Nurses' perceptions of the influence of leadership behaviours and organisational culture on patient safety incident reporting practices.

BACKGROUND: Patient safety is a priority for all healthcare organisations. Enhancing patient safety incident reporting practices requires effective leadership behaviours at all levels in healthcare organisations. AIM: To explore nurses' perceptions of the influence of nurse managers' leadership behaviours and organisational culture on patient safety incident reporting practices. METHOD: A descriptive, cross-sectional, correlational design was adopted with a convenience sample of 325 nurses from 15 Jordanian hospitals. RESULTS: Respondents had positive perceptions of their nurse managers' leadership behaviours and organisational culture. There was a significant positive relationship between leadership behaviours and organisational culture (r=0.423, P<0.001) and between leadership behaviours and actual incident-reporting practices (r=0.131, P<0.001). Additionally, there was a significant positive relationship between organisational culture and incident-reporting practices (r=0.250, P<0.001). CONCLUSION: Healthcare organisations must develop leaders who will foster a supportive and just culture that will enhance nurses' practice with regards to reporting patient safety incidents.

PET/MRI Assessment of Acute Cardiac Inflammation 1 Month After Left-Sided Breast Cancer Radiation Therapy.

Our purpose was to investigate the utility of 18F-FDG PET/MRI and serial blood work to detect early inflammatory responses and cardiac functionality changes at 1 mo after radiation therapy (RT) in patients with left-sided breast cancer. Methods: Fifteen left-sided breast cancer patients who enrolled in the RICT-BREAST study underwent cardiac PET/MRI at baseline and 1 mo after standard RT. Eleven patients received deep-inspiration breath-hold RT, whereas the others received free-breathing RT. A list-mode 18F-FDG PET scan with glucose suppression was acquired. Myocardial inflammation was quantified by the change in 18F-FDG SUVmean (based on body weight) and analyzed on the basis of the myocardial tissue associated with the left anterior descending, left circumflex, or right coronary artery territories. MRI assessments, including left ventricular functional and extracellular volumes (ECVs), were extracted from T1 (before and during a constant infusion of gadolinium) and cine images, respectively, acquired simultaneously during the PET acquisition. Cardiac injury and inflammation biomarker measurements of high-sensitivity troponin T, high-sensitivity C-reactive protein, and erythrocyte sedimentation rate were measured at the 1-mo follow-up and compared with preirradiation values. Results: At the 1-mo follow-up, a significant increase (10%) in myocardial SUVmean in left anterior descending segments (P = 0.04) and ECVs in slices at the apex (6%) and base (5%) was detected (P ≤ 0.02). Further, a significant reduction in left ventricular stroke volume (-7%) was seen (P < 0.02). No significant changes in any circulating biomarkers were seen at follow-up. Conclusion: Myocardial 18F-FDG uptake and functional MRI, including stroke volume and ECVs, were sensitive to changes at 1 mo after breast cancer RT, with findings suggesting an acute cardiac inflammatory response to RT.

Functional CT assessment of extravascular contrast distribution volume and myocardial perfusion in acute myocardial infarction.

PURPOSE: In a pig model of acute myocardial infarction (AMI), we validated a functional computed tomography (CT) technique for concomitant assessment of myocardial edema and ischemia through extravscualar contrast distribution volume (ECDV) and myocardial perfusion (MP) measurements from a single dynamic imaging session using a single contrast bolus injection. METHODS: In seven pigs, balloon catheter was used to occlude the distal left anterior descending artery for one hour followed by reperfusion. CT and cardiac magnetic resonance (CMR) imaging studies were acquired on 3 days and 12 ±â€¯3 day post ischemic insult. In each CT study, 0.7 ml/kg of iodinated contrast was intravenously injected at 3-4 ml/s before dynamic contrast-enhanced (DCE) cardiac images were acquired with breath-hold using a 64-row CT scanner. DCE cardiac images were analyzed with a model-based deconvolution to generate ECDV and MP maps. ECDV as an imaging marker of edema was validated against CMR T2 weighted imaging in normal and infarcted myocardium delineated from ex-vivo histological staining. RESULTS: ECDV in infarcted myocardium was significantly higher (p < 0.05) than that in normal myocardium on both days post AMI and was in agreement with the findings of CMR T2 weighted imaging. MP was significantly lower (p < 0.05) in the infarcted region compared to normal on both days post AMI. CONCLUSION: This imaging technique can rapidly and simultaneously assess myocardial edema and ischemia through ECDV and MP measurements, and may be useful for delineation of salvageable tissue within at-risk myocardium to guide reperfusion therapy.

Regional assessment of cardiac left ventricular myocardial function via MRI statistical features.

Automating the detection and localization of segmental (regional) left ventricle (LV) abnormalities in magnetic resonance imaging (MRI) has recently sparked an impressive research effort, with promising performances and a breadth of techniques. However, despite such an effort, the problem is still acknowledged to be challenging, with much room for improvements in regard to accuracy. Furthermore, most of the existing techniques are labor intensive, requiring delineations of the endo- and/or epi-cardial boundaries in all frames of a cardiac sequence. The purpose of this study is to investigate a real-time machine-learning approach which uses some image features that can be easily computed, but that nevertheless correlate well with the segmental cardiac function. Starting from a minimum user input in only one frame in a subject dataset, we build for all the regional segments and all subsequent frames a set of statistical MRI features based on a measure of similarity between distributions. We demonstrate that, over a cardiac cycle, the statistical features are related to the proportion of blood within each segment. Therefore, they can characterize segmental contraction without the need for delineating the LV boundaries in all the frames. We first seek the optimal direction along which the proposed image features are most descriptive via a linear discriminant analysis. Then, using the results as inputs to a linear support vector machine classifier, we obtain an abnormality assessment of each of the standard cardiac segments in real-time. We report a comprehensive experimental evaluation of the proposed algorithm over 928 cardiac segments obtained from 58 subjects. Compared against ground-truth evaluations by experienced radiologists, the proposed algorithm performed competitively, with an overall classification accuracy of 86.09% and a kappa measure of 0.73.

