## 4 Discussion
As a further step of this study, a meta-analysis was also carried out, pooling data from studies using monitoring devices adopted by at least four studies, including oximetry, as a part of the reference PSG or as a standalone method, based on statistical classifier models for the AHI cut-off of 5 events/h. It was concluded that oximetry based on machine learning algorithms may provide a simple and effective alternative to PSG for the diagnosis of moderate-severe pediatric OSA with a pooled sensitivity of 74% (95% CI: 66-80%) and a pooled specificity of 90% (95% CI: 85-94%) [9]. Notably, themeta-analysis was carried out only for the AHI threshold of 5 events/h (i.e., the currently accepted diagnostic threshold for moderate-to-severe OSA in children), as there was an insufficient number of studies reporting data on 1 and 10 events/h. The present study was specifically focused on the diagnostic accuracy of oximetry, as a part of the reference PSG or as a standalone method, compared to PSG for all the AHI diagnostic thresholds of pediatric OSA currently recommended by the American Academy of Sleep Medicine. To our knowledge, this is the first study to perform a meta-analysis of diagnostic accuracy for each AHI cut-off of 1, 5, and 10 events/h. A better understanding of the diagnostic performance of oximetry parameters could potentially increase accessibility to the diagnosis of pediatric OSA and support the value and usefulness of oximetry as a simplified screening modality. A further advantage could be to streamlining the referral procedure to sleep medicine specialists, reducing long waiting lists by establishing priorities for children already referred to specialists and improving the monitoring of pediatric OSA. Conducting a systematic review and meta-analysis in this field enables us to combine data from different studies (i.e., from a large sample size) in order to identify relevant findings for clinical practice and to appraise the methodological limitations of each primary study in order to guide future research. The findings of the present study indicate that, as the AHI threshold increases, the sensitivity of ODI3 also increases, with the highest sensitivity being observed at AHI \(\geq\) 10 (79% [95% CI 73; 84]), meaning that 79% of children with severe OSA will have a positive ODI3 at overnight oximetry. This indicates that the ability of the test to correctly identify children with OSA improves as the severity of the condition (measured by PSG-derived AHI) increases. The specificity of ODI3 is consistently high across all AHI thresholds, ranging from 84% at AHI \(\geq\) 1 to 88% at AHI \(\geq\) 10, meaning that 88% of children without severe OSA will have a negative ODI3 at overnight oximetry. This suggests that the test is reliable in correctly classifying children without the disease, particularly at higher AHI thresholds. A highly specific test means that there are few false positive results. In this context, it is important to observe that it would not be desirable to use a test with low specificity for the screening of pediatric OSA, since many children without the disease will screen positive, and potentially undergo unnecessary and expensive diagnostic procedures such as PSG. A further finding of the present study is that PPV decreases as the AHI threshold increases, with the highest PPV being observed at AHI \(\geq\) 1 (91% [95% CI 88; 93]), meaning that 91% of children who have a positive ODI3 at overnight oximetry will actually have OSA. The PPV decrease indicates that while the test is sensitive at higher AHI thresholds, the chance that children who result positive with the index test really have OSA diminishes slightly as the threshold increases. This could be ascribed to the fact that predictive values are determined by the sensitivity and specificity of the test, but it also depends on the prevalence of the disease in the population being tested, and in the articles included in our study, the prevalence decreases as the OSA severity increases.