scholarly journals Estimated plasma volume status is a modest predictor of true plasma volume excess in compensated chronic heart failure patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Christoph Ahlgrim ◽  
Philipp Birkner ◽  
Florian Seiler ◽  
Sebastian Grundmann ◽  
Christoph Bode ◽  
...  
Keyword(s):  
Heart Failure ◽  
Plasma Volume ◽  
Clinical Decision Making ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Blood Parameters ◽  
Volume Status ◽  
Predictive Values ◽  
Heart Failure Patients ◽  
Rebreathing Method

AbstractPlasma volume and especially plasma volume excess is a relevant predictor for the clinical outcome of heart failure patients. In recent years, estimated plasma volume based on anthropometric characteristics and blood parameters has been used whilst direct measurement of plasma volume has not entered clinical routine. It is unclear whether the estimation of plasma volume can predict a true plasma volume excess. Plasma volume was measured in 47 heart failure patients (CHF, 10 female) using an abbreviated carbon monoxide rebreathing method. Plasma volume and plasma volume status were also estimated based on two prediction formulas (Hakim, Kaplan). The predictive properties of the estimated plasma volume status to detect true plasma volume excess > 10% were analysed based on logistic regression and receiver operator characteristics. The area under the curve (AUC) to detect plasma volume excess based on calculation of plasma volume by the Hakim formula is 0.65 (with a positive predictive value (PPV) of 0.62 at a threshold of − 16.5%) whilst the AUC for the Kaplan formula is 0.72 (PPV = 0.67 at a threshold of − 6.3%). Only the estimated plasma volume status based on prediction of plasma volume by the Kaplan formula formally appears as an acceptable predictor of true plasma volume excess, whereas calculation based on the Hakim formula does not sufficiently predict a true plasma volume excess. The low positive predictive values for both methods suggest that plasma volume status estimation based on these formulas is not suitable for clinical decision making.

Abstract 112: Targeted Metabolomics Profiling of Heart Failure Patients Undergoing Mechanical Circulatory Support

2014 ◽  
Vol 115 (suppl_1) ◽  
Author(s):  
X'avia Chan ◽  
J.H. Howard Choi ◽  
Chelsea J.-T. Ju ◽  
Wei Wang ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Heart Failure ◽  
Functional Groups ◽  
Mechanical Circulatory Support ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Circulatory Support ◽  
Surgical Interventions ◽  
Heart Failure Patients ◽  
Metabolic Marker ◽  
Metabolic Fingerprint

Metabolomics investigations hold promise for the characterization of small molecules, metabolites, which govern the ultimate manifestation of cardiac phenotypes. In this study, we employed a mass spectrometry-based metabolomics approach to identify metabolic marker(s), which dynamically reflect the cardiac performance of heart failure patients amid the implantation of mechanical circulatory support. Using the MRM-based and triple quadrupole technology platform, we have quantified 266 metabolites native to human plasma and collected from thirteen heart failure patients. The temporal profile of these metabolites was sampled from 1 day prior to the implantation of mechanical circulatory support, as well as 1-, 3-, 5-, and 7-day following their surgical interventions. We identified subgroups of these metabolites with coordinated behaviors that are interesting to their diseased phenotypes. In a pair-wise correlation analysis, 36.8% (98 out of 266) of metabolites were significantly correlated. Intriguingly, majority of which (65 out of 98) are representing the functional groups of phosphatidylcholines; several of them are known to have close associations with the pathogenesis of cardiovascular diseases. In addition, there are 33 metabolites contributing to multiple functional groups, including twelve of them belong to sphingomyelines, ten of them in the family of lysophosphatidylcholines, eight amino acids (Gln, Ser, Ala, His, Lys, Gly, Thr, and Arg), as well as three fatty acids (eicosapentaenoic acid, pentadecenoic acid, and heptadecenoic acid). The behaviors of these 266 metabolites have constituted individualized metabolic fingerprints. Delineation of the intrinsic relationships among alterations in distinct metabolite groups and their reflected cardiac function will enable us to identify new metabolic markers aiding stratification and/or prediction on the clinical outcome of each individual patient undergoing the treatment of mechanical circulatory support. This personalized metabolic fingerprint will offer unique prognostic utilities, supporting clinical decision-making process to deliver intervention that is most effective and beneficial to an individual.

