Clinical usefulness of gene-expression profile to rule out acute rejection after heart transplantation: CARGO II.

AIMS A non-invasive gene-expression profiling (GEP) test for rejection surveillance of heart transplant recipients originated in the USA. A European-based study, Cardiac Allograft Rejection Gene Expression Observational II Study (CARGO II), was conducted to further clinically validate the GEP test performance. METHODS AND RESULTS Blood samples for GEP testing (AlloMap(®), CareDx, Brisbane, CA, USA) were collected during post-transplant surveillance. The reference standard for rejection status was based on histopathology grading of tissue from endomyocardial biopsy. The area under the receiver operating characteristic curve (AUC-ROC), negative (NPVs), and positive predictive values (PPVs) for the GEP scores (range 0-39) were computed. Considering the GEP score of 34 as a cut-off (>6 months post-transplantation), 95.5% (381/399) of GEP tests were true negatives, 4.5% (18/399) were false negatives, 10.2% (6/59) were true positives, and 89.8% (53/59) were false positives. Based on 938 paired biopsies, the GEP test score AUC-ROC for distinguishing ≥3A rejection was 0.70 and 0.69 for ≥2-6 and >6 months post-transplantation, respectively. Depending on the chosen threshold score, the NPV and PPV range from 98.1 to 100% and 2.0 to 4.7%, respectively. CONCLUSION For ≥2-6 and >6 months post-transplantation, CARGO II GEP score performance (AUC-ROC = 0.70 and 0.69) is similar to the CARGO study results (AUC-ROC = 0.71 and 0.67). The low prevalence of ACR contributes to the high NPV and limited PPV of GEP testing. The choice of threshold score for practical use of GEP testing should consider overall clinical assessment of the patient's baseline risk for rejection.

Effectiveness of Fluorescence-based Methods in Monitoring Progression of Noncavitated Caries-like Lesions on Smooth Surfaces

Although there has been a significant decrease in caries prevalence in developed countries, the slower progression of dental caries requires methods capable of detecting and quantifying lesions at an early stage. The aim of this study was to evaluate the effectiveness of fluorescence-based methods (DIAGNOdent 2095 laser fluorescence device [LF], DIAGNOdent 2190 pen [LFpen], and VistaProof fluorescence camera [FC]) in monitoring the progression of noncavitated caries-like lesions on smooth surfaces. Caries-like lesions were developed in 60 blocks of bovine enamel using a bacterial model of Streptococcus mutans and Lactobacillus acidophilus . Enamel blocks were evaluated by two independent examiners at baseline (phase I), after the first cariogenic challenge (eight days) (phase II), and after the second cariogenic challenge (a further eight days) (phase III) by two independent examiners using the LF, LFpen, and FC. Blocks were submitted to surface microhardness (SMH) and cross-sectional microhardness analyses. The intraclass correlation coefficient for intra- and interexaminer reproducibility ranged from 0.49 (FC) to 0.94 (LF/LFpen). SMH values decreased and fluorescence values increased significantly among the three phases. Higher values for sensitivity, specificity, and area under the receiver operating characteristic curve were observed for FC (phase II) and LFpen (phase III). A significant correlation was found between fluorescence values and SMH in all phases and integrated loss of surface hardness (ΔKHN) in phase III. In conclusion, fluorescence-based methods were effective in monitoring noncavitated caries-like lesions on smooth surfaces, with moderate correlation with SMH, allowing differentiation between sound and demineralized enamel.

