9,207 research outputs found

    Reduced functional measure of cardiovascular reserve predicts admission to critical care unit following kidney transplantation

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    Background: There is currently no effective preoperative assessment for patients undergoing kidney transplantation that is able to identify those at high perioperative risk requiring admission to critical care unit (CCU). We sought to determine if functional measures of cardiovascular reserve, in particular the anaerobic threshold (VO2AT) could identify these patients. Methods: Adult patients were assessed within 4 weeks prior to kidney transplantation in a University hospital with a 37-bed CCU, between April 2010 and June 2012. Cardiopulmonary exercise testing (CPET), echocardiography and arterial applanation tonometry were performed. Results: There were 70 participants (age 41.7614.5 years, 60% male, 91.4% living donor kidney recipients, 23.4% were desensitized). 14 patients (20%) required escalation of care from the ward to CCU following transplantation. Reduced anaerobic threshold (VO2AT) was the most significant predictor, independently (OR = 0.43; 95% CI 0.27โ€“0.68; p,0.001) and in the multivariate logistic regression analysis (adjusted OR = 0.26; 95% CI 0.12โ€“0.59; p = 0.001). The area under the receiveroperating- characteristic curve was 0.93, based on a risk prediction model that incorporated VO2AT, body mass index and desensitization status. Neither echocardiographic nor measures of aortic compliance were significantly associated with CCU admission. Conclusions: To our knowledge, this is the first prospective observational study to demonstrate the usefulness of CPET as a preoperative risk stratification tool for patients undergoing kidney transplantation. The study suggests that VO2AT has the potential to predict perioperative morbidity in kidney transplant recipients

    Proximal aortic stiffening in Turner patients may be present before dilation can be detected : a segmental functional MRI study

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    Background: To study segmental structural and functional aortic properties in Turner syndrome (TS) patients. Aortic abnormalities contribute to increased morbidity and mortality of women with Turner syndrome. Cardiovascular magnetic resonance (CMR) allows segmental study of aortic elastic properties. Method: We performed Pulse Wave Velocity (PWV) and distensibility measurements using CMR of the thoracic and abdominal aorta in 55 TS-patients, aged 13-59y, and in a control population (n = 38; 12-58y). We investigated the contribution of TS on aortic stiffness in our entire cohort, in bicuspid (BAV) versus tricuspid (TAV) aortic valve-morphology subgroups, and in the younger and older subgroups. Results: Differences in aortic properties were only seen at the most proximal aortic level. BAV Turner patients had significantly higher PWV, compared to TAV Turner (p = 0.014), who in turn had significantly higher PWV compared to controls (p = 0.010). BAV Turner patients had significantly larger ascending aortic (AA) luminal area and lower AA distensibility compared to both controls (all p < 0.01) and TAV Turner patients. TAV Turner had similar AA luminal areas and AA distensibility compared to Controls. Functional changes are present in younger and older Turner subjects, whereas ascending aortic dilation is prominent in older Turner patients. Clinically relevant dilatation (TAV and BAV) was associated with reduced distensibility. Conclusion: Aortic stiffening and dilation in TS affects the proximal aorta, and is more pronounced, although not exclusively, in BAV TS patients. Functional abnormalities are present at an early age, suggesting an aortic wall disease inherent to the TS. Whether this increased stiffness at young age can predict later dilatation needs to be studied longitudinally

    Estimating pulse wave velocity using mobile phone sensors

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    Pulse wave velocity has been recognised as an important physiological phenomenon in the human body, and its measurement can aid in the diagnosis and treatment of chronic diseases. It is the gold standard for arterial stiffness measurements, and it also shares a positive relationship with blood pressure and heart rate. There exist several methods and devices via which it can be measured. However, commercially available devices are more geared towards working health professionals and hospital settings, requiring a significant monetary investment and specialised training to operate correctly. Furthermore, most of these devices are not portable and thus generally not feasible for private home use by the common individual. Given its usefulness as an indicator of certain physiological functions, it is expected that having a more portable, affordable, and simple to use solution would present many benefits to both end users and healthcare professionals alike. This study investigated and developed a working model for a new approach to pulse wave velocity measurement, based on existing methods, but making use of novel equipment. The proposed approach made use of a mobile phone video camera and audio input in conjunction with a Doppler ultrasound probe. The underlying principle is that of a two-point measurement system utilising photoplethysmography and electrocardiogram signals, an existing method commonly found in many studies. Data was collected using the mobile phone sensors and processed and analysed on a computer. A custom program was developed in MATLAB that computed pulse wave velocity given the audio and video signals and a measurement of the distance between the two data acquisition sites. Results were compared to the findings of previous studies in the field, and showed similar trends. As the power of mobile smartphones grows, there exists potential for the work and methods presented here to be fully developed into a standalone mobile application, which would bring forth real benefits of portability and cost-effectiveness to the prospective user base

    Prediction of fluid responsiveness using respiratory variations in left ventricular stroke area by transoesophageal echocardiographic automated border detection in mechanically ventilated patients.

