In vivo compatibility of Dynesys(®) spinal implants: a case series of five retrieved periprosthetic tissue samples and corresponding implants

PURPOSE To determine whether particulate debris is present in periprosthetic tissue from revised Dynesys(®) devices, and if present, elicits a biological tissue reaction. METHODS Five Dynesys(®) dynamic stabilization systems consisting of pedicle screws (Ti alloy), polycarbonate-urethane (PCU) spacers and a polyethylene-terephthalate (PET) cord were explanted for pain and screw loosening after a mean of 2.86 years (1.9-5.3 years). Optical microscopy and scanning electron microscopy were used to evaluate wear, deformation and surface damage, and attenuated total reflectance Fourier transform infrared spectroscopy to assess surface chemical composition of the spacers. Periprosthetic tissue morphology and wear debris were determined using light microscopy, and PCU and PET wear debris by polarized light microscopy. RESULTS All implants had surface damage on the PCU spacers consistent with scratches and plastic deformation; 3 of 5 exhibited abrasive wear zones. In addition to fraying of the outer fibers of the PET cords in five implants, one case also evidenced cord fracture. The pedicle screws were unremarkable. Patient periprosthetic tissues around the three implants with visible PCU damage contained wear debris and a corresponding macrophage infiltration. For the patient revised for cord fracture, the tissues also contained large wear particles (>10 μm) and giant cells. Tissues from the other two patients showed comparable morphologies consisting of dense fibrous tissue with no inflammation or wear debris. CONCLUSIONS This is the first study to evaluate wear accumulation and local tissue responses for explanted Dynesys(®) devices. Polymer wear debris and an associated foreign-body macrophage response were observed in three of five cases.

A New Approach for Automatic Removal of Movement Artifacts in Near-Infrared Spectroscopy Time Series by Means of Acceleration Data

Abstract: Near-infrared spectroscopy (NIRS) enables the non-invasive measurement of changes in hemodynamics and oxygenation in tissue. Changes in light-coupling due to movement of the subject can cause movement artifacts (MAs) in the recorded signals. Several methods have been developed so far that facilitate the detection and reduction of MAs in the data. However, due to fixed parameter values (e.g., global threshold) none of these methods are perfectly suitable for long-term (i.e., hours) recordings or were not time-effective when applied to large datasets. We aimed to overcome these limitations by automation, i.e., data adaptive thresholding specifically designed for long-term measurements, and by introducing a stable long-term signal reconstruction. Our new technique (“acceleration-based movement artifact reduction algorithm”, AMARA) is based on combining two methods: the “movement artifact reduction algorithm” (MARA, Scholkmann et al. Phys. Meas. 2010, 31, 649–662), and the “accelerometer-based motion artifact removal” (ABAMAR, Virtanen et al. J. Biomed. Opt. 2011, 16, 087005). We describe AMARA in detail and report about successful validation of the algorithm using empirical NIRS data, measured over the prefrontal cortex in adolescents during sleep. In addition, we compared the performance of AMARA to that of MARA and ABAMAR based on validation data.

Partial lateralization of the nasopalatine nerve at the incisive foramen for ridge augmentation in the anterior maxilla prior to placement of dental implants: a retrospective case series evaluating self-reported data and neurosensory testing

The objective of this study was to assess implant therapy after a staged guided bone regeneration procedure in the anterior maxilla by lateralization of the nasopalatine nerve and vessel bundle. Neurosensory function following augmentative procedures and implant placement, assessed using a standardized questionnaire and clinical examination, were the primary outcome variables measured. This retrospective study included patients with a bone defect in the anterior maxilla in need of horizontal and/or vertical ridge augmentation prior to dental implant placement. The surgical sites were allowed to heal for at least 6 months before placement of dental implants. All patients received fixed implant-supported restorations and entered into a tightly scheduled maintenance program. In addition to the maintenance program, patients were recalled for a clinical examination and to fill out a questionnaire to assess any changes in the neurosensory function of the nasopalatine nerve at least 6 months after function. Twenty patients were included in the study from February 2001 to December 2010. They received a total of 51 implants after augmentation of the alveolar crest and lateralization of the nasopalatine nerve. The follow-up examination for questionnaire and neurosensory assessment was scheduled after a mean period of 4.18 years of function. None of the patients examined reported any pain, they did not have less or an altered sensation, and they did not experience a "foreign body" feeling in the area of surgery. Overall, 6 patients out of 20 (30%) showed palatal sensibility alterations of the soft tissues in the region of the maxillary canines and incisors resulting in a risk for a neurosensory change of 0.45 mucosal teeth regions per patient after ridge augmentation with lateralization of the nasopalatine nerve. Regeneration of bone defects in the anterior maxilla by horizontal and/or vertical ridge augmentation and lateralization of the nasopalatine nerve prior to dental implant placement is a predictable surgical technique. Whether or not there were clinically measurable impairments of neurosensory function, the patients did not report them or were not bothered by them.

