Assessing the improved discriminatory power of a new biomarker in prognostic models

Although the area under the receiver operating characteristic (AUC) is the most popular measure of the performance of prediction models, it has limitations, especially when it is used to evaluate the added discrimination of a new biomarker in the model. Pencina et al. (2008) proposed two indices, the net reclassification improvement (NRI) and integrated discrimination improvement (IDI), to supplement the improvement in the AUC (IAUC). Their NRI and IDI are based on binary outcomes in case-control settings, which do not involve time-to-event outcome. However, many disease outcomes are time-dependent and the onset time can be censored. Measuring discrimination potential of a prognostic marker without considering time to event can lead to biased estimates. In this dissertation, we have extended the NRI and IDI to survival analysis settings and derived the corresponding sample estimators and asymptotic tests. Simulation studies were conducted to compare the performance of the time-dependent NRI and IDI with Pencina’s NRI and IDI. For illustration, we have applied the proposed method to a breast cancer study.^ Key words: Prognostic model, Discrimination, Time-dependent NRI and IDI ^

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. ^

ACCURACY OF THE BRCAPRO RISK ASSESSMENT MODEL IN MALES PRESENTING TO MD ANDERSON FOR BRCA TESTING

ACCURACY OF THE BRCAPRO RISK ASSESSMENT MODEL IN MALES PRESENTING TO MD ANDERSON FOR BRCA TESTING Publication No. _______ Carolyn A. Garby, B.S. Supervisory Professor: Banu Arun, M.D. Hereditary Breast and Ovarian Cancer (HBOC) syndrome is due to mutations in BRCA1 and BRCA2 genes. Women with HBOC have high risks to develop breast and ovarian cancers. Males with HBOC are commonly overlooked because male breast cancer is rare and other male cancer risks such as prostate and pancreatic cancers are relatively low. BRCA genetic testing is indicated for men as it is currently estimated that 4-40% of male breast cancers result from a BRCA1 or BRCA2 mutation (Ottini, 2010) and management recommendations can be made based on genetic test results. Risk assessment models are available to provide the individualized likelihood to have a BRCA mutation. Only one study has been conducted to date to evaluate the accuracy of BRCAPro in males and was based on a cohort of Italian males and utilized an older version of BRCAPro. The objective of this study is to determine if BRCAPro5.1 is a valid risk assessment model for males who present to MD Anderson Cancer Center for BRCA genetic testing. BRCAPro has been previously validated for determining the probability of carrying a BRCA mutation, however has not been further examined particularly in males. The total cohort consisted of 152 males who had undergone BRCA genetic testing. The cohort was stratified by indication for genetic counseling. Indications included having a known familial BRCA mutation, having a personal diagnosis of a BRCA-related cancer, or having a family history suggestive of HBOC. Overall there were 22 (14.47%) BRCA1+ males and 25 (16.45%) BRCA2+ males. Receiver operating characteristic curves were constructed for the cohort overall, for each particular indication, as well as for each cancer subtype. Our findings revealed that the BRCAPro5.1 model had perfect discriminating ability at a threshold of 56.2 for males with breast cancer, however only 2 (4.35%) of 46 were found to have BRCA2 mutations. These results are significantly lower than the high approximation (40%) reported in previous literature. BRCAPro does perform well in certain situations for men. Future investigation of male breast cancer and men at risk for BRCA mutations is necessary to provide a more accurate risk assessment.

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.

External validation study of a clinical prediction rule for ambulation outcomes after traumatic spinal cord injury

The main objective of this study was to determine the external validity of a clinical prediction rule developed by the European Multicenter Study on Human Spinal Cord Injury (EM-SCI) to predict the ambulation outcomes 12 months after traumatic spinal cord injury. Data from the North American Clinical Trials Network (NACTN) data registry with approximately 500 SCI cases were used for this validity study. The predictive accuracy of the EM-SCI prognostic model was evaluated using calibration and discrimination based on 231 NACTN cases. The area under the receiver-operating-characteristics curve (ROC) curve was 0.927 (95% CI 0.894 – 0.959) for the EM-SCI model when applied to NACTN population. This is lower than the AUC of 0.956 (95% CI 0.936 – 0.976) reported for the EM-SCI population, but suggests that the EM-SCI clinical prediction rule distinguished well between those patients in the NACTN population who were able to achieve independent ambulation and those who did not achieve independent ambulation. The calibration curve suggests that higher the prediction score is, the better the probability of walking with the best prediction for AIS D patients. In conclusion, the EM-SCI clinical prediction rule was determined to be generalizable to the adult NACTN SCI population.^

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.

