Diagnostic Yield of Decubitus CT Myelography for Detection of CSF-Venous Fistulas.

BACKGROUND AND PURPOSE: Various imaging techniques have been described to detect CSF-Venous Fistulas (CVFs) in the setting of Spontaneous Intracranial Hypotension (SIH), including decubitus CT myelography (dCTM). The expected diagnostic yield of dCTM for CVF detection is not fully established. The purpose of this study was to assess the yield of dCTM among consecutive patients presenting for evaluation of possible SIH, and to examine what impact brain MRI findings of SIH had on diagnostic yield. MATERIALS AND METHODS: Single-center, retrospective cohort of consecutive patients presenting over a one-year period who underwent CTM and had no CSF identified in the epidural space. Patients with epidural CSF leaks were included in a secondary cohort. Subjects were grouped according to positioning for the myelogram, either decubitus or prone, and the presence of imaging findings of SIH on pre-procedure brain MRI. Diagnostic yields for each subgroup were calculated, and the yield of dCTM was compared to prone CTM. RESULTS: The study cohort included 302 subjects, including 247 patients with no epidural fluid. Diagnostic yield of dCTM for CVF detection among subjects with positive brain MRI and no epidural fluid was 73%. No CVFs were identified among subjects with negative brain imaging. Among subjects with epidural leak, brain MRI was negative for signs of SIH in 22%. Prone CTM identified a CVF less commonly than dCTM (43% vs. 73%, p=0.19), although the difference was not statistically significant in this small subgroup. CONCLUSIONS: We found a diagnostic yield of dCTM to be similar to the yield previously reported for digital subtraction myelography among patients with positive brain imaging. No CVFs were identified in patients with negative brain imaging; epidural CSF leaks accounted for all cases of patients who had SIH with negative brain imaging. This study provides useful data for counseling patients and helps establish a general benchmark for dCTM yield for CVF detection.ABBREVIATIONS: SIH = spontaneous intracranial hypotension; CVF = CSF-Venous Fistula; CTM = CT Myelography; dCTM = decubitus CT myelography; EBP = epidural blood patch.

Finite mixtures of mean-parameterized Conway-Maxwell-Poisson models.

For modeling count data, the Conway-Maxwell-Poisson (CMP) distribution is a popular generalization of the Poisson distribution due to its ability to characterize data over- or under-dispersion. While the classic parameterization of the CMP has been well-studied, its main drawback is that it is does not directly model the mean of the counts. This is mitigated by using a mean-parameterized version of the CMP distribution. In this work, we are concerned with the setting where count data may be comprised of subpopulations, each possibly having varying degrees of data dispersion. Thus, we propose a finite mixture of mean-parameterized CMP distributions. An EM algorithm is constructed to perform maximum likelihood estimation of the model, while bootstrapping is employed to obtain estimated standard errors. A simulation study is used to demonstrate the flexibility of the proposed mixture model relative to mixtures of Poissons and mixtures of negative binomials. An analysis of dog mortality data is presented. Supplementary Information: The online version contains supplementary material available at 10.1007/s00362-023-01452-x.

Rectangular multivariate normal prediction regions for setting reference regions in laboratory medicine.

Reference intervals are among the most widely used medical decision-making tools and are invaluable in the interpretation of laboratory results of patients. Moreover, when multiple biochemical analytes are measured on each patient, a multivariate reference region (MRR) is needed. Such regions are more desirable than separate univariate reference intervals since the latter disregard the cross-correlations among variables. Traditionally, assuming multivariate normality, MRRs have been constructed as ellipsoidal regions, which cannot detect componentwise extreme values. Consequently, MRRs are rarely used in actual practice. In order to address the above drawback of ellipsoidal reference regions, we propose a procedure to construct rectangular MRRs under multivariate normality. The rectangular MRR is computed using a prediction region criterion. However, since the population correlations are unknown, a parametric bootstrap approach is employed for computing the required prediction factor. Also addressed in this study is the computation of mixed reference intervals, which include both two-sided and one-sided prediction limits, simultaneously. Numerical results show that the parametric bootstrap procedure is quite accurate, with estimated coverage probabilities very close to the nominal level. Moreover, the expected volumes of the proposed rectangular regions are substantially smaller than the expected volumes obtained from Bonferroni simultaneous prediction intervals. We also explore the computation of covariate-dependent MRRs in a multivariate regression setting. Finally, we discuss real-life applications of the proposed methods, including the computation of reference ranges for the assessment of kidney function and for components of the insulin-like growth factor system in adults.

