Substitutions due to antiretroviral toxicity or contraindication in the first 3 years of antiretroviral therapy in a large South African cohort

INTRODUCTION: The patterns and reasons for antiretroviral therapy (ART) drug substitutions are poorly described in resource-limited settings. METHODS: Time to and reason for drug substitution were recorded in treatment-naive adults receiving ART in two primary care treatment programmes in Cape Town. The cumulative proportion of patients having therapy changed because of toxicity was described for each drug, and associations with these changes were explored in multivariate models. RESULTS: Analysis included 2,679 individuals followed for a median of 11 months. Median CD4+ T-cell count at baseline was 85 cells/microl. Mean weight was 59 kg, mean age was 32 years and 71% were women. All started non-nucleoside reverse transcriptase inhibitor-based ART (60% on efavrienz) and 75% started on stavudine (d4T). After 3 years, 75% remained in care on-site, of whom 72% remained on their initial regimen. Substitutions due to toxicity of nevirapine (8% by 3 years), efavirenz (2%) and zidovudine (8%) occurred early. Substitutions on d4T occurred in 21% of patients by 3 years, due to symptomatic hyperlactataemia (5%), lipodystrophy (9%) or peripheral neuropathy (6%), and continued to accumulate over time. Those at greatest risk of hyperlactataemia or lipodystrophy were women on ART > or =6 months, weighing > or =75 kg at baseline. DISCUSSION: A high proportion of adult patients are able to tolerate their initial ART regimen for up to 3 years. In most instances treatment-limiting toxicities occur early, but continue to accumulate over time in patients on d4T. Whilst awaiting other treatment options, the risks of known toxicities could be minimized through early identification of patients at the highest risk.

Incidence and outcome of progressive multifocal leukoencephalopathy over 20 years of the Swiss HIV Cohort Study

BACKGROUND: We investigated the incidence and outcome of progressive multifocal leukoencephalopathy (PML) in human immunodeficiency virus (HIV)-infected individuals before and after the introduction of combination antiretroviral therapy (cART) in 1996. METHODS: From 1988 through 2007, 226 cases of PML were reported to the Swiss HIV Cohort Study. By chart review, we confirmed 186 cases and recorded all-cause and PML-attributable mortality. For the survival analysis, 25 patients with postmortem diagnosis and 2 without CD4+ T cell counts were excluded, leaving a total of 159 patients (89 before 1996 and 70 during 1996-2007). RESULTS: The incidence rate of PML decreased from 0.24 cases per 100 patient-years (PY; 95% confidence interval [CI], 0.20-0.29 cases per 100 PY) before 1996 to 0.06 cases per 100 PY (95% CI, 0.04-0.10 cases per 100 PY) from 1996 onward. Patients who received a diagnosis before 1996 had a higher frequency of prior acquired immunodeficiency syndrome-defining conditions (P = .007) but similar CD4+ T cell counts (60 vs. 71 cells/microL; P = .25), compared with patients who received a diagnosis during 1996 or thereafter. The median time to PML-attributable death was 71 days (interquartile range, 44-140 days), compared with 90 days (interquartile range, 54-313 days) for all-cause mortality. The PML-attributable 1-year mortality rate decreased from 82.3 cases per 100 PY (95% CI, 58.8-115.1 cases per 100 PY) during the pre-cART era to 37.6 cases per 100 PY (95% CI, 23.4.-60.5 cases per 100 PY) during the cART era. In multivariate models, cART was the only factor associated with lower PML-attributable mortality (hazard ratio, 0.18; 95% CI, 0.07-0.50; P < .001), whereas all-cause mortality was associated with baseline CD4+ T cell count (hazard ratio per increase of 100 cells/microL, 0.52; 95% CI, 0.32-0.85; P = .010) and cART use (hazard ratio, 0.37; 95% CI, 0.19-0.75; P = .006). CONCLUSIONS: cART reduced the incidence and PML-attributable 1-year mortality, regardless of baseline CD4+ T cell count, whereas overall mortality was dependent on cART use and baseline CD4+ T cell count.

