Canullo Luigi, Radovanović Sandro, Delibasic Boris, Blaya Juan Antonio, Penarrocha David, Rakic Mia
Private Practice, Rome, Italy.
Centre for Business Decision-making, Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia.
Clin Oral Implants Res. 2017 May;28(5):512-519. doi: 10.1111/clr.12828. Epub 2016 Apr 14.
The primary aim of this study was to evaluate 23 pathogens associated with peri-implantitis at inner part of implant connections, in peri-implant and periodontal pockets between patients suffering peri-implantitis and participants with healthy peri-implant tissues; the secondary aim was to estimate the predictive value of microbiological profile in patients wearing dental implants using data mining methods.
Fifty participants included in the present case─control study were scheduled for collection of plaque samples from the peri-implant pockets, internal connection, and periodontal pocket. Real-time polymerase chain reaction was performed to quantify 23 pathogens. Three predictive models were developed using C4.5 decision trees to estimate the predictive value of microbiological profile between three experimental sites.
The final sample included 47 patients (22 healthy controls and 25 diseased cases), 90 implants (43 with healthy peri-implant tissues and 47 affected by peri-implantitis). Total and mean pathogen counts at inner portions of the implant connection, in peri-implant and periodontal pockets were generally increased in peri-implantitis patients when compared to healthy controls. The inner portion of the implant connection, the periodontal pocket and peri-implant pocket, respectively, presented a predictive value of microbiologic profile of 82.78%, 94.31%, and 97.5% of accuracy.
This study showed that microbiological profile at all three experimental sites is differently characterized between patients suffering peri-implantitis and healthy controls. Data mining analysis identified Parvimonas micra as a highly accurate predictor of peri-implantitis when present in peri-implant pocket while this method generally seems to be promising for diagnosis of such complex infections.
本研究的主要目的是评估种植体周围炎患者与种植体周围组织健康的参与者在种植体连接内部、种植体周围和牙周袋中的23种与种植体周围炎相关的病原体；次要目的是使用数据挖掘方法估计佩戴牙种植体患者微生物谱的预测价值。
本病例对照研究纳入的50名参与者计划从种植体周围袋、内部连接和牙周袋中采集菌斑样本。进行实时聚合酶链反应以定量23种病原体。使用C4.5决策树开发了三个预测模型，以估计三个实验部位之间微生物谱的预测价值。
最终样本包括47名患者（22名健康对照和25名患病病例），90颗种植体（43颗种植体周围组织健康，47颗受种植体周围炎影响）。与健康对照相比，种植体周围炎患者种植体连接内部、种植体周围和牙周袋中的病原体总数和平均计数普遍增加。种植体连接内部、牙周袋和种植体周围袋的微生物谱预测价值的准确率分别为82.78%、94.31%和97.5%。
本研究表明，种植体周围炎患者与健康对照在所有三个实验部位的微生物谱特征不同。数据挖掘分析确定，微小单胞菌存在于种植体周围袋中时是种植体周围炎的高度准确预测指标，而这种方法总体上似乎对诊断此类复杂感染很有前景。