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    BackgroundLeft ventricular stroke area by transoesophageal echocardiographic automated border detection has been shown to be strongly correlated to left ventricular stroke volume. Respiratory variations in left ventricular stroke volume or its surrogates are good predictors of fluid responsiveness in mechanically ventilated patients. We hypothesised that respiratory variations in left ventricular stroke area (DeltaSA) can predict fluid responsiveness.MethodsEighteen mechanically ventilated patients undergoing coronary artery bypass grafting were studied immediately after induction of anaesthesia. Stroke area was measured on a beat-to-beat basis using transoesophageal echocardiographic automated border detection. Haemodynamic and echocardiographic data were measured at baseline and after volume expansion induced by a passive leg raising manoeuvre. Responders to passive leg raising manoeuvre were defined as patients presenting a more than 15% increase in cardiac output.ResultsCardiac output increased significantly in response to volume expansion induced by passive leg raising (from 2.16 +/- 0.79 litres per minute to 2.78 +/- 1.08 litres per minute; p &lt; 0.01). DeltaSA decreased significantly in response to volume expansion (from 17% +/- 7% to 8% +/- 6%; p &lt; 0.01). DeltaSA was higher in responders than in non-responders (20% +/- 5% versus 10% +/- 5%; p &lt; 0.01). A cutoff DeltaSA value of 16% allowed fluid responsiveness prediction with a sensitivity of 92% and a specificity of 83%. DeltaSA at baseline was related to the percentage increase in cardiac output in response to volume expansion (r = 0.53, p &lt; 0.01).ConclusionDeltaSA by transoesophageal echocardiographic automated border detection is sensitive to changes in preload, can predict fluid responsiveness, and can quantify the effects of volume expansion on cardiac output. It has potential clinical applications

