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Are Predictors for Implant Bone Loss Act Constantly Across Long-Term Function?
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Are Predictors for Implant Bone Loss Act Constantly Across Long-Term Function?
Ronen Ofec, DMD* ; Liran Levin, DMD ; Daniel Nitzan, DMD ; Devorah Schwartz-Arad, DMD, PhD
* M.Sc. program in Biostatistics
* Praviate dental practice, Tel-Aviv, Israel
School of Mathematical sciences, Tel-Aviv university
Are Predictors for Implant Bone Loss Act Constantly Across Long-Term Function?
Ronen Ofec, DMD* ; Liran Levin, DMD ; Daniel Nitzan, DMD ; Devorah Schwartz-Arad, DMD, PhD
* M.Sc. program in Biostatistics
* Praviate dental practice, Tel-Aviv, Israel
Marginal Bone Loss (MBL)
• Implant success criteria for the research community
• Measurement for the diagnosis of Peri-implantitis
• No standardization for MBL measurement
• No consensus for the classification of acceptable vs. advanced bone loss
Purpose of the study
• To identify predictors for MBL in long term follow-up
• To evaluate the interaction between predictors
and function-time
• To construct statistical model with respect to clustered observations
Participants & Methods
• Historical prospective (retrospective) cohort study design
• 13 years ago, 256 patients who received totally 936
implants in a 2-stage protocol
• Schwartz-Arad Surgical center by a single surgeon
• Recall program, clinical and radiographic follow-up
The dependent variable
Marginal bone loss
No. exposed threads
Bone loss rate (mm per function year )
Acceptable vs. Advanced bone loss
Last x-ray
Statistical analysis & results
Prevalence of acceptable and advanced bone loss across function-time
Function time[month]
Ma
rgin
al b
on
e lo
ss
20 40 60 80 100 120 140
Acce
pta
ble
Ad
va
nce
d
0.0
0.2
0.4
0.6
0.8
1.0
20.7%
Modeling advanced bone loss
Step 1 : The relevant predictorsMethod : Logistic regression with forward step selectionResults : The sequence variables entered to the equation
1. Function time
2. Implant surface (cpTi, HA & TPS)
3. Premature spontaneous exposure4. Smoking status at 1st surgery
5. Implant Diameter
Modeling advanced bone loss
Step 2 : The interaction between function time and predictorsMethod : Deviance analysis for goodness of fitResults :
Model with interaction
Model without interaction
595
605
615
625
635
645
655
2 Years 3 Years 4 Years 5 Years 6 Years
Deviance
• Interaction terms yield in a better fit• The strongest interaction at 3 years of function
Modeling advanced bone loss
Step 3 : Final models for advanced bone lossMethod : General estimation equations (GEE)Results : Odds ratio at short and long term function-time
Function-time
< 3 Years
Function- time
≥ 3 Years
HA coatingProtective 0.26
[0.09-0.80]
Risk 2.51
[1.08-5.82]
Modeling advanced bone loss
Step 3 : Final models for advanced bone lossMethod : General estimation equations (GEE)Results : Odds ratio at short and long term function-time
Function-time
< 3 Years
Function- time
≥ 3 Years
HA coatingProtective 0.26
[0.09-0.80]
Risk 2.51
[1.08-5.82]
TPS surfaceRisk 8.79
[3.42-22.58]
Modeling advanced bone loss
Step 3 : Final models for advanced bone lossMethod : General estimation equations (GEE)Results : Odds ratio at short and long term function-time
Function-time
< 3 Years
Function- time
≥ 3 Years
HA coatingProtective 0.26
[0.09-0.80]
Risk 2.51
[1.08-5.82]
TPS surfaceRisk 8.79
[3.42-22.58]
Spontaneous
exposure
Risk 2.42
[1.35-4.35]
Modeling advanced bone loss
Step 3 : Final models for advanced bone lossMethod : General estimation equations (GEE)Results : Odds ratio at short and long term function-time
Function-time
< 3 Years
Function- time
≥ 3 Years
HA coatingProtective 0.26
[0.09-0.80]
Risk 2.51
[1.08-5.82]
TPS surfaceRisk 8.79
[3.42-22.58]
Spontaneous
exposure
Risk 2.42
[1.35-4.35]
Smoker at 1st
surgery
Risk 4.81
[2.13-10.88]
Modeling advanced bone loss
Step 3 : Final models for advanced bone lossMethod : General estimation equations (GEE)Results : Odds ratio at short and long term function-time
Function-time
< 3 Years
Function- time
≥ 3 Years
HA coatingProtective 0.26
[0.09-0.80]
Risk 2.51
[1.08-5.82]
TPS surfaceRisk 8.79
[3.42-22.58]
Spontaneous
exposure
Risk 2.42
[1.35-4.35]
Smoker at 1st
surgery
Risk 4.81
[2.13-10.88]
Implant
diameter
Protective 0.26
[0.12-0.58]
Forest plot for Odds ratioconfidence intervals
0.02 0.2 2 20
< 3 Years
Smoker
Spontaneousexposure
TPS surface
HA coating
O.R=1
Diameter
Forest plot for Odds ratioconfidence intervals
0.02 0.2 2 20
≥ 3 Years
Smoker
Spontaneousexposure
TPS surface
HA coating
O.R=1
Diameter
Forest plot for Odds ratioconfidence intervals
0.02 0.2 2 20
< 3 Years ≥ 3 Years
Smoker
Spontaneousexposure
TPS surface
HA coating
O.R=1
Diameter
Conclusions
• Predictors for MBL do not act constantly across function-time
• Research findings should be judged according to
the study follow-up period
• The importance of long term follow-up
• Standardization concerning MBL measurements
• Consensus for the classification of advanced bone loss
Thanks