Journal of

Clinical Medicine
ArticleOptimal Examination Sites for Periodontal Disease
Evaluation: Applying the Item Response Theory
Graded Response Model
Yoshiaki Nomura 1 , Toshiya Morozumi 2, * , Mitsuo Fukuda 3 , Nobuhiro Hanada 1 ,Erika Kakuta 4 , Hiroaki Kobayashi 5 , Masato Minabe 2 , Toshiaki Nakamura 6 , Yohei Nakayama 7 ,Fusanori Nishimura 8 , Kazuyuki Noguchi 6 , Yukihiro Numabe 9 , Yorimasa Ogata 7 ,Atsushi Saito 10 , Soh Sato 11 , Satoshi Sekino 9 , Naoyuki Sugano 12 , Tsutomu Sugaya 13 ,Fumihiko Suzuki 14 , Keiso Takahashi 15 , Hideki Takai 7 , Shogo Takashiba 16 , Makoto Umeda 17 ,Hiromasa Yoshie 18 , Atsutoshi Yoshimura 19 , Nobuo Yoshinari 20 and Taneaki Nakagawa 21
1
2
3
4
5
6
7

8
9
10
11
12
13
14
15
16
17
18
19
20

Department of Translational Research, Tsurumi University School of Dental Medicine, Yokohama
230-8501, Japan; nomura-y@tsurumi-u.ac.jp (Y.N.); hanada-n@tsurumi-u.ac.jp (N.H.)
Division of Periodontology, Department of Oral Interdisciplinary Medicine, Graduate School of Dentistry,
Kanagawa Dental University, Yokosuka 238-8580, Japan; minabe@kdu.ac.jp
Department of Periodontology, School of Dentistry, Aichi Gakuin University, Nagoya 464-8650, Japan;
fukuda-m@dpc.agu.ac.jp
Department of Oral Microbiology, Tsurumi University School of Dental Medicine,
Yokohama 230-8501, Japan; kakuta-erika@tsurumi-u.ac.jp
Department of Periodontology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental
University, Tokyo 113-8510, Japan; h-kobayashi.peri@tmd.ac.jp
Department of Periodontology, Kagoshima University Graduate School of Medical and Dental Sciences,
Kagoshima 890-8544, Japan; toshi-n@dent.kagoshima-u.ac.jp (T.N.); kazuperi@dent.kagoshima-u.ac.jp (K.N.)
Department of Periodontology, Nihon University School of Dentistry at Matsudo, Matsudo 271-8587, Japan;
nakayama.youhei@nihon-u.ac.jp (Y.N.); ogata.yorimasa@nihon-u.ac.jp (Y.O.);takai.hideki@nihon-u.ac.jp (H.T.)
Section of Periodontology, Division of Oral Rehabilitation, Faculty of Dental Science, Kyushu University,
Fukuoka 819-0395, Japan; fusanori@dent.kyushu-u.ac.jp
Department of Periodontology, School of Life Dentistry at Tokyo, The Nippon Dental University,
Tokyo 102-8159, Japan; numabe-y@tky.ndu.ac.jp (Y.N.); sekino-s@tky.ndu.ac.jp (S.S.)
Department of Periodontology, Tokyo Dental College, Tokyo 101-0061, Japan; atsaito@tdc.ac.jp
Department of Periodontology, School of life Dentistry at Niigata, The Nippon Dental University,
Niigata 951-8580, Japan; s-sato@ngt.ndu.ac.jp
Department of Periodontology, Nihon University School of Dentistry, Tokyo 101-8310, Japan;
sugano.naoyuki@nihon-u.ac.jp
Division of Periodontology and Endodontology, Department of Oral Health Science, Hokkaido University
Graduate School of Dental Medicine, Sapporo 060-8586, Japan; sugaya@den.hokudai.ac.jp
Division of Dental Anesthesiology, Department of Oral Surgery, Ohu University School of Dentistry,
Koriyama 963-8611, Japan; f-suzuki@den.ohu-u.ac.jp
Division of Periodontics, Department of Conservative Dentistry, Ohu University School of Dentistry,
Koriyama 963-8611, Japan; ke-takahashi@den.ohu-u.ac.jp
Department of Pathophysiology-Periodontal Science, Okayama University Graduate School of Medicine,
Dentistry and Pharmaceutical Sciences, Okayama 700-8525, Japan; stakashi@okayama-u.ac.jp
Department of Periodontology, Osaka Dental University, Hirakata 573-1121, Japan;
umeda-m@cc.osaka-dent.ac.jp
Division of Periodontology, Department of Oral Biological Science, Niigata University Graduate School of
Medical and Dental Sciences, Niigata 951-8514, Japan; yoshie119@galaxy.ocn.ne.jp
Department of Periodontology and Endodontology, Nagasaki University Graduate School of Biomedical
Sciences, Nagasaki 852-8588, Japan; ayoshi@nagasaki-u.ac.jp
Department of Periodontology, School of Dentistry, Matsumoto Dental University, Shiojiri 399-0781, Japan;
nobuo.yoshinari@mdu.ac.jp

J. Clin. Med. 2020, 9, 3754; doi:10.3390/jcm9113754

www.mdpi.com/journal/jcm

J. Clin. Med. 2020, 9, 3754

21*

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Department of Dentistry and Oral Surgery, School of Medicine, Keio University, Tokyo 60-8582, Japan;
tane@z6.keio.jp
Correspondence: morozumi@kdu.ac.jp; Tel.: +81-46-822-8855

