Comparison of Managing Factor Xa Inhibitor to Unfractionated Heparin Transitions by aPTT Versus a Treatment Guideline Utilizing Heparin Anti-Xa Levels.

BACKGROUND: There are inadequate data on the optimal strategy for transitioning factor Xa inhibitors (FXai; apixaban, rivaroxaban) to unfractionated heparin (UFH) infusions. OBJECTIVE: In patients transitioning from an FXai to an UFH infusion, this study compared the safety and efficacy of monitoring UFH infusions using an activated partial thromboplastin time (aPTT) titration scale versus utilizing an UFH-calibrated anti-Xa titration scale aided by a novel institutional guideline. METHODS: A retrospective cohort analysis was conducted on adult patients transitioning from an FXai to an UFH infusion at 2 medical centers from June 1, 2018, to November 1, 2020. One institution utilized aPTT while the other institution primarily used UFH-calibrated anti-Xa. The primary endpoint was a composite of death, major bleeding, or new thrombosis during the hospitalization with a planned noninferiority analysis. Secondary outcomes were also collected including the amount and duration of UFH administered between cohorts. RESULTS: The incidence rate of the primary composite endpoint was 6.3% in the anti-Xa group and 11% in the aPTT group (P < 0.001 for noninferiority, P = 0.138 for superiority) meeting noninferiority criteria. No statistical differences were seen in new thrombosis, major bleeding, or any bleeding. CONCLUSION AND RELEVANCE: This represents the first report of a comparison between aPTT versus anti-Xa monitoring in relation to clinical outcomes for patients transitioning from an FXai to an UFH infusion. A transition guideline primarily utilizing an UFH-calibrated anti-Xa assay appears to be a safe alternative to aPTT monitoring and can aid facilities in the management of patients during these complex transitions.

Transitioning hospitalized patients from rivaroxaban or apixaban to a continuous unfractionated heparin infusion: A retrospective review.

PURPOSE: To determine a patient's clinical course based on the use of an activated partial thromboplastin time (aPTT) or heparin anti-Xa assay when transitioning from rivaroxaban or apixaban to an unfractionated heparin infusion. METHODS: A retrospective chart review was conducted to investigate how unfractionated heparin infusions were managed at a tertiary care hospital in the setting of recent apixaban or rivaroxaban administration. Patients were separated into 2 cohorts based on the chosen heparin infusion monitoring assay: heparin anti-Xa or aPTT. The primary composite outcome was total number of bleeding and thrombotic events; the secondary composite outcome was average incidence of heparin infusion holds and rate changes per patient. RESULTS: Data were collected from 76 patients (heparin anti-Xa = 69, aPTT = 7). Due to the limited number of patients within the aPTT cohort, this data was excluded from the analysis, and heparin anti-Xa descriptive statistics were reported without statistical comparisons. In the heparin anti-Xa group, a total of 10 bleeds and 1 thrombus were discovered. Additionally, the average number of infusion holds and rate changes was 0.841 and 2.65 times per patient, respectively, for those patients monitored via heparin anti-Xa assay. CONCLUSION: In the presence of a recently administered oral anti-Xa anticoagulant, more down-titrations occurred in the initial 6 hours of the heparin infusion when measuring anti-Xa activity, and most up-titrations occurred after 36 hours. Baseline heparin anti-Xa activity may be a useful tool to identify patients with residual plasma concentrations of apixaban and rivaroxaban to help better individualize heparin therapy.

The significance of cooking for early hominin scavenging.

Meat scavenged by early Homo could have contributed importantly to a higher-quality diet. However, it has been suggested that because carrion would normally have been contaminated by bacteria it would have been dangerous and therefore eaten rarely prior to the advent of cooking. In this study, we quantified bacterial loads on two tissues apparently eaten by hominins, meat and bone marrow. We tested the following three hypotheses: (1) the bacterial loads on exposed surfaces of raw meat increase within 24 h to potentially dangerous levels, (2) simple roasting of meat on hot coals kills most bacteria, and (3) fewer bacteria grow on marrow than on meat, making marrow a relatively safe food. Our results supported all three hypotheses. Our experimental data imply that early hominins would have found it difficult to scavenge safely without focusing on marrow, employing strategies of carrion selection to minimize pathogen load, or cooking.

Fusion of white and gray matter geometry: a framework for investigating brain development.

Current neuroimaging investigation of the white matter typically focuses on measurements derived from diffusion tensor imaging, such as fractional anisotropy (FA). In contrast, imaging studies of the gray matter oftentimes focus on morphological features such as cortical thickness, folding and surface curvature. As a result, it is not clear how to combine findings from these two types of approaches in order to obtain a consistent picture of morphological changes in both gray and white matter. In this paper, we propose a joint investigation of gray and white matter morphology by combining geometrical information from white and the gray matter. To achieve this, we first introduce a novel method for computing multi-scale white matter tract geometry. Its formulation is based on the differential geometry of curve sets and is easily incorporated into a continuous scale-space framework. We then incorporate this method into a novel framework for "fusing" white and gray matter geometrical information. Given a set of fiber tracts originating in a particular cortical region, the key idea is to compute two scalar fields that represent geometrical characteristics of the white matter and of the surface of the cortical region. A quantitative marker is created by combining the distributions of these scalar values using Mutual Information. This marker can be then used in the study of normal and pathological brain structure and development. We apply this framework to a study on autism spectrum disorder in children. Our preliminary results support the view that autism may be characterized by early brain overgrowth, followed by reduced or arrested growth (Courchesne, 2004).

Identifying group discriminative and age regressive sub-networks from DTI-based connectivity via a unified framework of non-negative matrix factorization and graph embedding.