    ์ปคํ”„๋ฆฌ์Šค ๋ฐฉ์‹์˜ ์ฐฉ์šฉํ˜• ์—ฐ์† ํ˜ˆ์•• ๋ชจ๋‹ˆํ„ฐ๋ง ์‹œ์Šคํ…œ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๋ฐ”์ด์˜ค์—”์ง€๋‹ˆ์–ด๋ง์ „๊ณต, 2019. 2. ๊น€ํฌ์ฐฌ.๊ณ ํ˜ˆ์••์˜ ์กฐ๊ธฐ ์ง„๋‹จ๊ณผ ๊ณ ํ˜ˆ์•• ํ™˜์ž์˜ ํ˜ˆ์•• ๊ด€๋ฆฌ๋ฅผ ์œ„ํ•ด์„œ๋Š” ์ผ์ƒ์ƒํ™œ์—์„œ์˜ ์ง€์†์ ์ธ ํ˜ˆ์•• ๋ชจ๋‹ˆํ„ฐ๋ง์ด ์ค‘์š”ํ•˜๋‹ค. ๋งฅํŒŒ์ „๋‹ฌ์‹œ๊ฐ„ (Pulse transit time, PTT) ๊ธฐ๋ฐ˜์˜ ํ˜ˆ์•• ์ถ”์ • ๋ฐฉ์‹์ด ์ด๋ฅผ ๊ฐ€๋Šฅ์ผ€ ํ•˜๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ ๊ฐ€์žฅ ๊ฐ๊ด‘ ๋ฐ›๊ณ  ์žˆ์ง€๋งŒ, ๋งฅํŒŒ์ „๋‹ฌ์‹œ๊ฐ„์„ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์—ฌ๋Ÿฌ ์ธก์ • ์žฅ์น˜๋“ค์ด ํ•„์š”ํ•˜์—ฌ ์ผ์ƒ ์ƒํ™œ์—์„œ์˜ ์‚ฌ์šฉ์— ์ œ์•ฝ์ด ์žˆ์œผ๋ฉฐ, ๋˜ํ•œ ๋งฅํŒŒ์ „๋‹ฌ์‹œ๊ฐ„ ๋งŒ์„ ์ด์šฉํ•œ ์ˆ˜์ถ•๊ธฐ ํ˜ˆ์••(Systolic blood pressure, SBP) ์ถ”์ • ๋Šฅ๋ ฅ์€ ๋ถ€์กฑํ•จ์ด ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์˜ ์ฒซ ๋ฒˆ์งธ ๋ชฉ์ ์€ ๋งฅํŒŒ์ „๋‹ฌ์‹œ๊ฐ„ ์ธก์ • ์‹œ์Šคํ…œ์„ ์ฐฉ์šฉํ˜•์œผ๋กœ ๊ฐœ๋ฐœํ•˜์—ฌ ๊ฐ„ํŽธํ•˜๊ฒŒ ๋งฅํŒŒ์ „๋‹ฌ์‹œ๊ฐ„์„ ์ธก์ •ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•จ์œผ๋กœ์จ ์ผ์ƒ ์ƒํ™œ ์ค‘ ๋งฅํŒŒ์ „๋‹ฌ์‹œ๊ฐ„์„ ์ด์šฉํ•œ ์—ฐ์†์ ์ธ ํ˜ˆ์•• ๋ชจ๋‹ˆํ„ฐ๋ง์ด ๊ฐ€๋Šฅ์ผ€ ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๊ด‘์šฉ์ ๋งฅํŒŒ (Photoplethysmogram, PPG) ์™€ ์‹ฌ์ง„๋„ (Seismocardiogram, SCG)๋ฅผ ๋™์‹œ์— ์ธก์ •ํ•˜๋Š” ๊ฐ€์Šด ์ฐฉ์šฉํ˜• ๋‹จ์ผ ์žฅ์น˜๋ฅผ ๊ฐœ๋ฐœํ•˜์—ฌ, ์‹ฌ์ง„๋„๋กœ๋ถ€ํ„ฐ ๋Œ€๋™๋งฅ ํŒ๋ง‰์˜ ์—ด๋ฆฌ๋Š” ์‹œ์ ์„, ๊ด‘์šฉ์ ๋งฅํŒŒ๋กœ๋ถ€ํ„ฐ ๋งฅํŒŒ์˜ ๋„์ฐฉ ์‹œ์ ์„ ํŠน์ •ํ•˜์—ฌ ๋งฅํŒŒ ์ „๋‹ฌ ์‹œ๊ฐ„์„ ์ธก์ •ํ•˜์˜€๋‹ค. ๊ฐœ๋ฐœ๋œ ์‹œ์Šคํ…œ์€ ๋‚ฎ์€ ์ „๋ ฅ ์†Œ๋ชจ์™€ ์†Œํ˜•์˜ ๊ฐ„ํŽธํ•œ ๋””์ž์ธ์„ ํ†ตํ•ด 24์‹œ๊ฐ„ ๋™์•ˆ ์—ฐ์†์ ์œผ๋กœ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ์„ค๊ณ„๋˜์—ˆ๋‹ค. ์ธก์ •๋œ ์ƒ์ฒด์‹ ํ˜ธ๋กœ๋ถ€ํ„ฐ ์ถ”์ถœ๋œ ๋งฅํŒŒ์ „๋‹ฌ์‹œ๊ฐ„ ๋ฐ ๊ธฐํƒ€ ํ˜ˆ์•• ๊ด€๋ จ ๋ณ€์ˆ˜๋“ค์ด ๊ธฐ๊ธฐ์˜ ๋ฐ˜๋ณต ์ฐฉ์šฉ์—๋„ ๋ณ€ํ•˜์ง€ ์•Š์Œ์„ ๊ธ‰๊ฐ„๋‚ด์ƒ๊ด€๊ณ„์ˆ˜(Intra-class correlation, ICC) ๋ถ„์„์„ ํ†ตํ•ด ํ™•์ธํ•˜์˜€๊ณ  (ICC >0.8), ๋˜ํ•œ ๋ณธ ์‹œ์Šคํ…œ์—์„œ ์‚ฌ์šฉ๋œ ์‹ฌ์ง„๋„๊ฐ€ ๋Œ€๋™๋งฅ ํŒ๋ง‰์˜ ์—ด๋ฆฌ๋Š” ์‹œ์ ์˜ ๋ ˆํผ๋Ÿฐ์Šค๊ฐ€ ๋  ์ˆ˜ ์žˆ๋Š”์ง€๋„ ์‹ฌ์ €ํ•ญ์‹ ํ˜ธ(Impedancecardiogram, ICG)์™€์˜ ๋น„๊ต๋ฅผ ํ†ตํ•ด ๊ฒ€์ฆํ•˜์˜€๋‹ค(r=0.79ยฑ0.14). ๋‘˜์งธ๋กœ, ๊ฐœ๋ฐœ๋œ ์‹œ์Šคํ…œ์„ ์ด์šฉํ•˜์—ฌ ๊ธฐ์กด์˜ ๋งฅํŒŒ ์ „๋‹ฌ ์‹œ๊ฐ„๋งŒ์„ ์ด์šฉํ•œ ํ˜ˆ์•• ์ถ”์ • ๋ฐฉ์‹์„ ๋ณด์™„ํ•˜์—ฌ ์ˆ˜์ถ•๊ธฐ ํ˜ˆ์••์˜ ์ถ”์ • ๋Šฅ๋ ฅ์ด ํ–ฅ์ƒ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด, ์‹ฌ์ง„๋„์˜ ์ง„ํญ๊ณผ ๋งฅํŒŒ ์ „๋‹ฌ ์‹œ๊ฐ„์„ ๊ฐ™์ด ์‚ฌ์šฉํ•˜๋Š” ๋‹ค๋ณ€์ˆ˜ ๋ชจ๋ธ์„ ์ˆ˜์ถ•๊ธฐ ํ˜ˆ์•• ์ถ”์ •์„ ์œ„ํ•ด ์ œ์•ˆํ•˜์˜€๊ณ , ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ์œ ๋„๋œ ํ˜ˆ์•• ๋ณ€ํ™” ์ƒํ™ฉ์—์„œ, ๊ธฐ์กด์˜ ๋งฅํŒŒ์ „๋‹ฌ์‹œ๊ฐ„ ํ˜น์€ ๋งฅํŒŒ๋„๋‹ฌ์‹œ๊ฐ„ (Pulse arrival time, PAT) ๋งŒ์„ ์ด์šฉํ•œ ๋ชจ๋ธ๊ณผ ๊ทธ ์„ฑ๋Šฅ์„ ๋น„๊ตํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์ œ์•ˆ๋œ ๋ชจ๋ธ์ด ๊ฐ„๋‹จํ•œ ๊ต์ •์ ˆ์ฐจ๋ฅผ ํ†ตํ•ด ์—ฌ๋Ÿฌ ์‚ฌ๋žŒ์—๊ฒŒ ์ ์šฉ๋  ์ˆ˜ ์žˆ๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ์‚ดํŽด๋ณด์•˜๊ณ  ๋” ๋‚˜์•„๊ฐ€ ์ผ์ƒ ์ƒํ™œ์—์„œ์˜ ์‚ฌ์šฉ ๊ฐ€๋Šฅ์„ฑ์— ๋Œ€ํ•ด์„œ๋„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ๋กœ ์ œ์•ˆ๋œ ๋ชจ๋ธ์€ (1) ๊ธฐ์กด์˜ ๋งฅํŒŒ์ „๋‹ฌ์‹œ๊ฐ„ ํ˜น์€ ๋งฅํŒŒ๋„๋‹ฌ์‹œ๊ฐ„ ๋งŒ์„ ์ด์šฉํ•œ ๋ชจ๋ธ๋ณด๋‹ค ์ˆ˜์ถ•๊ธฐ ํ˜ˆ์•• ์ถ”์ • ๋Šฅ๋ ฅ ์ธก๋ฉด์—์„œ ๋” ์šฐ์ˆ˜ํ•˜์˜€๊ณ , (๊ฐ๊ฐ์˜ ํ‰๊ท ์ ˆ๋Œ€์˜ค์ฐจ๋Š” 4.57, 6.01, 6,11 mmHg ์˜€๋‹ค.) (2) ๊ฐ„๋‹จํ•œ ๊ต์ •์ ˆ์ฐจ๋งŒ์„ ํ†ตํ•ด์„œ ์—ฌ๋Ÿฌ ์‚ฌ๋žŒ์—๊ฒŒ ์ ์šฉ ๋˜์—ˆ์„ ๋•Œ์˜ ์ถ”์ • ๋Šฅ๋ ฅ์ด ๊ตญ์ œ ๊ธฐ์ค€์— ๋ถ€ํ•ฉํ•˜์˜€์œผ๋ฉฐ, (3) ์ผ์ƒ ์ƒํ™œ์—์„œ๋„ ์‚ฌ์šฉ์ž์˜ ์•„๋ฌด๋Ÿฐ ๊ฐœ์ž…์ด๋‚˜ ์ œ์•ฝ ์—†์ด ์ง€์†์ ์ธ ํ˜ˆ์•• ๋ชจ๋‹ˆํ„ฐ๋ง์ด ๊ฐ€๋Šฅํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•˜๋Š” ์ฐฉ์šฉํ˜• ์—ฐ์† ํ˜ˆ์•• ์ธก์ • ์‹œ์Šคํ…œ์€ ๊ฐ€์Šด์— ๋ถ€์ฐฉํ•˜๋Š” ๋‹จ์ผ ๊ธฐ๊ธฐ ํ˜•ํƒœ๋กœ ๊ทธ ์‚ฌ์šฉ์ด ๊ฐ„ํŽธํ•  ๋ฟ ์•„๋‹ˆ๋ผ ์ผ์ƒ์ƒํ™œ ์ค‘์—์„œ ๋งฅํŒŒ์ „๋‹ฌ์‹œ๊ฐ„๊ณผ ์‹ฌ์ง„๋„์˜ ์ง„ํญ์„ ์ด์šฉํ•˜์—ฌ ํ–ฅ์ƒ๋œ ์ˆ˜์ค€์˜ ์—ฐ์† ํ˜ˆ์•• ๋ชจ๋‹ˆํ„ฐ๋ง ์„ฑ๋Šฅ์„ ์ œ๊ณตํ•˜์˜€๋Š”๋ฐ”, ์ด๋ฅผ ์ด์šฉํ•œ ๋ชจ๋ฐ”์ผ ํ—ฌ์Šค์ผ€์–ด ์„œ๋น„์Šค์˜ ๊ฐ€๋Šฅ์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค.Continuous blood pressure (BP) monitoring is needed in daily life to enable early detection of hypertension and improve control of BP for hypertensive patients. Although the pulse transit time (PTT)-based BP estimation represents one of most promising approaches, its use in daily life is limited owing to the requirement of multi systems to measure PTT, and its performance in systolic blood pressure (SBP) estimation is not yet satisfactory. The first goal of this study is to develop a wearable system providing convenient measurement of the PTT, which facilitates continuous BP monitoring based on PTT in daily life. A single chest-worn device was developed measuring a photoplethysmogram (PPG) and a seismocardiogram (SCG) simultaneously, thereby obtaining PTT by using the SCG as timing reference of the aortic valve opening and the PPG as timing reference of pulse arrival. The presented device was designed to be compact and convenient to use, and to last for 24h by reducing power consumption of the system. The consistency of BP related parameters extracted from the system including PTT between repetitive measurements was verified by an intra-class correlation analysis, and it was over 0.8 for all parameters. In addition, the use of SCG as timing