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Interactive CardioVascular and Thoracic Surgery Advance Access





Published: Fri, 23 Feb 2018 00:00:00 GMT

Last Build Date: Fri, 23 Feb 2018 04:49:09 GMT

 



Statistical primer: performing repeated-measures analysis†

Fri, 23 Feb 2018 00:00:00 GMT

Abstract
Longitudinal data arise when repeated measurements are taken on the same individuals over time. Inference about between-group differences of within-subject change is usually of interest. This statistical primer for cardiothoracic and vascular surgeons aims to provide a short and practical introduction of biostatistical methods on how to analyse repeated-measures data. Several methodological approaches for analysing repeated measures will be introduced, ranging from simple approaches to advanced regression modelling. Design considerations of studies involving repeated measures are discussed, and the methods are illustrated with a data set measuring coronary sinus potassium in dogs after occlusion. Cardiothoracic and vascular surgeons should be aware of the myriad approaches available to them for analysing repeated-measures data, including the relative merits and disadvantages of each. It is important to present effective graphical displays of the data and to avoid arbitrary cross-sectional statistical comparisons.



Statistical primer: basics of survival analysis for the cardiothoracic surgeon†

Fri, 23 Feb 2018 00:00:00 GMT

Abstract
Survival analysis incorporates various statistical methods specific to data on time until an event of interest. While the event is often death, giving rise to the phrase ‘survival analysis’, the event might also be, for example, a reoperation. As such, it is sometimes referred to as ‘time-to-event analysis’. Censoring sets survival analysis apart from other analyses: at the end of the follow-up period, not all subjects have experienced the event of interest, and some subjects may drop out of the study prior to completion. Survival data for a group of subjects is usually visualized by the Kaplan–Meier estimator, representing the probability of a subject remaining free of the event during follow-up. There are several methods to compare survival between the study groups, for example, treatment arms, including the log-rank test and the Cox proportional hazards model. The log-rank test is an unadjusted non-parametric method, whereas the Cox proportional hazards model allows comparison while adjusting for multiple covariates. A principal assumption of the Cox proportional hazards model is that the relative hazard stays constant over time—the so-called proportionality. Specific methods exist for comparison of survival with the general population. This article describes the fundamental concepts every cardiothoracic surgeon should be aware of when analysing survival data and are illustrated with a clinical example.






Long-term outcomes using the stentless LivaNova-Sorin Pericarbon Freedom™ valve after aortic valve replacement†

Wed, 21 Feb 2018 00:00:00 GMT

Abstract
OBJECTIVES
Information on the long-term safety, efficacy and durability of third-generation stentless aortic valves is lacking.
METHODS
In this single-centre, single-surgeon retrospective observational study, between 2003 and 2015, consecutive, non-selected aortic valve replacement (AVR) patients were implanted with the LivaNova-Sorin Pericarbon Freedom™, a third-generation stentless aortic xenograft. Changes in clinical and echocardiographic parameters were examined, as were mortality, structural valve deterioration and reoperation, according to age at 5, 10 and 14 years.
RESULTS
The mean logistic EuroSCORE was 8.5% in 22 AVR patients (mean age 68.3 years; range 15–89 years). Many patients [n = 139 (43%)] underwent a concomitant procedure. Before AVR, 68.0% of patients were in New York Heart Association (NYHA) Class I or II, and at discharge, mean gradient was 10.0 ± 4.3 mmHg. Follow-up lasted up to 8.9 ± 2.8 years. At the last follow-up, 95.6% of patients were in New York Heart Association Class I or II, the mean gradient was 8.0 ± 3.5 mmHg (P < 0.001) and reduction in interventricular septum thickness and improvement of ejection fraction were significant (both P < 0.001). Early 30-day in-hospital mortality was 1.6% overall and 0% in the AVR-only population. Overall survival probability was 99.9%, 87.9% and 82.7% at 5, 10 and 14 years. Freedom from structural valve deterioration at 14 years was 67.5%, 88.9% and 68.2% in AVR patients overall, in those aged >70 years and in those aged 60–70 years, respectively. Freedom from reoperation at 14 years was 70.3%, 88.3% and 78.0% in the corresponding groups.
CONCLUSIONS
Sorin Pericarbon Freedom is a valuable aortic bioprosthesis with favourable haemodynamics, particularly in smaller annuli, and durability similar to that of stented valves, which make Sorin Pericarbon Freedom a useful option in AVR.



Can bioprosthetic valve thrombosis be promoted by aortic root morphology? An in vitro study†

Wed, 21 Feb 2018 00:00:00 GMT

Abstract
OBJECTIVES
Bioprosthetic valve thrombosis has been considered uncommon, but recent studies have shown that it is more frequent than previously thought. Insufficient washout of the aortic sinus is believed to be a risk factor for bioprosthetic valve thrombosis. The objective of this in vitro experiment was to investigate the impact of aortic root morphology on blood flow in the aortic sinus and to relate these results to in vivo data obtained in patients with a transcatheter aortic valve implant.
METHODS
Two compliant aortic root phantoms with different morphologies (symmetrical and patient-specific) were fabricated with silicone. A bioprosthetic aortic valve was inserted in both phantoms. Haemodynamic measurements were performed in a pulsatile flow-loop replicating physiological flow and pressure conditions. The flow in the aortic root was visualized by injecting contrast agent (CA). The distribution of the CA was captured by a high-speed camera, and image post-processing was performed to quantify CA distribution in the aortic sinus. The results were compared with angiographic images after a transcatheter aortic valve implant.
RESULTS
Blood flow in the aortic root and the washout of the sinus portion are significantly affected by aortic root morphology. CA arrives at the aortic sinus of the 2 phantoms at 0.09 s and 0.16 s after the valve opens in the symmetrical and the patient-specific phantoms, respectively. Delayed CA arrival was also observed in the patients with a transcatheter aortic valve implant.
CONCLUSIONS
Aortic root morphology affects the blood flow in the aortic sinus and may be a factor in bioprosthetic valve thrombosis. Therefore, patient-specific aortic root morphology should be considered when selecting and positioning a prosthesis.