Diffusion tensor imaging (DTI) offers rich insights into the physical characteristics of white matter (WM) fiber tracts and their development in the brain, facilitating a network representation of brain's traffic pathways. Such a network representation of brain connectivity has provided a novel means of investigating brain changes arising from pathology, development or aging. The high dimensionality of these connectivity networks necessitates the development of methods that identify the connectivity building blocks or sub-network components that characterize the underlying variation in the population. In addition, the projection of the subject networks into the basis set provides a low dimensional representation of it, that teases apart different sources of variation in the sample, facilitating variation-specific statistical analysis. We propose a unified framework of non-negative matrix factorization and graph embedding for learning sub-network patterns of connectivity by their projective non-negative decomposition into a reconstructive basis set, as well as, additional basis sets representing variational sources in the population like age and pathology. The proposed framework is applied to a study of diffusion-based connectivity in subjects with autism that shows localized sparse sub-networks which mostly capture the changes related to pathology and developmental variations.

PUNCH: Population Characterization of Heterogeneity.

Neuropsychiatric disorders are notoriously heterogeneous in their presentation, which precludes straightforward and objective description of the differences between affected and typical populations that therefore makes finding reliable biomarkers a challenge. This difficulty underlines the need for reliable methods to capture sample characteristics of heterogeneity using a single continuous measure, incorporating the multitude of scores used to describe different aspects of functioning. This study addresses this challenge by proposing a general method of identifying and quantifying the heterogeneity of any clinical population using a severity measure called the PUNCH (Population Characterization of Heterogeneity). PUNCH is a decision level fusion technique to incorporate decisions of various phenotypic scores, while providing interpretable weights for scores. We provide applications of our framework to simulated datasets and to a large sample of youth with Autism Spectrum Disorder (ASD). Next we stratify PUNCH scores in our ASD sample and show how severity moderates findings of group differences in diffusion weighted brain imaging data; more severely affected subgroups of ASD show expanded differences compared to age and gender matched healthy controls. Results demonstrate the ability of our measure in quantifying the underlying heterogeneity of the clinical samples, and suggest its utility in providing researchers with reliable severity assessments incorporating population heterogeneity.

Combining surface and fiber geometry: an integrated approach to brain morphology.

Despite the fact that several theories link cortical development and function to the development of white matter and its geometrical structure, the relationship between gray and white matter morphology has not been widely researched. In this paper, we propose a novel framework for investigating this relationship. Given a set of fiber tracts which connect to a particular cortical region, the key idea is to compute two scalar fields that represent geometrical characteristics of the white matter and of the surface of the cortical region. The distributions of these scalar values are then linked via Mutual Information, which results in a quantitative marker that can be used in the study of normal and pathological brain structure and development. We apply this framework to a population study on autism spectrum disorder in children.

Connectivity subnetwork learning for pathology and developmental variations.

Network representation of brain connectivity has provided a novel means of investigating brain changes arising from pathology, development or aging. The high dimensionality of these networks demands methods that are not only able to extract the patterns that highlight these sources of variation, but describe them individually. In this paper, we present a unified framework for learning subnetwork patterns of connectivity by their projective non-negative decomposition into a reconstructive basis set, as well as, additional basis sets representing development and group discrimination. In order to obtain these components, we exploit the geometrical distribution of the population in the connectivity space by using a graph-theoretical scheme that imposes locality-preserving properties. In addition, the projection of the subject networks into the basis set provides a low dimensional representation of it, that teases apart the different sources of variation in the sample, facilitating variation-specific statistical analysis. The proposed framework is applied to a study of diffusion-based connectivity in subjects with autism.

Multinomial probabilistic fiber representation for connectivity driven clustering.

The clustering of fibers into bundles is an important task in studying the structure and function of white matter. Existing technology mostly relies on geometrical features, such as the shape of fibers, and thus only provides very limited information about the neuroanatomical function of the brain. We advance this issue by proposing a multinomial representation of fibers decoding their connectivity to gray matter regions. We then simplify the clustering task by first deriving a compact encoding of our representation via the logit transformation. Furthermore, we define a distance between fibers that is in theory invariant to parcellation biases and is equivalent to a family of Riemannian metrics on the simplex of multinomial probabilities. We apply our method to longitudinal scans of two healthy subjects showing high reproducibility of the resulting fiber bundles without needing to register the corresponding scans to a common coordinate system. We confirm these qualitative findings via a simple statistical analyse of the fiber bundles.

Identifying sub-populations via unsupervised cluster analysis on multi-edge similarity graphs.

Pathologies like autism and schizophrenia are a broad set of disorders with multiple etiologies in the same diagnostic category. This paper presents a method for unsupervised cluster analysis using multi-edge similarity graphs that combine information from different modalities. The method alleviates the issues with traditional supervised classification methods that use diagnostic labels and are therefore unable to exploit or elucidate the underlying heterogeneity of the dataset under analysis. The framework introduced in this paper has the ability to employ diverse features that define different aspects of pathology obtained from different modalities to create a multi-edged graph on which clustering is performed. The weights on the multiple edges are optimized using a novel concept of 'holding power' that describes the certainty with which a subject belongs to a cluster. We apply the technique to two separate clinical populations of autism spectrum disorder (ASD) and schizophrenia (SCZ), where the multi-edged graph for each population is created by combining information from structural networks and cognitive scores. For the ASD-control population the method clusters the data into two classes and the SCZ-control population is clustered into four. The two classes in ASD agree with underlying diagnostic labels with 92% accuracy and the SCZ clustering agrees with 78% accuracy, indicating a greater heterogeneity in the SCZ population.