Utility of electrophysiological studies to predict arrhythmic events

AIM To evaluate the prognostic value of electrophysiological stimulation (EPS) in the risk stratification for tachyarrhythmic events and sudden cardiac death (SCD). METHODS We conducted a prospective cohort study and analyzed the long-term follow-up of 265 consecutive patients who underwent programmed ventricular stimulation at the Luzerner Kantonsspital (Lucerne, Switzerland) between October 2003 and April 2012. Patients underwent EPS for SCD risk evaluation because of structural or functional heart disease and/or electrical conduction abnormality and/or after syncope/cardiac arrest. EPS was considered abnormal, if a sustained ventricular tachycardia (VT) was inducible. The primary endpoint of the study was SCD or, in implanted patients, adequate ICD-activation. RESULTS During EPS, sustained VT was induced in 125 patients (47.2%) and non-sustained VT in 60 patients (22.6%); in 80 patients (30.2%) no arrhythmia could be induced. In our cohort, 153 patients (57.7%) underwent ICD implantation after the EPS. During follow-up (mean duration 4.8 ± 2.3 years), a primary endpoint event occurred in 49 patients (18.5%). The area under the receiver operating characteristic curve (AUROC) was 0.593 (95%CI: 0.515-0.670) for a left ventricular ejection fraction (LVEF) < 35% and 0.636 (95%CI: 0.563-0.709) for inducible sustained VT during EPS. The AUROC of EPS was higher in the subgroup of patients with LVEF ≥ 35% (0.681, 95%CI: 0.578-0.785). Cox regression analysis showed that both, sustained VT during EPS (HR: 2.26, 95%CI: 1.22-4.19, P = 0.009) and LVEF < 35% (HR: 2.00, 95%CI: 1.13-3.54, P = 0.018) were independent predictors of primary endpoint events. CONCLUSION EPS provides a benefit in risk stratification for future tachyarrhythmic events and SCD and should especially be considered in patients with LVEF ≥ 35%.

Predicting recurrence after unprovoked venous thromboembolism: prospective validation of the updated Vienna Prediction Model.

The updated Vienna Prediction Model for estimating recurrence risk after an unprovoked venous thromboembolism (VTE) has been developed to identify individuals at low risk for VTE recurrence in whom anticoagulation (AC) therapy may be stopped after 3 months. We externally validated the accuracy of the model to predict recurrent VTE in a prospective multicenter cohort of 156 patients aged ≥65 years with acute symptomatic unprovoked VTE who had received 3 to 12 months of AC. Patients with a predicted 12-month risk within the lowest quartile based on the updated Vienna Prediction Model were classified as low risk. The risk of recurrent VTE did not differ between low- vs higher-risk patients at 12 months (13% vs 10%; P = .77) and 24 months (15% vs 17%; P = 1.0). The area under the receiver operating characteristic curve for predicting VTE recurrence was 0.39 (95% confidence interval [CI], 0.25-0.52) at 12 months and 0.43 (95% CI, 0.31-0.54) at 24 months. In conclusion, in elderly patients with unprovoked VTE who have stopped AC, the updated Vienna Prediction Model does not discriminate between patients who develop recurrent VTE and those who do not. This study was registered at www.clinicaltrials.gov as #NCT00973596.

The utility of preoperative six-minute-walk distance in lung transplantation

RATIONALE The use of 6-minute-walk distance (6MWD) as an indicator of exercise capacity to predict postoperative survival in lung transplantation has not previously been well studied. OBJECTIVES To evaluate the association between 6MWD and postoperative survival following lung transplantation. METHODS Adult, first time, lung-only transplantations per the United Network for Organ Sharing database from May 2005 to December 2011 were analyzed. Kaplan-Meier methods and Cox proportional hazards modeling were used to determine the association between preoperative 6MWD and post-transplant survival after adjusting for potential confounders. A receiver operating characteristic curve was used to determine the 6MWD value that provided maximal separation in 1-year mortality. A subanalysis was performed to assess the association between 6MWD and post-transplant survival by disease category. MEASUREMENTS AND MAIN RESULTS A total of 9,526 patients were included for analysis. The median 6MWD was 787 ft (25th-75th percentiles = 450-1,082 ft). Increasing 6MWD was associated with significantly lower overall hazard of death (P < 0.001). Continuous increase in walk distance through 1,200-1,400 ft conferred an incremental survival advantage. Although 6MWD strongly correlated with survival, the impact of a single dichotomous value to predict outcomes was limited. All disease categories demonstrated significantly longer survival with increasing 6MWD (P ≤ 0.009) except pulmonary vascular disease (P = 0.74); however, the low volume in this category (n = 312; 3.3%) may limit the ability to detect an association. CONCLUSIONS 6MWD is significantly associated with post-transplant survival and is best incorporated into transplant evaluations on a continuous basis given limited ability of a single, dichotomous value to predict outcomes.

