| ORIGINAL ARTICLE |
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| Year : 2021 | Volume
: 8
| Issue : 3 | Page : 150-156 |
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A predictive logistic regression model for periodontal diseases
Md Zahid Hossain, Mohammad Ali Alshahrani, Abdulmajeed Saeed Alasmari, Khaled Mashoor Hyderah, Ahmed Zafer Alshabab, Mutaz Ali Hassan, Abdo Mohammed Abdulrazzaq
Department of Preventive Dental Sciences, College of Dentistry, Najran University, Najran, Kingdom of Saudi Arabia
Correspondence Address:
Dr. Md Zahid Hossain
Department of Preventive Dental Sciences, Division of Periodontics, College of Dentistry, Najran University, King Abdulaziz Road, Najran
Kingdom of Saudi Arabia
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/sjoralsci.sjoralsci_123_20
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Introduction: Periodontal diseases (gingivitis and periodontitis) are one of the main concerns for oral health affecting around 20%–50% of the world population.
Aims: The aim of this study was to formulate a predictive model for periodontal diseases in a selected population.
Materials and Methods: A hospital-based analytical study was carried out. Seven hundred male patients having different forms of periodontal diseases were included to explore the common features and possible risk factors related to periodontal diseases. Chi-squared test and t-test were performed for univariate analysis, and binary logistic regression model was adapted for multivariate analysis using SPSS v23.
Results and Discussion: Four hundred and seventy (67%) and 230 (33%) patients suffered from gingivitis and periodontitis, respectively. The mean age of patients with periodontitis (37.17 ± 11.52 years) was significantly higher than those with gingivitis (26.04 ± 10.83 years). Univariate analysis showed that plaque and calculus had statistically significant relationship with gingivitis 451 (72%). Systemic diseases 18 (72%) and patients' habits 39 (76%) had statistically significant relationship with periodontitis (P < 0.05). A logistic regression model was formulated including age, risk factors, and nationality. The model was tested, and its sensitivity, specificity, and accuracy for detecting periodontal diseases were equal to 83.3%, 67.2%, and 78.0%, respectively.
Conclusions: This model had a good fit and explained a significant proportion of variance in the outcome variable (periodontitis) R2 = 0.40, (χ2 (9) = 238.32, P < 0.001).
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