machine learning analysis. Soc Sci Med 2021; 291:114486. [PMID: 34700121 DOI: 10.1016/j.socscimed.2021.114486] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 10/11/2021] [Accepted: 10/12/2021] [Indexed: 01/21/2023]
Abstract
Prevalence of tooth loss has increased due to population aging. Tooth loss negatively affects the overall physical and social well-being of older adults. Understanding the role of socio-demographic and other predictors associated with tooth loss that are measured in non-clinical settings can be useful in community-level prevention. We used high-dimensional epidemiological data to investigate important factors in predicting tooth loss among older adults over a 6-year period of follow-up. Data was from participants of 2010 and 2016 waves of the Japan Gerontological Evaluation Study (JAGES). A total of 19,407 community-dwelling functionally independent older adults aged 65 and older were included in the analysis. Tooth loss was measured as moving from a higher number of teeth category at the baseline to a lower number of teeth category at the follow-up. Out of 119 potential predictors, age, sex, number of teeth, denture use, chewing difficulty, household income, employment, education, smoking, fruit and vegetable consumption, community participation, time since last health check-up, having a hobby, and feeling worthless were selected using Boruta algorithm. Within the 6-year follow-up, 3013 individuals (15.5%) reported incidence of tooth loss. People who experienced tooth loss were older (72.9 ± 5.2 vs 71.8 ± 4.7), and predominantly men (18.3% vs 13.1%). Extreme gradient boosting (XGBoost) machine learning prediction model had a mean accuracy of 90.5% (±0.9%). A visual analysis of machine learning predictions revealed that the prediction of tooth loss was mainly driven by demographic (older age), baseline oral health (having 10-19 teeth, wearing dentures), and socioeconomic (lower household income, manual occupations) variables. Predictors related to wide a range of determinants contribute towards tooth loss among older adults. In addition to oral health related and demographic factors, socioeconomic factors were important in predicting future tooth loss. Understanding the behaviour of these predictors can thus be useful in developing prevention strategies for tooth loss among older adults.
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Affiliation(s)
- Upul Cooray
- Department of International and Community Oral Health, Tohoku University Graduate School of Dentistry, Sendai, Japan.
| | - Richard G Watt
- Department of Epidemiology and Public Health, University College London, London, United Kingdom
| | - Georgios Tsakos
- Department of Epidemiology and Public Health, University College London, London, United Kingdom
| | - Anja Heilmann
- Department of Epidemiology and Public Health, University College London, London, United Kingdom
| | - Masanori Hariyama
- Intelligent Integrated Systems Laboratory, Graduate School of Information Sciences, Tohoku University, Miyagi, Japan
| | - Takafumi Yamamoto
- Department of International and Community Oral Health, Tohoku University Graduate School of Dentistry, Sendai, Japan
| | - Isuruni Kuruppuarachchige
- Department of Dental and Digital Forensics Tohoku University Graduate School of Dentistry, Sendai, Japan
| | - Katsunori Kondo
- Center for Preventive Medical Sciences, Chiba University, Chiba, Japan; Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Ken Osaka
- Department of International and Community Oral Health, Tohoku University Graduate School of Dentistry, Sendai, Japan
| | - Jun Aida
- Department of Oral Health Promotion, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan; Division for Regional Community Development, Liaison Center for Innovative Dentistry, Graduate School of Dentistry, Tohoku University, Sendai, Japan
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Rahim-Wöstefeld S, Kronsteiner D, ElSayed S, ElSayed N, Eickholz P, Pretzl B. Development of a prognostic tool: based on risk factors for tooth loss after active periodontal therapy. Clin Oral Investig 2021; 26:813-822. [PMID: 34435251 PMCID: PMC8791882 DOI: 10.1007/s00784-021-04060-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 06/27/2021] [Indexed: 11/30/2022]
Abstract
Objectives The aim of this study was to develop a prognostic tool to estimate long-term tooth retention in periodontitis patients at the beginning of active periodontal therapy (APT). Material and methods Tooth-related factors (type, location, bone loss (BL), infrabony defects, furcation involvement (FI), abutment status), and patient-related factors (age, gender, smoking, diabetes, plaque control record) were investigated in patients who had completed APT 10 years before. Descriptive analysis was performed, and a generalized linear-mixed model-tree was used to identify predictors for the main outcome variable tooth loss. To evaluate goodness-of-fit, the area under the curve (AUC) was calculated using cross-validation. A bootstrap approach was used to robustly identify risk factors while avoiding overfitting. Results Only a small percentage of teeth was lost during 10 years of supportive periodontal therapy (SPT; 0.15/year/patient). The risk factors abutment function, diabetes, and the risk indicator BL, FI, and age (≤ 61 vs. > 61) were identified to predict tooth loss. The prediction model reached an AUC of 0.77. Conclusion This quantitative prognostic model supports data-driven decision-making while establishing a treatment plan in periodontitis patients. In light of this, the presented prognostic tool may be of supporting value. Clinical relevance In daily clinical practice, a quantitative prognostic tool may support dentists with data-based decision-making. However, it should be stressed that treatment planning is strongly associated with the patient’s wishes and adherence. The tool described here may support establishment of an individual treatment plan for periodontally compromised patients.
