Imaging of pulmonary embolism and t-PA therapy effects using MDCT and liposomal iohexol blood pool agent – preliminary results in a rabbit model

Hypothesis and Objectives PEGylated liposomal blood pool contrast agents maintain contrast enhancement over several hours. This study aimed to evaluate (long-term) imaging of pulmonary arteries, comparing conventional iodinated contrast with a liposomal blood pool contrast agent. Secondly, visualization of the (real-time) therapeutic effects of tissue-Plasminogen Activator (t-PA) on pulmonary embolism (PE) was attempted. Materials and Methods Six rabbits (approximate 4 kg weight) had autologous blood clots injected through the superior vena cava. Imaging was performed using conventional contrast (iohexol, 350 mg I/ml, GE HealthCare, Princeton, NJ) at a dose of 1400 mgI per animal and after wash-out, animals were imaged using an iodinated liposomal blood pool agent (88 mg I/mL, dose 900 mgI/animal). Subsequently, five animals were injected with 2mg t-PA and imaging continued for up to 4 ½ hours. Results Both contrast agents identified PE in the pulmonary trunk and main pulmonary arteries in all rabbits. Liposomal blood pool agent yielded uniform enhancement, which remained relatively constant throughout the experiments. Conventional agents exhibited non uniform opacification and rapid clearance post injection. Three out of six rabbits had mistimed bolus injections, requiring repeat injections. Following t-PA, Pulmonary embolus volume (central to segmental) decreased in four of five treated rabbits (range 10–57%, mean 42%). One animal showed no response to t-PA. Conclusions Liposomal blood pool agents effectively identified acute PE without need for re-injection. PE resolution following t-PA was quantifiable over several hours. Blood pool agents offer the potential for repeated imaging procedures without need for repeated (nephrotoxic) contrast injections

Eye Movement Measures of Cognitive Control in Children with Tourette Syndrome

Tourette Syndrome begins in childhood and is characterized by uncontrollable repetitive actions like neck craning or hopping and noises such as sniffing or chirping. Worst in early adolescence, these tics wax and wane in severity and occur in bouts unpredictably, often drawing unwanted attention from bystanders. Making matters worse, over half of children with Tourette Syndrome also suffer from comorbid, or concurrent, disorders such as attention deficit hyperactivity disorder (ADHD) and obsessive-compulsive disorder (OCD). These disorders introduce anxious thoughts, impulsivity, inattention, and mood variability that further disrupt children with Tourette Syndrome from focusing and performing well at school and home. Thus, deficits in the cognitive control functions of response inhibition, response generation, and working memory have long been ascribed to Tourette Syndrome. Yet, without considering the effect of medication, age, and comorbidity, this is a premature attribution. This study used an infrared eye tracking camera and various computer tasks requiring eye movement responses to evaluate response inhibition, response generation, and working memory in Tourette Syndrome. This study, the first to control for medication, age, and comorbidity, enrolled 39 unmedicated children with Tourette Syndrome and 29 typically developing peers aged 10-16 years who completed reflexive and voluntary eye movement tasks and diagnostic rating scales to assess symptom severities of Tourette Syndrome, ADHD, and OCD. Children with Tourette Syndrome and comorbid ADHD and/or OCD, but not children with Tourette Syndrome only, took longer to respond and made more errors and distracted eye movements compared to typically-developing children, displaying cognitive control deficits. However, increasing symptom severities of Tourette Syndrome, ADHD, and OCD correlated with one another. Thus, cognitive control deficits were not specific to Tourette Syndrome patients with comorbid conditions, but rather increase with increasing tic severity, suggesting that a majority of Tourette Syndrome patients, regardless of a clinical diagnosis of ADHD and/or OCD, have symptoms of cognitive control deficits at some level. Therefore, clinicians should evaluate and counsel all families of children with Tourette Syndrome, with or without currently diagnosed ADHD and/or OCD, about the functional ramifications of comorbid symptoms and that they may wax and wane with tic severity.