Non-invasive assessment of functionally relevant coronary artery stenoses with quantitative CT perfusion: preliminary clinical experiences.

OBJECTIVES: We developed a quantitative Dynamic Contrast-Enhanced CT (DCE-CT) technique for measuring Myocardial Perfusion Reserve (MPR) and Volume Reserve (MVR) and studied their relationship with coronary stenosis. METHODS: Twenty-six patients with Coronary Artery Disease (CAD) were recruited. Degree of stenosis in each coronary artery was classified from catheter-based angiograms as Non-Stenosed (NS, angiographically normal or mildly irregular), Moderately Stenosed (MS, 50-80% reduction in luminal diameter), Severely Stenosed (SS, >80%) and SS with Collaterals (SSC). DCE-CT at rest and after dipyridamole infusion was performed using 64-slice CT. Mid-diastolic heart images were corrected for beam hardening and analyzed using proprietary software to calculate Myocardial Blood Flow (MBF, in mL∙min(-1)∙100 g(-1)) and Blood Volume (MBV, in mL∙100 g(-1)) parametric maps. MPR and MVR in each coronary territory were calculated by dividing MBF and MBV after pharmacological stress by their respective baseline values. RESULTS: MPR and MVR in MS and SS territories were significantly lower than those of NS territories (p < 0.05 for all). Logistic regression analysis identified MPR∙MVR as the best predictor of ≥50% coronary lesion than MPR or MVR alone. CONCLUSIONS: DCE-CT imaging with quantitative CT perfusion analysis could be useful for detecting coronary stenoses that are functionally significant.

Global assessment of cardiac function using image statistics in MRI.

The cardiac ejection fraction (EF) depends on the volume variation of the left ventricle (LV) cavity during a cardiac cycle, and is an essential measure in the diagnosis of cardiovascular diseases. It is often estimated via manual segmentation of several images in a cardiac sequence, which is prohibitively time consuming, or via automatic segmentation, which is a challenging and computationally expensive task that may result in high estimation errors. In this study, we propose to estimate the EF in real-time directly from image statistics using machine learning technique. From a simple user input in only one image, we build for all the images in a subject dataset (200 images) a statistic based on the Bhattacharyya coefficient of similarity between image distributions. We demonstrate that these statistics are non-linearly related to the LV cavity areas and, therefore, can be used to estimate the EF via an Artificial Neural Network (ANN) directly. A comprehensive evaluation over 20 subjects demonstrated that the estimated EFs correlate very well with those obtained from independent manual segmentations.

Assessment of regional myocardial function via statistical features in MR images.

Early and accurate detection of Left Ventricle (LV) regional wall motion abnormalities significantly helps in the diagnosis and followup of cardiovascular diseases. We present a regional myocardial abnormality detection framework based on image statistics. The proposed framework requires a minimal user interaction, only to specify initial delineation and anatomical landmarks on the first frame. Then, approximations of regional myocardial segments in subsequent frames were systematically obtained by superimposing the initial delineation on the rest of the frames. The proposed method exploits the Bhattacharyya coefficient to measure the similarity between the image distribution within each segment approximation and the distribution of the corresponding user-provided segment. Linear Discriminate Analysis (LDA) is applied to find the optimal direction along which the projected features are the most descriptive. Then a Linear Support Vector Machine (SVM) classifier is employed for each of the regional myocardial segments to automatically detect abnormally contracting regions of the myocardium. Based on a clinical dataset of 30 subjects, the evaluation demonstrates that the proposed method can be used as a promising diagnostic support tool to assist clinicians.

Tracking endocardial motion via multiple model filtering.

Tracking heart motion plays an essential role in the diagnosis of cardiovascular diseases. As such, accurate characterization of dynamic behavior of the left ventricle (LV) is essential in order to enhance the performance of motion estimation. However, a single Markovian model is not sufficient due to the substantial variability in typical heart motion. Moreover, dynamics of an abnormal heart could be very different from that of a normal heart. This study introduces a tracking approach based on multiple models, each matched to a different phase of the LV motion. First, the algorithm adopts a graph cut distribution matching method to tackle the problem of segmenting LV cavity from cardiac MR images, which is acknowledged as a difficult problem because of low contrast and photometric similarities between the heart wall and papillary muscles within the LV cavity. Second, interacting multiple model (IMM), an effective estimation algorithm for Markovian switching system, is devised subsequent to the segmentations to yield state estimates of the endocardial boundary points. The IMM also yields the model probability indicating the model that most closely matches the LV motion. The proposed method is evaluated quantitatively by comparison with independent manual segmentations over 2280 images acquired from 20 subjects, which demonstrated competitive results in comparisons with related recent methods.