Validation of Periodontal and Tooth Profile Classification System for determining Periodontitis Treatment Outcomes: Aimed at Precision Medicine

Transcription

1 Validation of Periodontal and Tooth Profile Classification System for determining Periodontitis Treatment Outcomes: Aimed at Precision Medicine By Ayushi Gupta BMHI, MBA, BDS

2 Outline Basis of the Study Study Background Objective of the study Introduction to datasets Analysis and Results Conclusions Strengths and Weakness Future Work Lessons Learned Challenges 2

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4 Scaling Root planning Vs Surgery 4

5 Basis of Study Not all patients respond favorably to a standardized care Specific characteristics help determine treatment outcomes Important to classify population according to background characteristics and covariates 5

6 Background CDC/AAP classification: Mild, Moderate, Severe (American Dental Association, 1986) Latent Class Analysis (LCA): Statistical method for identifying discrete classes of individuals on basis of set of observed categorical variable Dental Atherosclerosis Risk in Community Study (DARIC) derived 7 Periodontal and 7 Tooth Profile classes (PPC/TPC) developed at UNC School of dentistry (Thiago et al, Feb 2017) 6

7 PPC Calculation 7 full-mouth clinical periodontal measures (32*7=224) 1. 1 site with interproximal attachment level (IAL) 3 mm 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 7. Tooth status presence (present versus absent) 7 (Thiago et al, 2017)

8 TPC Calculation 1. Interproximal AL (<3 mm = 0, 1 site with 3 or 4 mm = 1, and 5 mm = 2) 2. Direct attachment level (measured at direct buccal and lingual <3 mm = 0, 1 site with 3 or 4 mm = 1, and 5 mm = 2) 3. Interproximal PD(<4 mm = 0, 1 site with 4 or 5 mm = 1, and 6 mm = 2) 4. Direct PD (<4 mm = 0, 1 site with 4 or 5 mm = 1, and 6 mm = 2); 5. Interproximal gingival recession (IGR, dichotomized as IGR 1 versus IGR >1); 6. Direct GR (measured at direct buccal and lingual, dichotomized as DGR 1 versus DGR >1); 7. (dichotomized at <3 versus 3 sites per tooth); 8. GI (dichotomized as GI = 0 versus GI 1); 9. Plaque Index PI (dichotomized as PI = 0 versus Pl 1); 10. Decayed coronal surface (DCS, dichotomized as DCS = 0 versus DCS 1); 11. Filled coronal surface (FCS, dichotomized as FCS = 0 versus FCS 1); 12. Decayed root surface (DRS, dichotomized as DRS = 0 versus DRS 1); 13. Filled root surface (FRS, dichotomized FRS = 0 versus FRS 1) 14. Presence/absence of full prosthetic crowns (Thiago et al,

9 Objective Latent Class Analysis(LCA) classification method is sensitive to determine outcome based patient specific treatment in the field of dentistry. The study analyzes, visualizes, and predicts patient treatment outcomes of two most commonly performed periodontal procedures, scaling/ root-planing(sc/rt) and surgery(surg) 9

10 Introduction to Datasets Total number of UNC Electronic Patient Record(EPR) subjects No. of SC/RP & Surgery visits in EPR dataset 9502 (Total Subjects) 2409 (SC/RT) 273 (Surgery) 6820 (Others) 10

11 Difference in days from the last exam visit to 1 st treatment visit & last treatment visit to 1 st after treatment exam visit 65 days cutoff 34 days cutoff 187 days cutoff 51 days cutoff t = Treatment Date visit e before = Exam visit just before treatment date e after = Exam visit just after treatment date 11

12 Process Flow Cleaned EPR datasets SAS PROC LCA (32*7=224 dichotomous variable) 7PPC/7TPC stratification of population 12

13 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 TPC-A TPC-B TPC-C TPC-D TPC-E TPC-F TPC-G Health Recession Crown GI Interproximal Disease Reduced Periodontium Severe Disease 13

14 How is LCA derived PPC a better outcome classifier than CDC/ADA Classification? BOP(Bleeding %) and PD(Pocket depth) change at PPC level BOP and PD change of measurements in CDC classification 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 Health Mild Moderate Severe 14