Received: 9 October 2020; Accepted: 19 November 2020; Published: 21 November 2020


Abstract: Periodontal examination data have a complex structure. For epidemiological studies,mass screenings, and public health use, a simple index that represents the periodontal conditionis necessary. Periodontal indices for partial examination of selected teeth have been developed.
However, the selected teeth vary between indices, and a justification for the selection of examination
teeth has not been presented. We applied a graded response model based on the item response
theory to select optimal examination teeth and sites that represent periodontal conditions. Data wereobtained from 254 patients who participated in a multicenter follow-up study. Baseline data wereobtained from initial follow-up. Optimal examination sites were selected using item information
calculated by graded response modeling. Twelve sites—maxillary 2nd premolar (palatal-medial),
1st premolar (palatal-distal), canine (palatal-medial), lateral incisor (palatal-central), central incisor(palatal-distal) and mandibular 1st premolar (lingual, medial)—were selected. Mean values forclinical attachment level, probing pocket depth, and bleeding on probing by full mouth examinations
were used for objective variables. Measuring the clinical parameters of these sites can predict theresults of full mouth examination. For calculating the periodontal index by partial oral examination,a justification for the selection of examination sites is essential. This study presents an evidence-based
partial examination methodology and its modeling.
Keywords: periodontitis; epidemiological index; item response theory; oral examination; diagnosis;
bleeding on probing

1. Introduction
Periodontal examination should be carried out precisely for the evaluation of periodontal diseases,
especially their clinical parameters. Improvement in or progression of periodontitis should be monitored
at the site-level along with periodontal treatment [1]. One of the important characteristics of periodontal
disease is the localization of infectious processes at specific sites, which eventually leads to tissue
destruction [2]. Therefore, the accumulated data obtained during periodontal examination is copiousand structurally complex [1]. Each of the 28 teeth has six examination sites for measuring parameterssuch as bleeding on probing (BOP), periodontal pocket probing depth (PD), and clinical attachment level(CAL). These measurements are evaluated several times throughout the course of periodontal disease
treatment. Representative summary statistics include the mean value of CAL and PD, maximum valueof CAL and PD, and percentage of sites with BOP (BOP%). Conventionally, summary statistics
have been used in several clinical trials for the patient-level evaluation of clinical parameters [3].
However, aggregating summary statistics like the mean value or maximum value can lead to the lossof information [4]. Full-mouth protocols are proven to be the most effective [5]. When time and labor
are limited, a simplified index may be necessary to represent the periodontal condition.
Several periodontal indices have been developed for epidemiological surveys andperiodontal disease screening: Periodontal disease index (PDI) [6,7], Periodontal Index (PI) [8],Community periodontal index (CPI or CPITN) [8,9], Gingival bone count [10], PMA index [11],Gingival index [12]. They are based on a partial-examination method in which the target teeth for
examination vary between the indices. A justification for the selection of these target teeth is not always
clear. However, the developers of epidemiological indices may notice that there are several teeth or
examination sites within the oral cavity that represent periodontal conditions at an individual level.

J. Clin. Med. 2020, 9, 3754

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In many educational and psychological studies, a latent variable is often used as the outcome
variable. These variables cannot be measured directly and are estimated by their response to theobservational items. Item response theory (IRT) modeling is an important methodology commonlyused for the development of tests to measure the ability by total score of test consisted of items withweighted scores [13]. Through the application of IRT, we can examine each item’s reliability andwhether it contributes to an overall construct [14,15]. Total score correspond to the sum of the valuesof periodontal examinations. Items correspond to the values of periodontal examinations. Therefore,
IRT is applicable for the indexes used in dental search.
IRT can be applied to the clinical parameters of periodontal disease. The progress of periodontal
disease at an individual level corresponds to ability. Susceptibility to the progress of PD, CAL, and BOP
corresponds to item difficulty. Discrimination parameter corresponds to the predictability of eachsite to represent all examination sites or teeth. Previous studies have shown that IRT can efficiently
characterize dental caries susceptibility [16,17]. IRT graded response modeling has also been appliedin the evaluation of existing measures in several clinical areas, such as those related to swallowing
and communication disorders [18,19]. Furthermore, by using IRT models with clinical diagnoses
from electronic health records, a constellation of high-risk patients could be identified [20]. Therefore,
by applying the IRT model to periodontal data, evidence-based target sites or teeth can be selected torepresent and reflect the periodontal conditions of all sites or teeth in the oral cavity.
This study aimed to identify the most reliable subset of teeth able to represent a full-mouth
periodontal diagnosis.
2. Materials and Methods
2.1. Study Design
2.1.1. SettingThis study was part of a clinical research project by the Japanese Society of Periodontology,
in cooperation with 17 facilities (one clinic and 16 university hospitals) in Japan for the diagnosis ofperiodontitis [1,21,22]. Two-hundred-fifty-four patients with chronic periodontitis were chosen between
February 2009 and February 2012 for this study, who had completed their active treatment regulatedby the Japanese health insurance system. All 254 patients who registered the study were analyzed.
2.1.2. Diagnosis
Each patient was diagnosed according to the guideline at the time (Guidelines of the American
Academy of Periodontology) [23]. One examiner from each institute (T.M., M.F., H.K., M.M., T.N.,
Y.N., K.N., S.S., N.S., S.S., T.S., F.S., H.T., H.Y., A.Y., N.Y. and T.N.) was chosen to carry out the oral
examinations. Each examiner was a periodontist licensed by the Japanese Society of Periodontology.
Intra- and inter-examiner calibration session were conducted at the beginning and middleof the study period. Diagnosis of periodontitis was based on the proposed criteria by theCenter for Disease Control and Prevention (CDC) in partnership with the American Academyof Periodontology (AAP) [24].
2.1.3. Patients
Each patient was ≥ 30 years of age, possessed at least 20 teeth, was systemically healthy, and had
not been administered immunosuppressive or anti-inflammatory drugs or systemic antibiotics within
3 months before the initiation of the investigation.

