USim: A New Device and App for Case-Specific, Intraoperative Ultrasound Simulation and Rehearsal in Neurosurgery. A Preliminary Study.

BACKGROUND: Intraoperative ultrasound (iUS) is an excellent aid for neurosurgeons to perform better and safer operations thanks to real time, continuous, and high-quality intraoperative visualization. OBJECTIVE: To develop an innovative training method to teach how to perform iUS in neurosurgery. METHODS: Patients undergoing surgery for different brain or spine lesions were iUS scanned (before opening the dura) in order to arrange a collection of 3-dimensional, US images; this set of data was matched and paired to preoperatively acquired magnetic resonance images in order to create a library of neurosurgical cases to be studied offline for training and rehearsal purposes. This new iUS training approach was preliminarily tested on 14 European neurosurgery residents, who participated at the 2016 European Association of Neurosurgical Societies Training Course (Sofia, Bulgaria). RESULTS: USim was developed by Camelot and the Besta NeuroSim Center as a dedicated app that transforms any smartphone into a "virtual US probe," in order to simulate iUS applied to neurosurgery on a series of anonymized, patient-specific cases of different central nervous system tumors (eg, gliomas, metastases, meningiomas) for education, simulation, and rehearsal purposes. USim proved to be easy to use and allowed residents to quickly learn to handle a US probe and interpret iUS semiotics. CONCLUSION: USim could help neurosurgeons learn neurosurgical iUS safely. Furthermore, neurosurgeons could simulate many cases, of different brain/spinal cord tumors, that resemble the specific cases they have to operate on. Finally, the library of cases would be continuously updated, upgraded, and made available to neurosurgeons.

Aortic reservoir pressure corresponds to cyclic changes in aortic volume: physiological validation in humans.

OBJECTIVE: Aortic reservoir pressure indices independently predict cardiovascular events and mortality. Despite this, there has never been a study in humans to determine whether the theoretical principles of the mathematically derived aortic reservoir pressure (RP(derived)) and excess pressure (XP(derived)) model have a real physiological basis. This study aimed to directly measure the aortic reservoir (AR(direct); by cyclic change in aortic volume) and determine its relationship with RP(derived), XP(derived), and aortic blood pressure (BP). APPROACH AND RESULTS: Ascending aortic BP and Doppler flow velocity were recorded via intra-arterial wire in 10 men (aged 62 ± 12 years) during coronary artery bypass surgery. Simultaneous ascending aortic transesophageal echocardiography was used to measure AR(direct). Published mathematical formulae were used to determine RP(derived) and XP(derived). AR(direct) was strongly and linearly related to RP(derived) during systole (r=0.988; P<0.001) and diastole (r=0.985; P<0.001). Peak cross-correlation (r=0.98) occurred at a phase lag of 0.004 s into the cardiac cycle, suggesting close temporal agreement between waveforms. The relationship between aortic BP and AR(direct) was qualitatively similar to the cyclic relationship between aortic BP and RP(derived), with peak cross-correlations occurring at identical phase lags (AR(direct) versus aortic BP, r=0.96 at 0.06 s; RP(derived) versus aortic BP, r=0.98 at 0.06 s). CONCLUSIONS: RP(derived) is highly correlated with changes in proximal aortic volume, consistent with its physiological interpretation as corresponding to the instantaneous volume of blood stored in the aorta. Thus, aortic reservoir pressure should be considered in the interpretation of the central BP waveform.

Patient-accessible tool for shared decision making in cardiovascular primary prevention: balancing longevity benefits against medication disutility.

BACKGROUND: Primary prevention guidelines focus on risk, often assuming negligible aversion to medication, yet most patients discontinue primary prevention statins within 3 years. We quantify real-world distribution of medication disutility and separately calculate the average utilities for a range of risk strata. METHOD AND RESULTS: We randomly sampled 360 members of the general public in London. Medication aversion was quantified as the gain in lifespan required by each individual to offset the inconvenience (disutility) of taking an idealized daily preventative tablet. In parallel, we constructed tables of expected gain in lifespan (utility) from initiating statin therapy for each age group, sex, and cardiovascular risk profile in the population. This allowed comparison of the widths of the distributions of medication disutility and of group-average expectation of longevity gain. Observed medication disutility ranged from 1 day to >10 years of life being required by subjects (median, 6 months; interquartile range, 1-36 months) to make daily preventative therapy worthwhile. Average expected longevity benefit from statins at ages ≥50 years ranges from 3.6 months (low-risk women) to 24.3 months (high-risk men). CONCLUSION: We can no longer assume that medication disutility is almost zero. Over one-quarter of subjects had disutility exceeding the group-average longevity gain from statins expected even for the highest-risk (ie, highest-gain) group. Future primary prevention studies might explore medication disutility in larger populations. Patients may differ more in disutility than in prospectively definable utility (which provides only group-average estimates). Consultations could be enriched by assessing disutility and exploring its reasons.

