How to identify subgroups in longitudinal clinical data: treatment response patterns in patients with a shortened dental arch

Abstract: Background

When dental patients seek care, treatments are not always successful,that is patients' oral health problems are not always eliminated or substantially reduced. Identifying these patients (treatment non-responders) is essential for clinical decision-making. Group-based trajectory modeling (GBTM) is rarely used in dentistry, but a promising statistical technique to identify non-responders in particular and clinical distinct patient groups in general in longitudinal data sets.
Aim

Using group-based trajectory modeling, this study aimed to demonstrate how to identify oral health-related quality of life (OHRQoL) treatment response patterns by the example of patients with a shortened dental arch (SDA).
Methods

This paper is a secondary data analysis of a randomized controlled clinical trial. In this trial SDA patients received partial removable dental prostheses replacing missing teeth up to the first molars (N = 79) either or the dental arch ended with the second premolar that was present or replaced by a cantilever fixed dental prosthesis (N = 71). Up to ten follow-up examinations (1-2, 6, 12, 24, 36, 48, 60, 96, 120, and 180 months post-treatment) continued for 15 years. The outcome OHRQoL was assessed with the 49-item Oral Health Impact Profile (OHIP). Exploratory GBTM was performed to identify treatment response patterns.
Results

Two response patterns could be identified - "responders" and "non-responders." Responders’ OHRQoL improved substantially and stayed primarily stable over the 15 years. Non-responders’ OHRQoL did not improve considerably over time or worsened. While the SDA treatments were not related to the 2 response patterns, higher levels of functional, pain-related, psychological impairment in particular, and severely impaired OHRQoL in general predicted a non-responding OHRQoL pattern after treatment. Supplementary, a 3 pattern approach has been evaluated.
Conclusions

Clustering patients according to certain longitudinal characteristics after treatment is generally important, but specifically identifying treatment in non-responders is central. With the increasing availability of OHRQoL data in clinical research and regular patient care, GBTM has become a powerful tool to investigate which dental treatment works for which patients

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch
• Anmerkungen: The journal of evidence based dental practice. - 23, 1 (2023) , 101794, ISSN: 1532-3390

Beteiligte, Orts- und Zeitangaben

• Ereignis: Veröffentlichung
• (wo): Freiburg
• (wer): Universität
• (wann): 2022
• Urheber: Schierz, Oliver ; Lee, Chi Hyun ; John, Mike Torsten ; Rauch, Angelika ; Reißmann, Daniel R. ; Marré, Birgit ; Luthardt, Ralph G. ; Rudolph, Heike ; Mundt, Torsten ; Hannak, Wolfgang ; Kohal, Ralf-Joachim ; Heydecke, Guido ; Kern, Matthias ; Hartmann, Sinsa ; Böning, Klaus ; Boldt, Julian ; Stark, Helmut ; Edelhoff, Daniel ; Wöstmann, Bernd ; Wolfart, Stefan ; Jahn, Florentine ; Walter, Michael

Weitere Informationen

• DOI: 10.1016/j.jebdp.2022.101794
• URN: urn:nbn:de:bsz:25-freidok-2311114
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 15.08.2025, 07:35 MESZ