scholarly journals IMPACT OF CMR ON MANAGEMENT AND CLINICAL DECISION-MAKING IN HEART FAILURE PATIENTS

2013 ◽  
Vol 61 (10) ◽  
pp. E826 ◽  
Author(s):  
Siddique Abbasi ◽  
Andrew Ertel ◽  
Ravi Shah ◽  
Tomas Neilan ◽  
Bobby Heydari ◽  
...  
Keyword(s):  
Heart Failure ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Heart Failure Patients

scholarly journals Effects of age on aerobic capacity in heart failure patients under beta-blocker therapy: Possible impact in clinical decision-making?

2013 ◽  
Vol 20 (6) ◽  
pp. 655-661 ◽  
Author(s):  
Emmanuel Gomes Ciolac ◽  
Edimar Alcides Bocchi ◽  
Miguel Morita Fernandes da Silva ◽  
Aline Cristina Tavares ◽  
Iram Soares Teixeira-Neto ◽  
...  
Keyword(s):  
Heart Failure ◽  
Decision Making ◽  
Aerobic Capacity ◽  
Beta Blocker ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Blocker Therapy ◽  
Heart Failure Patients ◽  
Beta Blocker Therapy

scholarly journals Impact of Grade Groups on Prostate Cancer-Specific and Other-Cause Mortality: Competing Risk Analysis from a Large Single Institution Series

Cancers ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1963
Author(s):  
Daimantas Milonas ◽  
Tomas Ruzgas ◽  
Zilvinas Venclovas ◽  
Mindaugas Jievaltas ◽  
Steven Joniau
Keyword(s):  
Clinical Decision Making ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Competing Risk ◽  
Grade Group ◽  
Tertiary Center ◽  
Stable Performance ◽  
Cancer Specific Mortality ◽  
Risk Of Cancer

Objective: To assess the risk of cancer-specific mortality (CSM) and other-cause mortality (OCM) using post-operative International Society of Urological Pathology Grade Group (GG) model in patients after radical prostatectomy (RP). Patients and Methods: Overall 1921 consecutive men who underwent RP during 2001 to 2017 in a single tertiary center were included in the study. Multivariate competing risk regression analysis was used to identify significant predictors and quantify cumulative incidence of CSM and OCM. Time-depending area under the curve (AUC) depicted the performance of GG model on prediction of CSM. Results: Over a median follow-up of 7.9-year (IQR 4.4-11.7) after RP, 235 (12.2%) deaths were registered, and 52 (2.7%) of them were related to PCa. GG model showed high and stable performance (time-dependent AUC 0.88) on prediction of CSM. Cumulative 10-year CSM in GGs 1 to 5 was 0.9%, 2.3%, 7.6%, 14.7%, and 48.6%, respectively; 10-year OCM in GGs was 15.5%, 16.1%, 12.6%, 17.7% and 6.5%, respectively. The ratio between 10-year CSM/OCM in GGs 1 to 5 was 1:17, 1:7, 1:2, 1:1, and 7:1, respectively. Conclusions: Cancer-specific and other-cause mortality differed widely between GGs. Presented findings could aid in personalized clinical decision making for active treatment.

The Optimal Plasma Volume Status in Heart Failure in Relation to Clinical Outcome

2019 ◽  
Vol 25 (4) ◽  
pp. 240-248 ◽  
Author(s):  
Pieter Martens ◽  
Petra Nijst ◽  
Matthias Dupont ◽  
Wilfried Mullens
Keyword(s):  
Heart Failure ◽  
Clinical Outcome ◽  
Plasma Volume ◽  
Volume Status
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