J. Clin. Med. 2020, 9, 3754

4 of 172.2. Research Data
In this study, we analyzed CAL, PD, BOP, plaque index (PlI), and tooth mobility. CAL wasmeasured at six sites for all of the remaining teeth (mesiobuccal, buccal, distobuccal, mesiolingual,lingual, and distolingual). The data of CAL were categorized as < 4, 4–5, and > 5 mm.
2.3. Statistical Analysis
2.3.1. IRT Modeling
Based on the IRT model for ordinal polytomous data, we applied a Graded Response Model [25–29].
Item difficulty, item discrimination, item information for the examined sites, and ability of the subjects
were calculated [30–34]. The R software with the ltm package was used to perform the IRT analysis [27].
To reduce the total number of examination sites, sites with small item information were removed fromthe IRT model. This procedure were based on a step-by-step analysis. Using the data for CAL, a model
was constructed for all examination sites (Model 1). Next, out of all 168 examination sites, 28 sites
representing the highest information for each tooth (sum of left and right side) were selected. An IRT
model was constructed using these 28 sites (Model 2). Out of these 28 sites, 12 sites in six teeth wereselected for depicting a higher information (Model 3). Finally, the data from the right and left side
were categorized as follows: at least one site with >5 mm CAL; at least one site with 4–5 mm CAL;
or both sites with <4 mm CAL. Even though there may be optimal examination sites for each clinicalparameter, the examination of numerous sites for each clinical parameter may be a laborious procedure
for an epidemiological examiner or clinician. IRT models for BOP and PD were constructed in thesame manner as for CAL.
2.3.2. Model Evaluation
For the scatterplot, regression analysis was carried out. Generalized linear models wereapplied. For optimal link functions, models were evaluated using Akaike’s information criteria [35].
Receiver operating characteristic (ROC) curve was used to analyze sensitivities and specificities.
The cutoff points were determined as the minimum difference between specificity and sensitivity [36,37].
The mean CAL of all examination sites and community periodontal index (CPI) were used as reference.
Diagnostic criteria by the CDC-AAP [24] was used. Statistical Package for the Social Sciences version
24.0 (IBM, Tokyo, Japan) was used to perform the analyses.
To compare the model to other studies, Sensitivity, relative bias (Severity) relative bias (Extent)
were calculated [38–40].
2.4. Ethical Approval
The study was conducted in compliance with the principles outlined in the Helsinki Declaration.
Informed written consent was obtained from each subject, and the protocol was approved by theInstitutional Review Board of each participating institution. The ethics committee members’ names
and reference numbers are listed in Appendix A.
3. Results
3.1. Descriptive Statistics of the Subjects Participated in this Study
Descriptive statistics of periodontal clinical parameters were the 3.1 mm for mean of CAL, 2.5 mm
for mean of PD, 15.0% for BOP%, and 0.3 for PlI.
3.2. Optimal Site Selection by IRT Modeling
The final model for CAL (Model 4) is shown in Table 1, accompanied with models for the remaining
clinical parameters. Item information and item response curves of Model 4 are shown in Figure S1.
Using these steps, 168 examination sites were narrowed down to six variables located at 12 sites

J. Clin. Med. 2020, 9, 3754

5 of 17(same sites on the right and left side). The results of each step from Model 1 to 4 are shown in Table S1.
A quick reference for the calculation of ability by Model 4 is presented in Appendix B.
Table 1. Final model (Model 4) for the clinical attachment level.
MaxillaExtrmt1
CAL

