Impact of Intraprocedural Pressure Changes on Hemodynamic Outcome During Self-Expanding TAVR.

INTRODUCTION: During the transcatheter aortic valve replacement (TAVR) procedure, hemodynamic measurements can be used to evaluate transcatheter heart valve (THV) performance. We hypothesized that the occurrence of a significant decrease in invasive aortic pressure immediately after annular contact by a self-expanding THV indicates effective annular sealing. This phenomenon could thus be used as a marker for the occurrence of paravalvular leak (PVL). METHODS: Thirty-eight patients undergoing TAVR procedure with a self-expandable Evolut R or Evolut Pro (Medtronic) valve prosthesis were included in the study. Drop in aortic pressure during valve expansion was defined as a decrease in systolic pressure of 30 mmHg immediately after annular contact. The primary endpoint was the occurrence of more than mild PVL immediately after valve implantation. RESULTS: A pressure drop was seen in 60.5% (23/38) of patients. More than mild PVL requiring balloon post-dilatation (BPD) was significantly more frequent in patients who did not have a systolic pressure decrease > 30 mmHg during valve implantation (46.7% [7/15] vs. 13.0% [3/23], respectively; p = 0.03). Patients without a systolic pressure decrease > 30 mmHg also had a lower mean cover index on computed tomography analysis (16.2% vs. 13.3%; p = 0.016). The 30-day outcomes were similar between the two groups, and echocardiography at 30 days demonstrated more than none/trace PVL in 21.1% (8/38) of patients, with no difference between the two groups. CONCLUSION: A decrease in aortic pressure after annular contact is associated with an increased probability of good hemodynamic outcome after self-expanding TAVR implantation. In addition to other methods, this parameter could be used as an additional marker for optimal valve positioning and hemodynamic outcome during the implantation procedure.

Mechanocardiography-Based Measurement System Indicating Changes in Heart Failure Patients during Hospital Admission and Discharge.

Heart failure (HF) is a disease related to impaired performance of the heart and is a significant cause of mortality and treatment costs in the world. During its progression, HF causes worsening (decompensation) periods which generally require hospital care. In order to reduce the suffering of the patients and the treatment cost, avoiding unnecessary hospital visits is essential, as hospitalization can be prevented by medication. We have developed a data-collection device that includes a high-quality 3-axis accelerometer and 3-axis gyroscope and a single-lead ECG. This allows gathering ECG synchronized data utilizing seismo- and gyrocardiography (SCG, GCG, jointly mechanocardiography, MCG) and comparing the signals of HF patients in acute decompensation state (hospital admission) and compensated condition (hospital discharge). In the MECHANO-HF study, we gathered data from 20 patients, who each had admission and discharge measurements. In order to avoid overfitting, we used only features developed beforehand and selected features that were not outliers. As a result, we found three important signs indicating the worsening of the disease: an increase in signal RMS (root-mean-square) strength (across SCG and GCG), an increase in the strength of the third heart sound (S3), and a decrease in signal stability around the first heart sound (S1). The best individual feature (S3) alone was able to separate the recordings, giving 85.0% accuracy and 90.9% accuracy regarding all signals and signals with sinus rhythm only, respectively. These observations pave the way to implement solutions for patient self-screening of the HF using serial measurements.

Prudent Antimicrobial Use Is Essential to Prevent the Emergence of Antimicrobial Resistance in Yersinia enterocolitica 4/O:3 Strains in Pigs.

Yersinia enterocolitica is a psychrotrophic zoonotic foodborne pathogen. Pigs are considered the main reservoir of Y. enterocolitica 4/O:3, which is the most commonly isolated bioserotype in many European countries. Consuming pork contaminated with Y. enterocolitica can be a health threat, and antimicrobial-resistant strains may complicate the treatment of the most severe forms of yersiniosis. We analyzed the antimicrobial resistance of 1,016 pathogenic porcine Y. enterocolitica 4/O:3 strains originating from Belgium, Estonia, Finland, Germany, Italy, Latvia, Russia, Spain, and the United Kingdom. Based on available reports, we also compared antimicrobial sales for food production animals in these countries, excluding Russia. Antimicrobial resistance profiles were determined using a broth microdilution method with VetMIC plates for 13 antimicrobial agents: ampicillin, cefotaxime, ceftiofur (CTF), chloramphenicol (CHL), ciprofloxacin, florfenicol, gentamicin, kanamycin, nalidixic acid (NAL), streptomycin (STR), sulfamethoxazole (SME), tetracycline (TET), and trimethoprim (TMP). The antimicrobial resistance of Y. enterocolitica 4/O:3 strains varied widely between the countries. Strains resistant to antimicrobial agents other than ampicillin were rare in Estonia, Finland, Latvia, and Russia, with prevalence of 0.7, 0.4, 0, and 8.3%, respectively. The highest prevalence of antimicrobial resistance was found in Spanish and Italian strains, with 98 and 61% of the strains being resistant to at least two antimicrobial agents, respectively. Resistance to at least four antimicrobial agents was found in 34% of Spanish, 19% of Italian, and 7.1% of English strains. Antimicrobial resistance was more common in countries where the total sales of antimicrobials for food production animals are high and orally administered medications are common. Our results indicate that antimicrobials should be used responsibly to treat infections, and parenteral medications should be preferred to orally administered mass medications.