Effects of buffering properties and undissociated acid concentration on dissolution of dental enamel in relation to pH and acid type

To quantify the relationships between buffering properties and acid erosion and hence improve models of erosive potential of acidic drinks, a pH-stat was used to measure the rate of enamel dissolution in solutions of citric, malic and lactic acids, with pH 2.4-3.6 and with acid concentrations adjusted to give buffer capacities (β) of 2-40 (mmol·l(-1))·pH(-1) for each pH. The corresponding undissociated acid concentrations, [HA], and titratable acidity to pH 5.5 (TA5.5) were calculated. In relation to β, the dissolution rate and the strength of response to β varied with acid type (lactic > malic ≥ citric) and decreased as pH increased. The patterns of variation of the dissolution rate with TA5.5 were qualitatively similar to those for β, except that increasing pH above 2.8 had less effect on dissolution in citric and malic acids and none on dissolution in lactic acid. Variations of the dissolution rate with [HA] showed no systematic dependence on acid type but some dependence on pH. The results suggest that [HA], rather than buffering per se, is a major rate-controlling factor, probably owing to the importance of undissociated acid as a readily diffusible source of H(+) ions in maintaining near-surface dissolution within the softened layer of enamel. TA5.5 was more closely correlated with [HA] than was β, and seems to be the preferred practical measure of buffering. The relationship between [HA] and TA5.5 differs between mono- and polybasic acids, so a separate analysis of products according to predominant acid type could improve multivariate models of erosive potential.

Cichlid species-area relationships are shaped by adaptive radiations that scale with area

A positive relationship between species richness and island size is thought to emerge from an equilibrium between immigration and extinction rates, but the influence of species diversification on the form of this relationship is poorly understood. Here, we show that within-lake adaptive radiation strongly modifies the species-area relationship for African cichlid fishes. The total number of species derived from in situ speciation increases with lake size, resulting in faunas orders of magnitude higher in species richness than faunas assembled by immigration alone. Multivariate models provide evidence for added influence of lake depth on the species-area relationship. Diversity of clades representing within-lake radiations show responses to lake area, depth and energy consistent with limitation by these factors, suggesting that ecological factors influence the species richness of radiating clades within these ecosystems. Together, these processes produce lake fish faunas with highly variable composition, but with diversities that are well predicted by environmental variables.

Social relationship correlates of major depressive disorder and depressive symptoms in Switzerland: nationally representative cross sectional study

BACKGROUND The quality and quantity of social relationships are associated with depression but there is less evidence regarding which aspects of social relationship are most predictive. We evaluated the relative magnitude and independence of the association of four social relationship domains with major depressive disorder and depressive symptoms. METHODS We analyzed a cross-sectional telephone interview and postal survey of a probability sample of adults living in Switzerland (N = 12,286). Twelve-month major depressive disorder was assessed via structured interview over the telephone using the Composite International Diagnostic Interview (CIDI). The postal survey assessed depressive symptoms as well as variables representing emotional support, tangible support, social integration, and loneliness. RESULTS Each individual social relationship domain was associated with both outcome measures, but in multivariate models being lonely and perceiving unmet emotional support had the largest and most consistent associations across depression outcomes (incidence rate ratios ranging from 1.55-9.97 for loneliness and from 1.23-1.40 for unmet support, p's < 0.05). All social relationship domains except marital status were independently associated with depressive symptoms whereas only loneliness and unmet support were associated with depressive disorder. CONCLUSIONS Perceived quality and frequency of social relationships are associated with clinical depression and depressive symptoms across a wide adult age spectrum. This study extends prior work linking loneliness to depression by showing that a broad range of social relationship domains are associated with psychological well-being.

