Use of the modified Schirmer test to measure salivary gland hypofunction/hyposalivation: Systematic review and meta-analysis.
Christoph Matthias SchoppmeierMalin JansonKarolin C HoeferIsabelle GrafMichael Jochen WichtAnna Greta BarbePublished in: European journal of oral sciences (2024)
Although dry mouth is a relatively common condition, salivary flow is not routinely measured in dental clinical practice. Moreover, existing data regarding the use of the modified Schirmer test (MST) for the screening of dry mouth has not been summarized. This systematic review and meta-analysis, conducted in accordance with the PRISMA guidelines, aimed to determine whether the modified Schirmer test can be used to identify dry mouth. The databases of PubMed, Scopus, ScienceDirect, and CENTRAL (CRD42023393843) were systematically searched to retrieve articles published until 9th November 2023. Among the 343 original articles retrieved, six met the inclusion criteria. A total of 1150 patients, comprising 710 (61.7%) women and 440 (38.3%) men (mean age, 47.1 ± 7.3 years), were included. The meta-analysis revealed a weak correlation coefficient of r ¯ $\bar{r}$ = 0.42 (95% Cl: 0.29-0.55) between MST and the unstimulated salivary flow rate. Therefore, while the MST might offer a simple and accessible alternative for initial screening in the future, especially in non-specialized settings, its variability in sensitivity and specificity, along with an actual lack of standardization, necessitates cautious interpretation. Further studies are necessary before recommending the test in clinical routine.
Keyphrases
- clinical practice
- systematic review
- end stage renal disease
- meta analyses
- chronic kidney disease
- newly diagnosed
- big data
- palliative care
- prognostic factors
- polycystic ovary syndrome
- randomized controlled trial
- magnetic resonance imaging
- case control
- metabolic syndrome
- computed tomography
- peritoneal dialysis
- type diabetes
- electronic health record
- deep learning
- patient reported outcomes
- oral health
- machine learning
- middle aged
- patient reported
- diffusion weighted imaging