Predicting the Probability of the Incidence of Maxillary Sinus Fungus Ball in Patients Using Nomogram Models
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Participants
2.2. Clinical Characteristics and CT Imaging Features
2.3. Statistical Analysis
3. Results
3.1. Descriptive Characteristics of the Participants
3.2. Features of CT Imaging
3.3. Logistic Regression Analysis
3.4. Nomograms for Predicting the Probability of the Incidence of MSFB
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variables | MSFB Group | UMRS Group | p Value |
---|---|---|---|
(n = 97) | (n = 158) | ||
Age, years (mean ± SD) | 59.61 ± 13.2 | 46.31 ± 14.8 | <0.001 *** |
Sex | |||
Male, n (%) | 33 (34.0) | 97 (61.4) | <0.001 *** |
Female, n (%) | 64 (66.0) | 61 (38.6) | |
Laboratory data | |||
WBC (×103/μL) | 6.69 ± 1.90 | 7.87 ± 2.41 | <0.001 *** |
Neutrophils count (×103/μL) | 3.97 ± 1.56 | 4.95 ± 2.13 | <0.001 *** |
Lymphocytes count (×103/μL) | 2.20 ± 0.722 | 2.27 ± 7.04 | 0.489 |
Eosinophils count (×102/μL) | 1.83 ± 1.91 | 1.78 ± 1.67 | 0.796 |
Platelet (×106/μL) | 2.50 ± 0.58 | 2.72 ± 0.63 | 0.006 ** |
Underlying condition | |||
Diabetes mellitus, n (%) | 19 (19.6) | 20 (12.7) | 0.136 |
Clinical presentations | |||
Rhinorrhea, n (%) | 60 (61.9) | 113 (71.5) | 0.109 |
Post nasal dripping, n (%) | 47 (48.5) | 85 (53.8) | 0.407 |
Nasal obstruction, n (%) | 43 (44.3) | 107 (67.7) | <0.001 *** |
Headache and facial pain, n (%) | 28 (28.9) | 43 (27.2) | 0.775 |
Hyposmia, n (%) | 25 (25.8) | 42 (26.6) | 0.887 |
CT Imaging Features | MSFB Group | UMRS Group | p Value |
---|---|---|---|
(n = 97) | (n = 158) | ||
Total opacification, n (%) | 47 (48.5) | 84 (53.2) | 0.465 |
Partial opacification, n (%) | 50 (51.5) | 74 (46.8) | 0.465 |
Irregular surface, n (%) | 41 (42.3) | 14 (8.9) | <0.001 *** |
Maxillary odontogenic pathology, n (%) | 16 (16.5) | 53 (33.5) | 0.003 ** |
Erosion of medial sinus wall, n (%) | 53 (54.6) | 12 (7.6) | <0.001 *** |
Sclerosis of lateral sinus wall, n (%) | 88 (90.7) | 109 (69.0) | <0.001 *** |
Intralesional hyperdensity, n (%) | 79 (81.4) | 9 (5.7) | <0.001 *** |
Variables | Univariate Regression Analysis | |
---|---|---|
Odds Ratio (95% CI) | p Value | |
Characteristics of patients | ||
Age (years) | 1.07 (1.04−1.09) | <0.001 *** |
Diabetes mellitus | 1.68 (0.85−3.33) | 0.138 |
Female sex (female versus male) | 3.08 (1.82−5.23) | <0.001 ** |
Laboratory data | ||
WBC count (×103/μL) | 0.77 (0.68−0.88) | 0.001 ** |
Neutrophils count (×103/μL) | 0.73 (0.62−0.87) | <0.001 *** |
Lymphocytes count (×103/μL) | 0.88 (0.61−1.27) | 0.480 |
Eosinophils count (×102/μL) | 1.00 (0.99−1.02) | 0.761 |
Platelet (×106/μL) | 0.55 (0.36−0.86) | 0.008 ** |
CT imaging features | ||
Total opacification | 0.83 (0.50−1.37) | 0.465 |
Partial opacification | 1.21 (0.73−2.00) | 0.465 |
Maxillary odontogenic pathology | 0.39 (0.21−0.73) | 0.003 ** |
Irregular surface | 7.53 (3.81−14.88) | <0.001 *** |
Erosion of medial sinus wall | 14.66 (7.19−29.85) | <0.001 *** |
Sclerosis of lateral sinus wall | 4.40 (2.05−9.44) | <0.001 *** |
Intralesional hyperdensity | 72.66 (31.20−169.21) | <0.001 *** |
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Fan, Y.-H.; Shih, K.-Y.; Wu, P.-W.; Huang, Y.-L.; Lee, T.-J.; Huang, C.-C.; Chang, P.-H.; Huang, C.-C. Predicting the Probability of the Incidence of Maxillary Sinus Fungus Ball in Patients Using Nomogram Models. Diagnostics 2023, 13, 3156. https://doi.org/10.3390/diagnostics13193156
Fan Y-H, Shih K-Y, Wu P-W, Huang Y-L, Lee T-J, Huang C-C, Chang P-H, Huang C-C. Predicting the Probability of the Incidence of Maxillary Sinus Fungus Ball in Patients Using Nomogram Models. Diagnostics. 2023; 13(19):3156. https://doi.org/10.3390/diagnostics13193156
Chicago/Turabian StyleFan, Yu-Hsi, Kai-Yi Shih, Pei-Wen Wu, Yen-Lin Huang, Ta-Jen Lee, Chi-Che Huang, Po-Hung Chang, and Chien-Chia Huang. 2023. "Predicting the Probability of the Incidence of Maxillary Sinus Fungus Ball in Patients Using Nomogram Models" Diagnostics 13, no. 19: 3156. https://doi.org/10.3390/diagnostics13193156