The diagnostic value of a 12-lead electrocardiogram in patients with cardiac tamponade due to a malignant pericardial effusion

Background. Cardiac tamponade can occur when a large amount of fluid, gas, singly or in combination, accumulating within the pericardium, compresses the heart causing circulatory compromise. Although previous investigators have found the 12-lead ECG to have a poor predictive value in diagnosing cardiac tamponade, very few studies have evaluated it as a follow up tool for ruling in or ruling out tamponade in patients with previously diagnosed malignant pericardial effusions. ^ Methods. 127 patients with malignant pericardial effusions at the MD Anderson Cancer Center were included in this retrospective study. While 83 of these patients had a cardiac tamponade diagnosed by echocardiographic criteria (Gold standard), 44 did not. We computed the sensitivity (Se), specificity (Sp), positive (PPV) and negative predictive values (NPV) for individual and combinations of ECG abnormalities. Individual ECG abnormalities were also entered singly into a univariate logistic regression model to predict tamponade. ^ Results. For patients with effusions of all sizes, electrical alternans had a Se, Sp, PPV and NPV of 22.61%, 97.61%, 95% and 39.25% respectively. These parameters for low voltage complexes were 55.95%, 74.44%, 81.03%, 46.37% respectively. The presence of all three ECG abnormalities had a Se = 8.33%, Sp = 100%, PPV = 100% and NPV = 35.83% while the presence of at least one of the three ECG abnormalities had a Se = 89.28%, Sp = 46.51%, PPV = 76.53%, NPV = 68.96%. For patients with effusions of all sizes electrical alternans had an OR of 12.28 (1.58–95.17, p = 0.016), while the presence of at least one ECG abnormality had an OR of 7.25 (2.9–18.1, p = 0.000) in predicting tamponade. ^ Conclusions. Although individual ECG abnormalities had low sensitivities, specificities, NPVs and PPVs with the exception of electrical alternans, the presence of at least one of the three ECG abnormalities had a high sensitivity in diagnosing cardiac tamponade. This could point to its potential use as a screening test with a correspondingly high NPV to rule out a diagnosis of tamponade in patients with malignant pericardial effusions. This could save expensive echocardiographic assessments in patients with previously diagnosed pericardial effusions. ^

Maximal exercise hemodynamics and risk of mortality in apparently healthy men and women

The use of exercise electrocardiography (ECG) to detect latent coronary heart disease (CHD) is discouraged in apparently healthy populations because of low sensitivity. These recommendations however, are based on the efficacy of evaluation of ischemia (ST segment changes) with little regard for other measures of cardiac function that are available during exertion. The purpose of this investigation was to determine the association of maximal exercise hemodynamic responses with risk of mortality due to all-causes, cardiovascular disease (CVD), and coronary heart disease (CHD) in apparently healthy individuals. Study participants were 20,387 men (mean age = 42.2 years) and 6,234 women (mean age = 41.9 years) patients of a preventive medicine center in Dallas, TX examined between 1971 and 1989. During an average of 8.1 years of follow-up, there were 348 deaths in men and 66 deaths in women. In men, age-adjusted all-cause death rates (per 10,000 person years) across quartiles of maximal systolic blood pressure (SBP) (low to high) were: 18.2, 16.2, 23.8, and 24.6 (p for trend $<$0.001). Corresponding rates for maximal heart rate were: 28.9, 15.9, 18.4, and 15.1 (p trend $<$0.001). After adjustment for confounding variables including age, resting systolic pressure, serum cholesterol and glucose, body mass index, smoking status, physical fitness and family history of CVD, risks (and 95% confidence interval (CI)) of all-cause mortality for quartiles of maximal SBP, relative to the lowest quartile, were: 0.96 (0.70-1.33), 1.36 (1.01-1.85), and 1.37 (0.98-1.92) for quartiles 2-4 respectively. Similar risks for maximal heart rate were: 0.61 (0.44-0.85), 0.69 (0.51-0.93), and 0.60 (0.41-0.87). No associations were noted between maximal exercise rate-pressure product mortality. Similar results were seen for risk of CVD and CHD death. In women, similar trends in age-adjusted all-cause and CVD death rates across maximal SBP and heart rate categories were observed. Sensitivity of the exercise test in predicting mortality was enhanced when ECG results were evaluated together with maximal exercise SBP or heart rate with a concomitant decrease in specificity. Positive predictive values were not improved. The efficacy of the exercise test in predicting mortality in apparently healthy men and women was not enhanced by using maximal exercise hemodynamic responses. These results suggest that an exaggerated systolic blood pressure or an attenuated heart rate response to maximal exercise are risk factors for mortality in apparently healthy individuals. ^