PD

BOP

PlI

Tooth
Mobility

Mandibular

2nd
Premolar

1st
PremolarCanineLateralIncisorCentralIncisor

1st
Premolar

PalatalPalatalPalatalPalatalPalatalLingualMedialDistalMedialCentralDistalMedial

0.28

0.45

0.65

1.13

0.75

0.53

Extrmt2

1.28

1.24

1.31

1.59

1.38

1.57

Discrimination

3.07

3.42

4.51

4.83

3.16

2.03

Item
information

5.47

5.92

7.97

7.93

5.02

3.27

Extrmt1

0.72

0.88

0.96

1.35

1.01

1.03

Extrmt2

1.46

1.48

1.39

1.79

1.45

1.63

Discrimination

3.89

3.38

4.89

4.63

3.90

3.39

Item
information

6.82

5.37

7.87

7.41

5.94

5.40

Extrmt1

1.30

1.22

1.56

1.45

1.63

1.95

Discrimination

3.70

7.71

2.93

16.96

2.48

2.09

Item
information

3.70

7.62

2.93

16.65

2.48

2.09

Extrmt1

0.38

0.47

0.35

0.27

0.47

0.82

Extrmt2

1.85

1.70

1.65

1.81

1.96

2.03

Discrimination

2.78

3.85

3.86

2.66

2.14

2.19

Item
information

5.25

7.44

7.50

5.03

3.85

3.76

Extrmt1

0.87

0.77

1.22

0.69

0.72

1.19

Extrmt2

1.90

1.85

2.21

1.79

1.99

2.56

Discrimination

2.41

2.53

2.98

3.81

3.23

2.07

Item
information

4.06

4.39

5.28

7.24

6.11

3.62

AIC

BIC

1796.06

1859.731198.731262.41686.60729.04

1965.74

2029.411445.901509.57

Extrmt: extremity parameters; CAL: clinical attachment level; PD: probing depth; BOP: bleeding on probing;
PlI: plaque index. For CAL, it shows the cutoff to discriminate CAL < 4 mm, CAL 4−5 mm, and CAL > 5 mm.
Extrmt1 discriminates CAL < 4 mm and (CAL 4−5 mm and CAL > 5 mm), and Extrmt 2 discriminates (CAL < 4 mm
and CAL 4–5 mm) and CAL > 5 mm. Discrimination: This parameter shows the height of item characteristic curves.
For the item response theory (IRT) analysis, CAL and PD were categorized as at least one site with >6 mm, at least
one site with 4−6 mm, or both sites with <4 mm on the left or right side. IRT analysis was carried out using a graded
response model. AIC: Akaike’s information criterion; BIC: Bayesian information criterion; both are fitness indices,in which small values are more suitable for a model fit.

3.3. Model Evaluation
3.3.1. Evaluation of Selected Sites
In clinical practice, the mean values of all examination sites are often used as summary statistics.
The selected 12 sites were evaluated using a scatter plot by plotting the mean values of each clinicalparameter against the mean values of the selected 12 sites. The results are shown in Figure 1. For eachclinical parameter, adequate co-relations were obtained.

J. Clin. Med. 2020, 9, x FOR PEER REVIEW

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parameter against the mean values of the selected 12 sites. The results are shown in Figure 1. For each
J. Clin. Med.
2020,parameter,
9, 3754
clinicaladequate co-relations were obtained.

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Figure 1. Scatter plot of the mean values of clinical parameters against the mean values of the selected
Figure 1. Scatter plot of the mean values of clinical parameters against the mean values of the selected
12 sites. (A) CAL: clinical attachment level. (B) PD: probing depth. (C) BOP: bleeding on probing.
12 sites. (A) CAL: clinical attachment level. (B) PD: probing depth. (C) BOP: bleeding on probing. (D)(D) PlI: plaque index. The selected 12 sites were the same sites that are listed in the Figure 1 legend.
PlI: plaque index. The selected 12 sites were the same sites that are listed in the Figure 1 legend.

3.3.2. Model Evaluation

3.3.2. Model Evaluation

The models
werewereevaluatedusing
correlation
betweenpredictive
andThe models
evaluatedusingtwo
twomethods:
methods: correlation
betweenpredictive
valuesvalues
andobserved
valuesvalues
and ROC
curve
analysis.
calculatedbyby
IRT
analysisindicates
the predictive
observed
and ROC
curve
analysis.Ability
Ability calculated
IRT
analysisindicates
the predictiveof the sample.
The scatterof theabilitycalculated
calculated using
using Modelthethemean
valuesvalue ofvalue
the sample.
The scatter
plotplotof theability
Model4 4against
againstmean
values of
CAL is illustrated
in Figure
2. A scatterall 168
examinationsites
sites(Model(Model 1)
CAL is ofillustrated
in Figure
2. A scatterplot plotof allof168
examination
1)isisalso
alsopresented
presented as a
as a reference. As the plot appears to be a curve, the generalized linear model was applied. The
reference. As the plot appears to be a curve, the generalized linear model was applied. The coefficient
coefficient and intercept were statistically significant. The scatter plot of the result of the generalized
and intercept
were statistically significant. The scatter plot of the result of the generalized linear model
linear model against the models for other clinical parameters is shown in Table S2.
J. Clin. Med. 2020, 9, x FOR PEER REVIEW
7 of 18
against the models for other clinical parameters is shown in Table S2.

Figure 2. Scatter plot of the mean value of the clinical attachment level against the ability calculated by
Figure 2. Scatter plot of the mean value of the clinical attachment level against the ability calculated
item response theory. Abilities are calculated using a graded response model under the item responseby item response theory. Abilities are calculated using a graded response model under the item
theory response
approach.
Abilities are calculated for all the 168 examined sites (A) and the selected six variables
theory approach. Abilities are calculated for all the 168 examined sites (A) and the selected
at the 12(B). Ability
sumof theweighted
scoresof eachitems.
In thiscase,abilitysixsitesvariablesat the 12meanssites (B).
Abilitymeans
sum of theweighted
scoresof eachitems.
In thiscase,indicate
the sumof theweighted
scorespositivescoresfor periodontal
examination.
abilityindicate
the sumof theofweighted
of positive for
periodontal examination.
The data from the same location site are combined with at least one site with 6 mm, at least onesite with 4–6 mm, or both sites 4 mm.
The plot appears to be a curve. Therefore, the generalized linear model is applied for the
relationship.
The selected sites were maxillary 2nd premolar (palatal-distal), maxillary 1st premolar (palatalmedial), maxillary Canine (palatal-distal), maxillary lateral incisor (palatal-central), maxillary central
incisor (palatal-medial), and mandibular 1st premolar (lingual-medial).