15 BOP and PD change of measurements at TPC level BOP and PD change of measurements in CDC classification Does Not classify at tooth level TPC-A TPC-B TPC-C TPC-D TPC-E TPC-F TPC-G Health Recession Crown GI Interproximal Disease Reduced Periodontium Severe Disease Health Mild Moderate Severe 15

16 Delta measure (After treatment Before treatment) Absolute percentage of population with PD change >1mm 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 PD.rt ppca ppcb ppcc ppcd ppce ppcf ppcg tpca tpcb tpcc tpcd tpce tpcf tpcg PD.sg tpca 0.21 NA NA 0.11 tpcb 0.33 NA NA tpcc 0.25 NA tpcd 0.37 NA NA tpce NA tpcf 0.20 NA NA tpcg NA NA BOP.rt tpca tpcb tpcc tpcd tpce tpcf tpcg BOP.sg tpca 0.04 NA NA 0.01 tpcb 0.00 NA NA 0.03 tpcc 0.04 NA tpcd 0.01 NA NA 0.01 tpce 0.07 NA tpcf NA NA tpcg NA NA TPC-A TPC-B TPC-C TPC-D TPC-E TPC-F TPC-G Health Recession Crown GI Interproximal Disease Reduced Periodontium Severe Disease Maximum number of people showed >1mm of PD improvement 16

17 Effect of smoking on PD and BOP 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 17

18 Effect of alcohol on PD and BOP 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 18

19 Effect of diabetes on PD and BOP 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 19

20 Conclusion Precise patient stratification at individual and tooth level. Detailed stratification than the traditional CDC/AAP classification Applicable to other datasets across different dental schools 20

21 Strengths and Limitations Strengths of the study: o Relevant large population with records past 5 years o Validated using 3 large population databases (DARIC, NHANES, PDS) o Classifies at individual tooth level Limitations of the study: o Age distribution of population (mean 58 years) o Irregularity of patient visits: Different cutoffs o No genetic intervention in the study 21

22 Future Work Combine EPR study with Periodontitis and Vascular Events (PAVE) study conducted at 5 clinical centers (the University at Buffalo, University of North Carolina, Boston University, Kaiser Permanente/Oregon Health and Sciences University, University of Maryland) (Offenbacher S et al, 2009) Incorporating PPC/TPC classification and risk scores in UNC patient reports for patient risk identification 22

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24 Lessons Learned Precision medicine : Scope in Dentistry Collectively process thoughts to build new model and tools R data analysis Self Motivation Communication skills 24

25 Challenges Non proficiency with SAS tools Messy/ Missing Data 25

26 Acknowledgements Special thanks to Dr. Di Wu Dr. Steve Offenbacher Mr. Kevin Moss Dr. Heidi Harkins Ms. Mariell Ruiz Dr. Javed Mostafa 26

27 References 1. Trombelli L, Farina R, Ferrari S, Pasetti P, Calura G. Comparison between two methods for periodontal risk assessment. Minerva Stomatol 2009;58: Busby M, Chapple L, Matthews R, Burke FJ, Chapple I. Continuing development of an oral health score for clinical audit. Br Dent J 2014;216:E Lang NP, Tonetti MS. Periodontal risk assessment (PRA) for patients in supportive periodontal therapy (SPT). Oral Health Prev Dent 2003;1: Chandra RV. Evaluation of a novel periodontal risk assessment model in patients presenting for dental care. Oral Health Prev Dent 2007; 5: Page RC, Krall EA, Martin J, Mancl L, Garcia RI. Validity and accuracy of a risk calculator in predicting periodontal disease. J Am Dent Assoc 2002; 133: Lindskog S, Blomlof J, Persson I, et al. Validation of an algorithm for chronic periodontitis risk assessment and prognostication: analysis of an inflammatory reactivity test and selected risk predictors. Journal of periodontology 2010; 81: Thiago Morelli et al. Periodontal profile classes predict periodontal Diseases Progression and Tooth Loss 8. Armitage GC. Development of a classification system for periodontal diseases and conditions. Ann Periodontol 1999;4: American Dental Association. Risk Management Series: Diagnosing and Managing the Periodontal Patient. Chicago: American Dental Association; Lang NP, Tonetti MS. Risk factor assessment tools for the prevention of periodontitis progression: a systematic review. J Clin Periodontol 2015; 42: S59 S Martin JA, Page RC, Kaye EK, Hamed MT, Loeb CF. Periodontitis severity plus risk as a tooth loss predictor. J Periodontal.2009 Feb;80(2): Offenbacher S Et al. Results from the Periodontitis and Vascular Events (PAVE) Study: a pilot multicentered, randomized, controlled trial to study effects of periodontal therapy in a secondary prevention model of cardiovascular disease. J Periodontol.2009 Feb;80(2): Thiago Morelli et al. Derivation and Validation of the Periodontal and Tooth Profile Classification System for Patient Stratification. Journal of Periodontology 2016; /jop Eke PI, Page RC, Wei L, Thornton-Evans G, Genco RJ. Update of the case definitions for population-based surveillance of periodontitis. Journal of periodontology 2012; 83: Lanza ST, Rhoades BL, Nix RL, Greenberg MT, Conduct Problems Prevention Research G. Modeling the interplay of multilevel risk factors for future academic and behavior problems: a person-centered approach. Dev Psychopathol 2010; 22:

28 References cont. Joensuu M, Mattila-Holappa P, Ahola K, et al. Clustering of adversity in young adults on disability pension due to mental disorders: a latent class analysis. Soc Psychiatry Psychiatr Epidemiol 2016; 51: Ferrat E, Audureau E, Paillaud E, et al. Four Distinct Health Profiles in Older Patients With Cancer: Latent Class Analysis of the Prospective ELCAPA Cohort. J Gerontol A Biol Sci Med Sci Socransky SS, Haffajee AD, Cugini MA, Smith C, Kent RL, Jr. Microbial complexes in subgingival plaque. J Clin Periodontol 1998; 25: Lang NP, Suvan JE, Tonetti MS. Risk factor assessment tools for the prevention of periodontitis progression a systematic review. J Clin Periodontol 2015;42 Suppl 16: S Takeuchi K, Furuta M, Takeshita T, et al. Serum antibody to Porphyromonas gingivalis and periodontitis progression: the Hisayama Study. J Clin Periodontol Papapanou PN, Neiderud AM, Disick E, Lalla E, Miller GC, Dahlen G. Longitudinal stability of serum immunoglobulin G responses to periodontal bacteria. J Clin Periodontol 2004; 31: Hwang AM, Stoupel J, Celenti R, Demmer RT, Papapanou PN. Serum antibody responses to periodontal microbiota in chronic and aggressive periodontitis: a postulate revisited. Journal of periodontology 2014;85: Offenbacher S, Divaris K, Barros SP, et al. Genome-wide association study of biologically-informed periodontal complex traits offers novel insights into the genetic basis of periodontal disease. Hum Mol Genet Lanza ST, Collins LM, Lemmon DR, Schafer JL. PROC LCA: A SAS Procedure for Latent Class Analysis. Struct Equ Modeling 2007;14: Henry KL, Muthen B. Multilevel Latent Class Analysis: An Application of Adolescent Smoking Typologies with Individual and Contextual Predictors. Struct Equ Modeling 2010;17: Xin X, Ming Q, Zhang J, Wang Y, Liu M, Yao S. Four Distinct Subgroups of Self-Injurious Behavior among Chinese Adolescents: Findings from a Latent Class Analysis. PLoS One 2016;11:e Hamza CA, Willoughby T. Nonsuicidal self-injury and suicidal behavior: a latent class analysis among young adults. PLoS One 2013;8:e Beck JD, Sharp T, Koch GG, Offenbacher S. A study of attachment loss patterns in survivor teeth at 18 months, 36 months and 5 years in communitydwelling older adults. J Periodontal Res 1997;32: Beck JD, Koch GG, Rozier RG, Tudor GE. Prevalence and risk indicators for periodontal attachment loss in a population of older community-dwelling blacks and whites. Journal of periodontology 1990;61: Beck JD, Koch GG, Offenbacher S. Attachment loss trends over 3 years in community-dwelling older adults. Journal of periodontology 1994;65: Lanza ST, Rhoades BL. Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment. Prev Sci 2013;14:

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