Retinal slit lamp video mosaicking

Purpose To this day, the slit lamp remains the first tool used by an ophthalmologist to examine patient eyes. Imaging of the retina poses, however, a variety of problems, namely a shallow depth of focus, reflections from the optical system, a small field of view and non-uniform illumination. For ophthalmologists, the use of slit lamp images for documentation and analysis purposes, however, remains extremely challenging due to large image artifacts. For this reason, we propose an automatic retinal slit lamp video mosaicking, which enlarges the field of view and reduces amount of noise and reflections, thus enhancing image quality. Methods Our method is composed of three parts: (i) viable content segmentation, (ii) global registration and (iii) image blending. Frame content is segmented using gradient boosting with custom pixel-wise features. Speeded-up robust features are used for finding pair-wise translations between frames with robust random sample consensus estimation and graph-based simultaneous localization and mapping for global bundle adjustment. Foreground-aware blending based on feathering merges video frames into comprehensive mosaics. Results Foreground is segmented successfully with an area under the curve of the receiver operating characteristic curve of 0.9557. Mosaicking results and state-of-the-art methods were compared and rated by ophthalmologists showing a strong preference for a large field of view provided by our method. Conclusions The proposed method for global registration of retinal slit lamp images of the retina into comprehensive mosaics improves over state-of-the-art methods and is preferred qualitatively.

Antifactor Xa Activity for the Management of Anticoagulation during Cardiac Surgery

Background In patients with autoimmune diseases associated with antiphospholipid antibodies, precise management of anticoagulation during extracorporeal circulation (ECC) is complicated. It was the aim of the present study to determine whether antifactor Xa (aXa) activity is useful in guiding heparin therapy during ECC. Methods In 15 patients undergoing cardiac surgery, anticoagulation with unfractionated heparin (UFH) and its reversal with protamine were guided using activated clotting time (ACT) (>400 second during ECC; ≤100 second for UFH reversal). For each ACT, the corresponding aXa activity levels were measured. Results A total of 144 blood samples were obtained. ACT and aXa activity were significantly correlated (r = 0.771, p< 0.0001, Spearman rank-order correlation). Using receiver operating characteristic curve (ROC) analyses, the cutoffvalues for aXa activity were 1.14 IU/mL (area under the ROC curve [AUC]: 0.89; inaccuracy rate: 9.4%) to predict ACT > 400 seconds and 0.55 IU/mL (AUC: 0.85; inaccuracy rate: 13.3%) for ACT ≤ 100 seconds. Conclusion AXa activity is strongly correlated with ACT, and therefore may be feasible for managing anticoagulation with UFH during ECC.

PICADAR: a diagnostic predictive tool for primary ciliary dyskinesia.

Symptoms of primary ciliary dyskinesia (PCD) are nonspecific and guidance on whom to refer for testing is limited. Diagnostic tests for PCD are highly specialised, requiring expensive equipment and experienced PCD scientists. This study aims to develop a practical clinical diagnostic tool to identify patients requiring testing.Patients consecutively referred for testing were studied. Information readily obtained from patient history was correlated with diagnostic outcome. Using logistic regression, the predictive performance of the best model was tested by receiver operating characteristic curve analyses. The model was simplified into a practical tool (PICADAR) and externally validated in a second diagnostic centre.Of 641 referrals with a definitive diagnostic outcome, 75 (12%) were positive. PICADAR applies to patients with persistent wet cough and has seven predictive parameters: full-term gestation, neonatal chest symptoms, neonatal intensive care admittance, chronic rhinitis, ear symptoms, situs inversus and congenital cardiac defect. Sensitivity and specificity of the tool were 0.90 and 0.75 for a cut-off score of 5 points. Area under the curve for the internally and externally validated tool was 0.91 and 0.87, respectively.PICADAR represents a simple diagnostic clinical prediction rule with good accuracy and validity, ready for testing in respiratory centres referring to PCD centres.