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The data from the same location site are combined with at least one site with >6 mm, at least onesite with 4–6 mm, or both sites <4 mm.
The plot appears to be a curve. Therefore, the generalized linear model is applied forthe relationship.
The selected sites were maxillary 2nd premolar (palatal-distal), maxillary 1st premolar(palatal-medial), maxillary Canine (palatal-distal), maxillary lateral incisor (palatal-central),maxillary central incisor (palatal-medial), and mandibular 1st premolar (lingual-medial).
Based on the ROC analysis, sensitivity, specificity, likelihood, and area under ROC curve (AUR)
are presented in Table 2. The results of the models for other clinical parameters are also shown in
Table 2. For the mean value of CAL >3 mm, sensitivity and specificity were 0.832 and 0.852, respectively,
and for >5 mm, they were 0.895 and 0.911, respectively. The ROC curve of each clinical parameter andvarious cutoff points are presented in Figure S2.
Table 2. Sensitivity, specificity, and area under the receiver operating characteristic curve for the six variables.

Mean CAL

Mean PD

BOP

PlI

>3 mm
>3.5 mm>4 mm>4.5 mm>5 mm>3 mm>3.5 mm>4 mm>4.5 mm
>5 mm
>2.5%
>5%
>10%
>20%
>25%
>10
>20
>30
>40
>50

Cutoff Point

Sensitivity

Specificity

AUR−0.190
0.193
0.546
1.033
1.082
0.703
0.891
1.202
1.391
1.548
−0.097−0.097−0.097−0.097−0.097−0.141
0.313
0.540
0.695
0.710

0.832
0.865
0.891
0.929
0.895
0.897
0.926
0.947
1.000
1.000
0.453
0.388
0.571
0.687
0.765
0.849
0.838
0.824
0.831
0.825

0.852
0.878
0.885
0.929
0.911
0.912
0.930
0.949
0.971
0.984
0.913
0.939
0.886
0.834
0.813
0.829
0.839
0.822
0.776
0.838

0.922
0.939
0.960
0.978
0.977
0.974
0.983
0.985
0.989
0.992
0.671
0.698
0.755
0.804
0.847
0.890
0.915
0.915
0.924
0.912

CAL: clinical attachment level; PD: probing depth; BOP: bleeding on probing; PlI: plaque index; AUR: area underreceiver operating characteristic curve. Cutoff points are set in abilities calculated using graded response theory.
Graded response theory is one of the models of item response theory.

3.4. Application of the CAL Model for Diagnosis of Periodontal Disease
The cutoff point, sensitivity, specificity, and AUR for the diagnosis of periodontal disease by the
CDC-AAP are presented in Table 3. ROC curves are shown in Figure S3. For moderate periodontitis,the simple mean CAL of all examination sites is most useful, followed by the mean CAL of optimal
examination sites. For severe periodontitis, the CPI is most useful. Simple mean CAL were notobtained similar AUR for CPI.

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Table 3. Receiver operating characteristic analysis for the selected optimal examination sites for the
diagnosis of periodontal disease.
Diagnosis

CAL mean(12 sites)
CAL mean(All sites)
CAL ability(6 value)
CAL ability(12 sites)
CPI

Moderate Periodontitis

Severe Periodontitis

CutoffSensitivity

Specificity

AUR

CutoffSensitivity

Specificity

AUR

2.20

0.82

0.88

0.91

2.65

0.75

0.72

0.85

2.21

0.91

0.92

0.96

3.04

0.78

0.78

0.88

−0.72

0.71

0.88

0.82

−0.47

0.86

0.62

0.83

−0.79

0.79

0.79

0.87

0.05

0.81

0.81

0.87

2

0.84

0.92

0.88

4

0.81

0.94

0.91

CPI: community periodontal index; CAL: clinical attachment level.

3.5. Prediction of Conventional Periodontal Indices by the CAL Model
The model was applied to conventionally use summary statistics, i.e., mean value of CAL, PD,
and BOP%. Clinically useful cutoff points were set for each index. The results are presented inTable 4. The best predictors were the mean values of the clinical parameters by the mean values of the
12 selected sites (e.g., mean CAL by the mean CAL of the 12 sites). For obtaining the mean values of
PD and BOP%, we were able to obtain higher values of the AUR compared to the CPI by calculating
the simple mean CAL of the 12 selected sites. Ability of CAL, weighted CAL, could obtain higher
AUR. All ROC curves are shown in Figure S4. By measuring the CAL, all other clinical parameters canbe predicted.
3.6. Model Evaluation by Prevalence, Severity, and Extent
The prevalence, relative bias for severity and extent of model 4 were 62%, −0.0035,
and −0.017, respectively.

