Time series analysis as input for clinical predictive modeling: Modeling cardiac arrest in a pediatric ICU

BACKGROUND: Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. METHODS: We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. RESULTS: Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9) training models for various data subsets; and 10) measuring model performance characteristics in unseen data to estimate their external validity. CONCLUSIONS: We have proposed a ten step process that results in data sets that contain time series features and are suitable for predictive modeling by a number of methods. We illustrated the process through an example of cardiac arrest prediction in a pediatric intensive care setting

Translational Research from an Informatics Perspective

Clinical and translational research (CTR) is an essential part of a sustainable global health system. Informatics is now recognized as an important en-abler of CTR and informaticians are increasingly called upon to help CTR efforts. The US National Institutes of Health mandated biomedical informatics activity as part of its new national CTR grant initiative, the Clinical and Translational Science Award (CTSA). Traditionally, translational re-search was defined as the translation of laboratory discoveries to patient care (bench to bedside). We argue, however, that there are many other kinds of translational research. Indeed, translational re-search requires the translation of knowledge dis-covered in one domain to another domain and is therefore an information-based activity. In this panel, we will expand upon this view of translational research and present three different examples of translation to illustrate the point: 1) bench to bedside, 2) Earth to space and 3) academia to community. We will conclude with a discussion of our local translational research efforts that draw on each of the three examples

Towards a Hybrid Method to Categorize Interruptions and Activities in Healthcare

Objective Interruptions are known to have a negative impact on activity performance. Understanding how an interruption contributes to human error is limited because there is not a standard method for analyzing and classifying interruptions. Qualitative data are typically analyzed by either a deductive or an inductive method. Both methods have limitations. In this paper a hybrid method was developed that integrates deductive and inductive methods for the categorization of activities and interruptions recorded during an ethnographic study of physicians and registered nurses in a Level One Trauma Center. Understanding the effects of interruptions is important for designing and evaluating informatics tools in particular and for improving healthcare quality and patient safety in general. Method The hybrid method was developed using a deductive a priori classification framework with the provision of adding new categories discovered inductively in the data. The inductive process utilized line-by-line coding and constant comparison as stated in Grounded Theory. Results The categories of activities and interruptions were organized into a three-tiered hierarchy of activity. Validity and reliability of the categories were tested by categorizing a medical error case external to the study. No new categories of interruptions were identified during analysis of the medical error case. Conclusions Findings from this study provide evidence that the hybrid model of categorization is more complete than either a deductive or an inductive method alone. The hybrid method developed in this study provides the methodical support for understanding, analyzing, and managing interruptions and workflow

Plasma cholesterol levels and brain development in preterm newborns.

BackgroundTo assess whether postnatal plasma cholesterol levels are associated with microstructural and macrostructural regional brain development in preterm newborns.MethodsSixty preterm newborns (born 24-32 weeks gestational age) were assessed using MRI studies soon after birth and again at term-equivalent age. Blood samples were obtained within 7 days of each MRI scan to analyze for plasma cholesterol and lathosterol (a marker of endogenous cholesterol synthesis) levels. Outcomes were assessed at 3 years using the Bayley Scales of Infant Development, Third Edition.ResultsEarly plasma lathosterol levels were associated with increased axial and radial diffusivities and increased volume of the subcortical white matter. Early plasma cholesterol levels were associated with increased volume of the cerebellum. Early plasma lathosterol levels were associated with a 2-point decrease in motor scores at 3 years.ConclusionsHigher early endogenous cholesterol synthesis is associated with worse microstructural measures and larger volumes in the subcortical white matter that may signify regional edema and worse motor outcomes. Higher early cholesterol is associated with improved cerebellar volumes. Further work is needed to better understand how the balance of cholesterol supply and endogenous synthesis impacts preterm brain development, especially if these may be modifiable factors to improve outcomes

Inhibition of Progenitor Dendritic Cell Maturation by Plasma from Patients with Peripartum Cardiomyopathy: Role in Pregnancy-associated Heart Disease

Dendritic cells (DCs) play dual roles in innate and adaptive immunity based on their functional maturity, and both innate and adaptive immune responses have been implicated in myocardial tissue remodeling associated with cardiomyopathies. Peripartum cardiomyopathy (PPCM) is a rare disorder which affects women within one month antepartum to five months postpartum. A high occurrence of PPCM in central Haiti (1 in 300 live births) provided the unique opportunity to study the relationship of immune activation and DC maturation to the etiology of this disorder. Plasma samples from two groups (n = 12) of age- and parity-matched Haitian women with or without evidence of PPCM were tested for levels of biomarkers of cardiac tissue remodeling and immune activation. Significantly elevated levels of GM-CSF, endothelin-1, proBNP and CRP and decreased levels of TGF- were measured in PPCM subjects relative to controls. Yet despite these findings, in vitro maturation of normal human cord blood derived progenitor dendritic cells (CBDCs) was significantly reduced (p < 0.001) in the presence of plasma from PPCM patients relative to plasma from post-partum control subjects as determined by expression of CD80, CD86, CD83, CCR7, MHC class II and the ability of these matured CBDCs to induce allo-responses in PBMCs. These results represent the first findings linking inhibition of DC maturation to the dysregulation of normal physiologic cardiac tissue remodeling during pregnancy and the pathogenesis of PPCM