Screening Subclinical Keratoconus With Placido-Based Corneal Indices

Purpose. To assess in a sample of normal, keratoconic, and keratoconus (KC) suspect eyes the performance of a set of new topographic indices computed directly from the digitized images of the Placido rings. Methods. This comparative study was composed of a total of 124 eyes of 106 patients from the ophthalmic clinics Vissum Alicante and Vissum Almería (Spain) divided into three groups: control group (50 eyes), KC group (50 eyes), and KC suspect group (24 eyes). In all cases, a comprehensive examination was performed, including the corneal topography with a Placidobased CSO topography system. Clinical outcomes were compared among groups, along with the discriminating performance of the proposed irregularity indices. Results. Significant differences at level 0.05 were found on the values of the indices among groups by means of Mann-Whitney-Wilcoxon nonparametric test and Fisher exact test. Additional statistical methods, such as receiver operating characteristic analysis and K-fold cross validation, confirmed the capability of the indices to discriminate between the three groups. Conclusions. Direct analysis of the digitized images of the Placido mires projected on the cornea is a valid and effective tool for detection of corneal irregularities. Although based only on the data from the anterior surface of the cornea, the new indices performed well even when applied to the KC suspect eyes. They have the advantage of simplicity of calculation combined with high sensitivity in corneal irregularity detection and thus can be used as supplementary criteria for diagnosing and grading KC that can be added to the current keratometric classifications.

Transferability of Regional Permafrost Disturbance Susceptibility Modelling Using Generalized Linear and Generalized Additive Models

To effectively assess and mitigate risk of permafrost disturbance, disturbance-p rone areas can be predicted through the application of susceptibility models. In this study we developed regional susceptibility models for permafrost disturbances using a field disturbance inventory to test the transferability of the model to a broader region in the Canadian High Arctic. Resulting maps of susceptibility were then used to explore the effect of terrain variables on the occurrence of disturbances within this region. To account for a large range of landscape charac- teristics, the model was calibrated using two locations: Sabine Peninsula, Melville Island, NU, and Fosheim Pen- insula, Ellesmere Island, NU. Spatial patterns of disturbance were predicted with a generalized linear model (GLM) and generalized additive model (GAM), each calibrated using disturbed and randomized undisturbed lo- cations from both locations and GIS-derived terrain predictor variables including slope, potential incoming solar radiation, wetness index, topographic position index, elevation, and distance to water. Each model was validated for the Sabine and Fosheim Peninsulas using independent data sets while the transferability of the model to an independent site was assessed at Cape Bounty, Melville Island, NU. The regional GLM and GAM validated well for both calibration sites (Sabine and Fosheim) with the area under the receiver operating curves (AUROC) N 0.79. Both models were applied directly to Cape Bounty without calibration and validated equally with AUROC's of 0.76; however, each model predicted disturbed and undisturbed samples differently. Addition- ally, the sensitivity of the transferred model was assessed using data sets with different sample sizes. Results in- dicated that models based on larger sample sizes transferred more consistently and captured the variability within the terrain attributes in the respective study areas. Terrain attributes associated with the initiation of dis- turbances were similar regardless of the location. Disturbances commonly occurred on slopes between 4 and 15°, below Holocene marine limit, and in areas with low potential incoming solar radiation

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).

