Does the Kushida morphometric model predict outcomes following maxillomandibular advancement surgery for obstructive sleep apnoea?

Morphometric measures and desaturations: Proposal for an index with improved accuracy for obstructive sleep apnea screening

STUDY OBJECTIVE

To evaluate the sensitivity and specificity of the combined Kushida morphometric model (KMM) and the oxygen desaturation index (ODI) for screening individuals with obstructive sleep apnea.

METHODS

Diagnostic test study with adults >18 years, both sexes, polysomnography, body mass index, neck circumference and intraoral measurements.

RESULTS

144 patients were invited; of these, 75 met the exclusion criteria. 55 individuals presented AHI ≥5 ev/h and 14, an AHI <5 ev/h. Three AHI cut-off points were evaluated: AHI ≥5, ≥15, ≥30 ev/h. When adopting the cut-off point of AHI ≥5 ev/h, the KMM showed sensitivity (SE) = 60.0 %, specificity (SP) = 71.4 % and 95 % confidence interval of the area under the curve (95 % CI of AUC) = 0.655; the combination of KMM and ODI (KMM + ODI) revealed SE = 73.0 %, SP = 71.4 % (95 % CI of AUC = 0.779) and the ODI showed SE = 76.4 % and SP = 92.9 % (95 % CI of AUC = 0.815). At the cut-off point of AHI ≥15 ev/h, the KMM presented SE = 64.1 %, SP = 76.7 % (95 % CI of AUC = 0.735); the KMM + ODI showed SE = 82.1 %, SP = 83.3 % (95 % CI of AUC = 0.895); and the ODI presented SE = 76.9 %, SP = 100.0 % (95 % CI of AUC = 0.903). For the cut-off point of AHI ≥30 ev/h, the KMM showed SE = 56.0 %, SP = 77.2 % (95 % CI of AUC = 0.722); the KMM + ODI revealed SE = 92.0 %, SP = 79.5 % (95 % CI of AUC = 0.926); and the ODI showed SE = 92.0 %, SP = 90.9 % (95 % CI of AUC = 0.941).

CONCLUSION

The combination of oxygen desaturation index and Kushida morphometric model improved the sensitivity and specificity of this model regardless of obstructive sleep apnea severity suggesting greater effectiveness in risk prediction.

Simple and Unbiased OSA Prescreening: Introduction of a New Morphologic OSA Prediction Score

PURPOSE

An early prescreening in suspected obstructive sleep apnea (OSA) patients is desirable to expedite diagnosis and treatment. However, the accuracy and applicability of current prescreening tools is insufficient. We developed and tested an unbiased scoring system based solely on objective variables, which focuses on the diagnosis of severe OSA and exclusion of OSA.

PATIENTS AND METHODS

The OSA prediction score was developed (n = 150) and validated (n = 50) within German sleep center patients that were recruited as part of the Sleep Apnea Global Interdisciplinary Consortium (SAGIC). Six objective variables that were easy to assess and highly correlated with the apnea-hypopnea index were chosen for the score, including some known OSA risk factors: body-mass index, neck circumference, waist circumference, tongue position, male gender, and age (for women only). To test the predictive ability of the score and identify score thresholds, the receiver-operating characteristics (ROC) and curve were calculated.

RESULTS

A score ≥8 for predicting severe OSA resulted in an area under the ROC curve (ROC-AUC) of 90% (95% confidence interval: 84%, 95%), test accuracy of 82% (75%, 88%), sensitivity of 82% (65%, 93%), specificity of 82% (74%, 88%), and positive likelihood ratio of 4.55 (3.00, 6.90). A score ≤5 for predicting the absence of OSA resulted in a ROC-AUC of 89% (83%, 94%), test accuracy of 80% (73%, 86%), sensitivity of 72% (55%, 85%), specificity of 83% (75%, 89%), and positive likelihood ratio of 4.20 (2.66, 6.61). Performance characteristics were comparable in the small validation sample.

CONCLUSION

We introduced a novel prescreening tool combining easily obtainable objective measures with predictive power and high general applicability. The proposed tool successfully predicted severe OSA (important due to its high risk of cardiovascular disease) and the exclusion of OSA (rarely a feature of previous screening instruments, but important for better differential diagnosis and treatment).