reference of the aortic valve opening was verified by comparing it with an impedance cardiogram (r = 0.79 ยฑ 0.14). Secondly, the algorithm improving the performance of the SBP estimation was developed by using the presented system. A multivariate model using SCG amplitude (SA) in conjunction with PTT was proposed for SBP estimation, and was compared with conventional models using only PTT or pulse arrival time (PAT) in various interventions inducing BP changes. Furthermore, we validated the proposed model against the general population with a simple calibration process and verified its potential for daily use. The results suggested that (1) the proposed model, which employed SA in conjunction with PTT for SBP estimation, outperformed the conventional univariate model using PTT or PAT (the mean absolute errors were of 4.57, 6.01, and 6.11 for the proposed, PTT, and PAT models, respectively)(2) for practical use, the proposed model showed potential to be generalized with a simple calibrationand (3) the proposed model and system demonstrated the potential for continuous BP monitoring in daily life without any intervention of users or regulations. In conclusion, the presented system provides an improved performance of continuous BP monitoring in daily life by using a combination of PTT and SA with a convenient and compact single chest-worn device, and thus, it can contribute to mobile healthcare services.CONTENTS Abstract i Contents v List of Tables ix List of Figures xi List of Abbreviations xvi Chapter 1 1 General Introduction 1.1. Blood pressure 2 1.2. Pulse transit time 6 1.3. Thesis objective 12 Chapter 2 14 Development of the Wearable Blood Pressure Monitoring System 2.1. Introduction 15 2.2. System overview 17 2.3. Bio-signal instrumentation 21 2.4. Power management 24 2.5. PCB and case design 25 2.6. Software Design 27 2.7. Signal Processing 30 2.8. Experimental setup 34 2.8.1. Repeatability test 34 2.8.2. Verification of SCG-based PEP 35 2.9. Results and Discussion 38 2.9.1. Repeatability test 38 2.9.2. Verification of SCG-based PEP 40 Chapter 3 43 Enhancement of PTT based BP estimation 3.1. Introduction 44 3.2. Method 47 3.2.1. Principle of BP estimation 47 3.2.2. Subjects 49 3.2.3. Study protocol 50 3.2.4. Data collection 56 3.2.5. Data analysis 60 3.2.6. Evaluation standard 64 3.3. Results 67 3.4. Discussion 96 Chapter 4 113 Conclusion 4.1. Thesis Summary and Contributions 114 4.2. Future Direction 116 Bibliography 118 Abstract in Korean 128Docto
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