Incidental findings in multislice computed tomography prior to transcatheter aortic valve implantation: frequency,clinical relevance and outcome

Multislice computed tomography (MSCT) has emerged as the mainstay in patients planned for transcatheter aortic valve implantation (TAVI). Incidental findings (IF) in MSCT are common. However, the exact incidence, clinical relevance and further consequences of IF are unclear and it is controversial whether IF adversely affect patients’ outcome. We analyzed MSCT data of 1050 patients screened for TAVI between January 2011 and December 2014. Median follow-up of patients was 20 months. In total, 3194 IF were identified, which were classified into clinically non-relevant IF (2872, 90%) and clinically relevant IF (322, 10%). In 25% of patients (258/1050) at least one clinically relevant IF was present. Age (80?±?7 vs. 80?±?7 years; p?=?0.198) and EuroSCORE II (3.6% [2.1–5.7] vs. 3.6% [2.1–5.9]; p?=?0.874) was similar between patients with and without a clinically relevant IF. TAVI was performed less frequently in patients with a clinically relevant IF (76% vs. 85%; p?<?0.001), with more patients receiving surgical aortic valve replacement in that group (14% vs. 11%; p?=?0.042), possibly due to the high rate of incidental aneurysms of the ascending aorta (n?=?48). If TAVI was performed mortality did not differ (30-days: 4% vs. 3%; p?=?0.339, 1-year: 11% vs. 14%; p?=?0.226) between patients with and without a clinically relevant IF. Our study is the largest study to analyze prevalence, clinical relevance and therapeutic consequences of IF during screening for TAVI. IF in pre-procedural MSCT are common and clinically relevant in one-quarter of patients. However, these findings had no impact on overall mortality.     

Assessment of Remote Myocardium Heterogeneity in Patients with Ventricular Tachycardia Using Texture Analysis of Late Iodine Enhancement (LIE) Cardiac Computed Tomography (cCT) Images

Purpose

Diffuse remodeling of myocardial extra-cellular matrix is largely responsible for left ventricle (LV) dysfunction and arrhythmias. Our hypothesis is that the texture analysis of late iodine enhancement (LIE) cardiac computed tomography (cCT) images may improve characterization of the diffuse extra-cellular matrix changes. Our aim was to extract volumetric extracellular volume (ECV) and LIE texture features of non-scarred (remote) myocardium from cCT of patients with recurrent ventricular tachycardia (rVT), and to compare these radiomic features with LV-function, LV-remodeling, and underlying cardiac disease.

Procedures

Forty-eight patients suffering from rVT were prospectively enrolled: 5/48 with idiopathic VT (IVT), 23/48 with post-ischemic dilated cardiomyopathy (ICM), 9/48 with idiopathic dilated cardiomyopathy (IDCM), and 11/48 with scars from a previous healed myocarditis (MYO). All patients underwent echocardiography to assess LV systolic and diastolic function and cCT with pre-contrast, angiographic, and LIE scan to obtain end-diastolic volume (EDV), ECV, and first-order texture parameters of Hounsfield Unit (HU) of remote myocardium in LIE [energy, entropy, HU-mean, HU-median, standard deviation (SD), and mean absolute deviation (MAD)].

Results

Energy, HU mean, and HU median by cCT texture analysis correlated with ECV (rho?=?0.5650, rho?=?0.5741, rho?=?0.5068; p?<?0.0005). cCT-derived ECV, HU-mean, HU-median, SD, and MAD correlated directly to EDV by cCT and inversely to ejection fraction by echocardiography (p?<?0.05). SD and MAD correlated with diastolic function by echocardiography (rho?=?0.3837, p?=?0.0071; rho?=?0.3330, p?=?0.0208). MYO and IVT patients were characterized by significantly lower values of SD and MAD when compared with ICM and IDCM patients, independently of LV-volume systolic and diastolic function.

Conclusions

Texture analysis of LIE may expand cCT capability of myocardial characterization. Myocardial heterogeneity (SD and MAD) was associated with LV dilatation, systolic and diastolic function, and is able to potentially identify the different patterns of structural remodeling characterizing patients with rVT of different etiology.

     

Enhancing Syndromic Surveillance With Online Respondent-Driven Detection

Objectives. We investigated the feasibility of combining an online chain recruitment method (respondent-driven detection) and participatory surveillance panels to collect previously undetected information on infectious diseases via social networks of participants.Methods. In 2014, volunteers from 2 large panels in the Netherlands were invited to complete a survey focusing on symptoms of upper respiratory tract infections and to invite 4 individuals they had met in the preceding 2 weeks to take part in the study. We compared sociodemographic characteristics among panel participants, individuals who volunteered for our survey, and individuals recruited via respondent-driven detection.Results. Starting from 1015 panel members, the survey spread through all provinces of the Netherlands and all age groups in 83 days. A total of 433 individuals completed the survey via peer recruitment. Participants who reported symptoms were 6.1% (95% confidence interval = 5.4, 6.9) more likely to invite contact persons than were participants who did not report symptoms. Participants with symptoms invited more symptomatic recruits to take part than did participants without symptoms.Conclusions. Our findings suggest that online respondent-driven detection can enhance identification of symptomatic patients by making use of individuals’ local social networks.Syndromic surveillance provides information necessary to monitor trends in disease incidence and implement and evaluate response plans.1,2 To date, most efforts have focused on developing systems based on data from inpatient and ambulatory care health records.3 In a majority of high-income countries, including the Netherlands, influenza surveillance is based on a combination of reports of influenza-like illness (ILI) collected by sentinel surveillance clinics and additional microbiological testing of subgroups of symptomatic patients.4 This type of system excludes symptomatic patients who do not visit a general practitioner, and such patients are likely to account for the majority of cases in most influenza outbreaks.5Many communicable diseases (e.g., influenza, severe acute respiratory syndrome, measles) spread largely between socially connected individuals, such as household members and schoolchildren, and they often occur in clusters.6,7 Therefore, cases of infection are expected to cluster in social networks (i.e., contacts of an infected individual are infected at a level of probability higher than that expected if the distribution was random), and clusters can be detected via local social networks of individuals reporting symptoms.Increased Internet use facilitated the emergence of participatory surveillance (PS) systems, which enable real-time monitoring of diseases through regular submission of syndromic information by volunteers.8,9 These systems provide information that is not collected in regular surveillance, such as the proportion of symptomatic individuals who actually visit a general practitioner and the proportion who are hospitalized.To test the feasibility of eliciting information about infections in local networks of symptomatic individuals, we combined a chain recruitment method with existing online PS platforms. Under certain conditions, such a recruitment method permits stepwise and controlled sampling of contacts of contacts, and so forth, in social networks in the general population.10 We asked PS volunteers to complete a questionnaire and to invite their contacts into the study. In this way, we collected data on chains of contacts to analyze whether other symptomatic individuals could be detected via the local social network of symptomatic respondents. Our aims were to determine whether respondents can be recruited via respondent-driven detection, to report on which individuals are reached, and to assess whether there is clustering of symptomatic patients.