Browsing by Author "Bhongmakapat, Thongchai"

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    Computed Tomography Characterization and Comparison With Polysomnography for Obstructive Sleep Apnea Evaluation
    (Elsevier, 2017) Chousangsuntorn, Khaisang; Bhongmakapat, Thongchai; Apirakkittikul, Navarat; Sungkarat, Witaya; Supakul, Nucharin; Laothamatas, Jiraporn; Radiology and Imaging Sciences, School of Medicine
    Purpose We hypothesized that computed tomography (CT) combined with portable polysomnography (PSG) might better visualize anatomic data related to obstructive sleep apnea (OSA). The present study evaluated the CT findings during OSA and assessed their associations with the PSG data and patient characteristics. Patients and Methods We designed a prospective cross-sectional study of patients with OSA. The patients underwent scanning during the awake state and apneic episodes. Associations of the predictor variables (ie, PSG data, respiratory disturbance index [RDI]), patient characteristics (body mass index [BMI], neck circumference [NC], and waist circumference [WC]), and outcome variables (ie, CT findings during apneic episodes) were assessed using logistic regression analysis. The CT findings during apneic episodes were categorized regarding the level of obstruction, single level (retropalatal [RP] or retroglossal [RG]) or multilevel (mixed RP and RG), degree of obstruction (partial or complete), and pattern of collapse (complete concentric collapse [CCC] or other patterns). Results A total of 58 adult patients with OSA were scanned. The mean ± standard deviation for the RDI, BMI, NC, and WC were 41.6 ± 28.55, 27.80 ± 5.43 kg/m2, 38.3 ± 4.3 cm, and 93.8 ± 13.6 cm, respectively. No variables distinguished between the presence of single- and multilevel airway obstruction in the present study. A high RDI (≥30) was associated with the presence of complete obstruction and CCC (odds ratio 6.33, 95% confidence interval 1.55 to 25.90; and odds ratio 3.77, 95% confidence interval 1.02 to 13.91, respectively) compared with those with a lesser RDI. Conclusions An increased RDI appears to be an important variable for predicting the presence of complete obstruction and CCC during OSA. Scanning during apneic episodes, using low-dose volumetric CT combined with portable PSG provided better anatomic and pathologic findings of OSA than did scans performed during the awake state.
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    Upper Airway Areas, Volumes, and Linear Measurements Determined on Computed Tomography During Different Phases of Respiration Predict the Presence of Severe Obstructive Sleep Apnea
    (Elsevier, 2017) Chousangsuntorn, Khaisang; Bhongmakapat, Thongchai; Apirakkittikul, Navarat; Sungkarat, Witaya; Supakul, Nucharin; Laothamatas, Jiraporn; Radiology and Imaging Sciences, School of Medicine
    Purpose The objective of this study was to analyze the potential of using low-dose volumetric computed tomography (CT) during different phases of respiration for identifying patients likely to have severe obstructive sleep apnea (OSA), defined as a respiratory disturbance index (RDI) higher than 30. Patients and Methods A prospective study was undertaken at the Ramathibodi Hospital (Bangkok, Thailand). Patients with diagnosed OSA (N = 82) were recruited and separated into group 1 (RDI, ≤30; n = 36) and group 2 (RDI, >30; n = 46). The 2 groups were scanned by low-dose volumetric CT while they were 1) breathing quietly, 2) at the end of inspiration, and 3) at the end of expiration. Values for CT variables were obtained from linear measurements on lateral scout images during quiet breathing and from the upper airway area and volume measurements were obtained on axial cross-sections during different phases of respiration. All CT variables were compared between study groups. A logistic regression model was constructed to calculate a patient's likelihood of having an RDI higher than 30 and the predictive value of each variable and of the final model. Results The minimum cross-sectional area (MCA) measured at the end of inspiration (cutoff point, ≤0.33 cm2) was the most predictive variable for the identification of patients likely to have an RDI higher than 30 (adjusted odds ratio [OR] = 5.50; 95% confidence interval [CI], 1.76-17.20; sensitivity, 74%; specificity, 72%,), followed by the MCA measured at the end of expiration (cutoff point, ≤0.21 cm2; adjusted OR = 3.28; 95% CI, 1.05-10.24; sensitivity, 70%; specificity, 68%). Conclusion CT scanning at the ends of inspiration and expiration helped identify patients with an RDI higher than 30 based on measurement of the MCA. Low-dose volumetric CT can be a useful tool to help the clinician rapidly identify patients with severe OSA and decide on the urgency to obtain a full-night polysomnographic study and to start treatment.