PURPOSE: To develop 3D personalized left ventricular (LV) models and thickening assessment framework for assessing regional wall thickening dysfunction and dyssynchrony in AMI patients.
STUDY TYPE: Retrospective study, diagnostic accuracy.
SUBJECTS: Forty-four subjects consisting of 15 healthy subjects and 29 AMI patients.
FIELD STRENGTH/SEQUENCE: 1.5T/steady-state free precession cine MRI scans; LGE MRI scans.
ASSESSMENT: Quantitative thickening measurements across all cardiac phases were correlated and validated against clinical evaluation of infarct transmurality by an experienced cardiac radiologist based on the American Heart Association (AHA) 17-segment model.
STATISTICAL TEST: Nonparametric 2-k related sample-based Kruskal-Wallis test; Mann-Whitney U-test; Pearson's correlation coefficient.
RESULTS: Healthy LV wall segments undergo significant wall thickening (P 50% transmurality) underwent remarkable wall thinning during contraction (thickening index [TI] = 1.46 ± 0.26 mm) as opposed to healthy myocardium (TI = 4.01 ± 1.04 mm). For AMI patients, LV that showed signs of thinning were found to be associated with a significantly higher percentage of dyssynchrony as compared with healthy subjects (dyssynchrony index [DI] = 15.0 ± 5.0% vs. 7.5 ± 2.0%, P
PURPOSE: To develop and validate a fully automated neural network regression-based algorithm for segmentation of the LV in cardiac MRI, with full coverage from apex to base across all cardiac phases, utilizing both short axis (SA) and long axis (LA) scans.
STUDY TYPE: Cross-sectional survey; diagnostic accuracy.
SUBJECTS: In all, 200 subjects with coronary artery diseases and regional wall motion abnormalities from the public 2011 Left Ventricle Segmentation Challenge (LVSC) database; 1140 subjects with a mix of normal and abnormal cardiac functions from the public Kaggle Second Annual Data Science Bowl database.
FIELD STRENGTH/SEQUENCE: 1.5T, steady-state free precession.
ASSESSMENT: Reference standard data generated by experienced cardiac radiologists. Quantitative measurement and comparison via Jaccard and Dice index, modified Hausdorff distance (MHD), and blood volume.
STATISTICAL TESTS: Paired t-tests compared to previous work.
RESULTS: Tested against the LVSC database, we obtained 0.77 ± 0.11 (Jaccard index) and 1.33 ± 0.71 mm (MHD), both metrics demonstrating statistically significant improvement (P < 0.001) compared to previous work. Tested against the Kaggle database, the signed difference in evaluated blood volume was +7.2 ± 13.0 mL and -19.8 ± 18.8 mL for the end-systolic (ES) and end-diastolic (ED) phases, respectively, with a statistically significant improvement (P < 0.001) for the ED phase.
DATA CONCLUSION: A fully automated LV segmentation algorithm was developed and validated against a diverse set of cardiac cine MRI data sourced from multiple imaging centers and scanner types. The strong performance overall is suggestive of practical clinical utility.
LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.
OBJECTIVES: 1. To assess the effects of CPAP on AoP in preterm infants (this may be compared to supportive care or mechanical ventilation). 2. To assess the effects of different CPAP delivery systems on AoP in preterm infants.
SEARCH METHODS: Searches were conducted in September 2022 in the following databases: Cochrane Library, MEDLINE, Embase, and CINAHL. We also searched clinical trial registries and the reference lists of studies selected for inclusion.
SELECTION CRITERIA: We included all randomised and quasi-randomised controlled trials (RCTs) in which researchers determined that CPAP was necessary for AoP in preterm infants (born before 37 weeks). Cross-over studies were also included, provided sufficient data were available for analysis.
DATA COLLECTION AND ANALYSIS: We used the standard methods of Cochrane and Cochrane Neonatal, including independent assessment of risk of bias and extraction of data by at least two review authors. Discrepancies were resolved by involvement of a third author. We used the GRADE approach to assess the certainty of evidence for the following outcomes: 1) failed CPAP; 2) apnoea; 3) adverse effects of CPAP.
MAIN RESULTS: We included four single-centre trials conducted in Malaysia, Spain, Germany, and North America, involving 138 infants with a mean/median gestation of 26 to 28 weeks. Two studies were parallel-group RCTs and two were cross-over trials. None of the studies compared CPAP with supportive care. All trials compared one form of CPAP with another. Two compared a variable flow device with ventilator CPAP, one compared two different variable flow devices, and one compared a variable flow device with bubble CPAP. Interventions were administered for periods ranging between six and 48 hours, with pressures between 4 and 6 cm H2O. We assessed all trials as having a high risk of bias for blinding of participants and personnel, and two studies for blinding of outcome assessors. We found a high risk of a carry-over effect in two studies where the washout period was not adequately described, and a high risk of bias in a study that appeared to use an analysis method not generally accepted for cross-over studies. Comparison 1. CPAP and supportive care compared to supportive care alone We did not identify any study for inclusion in this comparison. Comparison 2. CPAP delivered by different types of devices 2a. Variable flow compared to ventilator CPAP Two studies were included in this comparison. We are very uncertain whether there is any difference in the incidence of failed CPAP, defined as the need for mechanical ventilation (risk ratio (RR) 0.16, 95% confidence interval (CI) 0.01 to 2.90; 1 study, 26 participants; very low-certainty). We are very uncertain whether there is any difference in the frequency of apnoea events (mean difference (MD) per four-hour interval -0.10, 95% CI -1.30 to 1.10; 1 study, 26 participants; very low-certainty). We are uncertain whether there is any difference in adverse events. Neurodevelopmental outcomes were not reported. 2b. Variable flow compared to bubble CPAP We included one study in this comparison, but it did not report our pre-specified outcomes. 2c. Infant Flow variable flow CPAP compared to Medijet variable flow CPAP We are very uncertain whether there is any difference in the incidence of failed CPAP (RR 2.62, 95% CI 0.91 to 7.53; 1 study, 80 participants; very low-certainty). The frequency of apnoea was not reported, and we do not know whether there is any difference in adverse events. Neurodevelopmental outcomes were not reported. Comparison 3. CPAP compared to mechanical ventilation We did not identify any studies for inclusion in this comparison.