STUDY OF THE EFFECTS OF TOTAL MODULATION TRANSFER FUNCTION CHANGES ON OBSERVER PERFORMANCE USING CLINICAL MAMMOGRAMS

The main goal of this study was to relate physical changes in image quality measured by Modulation Transfer Function (MTF) to diagnostic accuracy.^ One Hundred and Fifty Kodak Min-R screen/film combination conventional craniocaudal mammograms obtained with the Pfizer Microfocus Mammographic system were selected from the files of the Department of Radiology, at M.D. Anderson Hospital and Tumor Institute.^ The mammograms included 88 cases with a variety of benign diagnosis and 62 cases with a variety of malignant biopsy diagnosis. The average age of the patient population was 55 years old. 70 cases presented calcifications with 30 cases having calcifications smaller than 0.5mm. 46 cases presented irregular bordered masses larger than 1 cm. 30 cases presented smooth bordered masses with 20 larger than 1 cm.^ Four separated copies of the original images were made each having a different change in the MTF using a defocusing technique whereby copies of the original were obtained by light exposure through different thicknesses (spacing) of transparent film base.^ The mammograms were randomized, and evaluated by three experienced mammographers for the degree of visibility of various anatomical breast structures and pathological lesions (masses and calicifications), subjective image quality, and mammographic interpretation.^ 3,000 separate evaluations were anayzed by several statistical techniques including Receiver Operating Characteristic curve analysis, McNemar test for differences between proportions and the Landis et al. method of agreement weighted kappa for ordinal categorical data.^ Results from the statistical analysis show: (1) There were no statistical significant differences in the diagnostic accuracy of the observers when diagnosing from mammograms with the same MTF. (2) There were no statistically significant differences in diagnostic accuracy for each observer when diagnosing from mammograms with the different MTF's used in the study. (3) There statistical significant differences in detail visibility between the copies and the originals. Detail visibility was better in the originals. (4) Feature interpretations were not significantly different between the originals and the copies. (5) Perception of image quality did not affect image interpretation.^ Continuation and improvement of this research ca be accomplished by: using a case population more sensitive to MTF changes, i.e., asymptomatic women with minimum breast cancer, more observers (including less experienced radiologists and experienced technologists) must collaborate in the study, and using a minimum of 200 benign and 200 malignant cases.^

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. ^

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.

Diagnóstico da exposição fetal ao álcool através de biomarcadores em mecônio

A grande prevalência do consumo de álcool por mulheres em idade reprodutiva aliada à gravidez não planejada expõe a gestante a um elevado risco de se alcoolizar em algum momento da gestação, principalmente no início do período gestacional em que a maioria delas ainda não tomou ciência do fato. Assim, torna-se extremamente relevante o desenvolvimento de métodos de detecção precoce de recém-nascidos em risco de desenvolvimento de problemas do espectro dos transtornos relacionados à exposição fetal ao álcool. O objetivo desse estudo foi desenvolver, validar e avaliar a eficácia de um método de quantificação de ésteres etílicos de ácidos graxos (FAEEs) no mecônio de recém-nascidos para avaliação da exposição fetal ao álcool. Os FAEEs avaliados foram: palmitato de etila, estearato de etila, oleato de etila e linoleato de etila.O método consistiu no preparo das amostras pela extração líquido-líquido utilizando água, acetona e hexano, seguida de extração em fase sólida empregando cartuchos de aminopropilsilica. A separação e quantificação dos analitos foi realizada por cromatografia em fase gasosa acoplada à espectrometria de massas. Os limites de quantificação (LQ) variaram entre 50-100ng/g. A curva de calibração foi linear de LQ até 2000ng/g para todos os analitos. A recuperação variou de 69,79% a 106,57%. Os analitos demonstraram estabilidade no ensaio de pós-processamento e em solução. O método foi aplicado em amostras de mecônio de 160 recém-nascidos recrutados em uma maternidade pública de Ribeirão Preto-SP. O consumo de álcool materno foi reportado utilizando questionários de rastreamento validados T-ACE e AUDIT e relatos retrospectivos da quantidade e frequência de álcool consumida ao longo da gestação. A eficácia do método analítico em identificar os casos positivos foi determinada pela curva Receiver Operating Characteristic (ROC). O consumo alcoólico de risco foi identificado pelo T-ACE em 31,3% das participantes e 50% reportaram o uso de álcool durante a gestação. 51,3% dos recém-nascidos apresentaram FAEEs em seu mecônio, sendo que 33,1% apresentaram altas concentrações para a somatória dos FAEEs (maior que 500ng/g), compatível com um consumo abusivo de álcool. O oleato de etila foi o biomarcador mais prevalente e o linoleato de etila foi o biomarcador que apresentou as maiores concentrações. Houve uma variabilidade no perfil de distribuição dos FAEEs entre os indivíduos, e discordâncias entre a presença de FAEEs e o consumo reportado pela mãe. A concentração total dos FAEEs nos mecônio mostrou-se como melhor indicador da exposição fetal ao álcool quando comparado com o uso de um único biomarcador. O ponto de corte para esta população foi de aproximadamente 600ng/g para uso tipo binge (três ou mais doses por ocasião) com sensibilidade de 71,43% e especificidade de 84,37%. Este estudo reforça a importância da utilização de métodos laboratoriais na identificação da exposição fetal ao álcool.