Generalized resistance resources of informal caregivers: A study of demands for care, competing demands, perception of burden and cognitive stress

This study examines the relationship among psychological resources (generalized resistance resources), care demands (demands for care, competing demands, perception of burden) and cognitive stress in a selected population of primary family caregivers. The study utilizes Antonovsky's Salutogenic Model of Health, specifically the concept of generalized resistance resources (GRRs), to analyze the relative effect of these resources on mediating cognitive stress, controlling for other care demands. The study is based on a sample of 784 eligible caregivers who (1) were relatives, (2) had the main responsibility for care, defined as a primary caregiver, and (3) provided a scaled stress score for the amount of overall care given to the care recipient (family member). The sample was drawn from the 1982 National Long-Term Care Survey (NLTCS) of individuals who assisted a given NLTCS sample person with ADL limitations.^ The study tests the following hypotheses: (a) There will be a negative relationship between generalized resistance resources (GRRs) and cognitive stress controlling for care demands (demands for care, competing demands, and perceptions of burden); (b) of the specific GRRs (material, cognitive, social, cultural-environmental) the social domain will represent the most significant factor predicting a decrease in cognitive stress; and (c) the social domain will be more significant for the female than the male primary family caregiver in decreasing cognitive stress.^ The study found that GRRs had a statistically significant mediating effect on cognitive stress, but the GRRs were a less significant predictor of stress than perception of burden and demands for care. Thus, although the analysis supported the underlying hypothesis, the specific hypothesis regarding GRRs' greater significance in buffering cognitive stress was not supported. Second, the results did not demonstrate the statistical significance or differences among the GRR domains. The hypothesis that the social GRR domain was most significant in mediating stress of family caregivers was not supported. Finally, the results confirmed that there are differences in the importance of social support help in mediating stress based on gender. It was found that gender and social support help were related to cognitive stress and gender had a statistically significant interaction effect with social support help. Implications for clinical practice, public health policy, and research are discussed. ^

A LAPLACE TRANSFORM PAIR MODEL TO DETERMINE BREMSSTRAHLUNG SPECTRA FROM ATTENUATION DATA (RADIATION, X-RAY, SPECTRAL MODELING)

Every x-ray attenuation curve inherently contains all the information necessary to extract the complete energy spectrum of a beam. To date, attempts to obtain accurate spectral information from attenuation data have been inadequate.^ This investigation presents a mathematical pair model, grounded in physical reality by the Laplace Transformation, to describe the attenuation of a photon beam and the corresponding bremsstrahlung spectral distribution. In addition the Laplace model has been mathematically extended to include characteristic radiation in a physically meaningful way. A method to determine the fraction of characteristic radiation in any diagnostic x-ray beam was introduced for use with the extended model.^ This work has examined the reconstructive capability of the Laplace pair model for a photon beam range of from 50 kVp to 25 MV, using both theoretical and experimental methods.^ In the diagnostic region, excellent agreement between a wide variety of experimental spectra and those reconstructed with the Laplace model was obtained when the atomic composition of the attenuators was accurately known. The model successfully reproduced a 2 MV spectrum but demonstrated difficulty in accurately reconstructing orthovoltage and 6 MV spectra. The 25 MV spectrum was successfully reconstructed although poor agreement with the spectrum obtained by Levy was found.^ The analysis of errors, performed with diagnostic energy data, demonstrated the relative insensitivity of the model to typical experimental errors and confirmed that the model can be successfully used to theoretically derive accurate spectral information from experimental attenuation data. ^

