The correlations of optimal AV delays by non-invasive (Finometer) Conclusions During acute biventricular pacing, at a fixed heart rate, changing the AV delay affects the cardiac mechanoenergetics. When an AV delay improves external cardiac work, compared to LBBB or a physiologically too short AV delay (eg, AV 40 ms), it also increases the myocardial oxygen consumption. However, only 1% more energy is consumed per 1.6% more external work (pressure3flow) done; as a result cardiac efficiency improves. Haemodynamic optimisation of AV delay can be achieved with high precision using non invasive beat-to-beat pressure measurements. This should enable routine haemodynamic optimisation (easily automated) of CRT devices in clinical practice.
was a secondary progressive decline to a lower plateau of +8.061.8 mm Hg (p¼0.004), Abstract 004 figure 1. The initial increment was caused by an immediate rise in flow by +9.162.4% (p¼0.007) which did not drop later. The secondary decline in pressure was caused by a delayed gradual decline in total peripheral resistance. Finometer-derived non-invasive blood pressure tracked invasive pressure closely (r¼0.97). Conclusion When AV delay is made more favourable, only the instant pressure increment is caused by increase in stroke volume. The secondary pressure decline is caused by systemic vasodilatation. Design of AV optimisation protocols, which face severe challenge of signal vs noise, might benefit from recognition that not all beats are equally informative: the first few after a transition are most signal-rich. Introduction A significant number of patients undergoing Cardiac Resynchronisation Therapy (CRT) do not remodel. Assessing global dyssynchrony has the potential to improve patient selection. We developed a framework for comparing measures of myocardial motion from cardiac magnetic resonance (CMR) imaging and evaluated the potential of these techniques to improve patient selection. Methods 48 patients recruited, (43 males, 63.8613.9 years), NYHA class 2.960.5, ejection fraction 2569%. Patients had LBBB (QRS 154624 ms). Acute haemodynamic response was measured at time of implant with a pressure wire in the LV measuring change in dP/ dt max . A >10% increase in LV-dP/dt max from baseline was considered an acute response. Decrease in end systolic volume (ESV) $15% at 6 months was used to determine remodelling. CMR was performed prior to CRT. A novel framework was developed. Key steps included: (1) detection of heart and myocardium segmentation from anatomical CMR cine images; (2) detection of endo and epi-cardial surfaces for wall thickening computation; (3) extraction of deformation fields within the myocardium for strain computation. A systolic dyssynchrony index (SDI) was produced for all parameters which included volume change, muscle thickening, radial, circumferential, longitudinal strain and combined strain. High SDI denoted dyssynchrony. Results Pre-implant ESV 175664 ml, post-implant ESV 155668 ml (p<0.01). 20 (44%) patients remodelled. We found a strong relationship between volume derived SDI and acute response (p¼0.008) and remodelling (p<0.001) (Abstract 005 figure 1). We found a weaker relationship with remodelling and muscle thickening SDI (p¼0.001) and no relationship with a SDI derived from strain indexes (Abstract 005 figure 2). Volume SDI $10% was highly sensitive (0.94) and specific (0.87) for predicting remodelling. Volume SDI was far superior for predicting remodelling than any other method. The intra-observer average difference for volume SDI Abstract 005 Figure 1 Shows the ANOVA plots for acute response and remodelling for QRS duration, volume and muscle thickening derived SDI. 005Abstract 005 Figure 2 Shows the ANOVA plots for acute response (top row) and remodelling (bottom row) for ...
Funding Acknowledgements Type of funding sources: None. Background Relative benefits of transvenous lead extraction (TLE) should be weighed against the risks on an individual basis. Observational studies have identified disparate risk factors for major adverse events (MAEs), however there are few available tools to aid the clinician risk-stratifying patients. Purpose The purpose of this study was to develop a machine learning (ML)-based risk stratification system to predict risk of MAEs following transvenous lead extraction (TLE). Methods Multiple ML models were derived from a training cohort of 3555 patients undergoing TLE to pre-procedurally identify patients at high and low risk of major adverse events (procedure-related major complication, including procedure-related death). The ML models were then tested on an independent, reference centre cohort of 1171 patients undergoing TLE. Clinical features were selected based on whether they were available in both datasets without large gaps. For model selection we compared the ML algorithms logistic regression, ridge regression, support vector machines (SVM), gradient boosting classifier and random forest. The best performing of these was then compared to a self-normalising network (SNN). Results In total 3122 cases with the required features were included from the training cohort with 53 MAEs (1.7%), and 998 cases in the test cohort with 24 MAEs (2.4%). SVM identified lead dwell time, left ventricular ejection fraction (LVEF), estimated glomerular filtration rate (eGFR), chronic respiratory disease, local infection, heart failure, sepsis and male gender as predictors of MAEs in order of decreasing importance (see figure). For the training data, the SVM provided a more sensitive result compared with SNN (0.83 vs 0.71) at the cost of specificity (0.63 vs 0.79). For the test data, the SVM identified MAEs in 12 out of 123 (9.8%) "high risk" patients, 10 out of 552 (1.8%) "medium risk" patients, and 2 out of 323 (0.006%) "low risk" patients. Conclusion Machine learning provided good discriminative capabilities for identifying patients in a "high risk" and "low risk" category for MAEs. Abstract Figure. 8 most important predictors of MAEs
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