A mixture model with Poisson and zero-truncated Poisson components to analyze road traffic accidents in Turkey.

The analysis of traffic accident data is crucial to address numerous concerns, such as understanding contributing factors in an accident's chain-of-events, identifying hotspots, and informing policy decisions about road safety management. The majority of statistical models employed for analyzing traffic accident data are logically count regression models (commonly Poisson regression) since a count - like the number of accidents - is used as the response. However, features of the observed data frequently do not make the Poisson distribution a tenable assumption. For example, observed data rarely demonstrate an equal mean and variance and often times possess excess zeros. Sometimes, data may have heterogeneous structure consisting of a mixture of populations, rather than a single population. In such data analyses, mixtures-of-Poisson-regression models can be used. In this study, the number of injuries resulting from casualties of traffic accidents registered by the General Directorate of Security (Turkey, 2005-2014) are modeled using a novel mixture distribution with two components: a Poisson and zero-truncated-Poisson distribution. Such a model differs from existing mixture models in literature where the components are either all Poisson distributions or all zero-truncated Poisson distributions. The proposed model is compared with the Poisson regression model via simulation and in the analysis of the traffic data.

Nonparametric hyperrectangular tolerance and prediction regions for setting multivariate reference regions in laboratory medicine.

Reference regions are widely used in clinical chemistry and laboratory medicine to interpret the results of biochemical or physiological tests of patients. There are well-established methods in the literature for reference limits for univariate measurements; however, limited methods are available for the construction of multivariate reference regions, since traditional multivariate statistical regions (e.g. confidence, prediction, and tolerance regions) are not constructed based on a hyperrectangular geometry. The present work addresses this problem by developing multivariate hyperrectangular nonparametric tolerance regions for setting the reference regions. The approach utilizes statistical data depth to determine which points to trim and then the extremes of the trimmed dataset are used as the faces of the hyperrectangular region. Also presented is a strategy for determining the number of points to trim based on previously established asymptotic results. An extensive coverage study shows the favorable performance of the proposed procedure for moderate to large sample sizes. The procedure is applied to obtain reference regions for addressing two important clinical problems: (1) assessing kidney function in adolescents and (2) characterizing insulin-like growth factor concentrations in the serum of adults.

Improving coverage probabilities for parametric tolerance intervals via bootstrap calibration.

Statistical tolerance intervals are commonly employed in biomedical and pharmaceutical research, such as in lifetime analysis, the assessment of biosimilarity of branded and generic versions of biopharmaceutical drugs, and in quality control of drug products to ensure that a specified proportion of the products are covered within established acceptance limits. Exact two-sided parametric tolerance intervals are only available for the normal distribution, while exact one-sided parametric tolerance limits are available for a limited number of distributions. Approximations to two-sided parametric tolerance intervals often use the Bonferroni correction on the one-sided tolerance interval calculation; however, this often incurs a higher coverage probability than the nominal level. Recently, the usage of a bootstrap calibration has been demonstrated as a way to improve coverage probabilities of tolerance intervals for very specific and complex distributional settings. We present a focused treatment on using a single-layer bootstrap calibration to improve the coverage probabilities of two-sided parametric tolerance intervals. Simulation results clearly demonstrate the improved coverage probabilities towards the nominal level over the uncalibrated setting. Applications to medical data for various parametric distributions also highlight the utility of constructing these calibrated tolerance intervals.