Certolizumab treatment during late pregnancy in patients with rheumatic diseases: Low drug levels in cord blood but possible risk for maternal infections. A case series of 13 patients

OBJECTIVE Due to reduction of immune-suppressive drugs, patients with rheumatic diseases can experience an increase in disease activity during pregnancy. In such cases, TNF-inhibitors may be prescribed. However, monoclonal antibodies with the Fc moiety are actively transported across the placenta, resulting in therapeutic drug levels in the newborn. As certolizumab (CZP) lacks the Fc moiety, it may bear a lower risk for the child. METHOD We report a case series of thirteen patients (5 with rheumatoid arthritis and 8 with spondyloarthritis) treated with CZP during late pregnancy to control disease activity. RESULT CZP measured in cord blood of eleven infants ranged between undetectable levels and 1μg/mL whereas the median CZP level of maternal plasma was 32.97μg/mL. Three women developed an infection during the third trimester, of whom one had a severe infection and one had an infection that resulted in a pre-term delivery. During the postpartum period, 6 patients remained on CZP while breastfeeding. CZP levels in the breast milk of two breastfeeding patients were undetectable. CONCLUSION The lack of the active transplacental transfer of CZP gives the possibility to treat inflammatory arthritis during late gestation without potential harm to the newborn. However, in pregnant women treated with TNF-inhibitors and prednisone, attention should be given to the increased susceptibility to infections, which might cause prematurity. CZP treatment can be continued while breastfeeding.

Psychological inquiries : in an series of essays, intended to illustrate the mutual relations of the physical organization and the mental faculties

by Sir Benjamin C. Brodie

Implementation of active surveillance cultures and associated infection control practices for the reduction of methicillin resistant Staphylococcus aureus in the healthcare setting: An interrupted time series analysis

OBJECTIVE. To determine the effectiveness of active surveillance cultures and associated infection control practices on the incidence of methicillin resistant Staphylococcus aureus (MRSA) in the acute care setting. DESIGN. A historical analysis of existing clinical data utilizing an interrupted time series design. ^ SETTING AND PARTICIPANTS. Patients admitted to a 260-bed tertiary care facility in Houston, TX between January 2005 through December 2010. ^ INTERVENTION. Infection control practices, including enhanced barrier precautions, compulsive hand hygiene, disinfection and environmental cleaning, and executive ownership and education, were simultaneously introduced during a 5-month intervention implementation period culminating with the implementation of active surveillance screening. Beginning June 2007, all high risk patients were cultured for MRSA nasal carriage within 48 hours of admission. Segmented Poisson regression was used to test the significance of the difference in incidence of healthcare-associated MRSA during the 29-month pre-intervention period compared to the 43-month post-intervention period. ^ RESULTS. A total of 9,957 of 11,095 high-risk patients (89.7%) were screened for MRSA carriage during the intervention period. Active surveillance cultures identified 1,330 MRSA-positive patients (13.4%) contributing to an admission prevalence of 17.5% in high-risk patients. The mean rate of healthcare-associated MRSA infection and colonization decreased from 1.1 per 1,000 patient-days in the pre-intervention period to 0.36 per 1,000 patient-days in the post-intervention period (P<0.001). The effect of the intervention in association with the percentage of S. aureus isolates susceptible to oxicillin were shown to be statistically significantly associated with the incidence of MRSA infection and colonization (IRR = 0.50, 95% CI = 0.31-0.80 and IRR = 0.004, 95% CI = 0.00003-0.40, respectively). ^ CONCLUSIONS. It can be concluded that aggressively targeting patients at high risk for colonization of MRSA with active surveillance cultures and associated infection control practices as part of a multifaceted, hospital-wide intervention is effective in reducing the incidence of healthcare-associated MRSA.^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.