Evaluation and recommendations of the oral health, oral function, and orofacial aesthetics-related measures of the ICHOM Standard Set for Cleft Lip and Palate.
L S van der Knaap-KindS OmbashiV Van RoeyL KragtP PetersonF JabbariE B WolviusS L VersnelPublished in: International journal of oral and maxillofacial surgery (2024)
This study was performed to evaluate the efficacy of outcome measures for the orofacial domain included in the International Consortium for Health Outcomes Measurement Standard Set for Cleft Lip and Palate (ICHOM-SCS). In this multicentre study involving two cleft centres, suggestions to optimize the type and timing of outcome measures were made based on data and clinical experience. Patient-reported outcome measures (PROMs) (CLEFT-Q Jaw, Teeth, Eating/Drinking; Child Oral Health Impact Profile-Oral Symptoms Scale (COHIP-OSS)) and clinical outcome measures (caries experience and dental occlusion) data were collected retrospectively for age 5, 8, 10, 12, 19, and 22 years. The data were categorized by cleft type and analysed within and between age groups using Spearman correlation, the distribution of responses per item, a two-sample test for equality of proportions, and effect plots. Most correlations between PROMs and clinical outcome measures were weak (r < 0.5), suggesting PROMs and clinical outcome measures complement each other. The COHIP-OSS and CLEFT-Q Eating/Drinking barely detected problems in any patient category and are no longer recommended. A suitable alternative appears complex to find; outcomes of this study and the recent literature doubt an added value. Similar problems were found in the CLEFT-Q Jaw at time-point 12 years. Therefore, time-points 15 and 17 years are currently suggested.
Keyphrases
- oral health
- patient reported outcomes
- mental health
- patient reported
- electronic health record
- systematic review
- physical activity
- type diabetes
- case report
- skeletal muscle
- sleep quality
- data analysis
- machine learning
- depressive symptoms
- insulin resistance
- deep learning
- artificial intelligence
- adipose tissue
- glycemic control
- clinical evaluation