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Table 4. Receiver operating characteristic curve analysis of the selected optimal examination sites for the mean values of pocket probing depth, clinical attachmentlevel, and bleeding on probing.
CutoffSensitivity

Specificity

AUR

CutoffMean CAL > 3.5 mm

Mean CAL

Sensitivity

Specificity

AUR

CutoffMean CAL > 4 mm

Sensitivity

Specificity

AUR

Mean CAL > 4.5 mm

CAL mean(12 sites)

3.04

0.92

0.90

0.96

3.52

0.91

0.91

0.98

3.95

0.93

0.94

0.99

CAL ability(6 values)

0.16

0.89

0.88

0.95

0.55

0.89

0.88

0.96

1.03

0.93

0.93

0.98

CAL ability(12 site)

0.35

0.89

0.90

0.96

0.69

0.93

0.93

0.97

0.90

0.93

0.93

0.99

CPI

4

0.72

0.77

0.77

2

0.96

0.36

0.81

4

0.86

0.69

0.78

Mean PD > 3.5 mm

Mean PD

Mean PD > 4 mm

Mean PD > 5 mm

CAL mean(12 sites)

3.90

0.08

0.11

0.96

4.13

0.95

0.92

0.97

5.04

1.00

0.96

0.99

CAL ability(6 values)

4.03

0.89

0.91

0.97

0.99

0.89

0.89

0.96

1.44

1.00

0.96

0.99

CAL ability(12 sites)

0.70

0.85

0.87

0.95

0.89

0.89

0.89

0.96

1.20

1.00

0.93

0.98

CPI

4

1.00

0.71

0.85

4

1.00

0.68

0.84

4

1.00

0.65

0.82

PD mean (12sites)

0.68

0.89

0.86

0.95

3.78

1.00

0.95

0.99

4.46

1.00

0.97

0.99

BOP > 10%BOP

BOP > 20%

BOP > 30%

CAL mean(12 sites)

2.65

0.64

0.65

0.71

2.96

0.76

0.75

0.83

3.26

0.82

0.80

0.89

CAL ability(6 values)

−0.26

0.65

0.62

0.69

0.12

0.74

0.77

0.80

0.46

0.77

0.82

0.86

CAL ability(12 sites)

0.03

0.63

0.66

0.67

0.31

0.74

0.75

0.80

0.60

0.75

0.85

0.85

CPI

4

0.59

0.79

0.72

4

0.75

0.76

0.80

4

0.84

0.72

0.81

BOP% mean(12 sites)

0.73

0.84

0.87

0.91

1.97

0.81

0.94

0.95

2.18

0.91

0.92

0.97

CAL: clinical attachment level; PD: probing depth; BOP: bleeding on probing; AUR: area under receiver operating characteristic curve; CPI: community periodontal index.

J. Clin. Med. 2020, 9, 3754

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4. Discussion
Public health applications of periodontal examination such as epidemiological surveys,
mass screenings, and community diagnosis, simplified indices that represent an indivisible diseasestatus are indispensable. For this purpose, several indices have been developed [6–12]. Developedby the World Health Organization, the CPI has been utilized in epidemiological surveys not just forcommunity diagnosis but also for the screening of periodontal disease. Originally, the index was
calculated through the examination of eight teeth [41]. It has currently been revised to include theexamination of all teeth [42]; however, the original method of examining just eight teeth is also still
applied. According to Japan’s Survey of Dental Diseases, national oral health surveys, which areconducted every six years, still use the original CPI examination method. However, we were unable
to find any justification for the tooth-selection methodology or periodontal indices used in thesesurveys [43].
Several methods that do not require oral examinations for the screening of periodontal disease
have been proposed [36,44–48]. These include questionnaires [49,50] and biochemical analysis of thesaliva [47] or gingival crevicular fluid [51]. However, the sensitivity and specificity of questionnaires
used for periodontitis screening is not high enough, and using biochemical analyses requires a special
measuring device. Therefore, oral examinations are still widely used for periodontal disease screenings.
In comparison to the CPI, our model was superior in predicting clinical parameters and almost
equivalent in diagnosing moderate periodontitis. Further, partial examination using the CPI requires
the examination of 60 sites; our model requires the examination of only 6–12 sites. Furthermore,
the examination sites presented by our model are more representative of the periodontal conditionsin an oral cavity. As shown in Table 4, the partial examination of these sites represents the meanvalue of each examined index by the mean value of all examination sites in the oral cavity. By simply
measuring the CAL, all other clinical parameters can be predicted. The model presented in this study
was derived using the IRT approach. The IRT model is very useful for the selection of items that havehigh information. However, IRT models can only process dichotomous variables or ordinal scale; they
are unable to process contentious variables. At this stage, a loss of information can occur. Therefore,
the simple mean of 12 selected sites is more suitable for calculating some of the predictions presented
in this study.
When the 12 selected sites are compared with other partial examination protocol, sampling sites are
predominantly small. Sampling sites of other protocols were 84 [38,39,52,53], 60 [54], and 56 [38,39,52].
Partial examination protocol by high number of examination site can detect small number of deep CAL
or PD. The sensitivity indicate to detect subject with at least one site of CAL > 4 mm. The sensitivity by
84 examination site was 92% [52], by 56 site was 66% [52] and by 28 site was 57% [52]. The sensitivity by
the 12 site in this study was 62%. In addition, relative bias of severity of the 12 sites, which estimate thedifference of mean value of CAL between full mouth examination, was −0.0035. This value protocols
by 84 site were 0.009 [52], −0.046 [38] and −0.01 [53]. The 12 sites based on statistical model may equal,in other partial examination protocol, more than 5 times higher numbers of examination sites.
In this study, the teeth selected for examination included premolars and anterior teeth; themolars were excluded. The molar is a double-rooted tooth with complex anatomical root morphology,
including root length, furcation area, and divergence of root and root trunk. Cervical enamel projections
and enamel pearls also occur commonly in molars and are considered to be risk factors for periodontal
disease; however, their occurrence varies among different individuals [55]. Additionally, molars haveto withstand high occlusal forces, which can contribute to periodontal tissue destruction. Therefore,
we excluded molars from representing oral examination sites in our analysis.
The periodontal disease index is used for assessing the periodontal status in epidemiologicalsurveys; six target teeth (maxillary right 1stmolar, maxillary left central incisor, maxillary left 1st
premolar, mandibular left 1st molar, mandibular left 1st molar, and mandibular right 1st premolar) arescored for the assessment of the disease. However, a sufficient justification for the selection of theseteeth has not been provided [6]. For the index, evidence is indispensable.