Terrain Controls and Landscape-Scale Susceptibility Modelling of Active-Layer Detachments, Sabine Peninsula, Melville Island, Nunavut

Modelling the susceptibility of permafrost slopes to disturbance can identify areas at risk to future disturbance and result in safer infrastructure and resource development in the Arctic. In this study, we use terrain attributes derived from a digital elevation model, an inventory of permafrost slope disturbances known as active-layer detachments (ALDs) and generalised additive modelling to produce a map of permafrost slope disturbance susceptibility for an area on northern Melville Island, in the Canadian High Arctic. By examining terrain variables and their relative importance, we identified factors important for initiating slope disturbance. The model was calibrated and validated using 70 and 30 per cent of a data-set of 760 mapped ALDs, including disturbed and randomised undisturbed samples. The generalised additive model calibrated and validated very well, with areas under the receiver operating characteristic curve of 0.89 and 0.81, respectively, demonstrating its effectiveness at predicting disturbed and undisturbed samples. ALDs were most likely to occur below the marine limit on slope angles between 3 and 10° and in areas with low values of potential incoming solar radiation (north-facing slopes).

Evidence of cardiac injury and arrhythmias in dogs with acute kidney injury

OBJECTIVES Cardiac involvement in the course of acute kidney injury is described in humans as cardiorenal syndrome type 3 but has received only limited attention in dogs. This study was designed to evaluate cardiac injury and dysfunction in acute kidney injury in dogs and its association with outcome. METHODS This prospective cohort study enrolled 24 client-owned dogs with acute kidney injury. Cardiac disorders were evaluated with thoracic radiographs, echocardiography, 24-hour Holter monitoring and cardiac troponin I concentrations within 2 days of admission and 7 to 10 days later. RESULTS Most dogs were diagnosed with leptospirosis (n=18, 75%) and presented with moderate-to-severe acute kidney injury, International Renal Interest Society grades III to V. Dogs with ê100 ventricular premature complexes per 24 hour in the first examination (n=8) had significantly higher initial cTnI concentrations (P=0·007) compared to dogs with fewer than 100. In receiver operating characteristic curve analysis, the number of ventricular premature complexes was predictive of outcome (AUC 0·83, P<0·001). CLINICAL SIGNIFICANCE Acute kidney injury seems to be associated with cardiac injury and arrhythmias in dogs. The data do not indicate a cardiac cause of poor outcome in dogs with increased number of ventricular premature complexes but the association may reflect the severity of disease.

Myocardial blood volume and perfusion reserve responses to combined dipyridamole and exercise stress: A quantitative approach to contrast stress echocardiography

Background: Qualitative interpretation of myocardial contrast echocardiography (MCE) improves the accuracy of wall-motion analysis for assessment of coronary artery disease (CAD). We examined the feasibility and accuracy of quantitative MCE for diagnosis of CAD. Methods: Dipyridamole/exercise stress MCE (destruction-replenishment protocol with real-time imaging) was performed in 90 patients undergoing quantitative coronary angiography, 48 of whom had significant (> 50%) stenoses. MCE was repeated with exercise alone in 18 patients. Myocardial blood flow (A*beta) was obtained from blood volume (A) and time to refill (beta). Results: Quantification of flow reserve was feasible in 88%. The mean A*beta reserve in the anterior wall was significantly impaired for patients with left anterior descending coronary artery disease (n = 28) compared with those with no disease (1.6 +/- 1.2 vs; 4.0 +/- 2.5, P <=.001). This reflected impaired beta reserve, with no difference in the A reserve. Applying a receiver operating characteristic curve derived cutoff of 2.0 for A*beta reserve, quantitative MCE was 76% sensitive and 71% specific for the diagnosis of significant left anterior descending coronary artery stenosis. Posterior circulation results were similar, with 78% sensitivity and 59% specificity for detection of posterior CAD. Overall, quantitative MCE was similarly sensitive to qualitative approach for diagnosis of CAD (88% vs 93%), but with lower specificity (52% vs 65%, P =.07). In 18 patients restudied with pure exercise stress, the mean myocardial blood flow reserve was less than after combined stress (2.1 +/- 1.6 vs 3.7 +/- 1.9, P =.01). Conclusion: Quantitative MCE is feasible for the diagnosis of CAD with dipyridamole/exercise stress. Dipyridamole prolongs postexercise hyperemia, augmenting the degree of hyperemia at the time of imaging.