The effect of innate compliance on the performance of a counterpulsation device

Cardiovascular disease (CVD) continues to be one of the top causes of mortality in the world. World Heart Organization (WHO) reported that in 2004, CVD contributed to almost 30% of death from estimated worldwide death figures of 58 million[1]. Heart failure treatment varies from lifestyle adjustment to heart transplantation; its aims are to reduce HF symptoms, prolong patient survival and minimize risk [2]. One alternative available in the market for HF treatment is Left Ventricular Assist Device (LVAD). Chronic Intermittent Mechanical Support (CIMS) device is a novel (LVAD) heart failure treatment using counterpulsation similar to Intra Aortic Balloon Pumps (IABP). However, the implantation site of the CIMS balloon is in the ascending aorta just distal to aortic valve contrasted with IABP in the descending aorta. Counterpulsation coupled with implantation close to the aortic valve enables comparable flow augmentation with reduced balloon volume. Two prototypes of the CIMS balloon were constructed using rapid prototyping: the straight-body model is a cylindrical tube with a silicone membrane lining with zero expansive compliance. The compliant-body model had a bulging structure that allowed the membrane to expand under native systolic pressure increasing the device’s static compliance to 1.5 mL/mmHg. This study examined the effect of device compliance and vascular compliance on counterpulsating flow augmentation. Both prototypes were tested on a two-element Windkessel model human mock circulatory loop (MCL). The devices were placed just distal to aortic valve and left coronary artery. The MCL mimicked HF with cardiac output of 3 L/min, left ventricular pressure of 85/15 mmHg, aortic pressure of 70/50 mmHg and left coronary artery flow rate of 66 mL/min. The mean arterial pressure (MAP) was calculated to be 57 mmHg. Arterial compliance was set to be1.25 mL/mmHg and 2.5 mL/mmHg. Inflation of the balloon was triggered at the dicrotic notch while deflation was at minimum aortic pressure prior to systole. Important haemodynamics parameters such as left ventricular pressure (LVP), aortic pressure (AoP), cardiac output (CO), left coronary artery flowrate (QcorMean), and dP (Peak aortic diastolic augmentation pressure – AoPmax ) were simultaneously recorded for both non-assisted mode and assisted mode. ANOVA was used to analyse the effect of both factors (balloon and arterial compliance) to flow augmentation. The results showed that for cardiac output and left coronary artery flowrate, there were significant difference between balloon and arterial compliance at p < 0.001. Cardiac output recorded maximum output at 18% for compliant body and stiff arterial compliance. Left coronary artery flowrate also recorded around 20% increase due to compliant body and stiffer arterial compliance. Resistance to blood ejection recorded highest difference for combination of straight body and stiffer arterial compliance. From these results it is clear that both balloon and arterial compliance are statistically significant factors for flow augmentation on peripheral artery and reduction of resistance. Although the result for resistance reduction was different from flow augmentation, these results serves as an important aspect which will influence the future design of the CIMS balloon and its control strategy. References: 1. Mathers C, Boerma T, Fat DM. The Global Burden of disease:2004 update. Geneva: World Heatlh Organization; 2008. 2. Jessup M, Brozena S. Heart Failure. N Engl J Med 2003;348:2007-18.

An artificial neural network stratifies the risks of reintervention and mortality after endovascular aneurysm repair:a retrospective observational study

Background Lifelong surveillance after endovascular repair (EVAR) of abdominal aortic aneurysms (AAA) is considered mandatory to detect potentially life-threatening endograft complications. A minority of patients require reintervention but cannot be predictively identified by existing methods. This study aimed to improve the prediction of endograft complications and mortality, through the application of machine-learning techniques. Methods Patients undergoing EVAR at 2 centres were studied from 2004-2010. Pre-operative aneurysm morphology was quantified and endograft complications were recorded up to 5 years following surgery. An artificial neural networks (ANN) approach was used to predict whether patients would be at low- or high-risk of endograft complications (aortic/limb) or mortality. Centre 1 data were used for training and centre 2 data for validation. ANN performance was assessed by Kaplan-Meier analysis to compare the incidence of aortic complications, limb complications, and mortality; in patients predicted to be low-risk, versus those predicted to be high-risk. Results 761 patients aged 75 +/- 7 years underwent EVAR. Mean follow-up was 36+/- 20 months. An ANN was created from morphological features including angulation/length/areas/diameters/ volume/tortuosity of the aneurysm neck/sac/iliac segments. ANN models predicted endograft complications and mortality with excellent discrimination between a low-risk and high-risk group. In external validation, the 5-year rates of freedom from aortic complications, limb complications and mortality were 95.9% vs 67.9%; 99.3% vs 92.0%; and 87.9% vs 79.3% respectively (p0.001) Conclusion This study presents ANN models that stratify the 5-year risk of endograft complications or mortality using routinely available pre-operative data.

Handling high level of censoring for endovascular aortic repair risk prediction

Feature selection is important in medical field for many reasons. However, selecting important variables is a difficult task with the presence of censoring that is a unique feature in survival data analysis. This paper proposed an approach to deal with the censoring problem in endovascular aortic repair survival data through Bayesian networks. It was merged and embedded with a hybrid feature selection process that combines cox's univariate analysis with machine learning approaches such as ensemble artificial neural networks to select the most relevant predictive variables. The proposed algorithm was compared with common survival variable selection approaches such as; least absolute shrinkage and selection operator LASSO, and Akaike information criterion AIC methods. The results showed that it was capable of dealing with high censoring in the datasets. Moreover, ensemble classifiers increased the area under the roc curves of the two datasets collected from two centers located in United Kingdom separately. Furthermore, ensembles constructed with center 1 enhanced the concordance index of center 2 prediction compared to the model built with a single network. Although the size of the final reduced model using the neural networks and its ensembles is greater than other methods, the model outperformed the others in both concordance index and sensitivity for center 2 prediction. This indicates the reduced model is more powerful for cross center prediction.

Examining factors associated with excess mortality in older people (age ≥ 70 years) with diabetes - a 10-year cohort study of older people with and without diabetes

Aims: To compare all-cause mortality in older people with or without diabetes and consider the associated risk of comorbidity and polypharmacy. Methods: A 10-year cohort study using data from the Health Innovation Network database (2003-2013) comparing mortality in people aged ≥ 70 years with diabetes (DM cohort) (n = 35 717) and without diabetes (No DM cohort) (n = 307 918). Results: The mean age of the DM cohort was 78.1 ± 5.8 years vs. 79.0 ± 6.3 years in the No DM cohort. Mean diabetes duration was 8.2 ± 8.1 years, and 30% had diabetes for > 10 years. The DM cohort had a greater comorbidity load and people in this cohort were prescribed more therapies than the No DM cohort. The 5- and 10-year survival rates were lower in the DM cohort at 64% and 39%, respectively, compared with 72% and 50% in the No DM cohort. The excess mortality in the DM cohort was greatest in those aged <75 years with longer duration diabetes, the relative hazard for mortality was higher in females. Although comorbidity and polypharmacy were associated with increased mortality risk in the DM cohort, this risk was lower compared with the No DM cohort. The hazard ratios (95% confidence interval) for comorbidities > 4 and medicines ≥ 7 were 1.29 (1.19 to 1.41) and 1.34 (1.25 to 1.43) in the DM cohort and 1.63 (1.57 to 1.70) and 1.48 (1.40 to 1.56) in the No DM cohort, respectively. Conclusions: There is significant excess mortality in older people with diabetes, which is unexplained by comorbidity or polypharmacy. This excess is greatest in the younger old with longer disease duration, suggesting that it may be related to the effect of diabetes exposure.