A Respiratory Motion Estimation Method Based on Inertial Measurement Units for Gated Positron Emission Tomography.

We present a novel method for estimating respiratory motion using inertial measurement units (IMUs) based on microelectromechanical systems (MEMS) technology. As an application of the method we consider the amplitude gating of positron emission tomography (PET) imaging, and compare the method against a clinically used respiration motion estimation technique. The presented method can be used to detect respiratory cycles and estimate their lengths with state-of-the-art accuracy when compared to other IMU-based methods, and is the first based on commercial MEMS devices, which can estimate quantitatively both the magnitude and the phase of respiratory motion from the abdomen and chest regions. For the considered test group consisting of eight subjects with acute myocardial infarction, our method achieved the absolute breathing rate error per minute of 0.44 ± 0.23 1/min, and the absolute amplitude error of 0.24 ± 0.09 cm, when compared to the clinically used respiratory motion estimation technique. The presented method could be used to simplify the logistics related to respiratory motion estimation in PET imaging studies, and also to enable multi-position motion measurements for advanced organ motion estimation.

A Computational Framework for Data Fusion in MEMS-Based Cardiac and Respiratory Gating.

Dual cardiac and respiratory gating is a well-known technique for motion compensation in nuclear medicine imaging. In this study, we present a new data fusion framework for dual cardiac and respiratory gating based on multidimensional microelectromechanical (MEMS) motion sensors. Our approach aims at robust estimation of the chest vibrations, that is, high-frequency precordial vibrations and low-frequency respiratory movements for prospective gating in positron emission tomography (PET), computed tomography (CT), and radiotherapy. Our sensing modality in the context of this paper is a single dual sensor unit, including accelerometer and gyroscope sensors to measure chest movements in three different orientations. Since accelerometer- and gyroscope-derived respiration signals represent the inclination of the chest, they are similar in morphology and have the same units. Therefore, we use principal component analysis (PCA) to combine them into a single signal. In contrast to this, the accelerometer- and gyroscope-derived cardiac signals correspond to the translational and rotational motions of the chest, and have different waveform characteristics and units. To combine these signals, we use independent component analysis (ICA) in order to obtain the underlying cardiac motion. From this cardiac motion signal, we obtain the systolic and diastolic phases of cardiac cycles by using an adaptive multi-scale peak detector and a short-time autocorrelation function. Three groups of subjects, including healthy controls (n = 7), healthy volunteers (n = 12), and patients with a history of coronary artery disease (n = 19) were studied to establish a quantitative framework for assessing the performance of the presented work in prospective imaging applications. The results of this investigation showed a fairly strong positive correlation (average r = 0.73 to 0.87) between the MEMS-derived (including corresponding PCA fusion) respiration curves and the reference optical camera and respiration belt sensors. Additionally, the mean time offset of MEMS-driven triggers from camera-driven triggers was 0.23 to 0.3 ± 0.15 to 0.17 s. For each cardiac cycle, the feature of the MEMS signals indicating a systolic time interval was identified, and its relation to the total cardiac cycle length was also reported. The findings of this study suggest that the combination of chest angular velocity and accelerations using ICA and PCA can help to develop a robust dual cardiac and respiratory gating solution using only MEMS sensors. Therefore, the methods presented in this paper should help improve predictions of the cardiac and respiratory quiescent phases, particularly with the clinical patients. This study lays the groundwork for future research into clinical PET/CT imaging based on dual inertial sensors.

Prevalence and Dynamics of Pathogenic Yersinia enterocolitica 4/O:3 Among Finnish Piglets, Fattening Pigs, and Sows.

Pigs are considered the main reservoir of Yersinia enterocolitica, and hence, understanding the ecology of this foodborne pathogen at the farm level is crucial. We calculated Bayesian estimates for the ability of a commercial enzyme-linked immunosorbent assay (ELISA) diagnostic test kit to detect antibodies against pathogenic Yersinia in pigs. The sensitivity and specificity of the test were 75.4% and 98.1%, respectively. We also studied the dynamics of Y. enterocolitica infection in 3 farrow-to-finish pig farms by following the same 30 pens of pigs through their lifetime from farrowing unit to slaughterhouse. Each farm was sampled 4 times, and 864 fecal and 730 serum samples were collected altogether. Pathogenic Y. enterocolitica 4/O:3 was isolated from 31.6% of the fecal samples by culturing, and Yersinia antibodies were detected in 38.2% of the serum samples with the commercial ELISA test. The pathogen was not isolated from farrowing units or all-in/all-out weaning units. However, in the weaning and fattening units using continuous management systems, the pathogen was isolated from every pen at some point of the study. After the pigs were transported into slaughterhouse, 150 tonsils were collected and 96.7% were positive by culturing. Among the strains isolated from feces and tonsils, 56 different genotypes of pathogenic Y. enterocolitica 4/O:3 were found by multilocus variable-number tandem-repeat analysis (MLVA). Finally, we collected tonsils of 266 sows from 115 farrowing farms, and Y. enterocolitica 4/O:3 was detected in 6.0% of the samples by the culture method, whereas 77.1% of the tonsils were serologically positive; the estimate for true seroprevalence was 95.8%. In conclusion, sows may not be the main source of Y. enterocolitica for piglets, although sows may still play a role in maintaining Y. enterocolitica in pig farms. Instead, pigs appear to get this foodborne pathogen mainly during the fattening period, especially if continuous management is applied.

