Predicting Implant Failure and Complications Using Cluster Analysis After Variable Selection: A Retrospective Study
Notice bibliographique
Résumé
BACKGROUND: Uneven data distribution (due to rare outcomes) and repeated measurements (from multiple implants per patient) hinder the creation of a precise oral implant failure risk model. PURPOSE: The aim of this study was to explore variable selection methods suitable for oral implant data, assess risk factors of early failure and postoperative complications, and apply the two-step cluster analysis to establish a risk prediction model for oral implant failure, providing a reference for clinical practice. MATERIALS AND METHODS: This study was a retrospective analysis, with early failure and postoperative complications serving as the outcome indicators. Given the repeated measurements and uneven distribution in oral implant data, our study conducted a comparative analysis between GEE and GEE with Firth penalization. This study evaluated the influencing factors screened by a more suitable model and utilized them for subsequent risk prediction. A two-step cluster analysis was applied to identify different subgroups of early failure and postoperative complications; their clinical characteristics were compared, and relevant risk prediction models were developed. RESULTS: Among a total of 677 patients and 1200 implants, 21 implants were lost prior to loading, and postoperative complications occurred in 74 patients involving 94 implants. The GEE model with Firth's penalty term indicated that non-submerged healing (p < 0.001), shorter implant length (p < 0.001), and thinner diameter (p = 0.007) were risk factors for early failure. The GEE model showed that non-submerged healing (p = 0.039) was a protective factor against postoperative complications, whereas unhealed extraction sockets at the implant site (p = 0.048), the use of bone substitutes (p = 0.008), and a history of periodontal disease (p = 0.009) were risk factors. Additionally, the use of bovine tendon-derived absorbable biomembranes (p = 0.036) may elevate the risk of postoperative complications. The two-step cluster analysis identified two patient subgroups, categorized as high-risk and low-risk, and the prediction model demonstrated good discrimination ability. CONCLUSIONS: Early failure data were highly imbalanced, and the incorporation of the Firth penalty term provided significant benefits. However, its effectiveness in managing postoperative complication data remained limited. Thus, a one-size-fits-all approach to variable screening may not have suited all types of imbalanced data. The analysis conducted in this study, using specific screening techniques, yielded more reliable influencing factors. Additionally, the developed two-step clustering model was capable of predicting high-risk patients for early failures and postoperative complications before surgery, aiding clinicians in devising personalized preventive measures to reduce incidence rates. TRIAL REGISTRATION: Clinical trial registration number: ChiCTR2300070420.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».