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Affiliation(s)
- Sonja Rahim-Wöstefeld
- Section of Periodontology, Department of Conservative Dentistry, Clinic for Oral, Dental and Maxillofacial Diseases, University Hospital Heidelberg, 69120, Heidelberg, Germany.
- Private Practice, 68159, Mannheim, Germany.
| | - Dorothea Kronsteiner
- Institute of Medical Biometry and Informatics (IMBI), University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Shirin ElSayed
- Section of Periodontology, Department of Conservative Dentistry, Clinic for Oral, Dental and Maxillofacial Diseases, University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Nihad ElSayed
- Section of Periodontology, Department of Conservative Dentistry, Clinic for Oral, Dental and Maxillofacial Diseases, University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Peter Eickholz
- Department of Periodontology, Center of Dentistry and Oral Medicine (Carolinum), Johann Wolfgang Goethe-University Frankfurt/Main, 60596, Frankfurt, Germany
| | - Bernadette Pretzl
- Section of Periodontology, Department of Conservative Dentistry, Clinic for Oral, Dental and Maxillofacial Diseases, University Hospital Heidelberg, 69120, Heidelberg, Germany
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El Sayed N, Rahim-Wöstefeld S, Stocker F, Behnisch R, Eickholz P, Pretzl B. The 2018 classification of periodontal diseases. Its predictive value for tooth loss. J Periodontol 2021; 93:560-569. [PMID: 34272728 DOI: 10.1002/jper.21-0211] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 05/25/2021] [Accepted: 05/26/2021] [Indexed: 11/11/2022]
Abstract
BACKGROUND Predictive models and assessment tools for disease susceptibility and progression are necessary to enhance personalized medicine. The aim of this study is to assess the predictive accuracy of using the 2018 classification to predict likelihood of tooth loss. MATERIAL AND METHODS 134 patients were screened 10 years after periodontal therapy. Data were extracted from 82 patients' records and periodontal diagnoses were assigned according to the 1999 and 2018 classifications at baseline, whereas patient- and tooth-related parameters were documented at baseline and at reexamination. Statistical analysis included descriptive statistics, hurdle regression with a zero and count model as well as logistic regression. RESULTS Significantly more teeth were lost during SPT in patients with Stage IV or Grade C (p<0.05). Patients' adherence seems to have an impact on the predictability of the 2018 classification (p<0.001). In comparison, neither classification system alone (1999 vs. 2018) showed a high predictive value for tooth loss (Area under the curve (AUC) = 59.2% vs. 58.2%). CONCLUSION Class III and IV/grade C of the 2018 classification of periodontal diseases show similar predictive accuracy for tooth loss as severe cases in the former classification. Patients adherence seems to influence the prognostic value of the classification. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Nihad El Sayed
- Section of Periodontology, Department of Conservative Dentistry, Clinic for Oral, Dental and Maxillofacial Diseases, University Hospital Heidelberg, Heidelberg, 69120, Germany
| | | | - Florian Stocker
- Section of Periodontology, Department of Conservative Dentistry, Clinic for Oral, Dental and Maxillofacial Diseases, University Hospital Heidelberg, Heidelberg, 69120, Germany
| | - Rouven Behnisch
- Institute of Medical Biometry and Informatics (IMBI), University Hospital Heidelberg, Heidelberg, 69120, Germany
| | - Peter Eickholz
- Department of Periodontology, Center of Dentistry and Oral Medicine (Carolinum), Johann Wolfgang Goethe-University Frankfurt/Main, Frankfurt, 60596, Germany
| | - Bernadette Pretzl
- Section of Periodontology, Department of Conservative Dentistry, Clinic for Oral, Dental and Maxillofacial Diseases, University Hospital Heidelberg, Heidelberg, 69120, Germany
