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Originalartikel | erschienen - Druck | peer reviewed

Development and External Validation of a Multivariable Prediction Model to Identify Nondiabetic Hyperglycemia and Undiagnosed Type 2 Diabetes: Diabetes Risk Assessment in Dentistry Score (DDS)


JOURNAL OF DENTAL RESEARCH 2023 / Februar ; 102(2): 170 - 177






Bibliometrische Indikatoren



Impact Factor = 5,7

Zitierhäufigkeit nach WOS = 6

DOI = 10.1177/00220345221129807

PubMed-ID = 36254392


Autoren

Yonel Z*, Kocher T1, Chapple I, Dietrich T, Völzke H2, Nauck M3, Collins G, Gray L, Holtfreter B1


Abstract

The aim of this study was to develop and externally validate a score for use in dental settings to identify those at risk of undiagnosed nondiabetic hyperglycemia (NDH) or type 2 diabetes (T2D). The Studies of Health in Pomerania (SHIP) project comprises 2 representative population-based cohort studies conducted in northeast Germany. SHIP-TREND-0, 2008 to 2012 (the development data set) had 3,339 eligible participants, with 329 having undiagnosed NDH or T2D. Missing data were replaced using multiple imputation. Potential covariates were selected for inclusion in the model using backward elimination. Heuristic shrinkage was used to reduce overfitting, and the final model was adjusted for optimism. We report the full model and a simplified paper-based point-score system. External validation of the model and score employed an independent data set comprising 2,359 participants with 357 events. Predictive performance, discrimination, calibration, and clinical utility were assessed. The final model included age, sex, body mass index, smoking status, first-degree relative with diabetes, presence of a dental prosthesis, presence of mobile teeth, history of periodontal treatment, and probing pocket depths ≥5 mm as well as prespecified interaction terms. In SHIP-TREND-0, the model area under the curve (AUC) was 0.72 (95% confidence interval [CI] 0.69, 0.75), calibration in the large was -0.025. The point score AUC was 0.69 (95% CI 0.65, 0.72), with sensitivity of 77.0 (95% CI 76.8, 77.2), specificity of 51.5 (95% CI 51.4, 51.7), negative predictive value of 94.5 (95% CI 94.5, 94.6), and positive predictive value of 17.0 (95% CI 17.0, 17.1). External validation of the point score gave an AUC of 0.69 (95% CI 0.66, 0.71), sensitivity of 79.2 (95% CI 79.0, 79.4), specificity of 49.9 (95% CI 49.8, 50.00), negative predictive value 91.5 (95% CI 91.5, 91.6), and positive predictive value of 25.9 (95% CI 25.8, 26.0). A validated prediction model involving dental variables can identify NDH or undiagnosed T2DM. Further studies are required to validate the model for different European populations.

Veröffentlicht in

JOURNAL OF DENTAL RESEARCH


Jahr 2023
Monat/Hj. Februar
Impact Factor (2023) 5,7
Volume 102
Issue 2
Seiten 170 - 177
Open Access nein
Peer reviewed ja
Artikelart Originalartikel
Artikelstatus erschienen - Druck
DOI 10.1177/00220345221129807
PubMed-ID 36254392

Allgemeine Daten zur Fachzeitschrift

Kurzbezeichnung: J DENT RES
ISSN: 0022-0345
eISSN: 1544-0591
Land: USA
Sprache: English
Kategorie(n):
  • DENTISTRY, ORAL SURGERY & MEDICINE


Impact Factor Entwicklung

Jahr Impact Factor
2008 3,142
2009 3,458
2010 3,773
2011 3,486
2012 3,826
2013 4,144
2014 4,139
2015 4,602
2016 4,755
2017 5,38
2018 5,125
2019 4,914
2020 6,116
2021 8,924
2022 7,6
2023 5,7

Forschungsschwerpunkt der Universität



Beteiligte Departments

Community Medicine

Projekte

Development of a prediction rule for diagnosing previously unknown diabetes mellitus using information on the dental status

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