A novel fully automated method for mitral regurgitant orifice area quantification.

BACKGROUND: Effective regurgitant orifice area (EROA) in mitral regurgitation (MR) is difficult to quantify. Clinically it is measured using the proximal isovelocity surface area (PISA) method, which is intrinsically not automatable, because it requires the operator to manually identify the mitral valve orifice. We introduce a new fully automated algorithm, ("AQURO"), which calculates EROA directly from echocardiographic colour M-mode data, without requiring operator input. METHODS: Multiple PISA measurements were compared to multiple AQURO measurements in twenty patients with MR. For PISA analysis, three mutually blinded observers measured EROA from the four stored video loops. For AQURO analysis, the software automatically processed the colour M-mode datasets and analysed the velocity field in the flow-convergence zone to extract EROA directly without any requirement for manual radius measurement. RESULTS: Reproducibility, measured by intraclass correlation (ICC), for PISA was 0.80, 0.83 and 0.83 (for 3 observers respectively). Reproducibility for AQURO was 0.97. Agreement between replicate measurements calculated using Bland-Altman standard deviation of difference (SDD) was 21,17 and 17mm(2)for the three respective observers viewing independent video loops using PISA. Agreement between replicate measurements for AQURO was 6, 5 and 7mm(2)for automated analysis of the three pairs of datasets. CONCLUSIONS: By eliminating the need to identify the orifice location, AQURO avoids an important source of measurement variability. Compared with PISA, it also reduces the analysis time allowing analysis and averaging of data from significantly more beats, improving the consistency of EROA quantification. AQURO, being fully automated, is a simple, effective enhancement for EROA quantification using standard echocardiographic equipment.

Evidence-based recommendations for PISA measurements in mitral regurgitation: systematic review, clinical and in-vitro study.

BACKGROUND: Guidelines for quantifying mitral regurgitation (MR) using "proximal isovelocity surface area" (PISA) instruct operators to measure the PISA radius from valve orifice to Doppler flow convergence "hemisphere". Using clinical data and a physically-constructed MR model we (A) analyse the actually-observed colour Doppler PISA shape and (B) test whether instructions to measure a "hemisphere" are helpful. METHODS AND RESULTS: In part A, the true shape of PISA shells was investigated using three separate approaches. First, a systematic review of published examples consistently showed non-hemispherical, "urchinoid" shapes. Second, our clinical data confirmed that the Doppler-visualized surface is non-hemispherical. Third, in-vitro experiments showed that round orifices never produce a colour Doppler hemisphere. In part B, six observers were instructed to measure hemisphere radius rh and (on a second viewing) urchinoid distance (du) in 11 clinical PISA datasets; 6 established experts also measured PISA distance as the gold standard. rh measurements, generated using the hemisphere instruction significantly underestimated expert values (-28%, p<0.0005), meaning r(h)(2) was underestimated by approximately 2-fold. du measurements, generated using the non-hemisphere instruction were less biased (+7%, p=0.03). Finally, frame-to-frame variability in PISA distance was found to have a coefficient of variation (CV) of 25% in patients and 9% in in-vitro data. Beat-to-beat variability had a CV of 15% in patients. CONCLUSIONS: Doppler-visualized PISA shells are not hemispherical: we should avoid advising observers to measure a hemispherical radius because it encourages underestimation of orifice area by approximately two-fold. If precision is needed (e.g. to detect changes reliably) multi-frame averaging is essential.

When is an optimization not an optimization? Evaluation of clinical implications of information content (signal-to-noise ratio) in optimization of cardiac resynchronization therapy, and how to measure and maximize it.

Impact of variability in the measured parameter is rarely considered in designing clinical protocols for optimization of atrioventricular (AV) or interventricular (VV) delay of cardiac resynchronization therapy (CRT). In this article, we approach this question quantitatively using mathematical simulation in which the true optimum is known and examine practical implications using some real measurements. We calculated the performance of any optimization process that selects the pacing setting which maximizes an underlying signal, such as flow or pressure, in the presence of overlying random variability (noise). If signal and noise are of equal size, for a 5-choice optimization (60, 100, 140, 180, 220 ms), replicate AV delay optima are rarely identical but rather scattered with a standard deviation of 45 ms. This scatter was overwhelmingly determined (ρ = -0.975, P < 0.001) by Information Content, [Formula: see text], an expression of signal-to-noise ratio. Averaging multiple replicates improves information content. In real clinical data, at resting, heart rate information content is often only 0.2-0.3; elevated pacing rates can raise information content above 0.5. Low information content (e.g. <0.5) causes gross overestimation of optimization-induced increment in VTI, high false-positive appearance of change in optimum between visits and very wide confidence intervals of individual patient optimum. AV and VV optimization by selecting the setting showing maximum cardiac function can only be accurate if information content is high. Simple steps to reduce noise such as averaging multiple replicates, or to increase signal such as increasing heart rate, can improve information content, and therefore viability, of any optimization process.