Identification of 2-Piperidone as a Biomarker of CYP2E1 Activity Through Metabolomic Phenotyping

Cytochrome P450 2E1 (CYP2E1) is a key enzyme in the metabolic activation of many low molecular weight toxicants and also an important contributor to oxidative stress. A noninvasive method to monitor CYP2E1 activity in vivo would be of great value for studying the role of CYP2E1 in chemical-induced toxicities and stress-related diseases. In this study, a mass spectrometry-based metabolomic approach was used to identify a metabolite biomarker of CYP2E1 through comparing the urine metabolomes of wild-type (WT), Cyp2e1-null, and CYP2E1-humanized mice. Metabolomic analysis with multivariate models of urine metabolites revealed a clear separation of Cyp2e1-null mice from WT and CYP2E1-humanized mice in the multivariate models of urine metabolomes. Subsequently, 2-piperidone was identified as a urinary metabolite that inversely correlated to the CYP2E1 activity in the three mouse lines. Backcrossing of WT and Cyp2e1-null mice, together with targeted analysis of 2-piperidone in mouse serum, confirmed the genotype dependency of 2-piperidone. The accumulation of 2-piperidone in the Cyp2e1-null mice was mainly caused by the changes in the biosynthesis and degradation of 2-piperidone because compared with the WT mice, the conversion of cadaverine to 2-piperidone was higher, whereas the metabolism of 2-piperidone to 6-hydroxy-2-piperidone was lower in the Cyp2e1-null mice. Overall, untargeted metabolomic analysis identified a correlation between 2-piperidone concentrations in urine and the expression and activity of CYP2E1, thus providing a noninvasive metabolite biomarker that can be potentially used in to monitor CYP2E1 activity.

Prognostic factors and outcomes in primary urethral cancer: results from the international collaboration on primary urethral carcinoma.

PURPOSE To evaluate risk factors for survival in a large international cohort of patients with primary urethral cancer (PUC). METHODS A series of 154 patients (109 men, 45 women) were diagnosed with PUC in ten referral centers between 1993 and 2012. Kaplan-Meier analysis with log-rank test was used to investigate various potential prognostic factors for recurrence-free (RFS) and overall survival (OS). Multivariate models were constructed to evaluate independent risk factors for recurrence and death. RESULTS Median age at definitive treatment was 66 years (IQR 58-76). Histology was urothelial carcinoma in 72 (47 %), squamous cell carcinoma in 46 (30 %), adenocarcinoma in 17 (11 %), and mixed and other histology in 11 (7 %) and nine (6 %), respectively. A high degree of concordance between clinical and pathologic nodal staging (cN+/cN0 vs. pN+/pN0; p < 0.001) was noted. For clinical nodal staging, the corresponding sensitivity, specificity, and overall accuracy for predicting pathologic nodal stage were 92.8, 92.3, and 92.4 %, respectively. In multivariable Cox-regression analysis for patients staged cM0 at initial diagnosis, RFS was significantly associated with clinical nodal stage (p < 0.001), tumor location (p < 0.001), and age (p = 0.001), whereas clinical nodal stage was the only independent predictor for OS (p = 0.026). CONCLUSIONS These data suggest that clinical nodal stage is a critical parameter for outcomes in PUC.

Association of Trait Resilience With Peritraumatic and Posttraumatic Stress in Patients With Myocardial Infarction.

OBJECTIVE Acute myocardial infarction (MI) is a life-threatening condition, leading to immediate fear and distress in many patients. Approximately 18% of patients develop posttraumatic stress disorder in the aftermath of MI. Trait resilience has shown to be a protective factor for the development of posttraumatic stress disorder. However, whether this buffering effect has already an impact on peritraumatic distress and applies to patients with MI is elusive. METHODS We investigated 98 consecutive patients with acute MI within 48 hours after having reached stable circulatory conditions and 3 months thereafter. Peritraumatic distress was assessed retrospectively with three single-item questions about pain, fear, and helplessness during MI. All patients completed the Posttraumatic Diagnostic Scale (PDS) and the Resilience Scale to self-rate posttraumatic stress and trait resilience. RESULTS Multivariate models adjusting for sociodemographic and medical factors showed that trait resilience was not associated with peritraumatic distress, but significantly so with posttraumatic stress. Patients with greater trait resilience showed lower PDS scores (b = -0.06, p < .001). There was no significant relationship between peritraumatic distress scores and PDS scores; resilience did not emerge as a moderator of this relationship. CONCLUSIONS The findings suggest that trait resilience does not buffer the perception of acute MI as stressful per se but may enhance better coping with the traumatic experience in the longer term, thus preventing the development of MI-associated posttraumatic stress. Trait resilience may play an important role in posttraumatic stress symptoms triggered by medical diseases such as acute MI.