A STUDY TO INVESTIGATE DIET DIVERSITY AS A NUTRITION ASSESSMENT INSTRUMENT

Nutrient intake and specific food item data from 24-hour dietary recalls were utilized to study the relationship between measures of diet diversity and dietary adequacy in a population of white females of child-bearing age and socioeconomic subgroups of that population. As the basis of the diet diversity measures, twelve food groups were constructed from the 24-hour recall data and the number of unique foods per food group counted and weighted according to specified weighting schemes. Utilizing these food groups, nine diet diversity indices were developed.^ Sensitivity/specificity analysis was used to determine the ability of varying levels of selected diet diversity indices to identify individuals above and below preselected intakes of different nutrients. The true prevalence proportions, sensitivity and specificity, false positive and false negative rates, and positive predictive values observed at the selected levels of diet diversity indices were investigated in relation to the objectives and resources of a variety of nutrition improvement programs. Diet diversity indices constructed from the total population data were evaluated as screening tools for respondent nutrient intakes in each of the socioeconomic subgroups as well.^ The results of the sensitivity/specificity analysis demonstrated that the false positive rate, the false negative rate, or both were too high at each diversity cut-off level to validate the widespread use of any of the diversity indices in the dietary assessment of the study population. Although diet diversity has been shown to be highly correlated with the intakes of a number of nutrients, the diet diversity indices constructed in this study did not adequately represent nutrient intakes in the diet as reported, in this study, intakes as reported in the 24-hour dietary recall. Specific cut-off levels of selected diversity indices might have limited application in some nutrition programs. The results were applicable to the sensitivity/specificity analyses in the socioeconomic subgroups as well as in the total population. ^

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.

Inappropriate imaging utilization in Texas: Geographic variation and predictive factors

Objective::Describe and understand regional differences and associated multilevel factors (patient, provider and regional) to inappropriate utilization of advance imaging tests in the privately insured population of Texas. Methods: We analyzed Blue Cross Blue Shield of Texas claims dataset to study the advance imaging utilization during 2008-2010 in the PPO/PPO+ plans. We used three of CMS "Hospital Outpatient Quality Reporting" imaging efficiency measures. These included ordering MRI for low back pain without prior conservative management (OP-8) and utilization of combined with and without contrast abdominal CT (OP-10) and thorax CT (OP-11). Means and variation by hospital referral regions (HRR) in Texas were measured and a multilevel logistic regression for being a provider with high values for any the three OP measures was used in the analysis. We also analyzed OP-8 at the individual level. A multilevel logistic regression was used to identify predictive factors for having an inappropriate MRI for low back pain. Results: Mean OP-8 for Texas providers was 37.89%, OP-10 was 29.94% and OP-11 was 9.24%. Variation was higher for CT measure. And certain HRRs were consistently above the mean. Hospital providers had higher odds of high OP-8 values (OP-8: OR, 1.34; CI, 1.12-1.60) but had smaller odds of having high OP-10 and OP-11 values (OP-10: OR, 0.15; CI, 0.12-0.18; OP-11: OR, 0.43; CI, 0.34-0.53). Providers with the highest volume of imaging studies performed, were less likely to have high OP-8 measures (OP-8: OR, 0.58; CI, 0.48-0.70) but more likely to perform combined thoracic CT scans (OP-11: OR, 1.62; CI, 1.34-1.95). Males had higher odds of inappropriate MRI (OR, 1.21; CI, 1.16-1.26). Pattern of care in the six months prior to the MRI event was significantly associated with having an inappropriate MRI. Conclusion::We identified a significant variation in advance imaging utilization across Texas. Type of facility was associated with measure performance, but the associations differ according to the type of study. Last, certain individual characteristics such as gender, age and pattern of care were found to be predictors of inappropriate MRIs.^