Cardiac monitoring of dogs via smartphone mechanocardiography: a feasibility study.

BACKGROUND: In the context of monitoring dogs, usually, accelerometers have been used to measure the dog's movement activity. Here, we study another application of the accelerometers (and gyroscopes)-seismocardiography (SCG) and gyrocardiography (GCG)-to monitor the dog's heart. Together, 3-axis SCG and 3-axis GCG constitute of 6-axis mechanocardiography (MCG), which is inbuilt to most modern smartphones. Thus, the objective of this study is to assess the feasibility of using a smartphone-only solution to studying dog's heart. METHODS: A clinical trial (CT) was conducted at the University Small Animal Hospital, University of Helsinki, Finland. 14 dogs (3 breeds) including 18 measurements (about one half of all) where the dog's status was such that it was still and not panting were further selected for the heart rate (HR) analysis (each signal with a duration of 1 min). The measurement device in the CT was a custom Holter monitor including synchronized 6-axis MCG and ECG. In addition, 16 dogs (9 breeds, one mixed-breed) were measured at home settings by the dog owners themselves using Sony Xperia Android smartphone sensor to further validate the applicability of the method. RESULTS: The developed algorithm was able to select 10 good-quality signals from the 18 CT measurements, and for 7 of these, the automated algorithm was able to detect HR with deviation below or equal to 5 bpm (compared to ECG). Further visual analysis verified that, for approximately half of the dogs, the signal quality at home environment was sufficient for HR extraction at least in some signal locations, while the motion artifacts due to dog's movements are the main challenges of the method. CONCLUSION: With improved data analysis techniques for managing noisy measurements, the proposed approach could be useful in home use. The advantage of the method is that it can operate as a stand-alone application without requiring any extra equipment (such as smart collar or ECG patch).

Gyrocardiography: A New Non-invasive Monitoring Method for the Assessment of Cardiac Mechanics and the Estimation of Hemodynamic Variables.

Gyrocardiography (GCG) is a new non-invasive technique for assessing heart motions by using a sensor of angular motion - gyroscope - attached to the skin of the chest. In this study, we conducted simultaneous recordings of electrocardiography (ECG), GCG, and echocardiography in a group of subjects consisting of nine healthy volunteer men. Annotation of underlying fiducial points in GCG is presented and compared to opening and closing points of heart valves measured by a pulse wave Doppler. Comparison between GCG and synchronized tissue Doppler imaging (TDI) data shows that the GCG signal is also capable of providing temporal information on the systolic and early diastolic peak velocities of the myocardium. Furthermore, time intervals from the ECG Q-wave to the maximum of the integrated GCG (angular displacement) signal and maximal myocardial strain curves obtained by 3D speckle tracking are correlated. We see GCG as a promising mechanical cardiac monitoring tool that enables quantification of beat-by-beat dynamics of systolic time intervals (STI) related to hemodynamic variables and myocardial contractility.

A real-time approach for heart rate monitoring using a Hilbert transform in seismocardiograms.

Heart rate monitoring helps in assessing the functionality and condition of the cardiovascular system. We present a new real-time applicable approach for estimating beat-to-beat time intervals and heart rate in seismocardiograms acquired from a tri-axial microelectromechanical accelerometer. Seismocardiography (SCG) is a non-invasive method for heart monitoring which measures the mechanical activity of the heart. Measuring true beat-to-beat time intervals from SCG could be used for monitoring of the heart rhythm, for heart rate variability analysis and for many other clinical applications. In this paper we present the Hilbert adaptive beat identification technique for the detection of heartbeat timings and inter-beat time intervals in SCG from healthy volunteers in three different positions, i.e. supine, left and right recumbent. Our method is electrocardiogram (ECG) independent, as it does not require any ECG fiducial points to estimate the beat-to-beat intervals. The performance of the algorithm was tested against standard ECG measurements. The average true positive rate, positive prediction value and detection error rate for the different positions were, respectively, supine (95.8%, 96.0% and ≃0.6%), left (99.3%, 98.8% and ≃0.001%) and right (99.53%, 99.3% and ≃0.01%). High correlation and agreement was observed between SCG and ECG inter-beat intervals (r > 0.99) for all positions, which highlights the capability of the algorithm for SCG heart monitoring from different positions. Additionally, we demonstrate the applicability of the proposed method in smartphone based SCG. In conclusion, the proposed algorithm can be used for real-time continuous unobtrusive cardiac monitoring, smartphone cardiography, and in wearable devices aimed at health and well-being applications.