Correlation Between Sonographic and In Vivo Measurement of A1 Pulleys in Trigger Fingers

The thickness of 210 A1 pulleys of 21 male and female healthy volunteers in two different age groups (20-35 y and 50-70 y) were measured by ultrasound. In a second group, the thickness of 15 diseased A1 pulleys and 15 A1 pulleys of the corresponding other hand of 10 patients with the clinical diagnosis of trigger finger were measured by ultrasound. During open trigger finger release, a strip of A1 pulley was excised and immediately measured using an electronic caliper. The average pulley thickness of healthy volunteers was 0.43-0.47 mm, compared to 0.77-0.79 mm in patients with trigger finger. Based on the receiver operating characteristic (ROC) curve, a diagnostic cut-off value of the pulley thickness at 0.62 mm was defined in order to differ a trigger finger from a healthy finger (sensitivity and specificity of 85%). The correlation between sonographic and effective intra-operative measurements of pulley thickness was linear and very strong (Pearson coefficient 0.86-0.90). In order to distinguish between healthy and diseased A1 pulleys, 0.62 mm is a simple value to use, which can be applied regardless of age, sex, body mass index (BMI) and height in adults.

Application of phosphatidylethanol (PEth) in whole blood in comparison to ethyl glucuronide in hair (hEtG) in driving aptitude assessment (DAA)

For driving aptitude assessment (DAA), the analysis of several alcohol biomarkers is essential for the detection of alcohol intake besides psycho-medical exploration. In Switzerland, EtG in hair (hEtG) is often the only direct marker for abstinence monitoring in DAA. Therefore, the suitability of phosphatidylethanol (PEth) was investigated as additional biomarker. PEth 16:0/18:1 and 16:0/18:2 were determined by online-SPE-LC-MS/MS in 136 blood samples of persons undergoing DAA and compared to hEtG, determined in hair segments taken at the same time. With a PEth 16:0/18:1 threshold of 210 ng/mL for excessive alcohol consumption, all (n = 30) but one tested person also had hEtG values ≥30 pg/mg. In 54 cases, results are not in contradiction to an abstinence as neither PEth (<20 ng/mL) nor hEtG (<7 pg/mg) was detected. In eight cases, both markers showed moderate consumption. Altogether, PEth and hEtG were in accordance in 68 % of the samples, although covering different time periods of alcohol consumption. With receiver operating characteristic analysis, PEth was evaluated to differentiate abstinence, moderate, and excessive alcohol consumption in accordance with hEtG limits. A PEth 16:0/18:1 threshold of 150 ng/mL resulted in the best sensitivity (70.6 %) and specificity (98.8 %) for excessive consumption. Values between 20 and 150 ng/mL passed for moderate consumption, values <20 ng/mL passed for abstinence. As PEth mostly has a shorter detection window (2-4 weeks) than hEtG (up to 6 months depending on hair length), changes in drinking behavior can be detected earlier by PEth than by hEtG analysis alone. Therefore, PEth helps to improve the diagnostic information and is a valuable additional alcohol marker for DAA.

Clinical prediction of large vessel occlusion in anterior circulation stroke: mission impossible?

Simple clinical scores to predict large vessel occlusion (LVO) in acute ischemic stroke would be helpful to triage patients in the prehospital phase. We assessed the ability of various combinations of National Institutes of Health Stroke Scale (NIHSS) subitems and published stroke scales (i.e., RACE scale, 3I-SS, sNIHSS-8, sNIHSS-5, sNIHSS-1, mNIHSS, a-NIHSS items profiles A-E, CPSS1, CPSS2, and CPSSS) to predict LVO on CT or MR arteriography in 1085 consecutive patients (39.4 % women, mean age 67.7 years) with anterior circulation strokes within 6 h of symptom onset. 657 patients (61 %) had an occlusion of the internal carotid artery or the M1/M2 segment of the middle cerebral artery. Best cut-off value of the total NIHSS score to predict LVO was 7 (PPV 84.2 %, sensitivity 81.0 %, specificity 76.6 %, NPV 72.4 %, ACC 79.3 %). Receiver operating characteristic curves of various combinations of NIHSS subitems and published scores were equally or less predictive to show LVO than the total NIHSS score. At intersection of sensitivity and specificity curves in all scores, at least 1/5 of patients with LVO were missed. Best odds ratios for LVO among NIHSS subitems were best gaze (9.6, 95 %-CI 6.765-13.632), visual fields (7.0, 95 %-CI 3.981-12.370), motor arms (7.6, 95 %-CI 5.589-10.204), and aphasia/neglect (7.1, 95 %-CI 5.352-9.492). There is a significant correlation between clinical scores based on the NIHSS score and LVO on arteriography. However, if clinically relevant thresholds are applied to the scores, a sizable number of LVOs are missed. Therefore, clinical scores cannot replace vessel imaging.

