Pulmonary Hypertension: A Nomogram Based on CT Pulmonary Angiographic Data for Prediction in Patients without Pulmonary Embolism.

PURPOSE: To use cardiovascular data from computerized tomographic (CT) pulmonary angiography for facilitating the identification of pulmonary hypertension (PH) in patients without acute pulmonary embolism. MATERIALS AND METHODS: The institutional human research committee approved this retrospective study; informed consent was waived. Patients without pulmonary embolism who underwent CT pulmonary angiography and echocardiography within 24 hours of each other between December 2008 and October 2012 were retrospectively identified. The diameters of the pulmonary artery, aorta, and right and left ventricles and the severity of reflux of contrast material were assessed. The volumes of each cardiac compartment were calculated. Doppler echocardiography served as a reference standard for PH. A prediction model for PH was built by using backward logistic regression and was presented on a nomogram. The prediction model was evaluated with 10-fold cross-validation, and a test group of patients was studied between November 2012 and June 2014. RESULTS: The final study group included 182 patients, of whom 98 (54%) were given a diagnosis of PH on the basis echocardiographic results. Age of 67 years or older (odds ratio [OR] = 4.46), reflux grade of 3 or higher (OR = 2.63), right atrial volume of greater than or equal to 106 cm(3) (OR = 3.59), pulmonary artery diameter greater than or equal to 28 mm (OR = 2.52) and pulmonary artery diameter to aorta diameter ratio of greater than or equal to 0.86 (OR = 2.17) were independently associated with PH. The logistic model showed good discrimination ability (area under the curve = 0.844, discrimination slope = 0.359). Tenfold cross-validation showed 85.7% sensitivity, 60.7% specificity, 71.3% positive predictive value, and 76.1% negative predictive value for identification of PH, while the test group showed similar results (84.1%, 60.5%, 71.2%, and 76.7%, respectively). CONCLUSION: Cardiovascular data derived from CT pulmonary angiography are associated with PH, and a nomogram can be created that may facilitate identification of PH after exclusion of acute pulmonary embolism.

The association of OSA with insulin resistance, inflammation and metabolic syndrome.

Obstructive sleep apnea (OSA) shares many cardiovascular risk factors with metabolic syndrome, including obesity, hypertension, insulin resistance, and pro-inflammatory state. This study aimed to examine the possible association of OSA severity with insulin resistance, inflammation and the metabolic syndrome. Ninety eight patients suspected for OSA (54.9+/-13.1 years) were studied. Overnight polysomnography and blood sampling was taken for glucose, insulin, high-density lipoprotein(HDL)-cholesterol, triglycerides, high-sensitivity C-reactive protein (Hs-CRP), and serum amyloid A (S-AA). Insulin resistance was estimated by the homeostatic model assessment (HOMA). Each patient was assigned a metabolic score according to the number of discrete components of metabolic syndrome identified, and categorized by OSA severity. Nine patients had primary snoring, nine had mild, 27 moderate and 53 severe OSA. Metabolic score increased from 1.56+/-1.01 to 2.92+/-1.20 with OSA severity (p=0.004), and was correlated independently with apnea hypopnea index (AHI; r=0.432, p=0.001) and with body mass index (BMI; r=0.518 p=0.001). Hs-CRP increased from 3.44+/-4.25 to 5.87+/-4.76mg/dL with OSA severity (p=0.066) and correlated with AHI (r=0.348; p=0.002). Insulin resistance, correlated significantly with AHI (r=0.390 p=0.021). Inflammation, insulin resistance and metabolic syndrome increase with OSA severity. The number of cardinal features of metabolic syndrome increases with an increase in OSA severity, regardless of the BMI.