AUTHORS' CONCLUSIONS: Due to the limited available evidence, we are very uncertain whether any CPAP device is more effective than other forms of supportive care, other CPAP devices, or mechanical ventilation for the prevention and treatment of AoP. The devices used in these studies included two types of variable flow CPAP device: bubble CPAP and ventilator CPAP. For each comparison, data were only available from a single study. There are theoretical reasons why these devices might have different effects on AoP, therefore further trials are indicated.
MATERIALS AND METHODS: A randomised control study with a two-group, single-blind design and baseline evaluation was selected. Social media sites were used to advertise for participants, who were then admitted after meeting the requirements. Participants who met the eligibility requirements were randomly split into two groups. Each group received a total of three sessions of online therapy (MT or CT), once every two weeks, as well as one phone call per week as reinforcement. At the beginning and end of the intervention, participants completed questionnaires (1st week and 5th week). Generalized Estimating Equation (GEE) statistical analysis was used to analyse all the variables.
RESULTS: The MT group experienced a statistically significant decrease in cigarette consumption (β: -3.50, 95% Wald CI: - 4.62, -2.39) compared to the CT group over time. Furthermore, the MT group demonstrated significant improvements in their scores for the AAQ-2, anxiety, stress, depression and mindfulness compared to the CT group.
CONCLUSION: Online MT is more successful at assisting smokers in lowering their daily cigarette intake and supporting their mental health during the smoking cessation process. Further longitudinal comparisons of the effectiveness of online MT should be undertaken using online platforms in future studies.
METHODS: A 3-step framework was proposed, consisting of: (1) 3D LV model reconstruction from motion-corrected 4D cine-MRI; (2) Registration of 2D LGE-MRI with 4D cine-MRI; (3) LV contour extraction from the intersection of LGE slices with the LV model. The framework was evaluated against cardiac MRI data from 27 patients scanned within 6 months after acute myocardial infarction. We compared the use of local Pearson's correlation (LPC) and normalized mutual information (NMI) as similarity measures for the registration. The use of 2 and 6 long-axis (LA) cine-MRI scans was also compared. The accuracy of the framework was evaluated using manual segmentation, and the interobserver variability of the scar volume derived from the segmented LV was determined using Bland-Altman analysis.
RESULTS: LPC outperformed NMI as a similarity measure for the proposed framework using 6 LA scans, with Hausdorrf distance (HD) of 1.19 ± 0.53 mm versus 1.51 ± 2.01 mm (endocardial) and 1.21 ± 0.48 mm versus 1.46 ± 1.78 mm (epicardial), respectively. Segmentation using 2 LA scans was comparable to 6 LA scans with a HD of 1.23 ± 0.70 mm (endocardial) and 1.25 ± 0.74 mm (epicardial). The framework yielded a lower interobserver variability in scar volumes compared with manual segmentation.
CONCLUSION: The framework showed high accuracy and robustness in delineating LV in LGE-MRI and allowed for bidirectional mapping of information between LGE- and cine-MRI scans, crucial in personalized model studies for treatment planning.
APPROACH: Several master templates are initially generated by applying principal component analysis to data obtained from the PhysioNet MIMIC II database. The master template is then updated with each incoming clean PPG pulse. The correlation coefficient is used to classify the PPG pulse into either good or bad quality categories. The performance of our algorithm was evaluated using data obtained from two different sources: (i) our own data collected from 19 healthy subjects using the wearable Sotera Visi Mobile system (Sotera Wireless Inc.) as they performed various movement types; and (ii) ICU data provided by the PhysioNet MIMIC II database. The developed algorithm was evaluated against a manually annotated 'gold standard' (GS).
MAIN RESULTS: Our algorithm achieved an overall accuracy of 91.5% ± 2.9%, with a sensitivity of 94.1% ± 2.7% and a specificity of 89.7% ± 5.1%, when tested on our own data. When applying the algorithm to data from the PhysioNet MIMIC II database, it achieved an accuracy of 98.0%, with a sensitivity and specificity of 99.0% and 96.1%, respectively.
SIGNIFICANCE: The proposed method is simple and robust against individual variations in the PPG characteristics, thus making it suitable for a diverse range of datasets. Integration of the proposed artefact detection technique into remote monitoring devices could enhance reliability of the PPG-derived physiological parameters.