A conditional Mdm2 allele shows the importance of Mdm2 in heart development

Over 50% of sporadic tumors in humans have a p53 mutation highlighting its importance as a tumor suppressor. Considering additional mutations in other genes involved in p53 pathways, every tumor probably has mutant p53 or impaired p53-mediated functions. In response to a variety of cellular and genotoxic stresses, p53, mainly through its transcriptional activity, induces pathways involved in apoptosis and growth arrest. In these circumstances and under normal situations, p53 must be tightly regulated. Mdm2 is an important regulator of p53. Mdm2 inhibits p53 function by binding and blocking its transactivation domain. In addition, Mdm2 helps target p53 for degradation through its E3 ligase activity. Mdm2 null mice are embryonic lethal due to apoptosis in the blastocysts. However, a p53 null background rescues this lethality demonstrating the importance of the p53-Mdm2 interaction, particularly during development. The lethality of the Mdm2 null mouse prior to implantation limits the ability to investigate the role of Mdm2 in regulating p53 in a temporal and tissue specific manner. Does p53 need to be regulated in all tissues throughout the life of a mouse? Does Mdm2 always have to regulate it? To address these questions, we created a conditional Mdm2 allele. The conditional allele, Mdm2FM, in the presence of Cre recombinase results in the deletion of exons 5 and 6 of Mdm2 (most of the p53 binding domain) and represents a null allele. ^ The Mdm2FM allele was crossed with a heart muscle specific Cre expressing mouse (α-myosin heavy chain promoter driven Cre) to ask whether Mdm2 acts as a negative regulator of p53 in the heart. The heart is the most prominent organ early in embryogenesis and is shaped by cell death and proliferation. p53 does not appear to be active in the heart in response to some types of stress, so it remained to be determined if it has to be regulated in normal heart development. Loss of Mdm2 in the heart results in heart defects as early as E9.5. Loss of Mdm2 results in stabilized p53 and apoptosis. This apoptosis leads to a thinning of the myocardial wall particularly in the ventricles and abnormal ventricular structure. Eventually the abnormal heart fails resulting in lethality by E13.5. The embryonic lethality is rescued in a p53 null background. Thus, Mdm2 is important in regulating p53 in the development of the heart. ^

Exploring race/ethnicity as a risk factor for mortality in newborns following congenital heart surgery at a center of excellence

The purpose of this study was to determine if race/ethnicity was a significant risk factor for hospital mortality in children following congenital heart surgery in a contemporary sample of newborns with congenital heart disease. Unlike previous studies that utilized administrative databases, this study utilized clinical data collected at the point of care to examine racial/ethnic outcome differences in the context of the patients' clinical condition and their overall perioperative experience. A retrospective cohort design was used. The study sample consisted of 316 newborns (<31 days of age) who underwent congenital heart surgery between January 2007 through December 2009. A multivariate logistic regression model was used to determine the impact of race/ethnicity, insurance status, presence of a spatial anomaly, prenatal diagnosis, postoperative sepsis, cardiac arrest, respiratory failure, unplanned reoperation, and total length of stay in the intensive care unit on outcomes following congenital heart surgery in newborns. The study findings showed that the strongest predictors of hospital mortality following congenital heart surgery in this cohort were postoperative cardiac arrest, postoperative respiratory failure, having a spatial anomaly, and total ICU LOS. Race/ethnicity and insurance status were not significant risk factors. The institution where this study was conducted is designated as a center of excellence for congenital heart disease. These centers have state-of-the-art facilities, extensive experience in caring for children with congenital heart disease, and superior outcomes. This study suggests that optimal care delivery for newborns requiring congenital heart surgery at a center of excellence portends exceptional outcomes and this benefit is conferred upon the entire patient population despite the race/ethnicity of the patients. From a public health and health services view, this study also contributes to the overall body of knowledge on racial/ethnic disparities in children with congenital heart defects and puts forward the possibility of a relationship between quality of care and racial/ethnic disparities. Further study is required to examine the impact of race/ethnicity on the long-term outcomes of these children as they encounter the disparate components of the health care delivery system. There is also opportunity to study the role of race/ethnicity on the hospital morbidity in these patients considering current expectations for hospital survival are very high, and much of the current focus for quality improvement rests in minimizing the development of patient morbidities.^

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.