Bayesian generalized linear models for meta-analysis of diagnostic tests

With the recognition of the importance of evidence-based medicine, there is an emerging need for methods to systematically synthesize available data. Specifically, methods to provide accurate estimates of test characteristics for diagnostic tests are needed to help physicians make better clinical decisions. To provide more flexible approaches for meta-analysis of diagnostic tests, we developed three Bayesian generalized linear models. Two of these models, a bivariate normal and a binomial model, analyzed pairs of sensitivity and specificity values while incorporating the correlation between these two outcome variables. Noninformative independent uniform priors were used for the variance of sensitivity, specificity and correlation. We also applied an inverse Wishart prior to check the sensitivity of the results. The third model was a multinomial model where the test results were modeled as multinomial random variables. All three models can include specific imaging techniques as covariates in order to compare performance. Vague normal priors were assigned to the coefficients of the covariates. The computations were carried out using the 'Bayesian inference using Gibbs sampling' implementation of Markov chain Monte Carlo techniques. We investigated the properties of the three proposed models through extensive simulation studies. We also applied these models to a previously published meta-analysis dataset on cervical cancer as well as to an unpublished melanoma dataset. In general, our findings show that the point estimates of sensitivity and specificity were consistent among Bayesian and frequentist bivariate normal and binomial models. However, in the simulation studies, the estimates of the correlation coefficient from Bayesian bivariate models are not as good as those obtained from frequentist estimation regardless of which prior distribution was used for the covariance matrix. The Bayesian multinomial model consistently underestimated the sensitivity and specificity regardless of the sample size and correlation coefficient. In conclusion, the Bayesian bivariate binomial model provides the most flexible framework for future applications because of its following strengths: (1) it facilitates direct comparison between different tests; (2) it captures the variability in both sensitivity and specificity simultaneously as well as the intercorrelation between the two; and (3) it can be directly applied to sparse data without ad hoc correction. ^

The diagnostic value of a 12-lead electrocardiogram in patients with cardiac tamponade due to a malignant pericardial effusion

Background. Cardiac tamponade can occur when a large amount of fluid, gas, singly or in combination, accumulating within the pericardium, compresses the heart causing circulatory compromise. Although previous investigators have found the 12-lead ECG to have a poor predictive value in diagnosing cardiac tamponade, very few studies have evaluated it as a follow up tool for ruling in or ruling out tamponade in patients with previously diagnosed malignant pericardial effusions. ^ Methods. 127 patients with malignant pericardial effusions at the MD Anderson Cancer Center were included in this retrospective study. While 83 of these patients had a cardiac tamponade diagnosed by echocardiographic criteria (Gold standard), 44 did not. We computed the sensitivity (Se), specificity (Sp), positive (PPV) and negative predictive values (NPV) for individual and combinations of ECG abnormalities. Individual ECG abnormalities were also entered singly into a univariate logistic regression model to predict tamponade. ^ Results. For patients with effusions of all sizes, electrical alternans had a Se, Sp, PPV and NPV of 22.61%, 97.61%, 95% and 39.25% respectively. These parameters for low voltage complexes were 55.95%, 74.44%, 81.03%, 46.37% respectively. The presence of all three ECG abnormalities had a Se = 8.33%, Sp = 100%, PPV = 100% and NPV = 35.83% while the presence of at least one of the three ECG abnormalities had a Se = 89.28%, Sp = 46.51%, PPV = 76.53%, NPV = 68.96%. For patients with effusions of all sizes electrical alternans had an OR of 12.28 (1.58–95.17, p = 0.016), while the presence of at least one ECG abnormality had an OR of 7.25 (2.9–18.1, p = 0.000) in predicting tamponade. ^ Conclusions. Although individual ECG abnormalities had low sensitivities, specificities, NPVs and PPVs with the exception of electrical alternans, the presence of at least one of the three ECG abnormalities had a high sensitivity in diagnosing cardiac tamponade. This could point to its potential use as a screening test with a correspondingly high NPV to rule out a diagnosis of tamponade in patients with malignant pericardial effusions. This could save expensive echocardiographic assessments in patients with previously diagnosed pericardial effusions. ^

Bayesian and survival model for tumor relapse status and disease-specific survival, with applications to breast cancer

