1. Classification method of dental electronic health record (EHR) data potentially improves precision treatment
Di Wu1,2, Ayushi Gupta1, Kevin Moss1, Thiago Morelli1, James D Beck1, Steven Offenbacher1
UNC Chapel Hill, 1Department of Periodontics, 2Department of Biostatistics
Background
Results (Continued)
Exclusion Criteria
Have complete PD measure at 6 sites
The effects of the two treatments can’t be separated due to lack of an exam between them
Accurate classification of disease phenotypes is important to both mechanism studies and the clinical decisions, in the oral health field [1].
Recent, Latent Class Analysis (LCA) based clustering method at the patient level and the tooth level, was developed [2].
The classification model built based on Dental Atherosclerosis Risk in Community Study (ARICs) [3] cohort provides parameters for other dental cohort to classify individuals into seven latent periodontal profile classes (PPC, person level) classes based on 224 dichotomous variables (from 32 teeth).
For PPC, seven tooth-level clinical parameters included 1) ≥1 site with interproximal attachment level (IAL) 2) ≥1 site with PD ≥4 mm; 3) extent of bleeding on probing (BOP, dichotomized at 50% or ≥3 sites per tooth); 4) gingival inflammation index14 (GI, dichotomized as GI = 0 versus GI ≥1); 5) plaque index15 (PI, dichotomized as PI = 0 versus Pl ≥1); 6) presence/absence of full prosthetic crowns for each tooth; and 7) tooth status presence (present versus absent).
The tooth-level LCA classified teeth into seven latent tooth profile classes (TPCs, tooth level), based on 14 categorical clinical parameters. 1) IAL 2) direct attachment level 3) interproximal PD 4) direct PD; 5) interproximal gingival recession 6) direct GR 7 8) GI14 ; 9) PI15; 10) decayed coronal surface 11) filled coronal surface 12) decayed root surface 13) filled root and 14) presence/absence of full prosthetic crowns.
Evidence-based dental precision treatment can be evaluated using the electronic patient record (EPR)
We apply the LCA based method to our dental EPR data to evaluate the results, e.g., probing depths (PD) and bleeding on probing (BOP), of scaling and root planing (SC/RT) vs surgery (sg), stratified via PPC and TPC.
No significant change in CDC standard classes
(0, health; 1, mild; 2, moderate; 3, severe) person level
Number of visits, close to number of patients
surgery
Time gap between clinical exam and treatment, for before-treatment exam and after-treatment exam. Surgery treatment group has larger time gap than the ‘scaling and root planing’ group. This suggests different time cutoff to match exam and treatment..
X: Number of teeth (per visit), that have all 6 sites of PD measured
Y: number of patients
surgery
Scaling and root planing
Data Processing
9502 patients in all 5 datasets
9237 patients, PD at all 6 locations
3704 patients, treatments of Surgery or SC/RT
2622 visits (patient + treatment)
with both pre and post treatment exam, including 213 surgery and 2409 SC/RT for 2528 patients
2270 visits, with reliable (AL information) PPCs,
including 172 surgery and 2098 SC/RT for 2197 patients
1787 visits, with TPC results at the tooth level,
Including 1646 SC/RT and 141 surgery for 1728 patients
Data Analysis
SAS and R statistical languages
Average changed PD and BOP (post-pre treatment) was calculated for each TPC and PPC, for each of the two treatments.
Days between exam and treatment
surgery
Scaling and root planing
Pre-treatment
Post-treatment
Conclusion and Future
Severe teeth in severe patients have more significant improvement with surgery.
Classification method reveals the different treatment effects in different PPC classes, while the CDC/ADA standard classification (at person level) hasn’t shown different treatment effects.
LCA classification method with 7 PPCs and 7 TPCs is more sensitive for detecting certain treatment responses than other methods.
Interaction between these categories with covariates, e.g., age, smoking and diabetes could be further investigated.
Missing data at clinical measurements in EHR needs to be handled potentially.
Results
Distribution of TPC by PPC
Objectives
TPC color
We hypothesized that LCA methods using clinical measures to better refine clinical phenotypes (at a 7-level tooth class and 7-level subject class) and applied to harmonize data extracted from dental school periodontal databases might provide insight into creating effective measures of treatment outcomes.
Y-axis:TPC
x-axis: PPC
Surgery
SC/RT
Comparison of average PD (mm) and BOP (0/1) between after-treatment and before-treatment exams per TPC per PPC.
Blue Arrow indicates significant P value at 0.05 in t test when comparing the surgery to the SC/RT group.
References
Method
Eke PI, Dye BA, Wei L, Slade GD, Thornton-Evans GO, Borgnakke WS, Taylor GW, Page RC, Beck JD, Genco RJ. Update on Prevalence of Periodontitis in Adults in the United States: NHANES 2009 to J Periodontol May;86(5): doi: /jop Epub Feb 17.
Morelli T, Moss KL, Beck James, Preisser JS, Wu D, Divaris K, and Offenbacher S. Derivation and Validation of the Periodontal and Tooth Profile Classification System for Patient Stratifica-tion. J. Periodontol 2017;88:
Beck JD, Elter JR, Heiss G, Couper D, Mauriello SM, Offenbacher S. Relationship of periodontal disease to carotid artery intima-media wall thickness: the atherosclerosis risk in communities (ARIC) study. Arterioscler Thromb Vasc Biol Nov;21(11):
Dataset
dental school periodontal databases in , local dental EPR software
Inclusion Criteria
In all 5 databases, e.g., exam, history, demographic etc.
Having received at least one of the two treatments
PPC-A
PPC-B
PPC-C
PPC-D
PPC-E
PPC-F
PPC-G
Health
Mild Disease
High GI
Tooth Loss
Posterior Disease
Severe Tooth Loss
Severe Disease
ACKNOWLEDGEMENTS
Work supported by UL1-TR001111, DE
TPC-A
TPC-B
TPC-C
TPC-D
TPC-E
TPC-F
TPC-G
Health
Recession
Crown GI
Interproximal Disease
Reduced Periodontium
Severe
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