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Carvalho R, Botelho J, Machado V, Mascarenhas P, Alcoforado G, Mendes JJ, Chambrone L. Predictors of tooth loss during long-term periodontal maintenance: An updated systematic review. J Clin Periodontol 2021; 48:1019-1036. [PMID: 33998031 DOI: 10.1111/jcpe.13488] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 04/27/2021] [Accepted: 04/29/2021] [Indexed: 12/24/2022]
Abstract
AIM To evaluate the risk factors / predictors of tooth loss in patients with periodontitis who underwent periodontal therapy and long-term periodontal maintenance (PM). MATERIAL AND METHODS PubMed, CENTRAL, EMBASE, Web of Science, LILACS and Scholar were searched up to and including September 2020. Studies limited to periodontitis patients who underwent active periodontal therapy (APT) and followed a regular PM programme with 5 years follow-up minimum were eligible for inclusion in this review. Studies were included if they reported data on tooth loss during PM. Random effects meta-analyses of number of tooth loss per patient per year were conducted. RESULTS Thirty-six papers regarding thirty-three studies were included in this review, with three prospective 30 retrospective trials. Subgroup meta-analysis showed no differences between prospective and retrospective studies, with an average of 0.1 tooth loss per year per patient (p < 0.001). Maxillary and molar teeth were more susceptible to be extracted during long-term PM. Baseline characteristics (smoking, diabetes mellitus, cardiovascular disease, being male and teeth with furcation lesions) showed no significance as predictor of tooth loss through meta-regression. The percentage of tooth loss due to periodontal reasons ranged from 0.45% to 14.4%. The individual outcomes in each study evidenced different patient-related factors (age and smoking) and tooth-related factors (i.e. tooth type and location) were associated with tooth loss during PM. CONCLUSION The majority of patients undergoing long-term PM have not lost teeth. On average, long-term PM effectively causes the loss of 1 tooth per patient every 10 years. Additional prospective trials may confirm these results.
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Affiliation(s)
- Rui Carvalho
- Clinical Research Unit (CRU), Centro de Investigação Interdisciplinar Egas Moniz (CiiEM), Egas Moniz-Cooperativa de Ensino Superior, Caparica, Almada, Portugal
| | - João Botelho
- Clinical Research Unit (CRU), Centro de Investigação Interdisciplinar Egas Moniz (CiiEM), Egas Moniz-Cooperativa de Ensino Superior, Caparica, Almada, Portugal.,Periodontology Department, Egas Moniz Dental Clinic (EMDC), Egas Moniz-Cooperativa de Ensino Superior, Caparica, Almada, Portugal
| | - Vanessa Machado
- Clinical Research Unit (CRU), Centro de Investigação Interdisciplinar Egas Moniz (CiiEM), Egas Moniz-Cooperativa de Ensino Superior, Caparica, Almada, Portugal.,Periodontology Department, Egas Moniz Dental Clinic (EMDC), Egas Moniz-Cooperativa de Ensino Superior, Caparica, Almada, Portugal
| | - Paulo Mascarenhas
- Evidence-Based Hub, CiiEM, Egas Moniz-Cooperativa de Ensino Superior, Caparica, Almada, Portugal
| | - Gil Alcoforado
- Clinical Research Unit (CRU), Centro de Investigação Interdisciplinar Egas Moniz (CiiEM), Egas Moniz-Cooperativa de Ensino Superior, Caparica, Almada, Portugal.,Periodontology Department, Egas Moniz Dental Clinic (EMDC), Egas Moniz-Cooperativa de Ensino Superior, Caparica, Almada, Portugal
| | - José João Mendes
- Clinical Research Unit (CRU), Centro de Investigação Interdisciplinar Egas Moniz (CiiEM), Egas Moniz-Cooperativa de Ensino Superior, Caparica, Almada, Portugal.,Evidence-Based Hub, CiiEM, Egas Moniz-Cooperativa de Ensino Superior, Caparica, Almada, Portugal
| | - Leandro Chambrone
- Evidence-Based Hub, CiiEM, Egas Moniz-Cooperativa de Ensino Superior, Caparica, Almada, Portugal.,Graduate Dentistry Program, School of Dentistry, Ibirapuera University, São Paulo, Brazil.,Unit of Basic Oral Investigation (UIBO), Universidad El Bosque, Bogota, Colombia
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