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To establish a pre-test probability model of coronary heart disease (CHD) combined with cardiopulmonary exercise test (CPET) indexes and to compare the clinical effectiveness with Duke clinical score (DCS) and updated Diamond-Forrester model (UDFM), thus further explore the predictive value. 342 cases were used to establish the prediction model equation and another 80 cases were used to verify the effectiveness. The patients were divided into CHD group (n = 157) and non-CHD group (n = 185) according to coronary artery stenosis degree >50% or not. Combining DCS and UDFM as reference models with CPET indexes, a multivariate logistic regression model was established. The area under the ROC curve of the three models were calculated to compare the predictive effectiveness. There were significant differences in gender, chest pain type, myocardial infarction history, hypertension history, smoking, pathological Q wave and ST-T change between two groups (P < 0.01), as well as age, LVEF, heart rate at anaerobic domain, peak oxygen uptake in kilograms of body weight, percentage of peak oxygen uptake to the predicted value, the oxygen uptake efficiency slope and carbon dioxide ventilation equivalent slope (P < 0.05). Multivariate analysis showed gender, age, chest pain type, myocardial infarction history, hypertension history, smoking, pathological Q wave, ST-T change, and peak oxygen pulse were independent risk factors of CHD. The pre-test probability model of CHD combined with CPET indexes has good distinguish and calibrate ability, its prediction accuracy is slightly better than DCS and UDFM, which still needs to be verified externally in more samples. [ABSTRACT FROM AUTHOR]

To establish a pre-test probability model of coronary heart disease (CHD) combined with cardiopulmonary exercise test (CPET) indexes and to compare the clinical effectiveness with Duke clinical score (DCS) and updated Diamond-Forrester model (UDFM), thus further explore the predictive value. 342 cases were used to establish the prediction model equation and another 80 cases were used to verify the effectiveness. The patients were divided into CHD group (n = 157) and non-CHD group (n = 185) according to coronary artery stenosis degree >50% or not. Combining DCS and UDFM as reference models with CPET indexes, a multivariate logistic regression model was established. The area under the ROC curve of the three models were calculated to compare the predictive effectiveness. There were significant differences in gender, chest pain type, myocardial infarction history, hypertension history, smoking, pathological Q wave and ST-T change between two groups (P < 0.01), as well as age, LVEF, heart rate at anaerobic domain, peak oxygen uptake in kilograms of body weight, percentage of peak oxygen uptake to the predicted value, the oxygen uptake efficiency slope and carbon dioxide ventilation equivalent slope (P < 0.05). Multivariate analysis showed gender, age, chest pain type, myocardial infarction history, hypertension history, smoking, pathological Q wave, ST-T change, and peak oxygen pulse were independent risk factors of CHD. The pre-test probability model of CHD combined with CPET indexes has good distinguish and calibrate ability, its prediction accuracy is slightly better than DCS and UDFM, which still needs to be verified externally in more samples. [ABSTRACT FROM AUTHOR]

Minimally invasive interventional diagnosis and treatment center, Affiliated Zhongshan Hospital of Dalian University, No.6 Jie Fang Street, Zhongshan District, Dalian 116001, Liaoning Province, China. These authors contributed equally: Si Xu Liu, Sheng Qin Yu, Guang Sheng Zhao and Feng Zhi Sun. [Extracted from the article]

Quantitatively determining physiological parameters at a microscopic level in the retina furthers the understanding of the molecular pathways of blinding diseases, such as diabetic retinopathy and glaucoma. An essential parameter, which has yet to be quantified noninvasively, is the retinal oxygen metabolic rate (rMRO2). Quantifying rMRO2 is challenging because two parameters, the blood flow rate and hemoglobin oxygen saturation (sO2), must be measured together. We combined photoacoustic ophthalmoscopy (PAOM) with spectral domain-optical coherence tomography (SD-OCT) to tackle this challenge, in which PAOM measured the sO2 and SD-OCT mapped the blood flow rate. We tested the integrated system on normal wild-type rats, in which the measured rMRO2 was 297.86 ± 70.23 nl/minute. This quantitative method may shed new light on both fundamental research and clinical care in ophthalmology in the future. [ABSTRACT FROM AUTHOR]