J. Clin. Med. 2020, 9, 3754

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In this study, optimal sites were selected based in the item information through the models
presented in Table S1, and Table 1. Twelve sites: maxillary 2nd premolar (palatal-medial), 1st premolar(palatal-distal), 3 canine (palatal-medial), lateral incisor (palatal-central), central incisor (palatal-distal)and mandibular 1st premolar (lingual-medial) selected in this study were based on statistical modelingand represented the periodontal conditions. a full mouth examination is a best method; however,as time and labor are limited, partial examination may be applicable. Partial examination of these sites
may be useful tool for epidemiological studies, mass screenings, and public health use.
There are several limitations in this study. The study population was consisted of the patients who
experienced active periodontal treatment. The wider population is necessary to confirm the robustnessof the model presented in this study. However, several partial-mouth assessments were not basedon the statistical modeling. The strength of the model presented in this study was based on the IRTmodel, and weights for the site were calculated to improve the predictive values.
5. Conclusions
For calculating the periodontal index by partial oral examination, a justification for the selection of
examination sites is necessary. This study presents an evidence-based partial examination method andits modeling. The 12 sites presented in this study almost equal to other partial examination protocol,
which have more than 5 times the number of sampling sites.
Supplementary Materials: The attached supplementary materials are available online at http://www.mdpi.com/
2077-0383/9/11/3754/s1, Figure S1: Item response curve and item information curves by the selected six values of
the clinical attachment level, Figure S2: ROC curves by the selected six values, Figure S3: ROC curves for the
diagnosis of periodontal disease by the selected site, Figure S4: ROC curves for clinical parameters by the selectedsite, Table S1: Constructed models, Table S2: Generalized linear model to predict the mean value of the clinicalattachment level by the selected site
Author Contributions: Conceptualization, Y.N. (Yoshiaki Nomura) and T.M.; methodology, Y.N. (Yoshiaki Nomura);software, Y.N. (Yoshiaki Nomura); validation, E.K. and N.H.; formal analysis, Y.N. (Yoshiaki Nomura);
investigation, T.M., M.F., H.K., M.M., T.N. (Taneaki Nakagawa), Y.N. (Yukihiro Numabe), F.N., K.N., Y.N.
(Yohei Nakayama), Y.O., A.S., S.S. (Soh Sato), N.S., S.S. (Satoshi Sekino), T.S., F.S., K.T., H.T., M.U., H.Y., A.Y., N.Y.
and T.N. (Toshiaki Nakamura); data curation, S.T.; writing—original draft preparation, Y.N. (Yoshiaki Nomura);
writing—review and editing, Y.N. (Yoshiaki Nomura) and T.M.; visualization, Y.N. (Yoshiaki Nomura);supervision, T.M. and H.Y.; project administration, T.M., T.N. (Taneaki Nakagawa) and H.Y.; funding acquisition,
T.N. (Taneaki Nakagawa) and H.Y. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by a clinical research project grant from the Japanese Society of Periodontology
for the diagnosis of periodontitis.
Acknowledgments: The authors thank Toshihide Noguchi, Masamitsu Kawanami, Koichi Ito, Yuichi Izumi,
Yoshitaka Hara, Osamu Fujise, Yuzo Abe, Tomoo Kono, Asako Makino-Oi, and Chie Fukaya, for their advice anduseful comments.
Conflicts of Interest: The authors declare no conflict of interest.

Appendix A
List of ethical committees and their approval numbers.
Institution

Name of the Ethics Committee

Reference Number

Niigata University

The regional ethical committee of theFaculty of Dentistry, Niigata University

20-R17-08-06

Keio University

Keio University School of Medicine,
Ethics Committee

20080096

Hokkaido University

Institutional Review Board for ClinicalResearch of Hokkaido University Hospital008-0113

Ohu University

Ohu University Research
Ethics Committee

52

J. Clin. Med. 2020, 9, 3754

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Institution

Name of the Ethics Committee

Reference Number

School of life Dentistry at Niigata,
The Nippon Dental University

The Ethical Review Committee of The
Nippon Dental University School of Life
Dentistry at Niigata

151

Tokyo Dental College

Ethics Committee of Tokyo Dental College

208

Bunkyo-Dori Dental Clinic

The regional ethical committee of theFaculty of Dentistry, Niigata University

20-R17-08-06

Nihon University School of
Dentistry at Matsudo

Ethics Committee in Nihon University
School of Dentistry at Matsudo

EC 08-014

Tokyo Medical and
Dental University

Dental Research Ethics Committee ofTokyo Medical and Dental University

660

Nihon University Schoolof Dentistry

Ethical Committee of Nihon University
School of Dentistry

EP08D016

School of Life Dentistry at Tokyo,
The Nippon Dental University

The Institutional Review Board of Nippon
Dental University.

