Assessment of Thoracic Blood Volume by Computerized Tomography in Patients With Heart Failure and Periodic Breathing

Jorgenson, Caitlin C.; Chase, Steven C.; Olson, Lyle J.; Johnson, Bruce D.

doi:10.1016/j.cardfail.2018.04.004

Cited by 3 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 7 Even so, we believe that there is a role for the ULN moving forward, and we incorporate it here. An increase in DLNO and DLCO above the ULN may not be pathologic; an increase could be caused by a negative intrathoracic pressure during breath-hold (Müeller manoeuvre), 65 66 or lung size may be very large. In fact, most nationally ranked swimmers have DLNO and DLCO above the ULN, 67 which are strongly associated with their large lung volumes.…”

Section: Discussionmentioning

confidence: 99%

Reference equations for pulmonary diffusing capacity using segmented regression show similar predictive accuracy as GAMLSS models

Zavorsky

Cao

2022

BMJ Open Resp Res

View full text Add to dashboard Cite

PurposeTo determine whether generalised additive models of location, scale and shape (GAMLSS) developed for pulmonary diffusing capacity are superior to segmented (piecewise) regression models, and to update reference equations for pulmonary diffusing capacity for carbon monoxide (DLCO) and nitric oxide (DLNO), which may be affected by the equipment used for its measurement.MethodsData were pooled from five studies that developed reference equations for DLCO and DLNO (n=530 F/546 M; 5–95 years old, body mass index 12.4–39.0 kg/m2). Reference equations were created for DLCO and DLNO using both GAMLSS and segmented linear regression. Cross-validation was applied to compare the prediction accuracy of the two models as follows: 80% of the pooled data were used to create the equations, and the remaining 20% was used to examine the fit. This was repeated 100 times. Then, the root-mean-square error was compared between both models.ResultsIn males, GAMLSS models were 7% worse to 3% better compared to segmented regression for DLCO and DLNO. In females, GAMLSS models were 2% worse to 5% better compared to segmented linear regression for DLCO and DLNO. The Hyp'Air Compact measured DLNO and alveolar volume (VA) that was approximately 16–20 mL/min/mm Hg and 0.2–0.4 L higher, respectively, compared to the Jaeger MasterScreen Pro. The measured DLCO was similar between devices after controlling for altitude.ConclusionsFor the development of pulmonary function reference equations, we propose that segmented linear regression can be used instead of GAMLSS due to its simplicity, especially when the predictive accuracy is similar between the two models, overall.

show abstract

Section: Discussionmentioning

confidence: 99%

Reference equations for pulmonary diffusing capacity using segmented regression show similar predictive accuracy as GAMLSS models

Zavorsky

Cao

2022

BMJ Open Resp Res

View full text Add to dashboard Cite

show abstract

Subclinical Pulmonary Congestion and Abnormal Hemodynamics in Heart Failure With Preserved Ejection Fraction

Jain

Tschirren²,

Reddy³

et al. 2022

JACC: Cardiovascular Imaging

View full text Add to dashboard Cite

Effects of exercise on thoracic blood volumes, lung fluid accumulation, and pulmonary diffusing capacity in heart failure with preserved ejection fraction

Fermoyle

Stewart

Borlaug

et al. 2020

American Journal of Physiology-Regulatory, Integrative and Comp

View full text Add to dashboard Cite

Background- Heart failure with preserved ejection fraction (HFpEF) patients experience symptoms of exertional dyspnea which may be related to lung fluid accumulation during exercise. A CT-based method was used to measure exercise-induced changes in extravascular lung fluid content and thoracic blood volumes and determine the effect of lung fluid on lung diffusing capacity for carbon monoxide (DLCO) in stable HFpEF subjects and healthy controls. Methods- Nine subjects with HFpEF (Age 68±8y, BMI 32.1±2.6kg/m2) and eight healthy controls (62±9y, 23.8±2.4kg/m2) performed triplicate re-breathe DLCO/DLNO tests in a supine position at rest and duplicate measurements during two 5 min submaximal exercise stages (15 and 35W) and recovery. Subjects subsequently performed a 5 min exercise bout (35W) inside a CT scanner, and extravascular lung fluid content and thoracic blood volumes were quantified at rest and immediately following exercise from thoracic and contrast perfusion scans, respectively. Results-Subjects with HFpEF had a higher lung fluid content at rest compared to controls (Mean±SD, HFpEF: 14.4±1.7%, Control: 12.8±1.7%, p=0.043) and a higher lung fluid content following exercise (15.2±2.0% vs. 12.6±1.5%, p=0.009). Higher lung fluid content was associated with a lower DLCO and alveolar-capillary membrane conductance (Dm) in subjects with HFpEF (DLCO: R=-0.57, p=0.022, Dm: R=-0.61, p=0.012), but not in controls. Pulmonary blood volume was not altered by exercise and was similar between groups. Conclusions-Submaximal exercise elicited a greater accumulation of lung fluid in HFpEF compared to controls, and lung fluid content was negatively correlated with lung diffusing capacity and alveolar-capillary membrane conductance in HFpEF.

show abstract

Assessment of Thoracic Blood Volume by Computerized Tomography in Patients With Heart Failure and Periodic Breathing

Abstract: Subjects with HF and PB at rest have greater centralization of blood volume.

Cited by 3 publications

References 17 publications

Reference equations for pulmonary diffusing capacity using segmented regression show similar predictive accuracy as GAMLSS models

Reference equations for pulmonary diffusing capacity using segmented regression show similar predictive accuracy as GAMLSS models

Subclinical Pulmonary Congestion and Abnormal Hemodynamics in Heart Failure With Preserved Ejection Fraction

Effects of exercise on thoracic blood volumes, lung fluid accumulation, and pulmonary diffusing capacity in heart failure with preserved ejection fraction

Contact Info

Product

Resources

About