Understanding Uncertainties in Model-Based Predictions of Aedes aegypti Population Dynamics

Dengue is one of the most important insect-vectored human viral diseases. The principal vector is Aedes aegypti, a mosquito that lives in close association with humans. Currently, there is no effective vaccine available and the only means for limiting dengue outbreaks is vector control. To help design vector control strategies, spatial models of Ae. aegypti population dynamics have been developed. However, the usefulness of such models depends on the reliability of their predictions, which can be affected by different sources of uncertainty including uncertainty in the model parameter estimation, uncertainty in the model structure, measurement errors in the data fed into the model, individual variability, and stochasticity in the environment. This study quantifies uncertainties in the mosquito population dynamics predicted by Skeeter Buster, a spatial model of Ae. aegypti, for the city of Iquitos, Peru. The uncertainty quantification should enable us to better understand the reliability of model predictions, improve Skeeter Buster and other similar models by targeting those parameters with high uncertainty contributions for further empirical research, and thereby decrease uncertainty in model predictions

Genomic analysis of diet composition finds novel loci and associations with health and lifestyle

We conducted genome-wide association studies (GWAS) of relative intake from the macronutrients fat, protein, carbohydrates, and sugar in over 235,000 individuals of European ancestries. We identified 21 unique, approximately independent lead SNPs. Fourteen lead SNPs are uniquely associated with one macronutrient at genome-wide significance (P < 5 × 10−8), while five of the 21 lead SNPs reach suggestive significance (P < 1 × 10−5) for at least one other macronutrient. While the phenotypes are genetically correlated, each phenotype carries a partially unique genetic architecture. Relative protein intake exhibits the strongest relationships with poor health, including positive genetic associations with obesity, type 2 diabetes, and heart disease (rg ≈ 0.15–0.5). In contrast, relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood deprivation (|rg| ≈ 0.1–0.3) and positive genetic correlations with physical activity (rg ≈ 0.1 and 0.2). Relative fat intake has no consistent pattern of genetic correlations with poor health but has a negative genetic correlation with educational attainment (rg ≈−0.1). Although our analyses do not allow us to draw causal conclusions, we find no evidence of negative health consequences associated with relative carbohydrate, sugar, or fat intake. However, our results are consistent with the hypothesis that relative protein intake plays a role in the etiology of metabolic dysfunction

Waist circumference, abdominal obesity, and depression among overweight and obese U.S. adults: national health and nutrition examination survey 2005-2006

<p>Abstract</p> <p>Background</p> <p>Obesity is associated with an increased risk of mental illness; however, evidence linking body mass index (BMI)-a measure of overall obesity, to mental illness is inconsistent. The objective of this study was to examine the association of depressive symptoms with waist circumference or abdominal obesity among overweight and obese U.S. adults.</p> <p>Methods</p> <p>A cross-sectional, nationally representative sample from the 2005-2006 National Health and Nutrition Examination Survey was used. We analyzed the data from 2,439 U.S. adults (1,325 men and 1,114 nonpregnant women) aged ≥ 20 years who were either overweight or obese with BMI of ≥ 25.0 kg/m². Abdominal obesity was defined as waist circumference of > 102 cm for men and > 88 cm for women. Depressive symptoms (defined as having major depressive symptoms or moderate-to-severe depressive symptoms) were assessed by the Patient Health Questionnaire-9 diagnostic algorithm. The prevalence and the odds ratios (ORs) with 95% confidence intervals (CIs) for having major depressive symptoms and moderate-to-severe depressive symptoms were estimated using logistic regression analysis.</p> <p>Results</p> <p>After multivariate adjustment for demographics and lifestyle factors, waist circumference was significantly associated with both major depressive symptoms (OR: 1.03, 95% CI: 1.01-1.05) and moderate-to-severe depressive symptoms (OR: 1.02, 95% CI: 1.01-1.04), and adults with abdominal obesity were significantly more likely to have major depressive symptoms (OR: 2.18, 95% CI: 1.35-3.59) or have moderate-to-severe depressive symptoms (OR: 2.56, 95% CI: 1.34-4.90) than those without. These relationships persisted after further adjusting for coexistence of multiple chronic conditions and persisted in participants who were overweight (BMI: 25.0-< 30.0 kg/m²) when stratified analyses were conducted by BMI status.</p> <p>Conclusion</p> <p>Among overweight and obese U.S. adults, waist circumference or abdominal obesity was significantly associated with increased likelihoods of having major depressive symptoms or moderate-to-severe depressive symptoms. Thus, mental health status should be monitored and evaluated in adults with abdominal obesity, particularly in those who are overweight.</p