Breast cancer is the most common non-skin cancer and the second leading cause of cancer-related death in women in the United States. Studies on ipsilateral breast tumor relapse (IBTR) status and disease-specific survival will help guide clinic treatment and predict patient prognosis.^ After breast conservation therapy, patients with breast cancer may experience breast tumor relapse. This relapse is classified into two distinct types: true local recurrence (TR) and new ipsilateral primary tumor (NP). However, the methods used to classify the relapse types are imperfect and are prone to misclassification. In addition, some observed survival data (e.g., time to relapse and time from relapse to death)are strongly correlated with relapse types. The first part of this dissertation presents a Bayesian approach to (1) modeling the potentially misclassified relapse status and the correlated survival information, (2) estimating the sensitivity and specificity of the diagnostic methods, and (3) quantify the covariate effects on event probabilities. A shared frailty was used to account for the within-subject correlation between survival times. The inference was conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in softwareWinBUGS. Simulation was used to validate the Bayesian method and assess its frequentist properties. The new model has two important innovations: (1) it utilizes the additional survival times correlated with the relapse status to improve the parameter estimation, and (2) it provides tools to address the correlation between the two diagnostic methods conditional to the true relapse types.^ Prediction of patients at highest risk for IBTR after local excision of ductal carcinoma in situ (DCIS) remains a clinical concern. The goals of the second part of this dissertation were to evaluate a published nomogram from Memorial Sloan-Kettering Cancer Center, to determine the risk of IBTR in patients with DCIS treated with local excision, and to determine whether there is a subset of patients at low risk of IBTR. Patients who had undergone local excision from 1990 through 2007 at MD Anderson Cancer Center with a final diagnosis of DCIS (n=794) were included in this part. Clinicopathologic factors and the performance of the Memorial Sloan-Kettering Cancer Center nomogram for prediction of IBTR were assessed for 734 patients with complete data. Nomogram for prediction of 5- and 10-year IBTR probabilities were found to demonstrate imperfect calibration and discrimination, with an area under the receiver operating characteristic curve of .63 and a concordance index of .63. In conclusion, predictive models for IBTR in DCIS patients treated with local excision are imperfect. Our current ability to accurately predict recurrence based on clinical parameters is limited.^ The American Joint Committee on Cancer (AJCC) staging of breast cancer is widely used to determine prognosis, yet survival within each AJCC stage shows wide variation and remains unpredictable. For the third part of this dissertation, biologic markers were hypothesized to be responsible for some of this variation, and the addition of biologic markers to current AJCC staging were examined for possibly provide improved prognostication. The initial cohort included patients treated with surgery as first intervention at MDACC from 1997 to 2006. Cox proportional hazards models were used to create prognostic scoring systems. AJCC pathologic staging parameters and biologic tumor markers were investigated to devise the scoring systems. Surveillance Epidemiology and End Results (SEER) data was used as the external cohort to validate the scoring systems. Binary indicators for pathologic stage (PS), estrogen receptor status (E), and tumor grade (G) were summed to create PS+EG scoring systems devised to predict 5-year patient outcomes. These scoring systems facilitated separation of the study population into more refined subgroups than the current AJCC staging system. The ability of the PS+EG score to stratify outcomes was confirmed in both internal and external validation cohorts. The current study proposes and validates a new staging system by incorporating tumor grade and ER status into current AJCC staging. We recommend that biologic markers be incorporating into revised versions of the AJCC staging system for patients receiving surgery as the first intervention.^ Chapter 1 focuses on developing a Bayesian method to solve misclassified relapse status and application to breast cancer data. Chapter 2 focuses on evaluation of a breast cancer nomogram for predicting risk of IBTR in patients with DCIS after local excision gives the statement of the problem in the clinical research. Chapter 3 focuses on validation of a novel staging system for disease-specific survival in patients with breast cancer treated with surgery as the first intervention. ^