qiuping zheng

The oral health-related quality of life in diabetic patients is regarded as a significant factor for assessing their dental health. This study aimed to examine the current research state, frequently utilized research instruments, and factors impacting the oral health quality of life in individuals with diabetes.

Our review was conducted according to the PRISMA extended guidelines for scoping review. We conducted a literature review on the oral health and quality of life of diabetic patients using PubMed, Embase, and additional databases. This research proposal has been formally submitted to the Open Science Framework.

Out of 3827 materials, merely 17 publications satisfied the review requirements for our study. The search period extended from the inception of the library until Feb 5, 2024. The research encompassed seven countries, including China, the United States, and Iran, utilizing the Oral Health Evaluation Index for the Elderly and the Oral Health Impact Scale as prevalent assessment instruments. The quality of life connected to oral health was predominantly low among diabetic patients, influenced by socio-demographic characteristics, oral health status, biochemical indicators, psychosocial elements, lifestyle choices, and oral-related factors.

The oral health-related quality of life among diabetic patients is typically diminished. Oral health care professionals must devise strategies to promptly identify, assess, and manage the factors influencing the oral health-related quality of life in diabetic patients, while incorporating necessary preventive measures and screenings to enhance oral disease prevention in routine evaluations.

We used the Predictive Model Bias Risk Assessment tool (PROBAST) tool to systematically evaluate the existing models worldwide, in order to provide a reference for clinical staff to select and optimize DFU recurrence risk prediction models.

Literature on DFU recurrence risk prediction model construction published in CNKI, China Biomedical Literature Database, Vipu China Knowledge, China Biomedical Literature Database, Vipu Chinese Journal Service Platform, Wanfang Data Knowledge Service Platform, Embase, PubMed, Web of Science, Cochrane Library and other databases were systematically searched. The search period was until January 29, 2024, encompassing all relevant studies published up to that date. Literature screening and data extraction were conducted by two researchers, and the PROBAST was used to evaluate the bias risk and applicability of the included literature.

Finally, 9 literatures were included, 13 prediction models were established, and the area under the AUC or C-index ranged from 0.660 to 0.943. Nine models were validated internally and one model was validated externally. All the models constructed in the included literature are of high-risk bias, and the applicability of the models is reasonable. Common predictors in the prediction model were Wagner scale, glycosylated hemoglobin, and diabetic peripheral neuropathy.

Although most of the existing DFU risk prediction models have good prediction performance, they all have high risk of bias. It is suggested that researchers should update the existing models in the future, and future modeling studies should follow the reporting norms, so as to develop a scientific, effective and convenient risk prediction model that is more conducive to clinical practice.