2-1-22

Matsumoto Dental University

The Ethics Committee of Matsumoto
Dental University

0090

Aichi Gakuin University

Ethics Committee: Aichi Gakuin
University, School of Dentistry

158

Osaka Dental University

the Ethics Committee of Osaka
Dental University

80712

Kyushu University

Ethical Committee of Kyushu University
Faculty of Dental Science

20-11

Nagasaki University

The Ethics Committee,
Nagasaki University Graduate School of
Biomedical Sciences

0846-2

Kagoshima University

Ethical Committee of Kagoshima
University Medical and Dental Hospital.

20-58

Appendix B
Quick reference for the calculation of ability by Model 4.
MaxillaMandibular

5

4

3

2

1

4

PalatalPalatalPalatalPalatalPalatalLingualMedialDistalMedialCentralDistalMedial

1

1

1

1

1

1

−0.932

1

1

1

1

1

2

−0.442

1

1

1

1

2

1

−0.223

1

1

1

1

2

2

0.052

1

1

1

1

3

2

0.159

1

1

1

2

1

1

0.16

1

1

2

1

1

1

−0.013

1

1

2

1

1

2

0.21

1

1

2

1

2

1

0.321

Ability

J. Clin. Med. 2020, 9, 3754

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MaxillaMandibular

5

4

3

2

1

4

PalatalPalatalPalatalPalatalPalatalLingualMedialDistalMedialCentralDistalMedial

1

1

3

1

1

1

0.108

1

1

3

3

2

2

1.315

1

2

1

1

1

1

−0.506

1

2

1

1

1

2

−0.156

1

2

1

1

1

3

−0.066

1

2

1

1

2

1

0.009

1

2

2

1

1

3

0.434

1

2

2

1

2

2

0.575

1

2

2

1

3

1

0.566

1

2

2

2

2

1

0.828

1

3

1

1

2

2

0.293

1

3

2

2

2

1

0.887

2

1

1

1

1

1

−0.306

2

1

1

1

1

2

−0.008

2

1

1

1

2

1

0.135

2

1

1

2

2

1

0.65

2

1

2

1

1

1

0.28

2

1

2

1

1

3

0.522

2

1

2

2

2

3

1.038

2

1

2

3

3

2

1.334

2

1

3

1

1

1

0.468

2

1

3

1

3

1

0.887

2

2

1

1

1

1

−0.053

2

2

1

1

1

2

0.176

2

2

1

1

1

3

0.262

2

2

1

1

2

2

0.449

2

2

1

1

2

3

0.53

2

2

1

2

1

1

0.542

2

2

2

1

1

1

0.411

2

2

2

1

1

2

0.549

2

2

2

1

2

2

0.727

2

2

2

1

3

2

0.847

2

2

2

2

2

2

1.027

2

2

2

2

3

3

1.228

2

2

2

3

2

1

1.121

2

2

3

1

1

1

0.613

Ability

J. Clin. Med. 2020, 9, 3754

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MaxillaMandibular

5

4

3

2

1

4

PalatalPalatalPalatalPalatalPalatalLingualMedialDistalMedialCentralDistalMedial

2

2

3

2

2

2

1.229

2

2

3

2

2

3

1.31

2

2

3

2

3

2

1.373

2

3

1

1

1

1

0.023

2

3

1

1

1

3

0.339

2

3

2

1

2

1

0.68

2

3

2

1

2

2

0.785

2

3

2

1

3

3

0.991

2

3

2

2

2

2

1.083

2

3

2

2

3

2

1.213

2

3

3

1

2

3

1.081

2

3

3

1

3

1

1.048

2

3

3

2

1

1

1.097

2

3

3

2

2

3

1.38

2

3

3

3

2

3

1.701

3

1

1

1

2

3

0.61

3

1

1

1

3

2

0.663

3

1

3

3

3

3

1.979

3

2

1

1

1

2

0.393

3

2

2

1

2

1

0.773

3

2

2

1

2

2

0.868

3

2

2

1

3

2

0.995

3

2

2

2

3

1

1.209

3

2

2

3

3

3

1.684

3

2

3

2

1

1

1.181

3

2

3

2

3

2

1.521

3

2

3

3

3

1

1.801

3

2

3

3

3

3

2.042

3

3

1

1

1

2

0.468

3

3

2

1

2

2

0.927

3

3

2

2

1

3

1.185

3

3

2

2

2

1

1.137

3

3

2

2

3

3

1.442

3

3

2

3

2

2

1.475

3

3

2

3

3

2

1.664

3

3

3

1

2

3

1.243

3

3

3

1

3

1

1.222

Ability

J. Clin. Med. 2020, 9, 3754

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MaxillaMandibular

5

4

3

2

1

4

PalatalPalatalPalatalPalatalPalatalLingualMedialDistalMedialCentralDistalMedial

3

3

3

2

1

3

1.439

3

3

3

2

2

1

1.362

3

3

3

2

2

3

1.53

3

3

3

3

2

3

1.9

3

3

3

3

3

2

2.012

3

3

3

3

3

3

2.228

Ability

1: CAL <4mm; 2: CAL = 4–6mm; 3: CAL >6mm.

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