Influence of Adrenal Venous Sampling on Management in Patients with Primary Aldosteronism Independent of Lateralization on Cross-Sectional Imaging.

BACKGROUND: Patients with primary aldosteronism undergo imaging of the adrenal glands after confirmation of the disease. Adrenal venous sampling (AVS) is a useful adjunct to imaging, and advocates believe that AVS should be performed before surgical management. Others argue that patients with unilateral lesions on imaging do not require AVS. Although AVS accuracy has been established, few studies have evaluated how AVS alters management. Our study aimed to determine how AVS affected management of these patients. STUDY DESIGN: Patient data were collected retrospectively from the electronic medical records at a single institution. Patients aged 18 years or older who underwent AVS with successful adrenal vein cannulation from 2007 to 2016 were included. The laterality of AVS was compared with laterality of preprocedural imaging for each patient. The management plan before AVS was determined by laterality on preprocedural imaging. The primary outcomes were management of primary aldosteronism, change in management compared with the plan before AVS, and antihypertensive medication use after therapy. RESULTS: Seventy-four patients had successful adrenal venous cannulation. Thirty-three (44.6%) patients had AVS lateralization that was concordant with preprocedural imaging. Forty-one (55.4%) patients had AVS lateralization that was non-concordant with preprocedural imaging. There was a change in management in 29 (39.2%) patients. CONCLUSIONS: Adrenal venous sampling can delineate the source of aldosterone hypersecretion, and often this is not concordant with cross-sectional imaging. We found that many patients avoided a potentially non-curative operation due to AVS. Adrenal venous sampling frequently alters the management of aldosteronomas and should be highly considered in patients before surgical intervention.

Increased white matter metabolic rates in autism spectrum disorder and schizophrenia.

Both autism spectrum disorder (ASD) and schizophrenia are often characterized as disorders of white matter integrity. Multimodal investigations have reported elevated metabolic rates, cerebral perfusion and basal activity in various white matter regions in schizophrenia, but none of these functions has previously been studied in ASD. We used 18fluorodeoxyglucose positron emission tomography to compare white matter metabolic rates in subjects with ASD (n = 25) to those with schizophrenia (n = 41) and healthy controls (n = 55) across a wide range of stereotaxically placed regions-of-interest. Both subjects with ASD and schizophrenia showed increased metabolic rates across the white matter regions assessed, including internal capsule, corpus callosum, and white matter in the frontal and temporal lobes. These increases were more pronounced, more widespread and more asymmetrical in subjects with ASD than in those with schizophrenia. The highest metabolic increases in both disorders were seen in the prefrontal white matter and anterior limb of the internal capsule. Compared to normal controls, differences in gray matter metabolism were less prominent and differences in adjacent white matter metabolism were more prominent in subjects with ASD than in those with schizophrenia. Autism spectrum disorder and schizophrenia are associated with heightened metabolic activity throughout the white matter. Unlike in the gray matter, the vector of white matter metabolic abnormalities appears to be similar in ASD and schizophrenia, may reflect inefficient functional connectivity with compensatory hypermetabolism, and may be a common feature of neurodevelopmental disorders.

Choosing a coverage probability for forecasting the incidence of cancer.

Loddon Mallee Integrated Cancer Service plays a key role in planning the delivery of cancer services in the Loddon Mallee Region of Victoria, Australia. Such planning relies on the accuracy of forecasting the incidence of cancer. Perhaps more importantly is the need to reflect the uncertainty of these forecasts, which is usually carried out through prediction intervals. Standard confidence levels (e.g., 90% or 95%) are typically employed when forecasting the incidence of cancer, but decision-theoretic approaches are available to help choose an optimal coverage probability by minimizing the combined risk of the interval width and noncoverage of the interval. We proceed with the decision-theoretic framework and discuss some general strategies for defining candidate loss functions for forecasting the incidence of cancer, such as the data we analyze for the Loddon Mallee Region.