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.

Is ultrasound perfusion imaging capable of detecting mismatch? A proof-of-concept study in acute stroke patients.

In this study, we compared contrast-enhanced ultrasound perfusion imaging with magnetic resonance perfusion-weighted imaging or perfusion computed tomography for detecting normo-, hypo-, and nonperfused brain areas in acute middle cerebral artery stroke. We performed high mechanical index contrast-enhanced ultrasound perfusion imaging in 30 patients. Time-to-peak intensity of 10 ischemic regions of interests was compared to four standardized nonischemic regions of interests of the same patient. A time-to-peak >3 s (ultrasound perfusion imaging) or >4 s (perfusion computed tomography and magnetic resonance perfusion) defined hypoperfusion. In 16 patients, 98 of 160 ultrasound perfusion imaging regions of interests of the ischemic hemisphere were classified as normal, and 52 as hypoperfused or nonperfused. Ten regions of interests were excluded due to artifacts. There was a significant correlation of the ultrasound perfusion imaging and magnetic resonance perfusion or perfusion computed tomography (Pearson`s chi-squared test 79.119, p < 0.001) (OR 0.1065, 95% CI 0.06-0.18). No perfusion in ultrasound perfusion imaging (18 regions of interests) correlated highly with diffusion restriction on magnetic resonance imaging (Pearson's chi-squared test 42.307, p < 0.001). Analysis of receiver operating characteristics proved a high sensitivity of ultrasound perfusion imaging in the diagnosis of hypoperfused area under the curve, (AUC = 0.917; p < 0.001) and nonperfused (AUC = 0.830; p < 0.001) tissue in comparison with perfusion computed tomography and magnetic resonance perfusion. We present a proof of concept in determining normo-, hypo-, and nonperfused tissue in acute stroke by advanced contrast-enhanced ultrasound perfusion imaging.

Mean arterial pressure and vasopressor load after out-of-hospital cardiac arrest: Associations with one-year neurologic outcome

THE AIM OF THE STUDY There are limited data on blood pressure targets and vasopressor use following cardiac arrest. We hypothesized that hypotension and high vasopressor load are associated with poor neurological outcome following out-of-hospital cardiac arrest (OHCA). METHODS We included 412 patients with OHCA included in FINNRESUSCI study conducted between 2010 and 2011. Hemodynamic data and vasopressor doses were collected electronically in one, two or five minute intervals. We evaluated thresholds for time-weighted (TW) mean arterial pressure (MAP) and outcome by receiver operating characteristic (ROC) curve analysis, and used multivariable analysis adjusting for co-morbidities, factors at resuscitation, an illness severity score, TW MAP and total vasopressor load (VL) to test associations with one-year neurologic outcome, dichotomized into either good (1-2) or poor (3-5) according to the cerebral performance category scale. RESULTS Of 412 patients, 169 patients had good and 243 patients had poor one-year outcomes. The lowest MAP during the first six hours was 58 (inter-quartile range [IQR] 56-61) mmHg in those with a poor outcome and 61 (59-63) mmHg in those with a good outcome (p<0.01), and lowest MAP was independently associated with poor outcome (OR 1.02 per mmHg, 95% CI 1.00-1.04, p=0.03). During the first 48h the median (IQR) of the TW mean MAP was 80 (78-82) mmHg in patients with poor, and 82 (81-83) mmHg in those with good outcomes (p=0.03) but in multivariable analysis TWA MAP was not associated with outcome. Vasopressor load did not predict one-year neurologic outcome. CONCLUSIONS Hypotension occurring during the first six hours after cardiac arrest is an independent predictor of poor one-year neurologic outcome. High vasopressor load was not associated with poor outcome and further randomized trials are needed to define optimal MAP targets in OHCA patients.