Is the sedimentation sign associated with spinal stenosis surgical treatment effect in SPORT?

STUDY DESIGN Subgroup analysis of the lumbar spinal stenosis (LSS) without degenerative spondylolisthesis diagnostic cohort of the Spine Patient Outcomes Research Trial multicenter randomized clinical trial with a concurrent observational cohort. OBJECTIVE To determine if sedimentation sign on magnetic resonance image can help with LSS treatment decisions. SUMMARY OF BACKGROUND DATA LSS is one of the most common reasons for surgery in the US elderly, but there is a dearth of reliable diagnostic tools that give a clear indication for surgery. Recent studies have suggested that positive sedimentation sign on magnetic resonance image may be a possible prognostic indicator. METHODS All patients with LSS in both the randomized and observational cohorts had imaging-confirmed stenosis, were surgical candidates, and had neurogenic claudication for at least 12 weeks prior to enrollment. Patients were categorized as "mild," "moderate," or "severe" according to stenosis severity. Of the 654 patients with LSS enrolled in Spine Patient Outcomes Research Trial, complete T2-weighted axial and sagittal digitized images of 115 patients were available for retrospective review. An independent orthopedic spine surgeon evaluated these deidentified Digital Imaging and Communications in Medicine files for the sedimentation sign. RESULTS Sixty-six percent (76/115) of patients were found to have a positive sedimentation sign. Those with a positive sedimentation sign were more likely to have stenosis at L2-L3 (33% vs. 10% P=0.016) or L3-L4 76% vs. 51%, P=0.012), and to have severe (72% vs. 33%, P<0.0001) central stenosis (93% vs. 67% P<0.001) at 2 or more concurrent levels (57% vs. 18%, P=0.01). In multivariate models, the surgical treatment effect was significantly larger in the positive sedimentation sign group for Oswestry Disability Index (-16 vs. -7; P=0.02). CONCLUSION A positive sedimentation sign was associated with a small but significantly greater surgical treatment effect for Oswestry Disability Index in patients with symptomatic LSS, after adjusting for other demographic and imaging features. These findings suggest that positive sedimentation sign may potentially be a useful adjunct to help guide an informed treatment choice regarding surgery for LSS. LEVEL OF EVIDENCE 2.

Influence of previous surgery on patient-rated outcome after surgery for degenerative disorders of the lumbar spine.

PURPOSE Few studies have used multivariate models to quantify the effect of multiple previous spine surgeries on patient-oriented outcome after spine surgery. This study sought to quantify the effect of prior spine surgery on 12-month postoperative outcomes in patients undergoing surgery for different degenerative disorders of the lumbar spine. METHODS The study included 4940 patients with lumbar degenerative disease documented in the Spine Tango Registry of EUROSPINE, the Spine Society of Europe, from 2004 to 2015. Preoperatively and 12 months postoperatively, patients completed the multidimensional Core Outcome Measures Index (COMI; 0-10 scale). Patients' medical history and surgical details were recorded using the Spine Tango Surgery 2006 and 2011 forms. Multiple linear regression models were used to investigate the relationship between the number of previous surgeries and the 12-month postoperative COMI score, controlling for the baseline COMI score and other potential confounders. RESULTS In the adjusted model including all cases, the 12-month COMI score showed a 0.37-point worse value [95 % confidence intervals (95 % CI) 0.29-0.45; p < 0.001] for each additional prior spine surgery. In the subgroup of patients with lumbar disc herniation, the corresponding effect was 0.52 points (95 % CI 0.27-0.77; p < 0.001) and in lumbar degenerative spondylolisthesis, 0.40 points (95 % CI 0.17-0.64; p = 0.001). CONCLUSIONS We were able to demonstrate a clear "dose-response" effect for previous surgery: the greater the number of prior spine surgeries, the systematically worse the outcome at 12 months' follow-up. The results of this study can be used when considering or consenting a patient for further surgery, to better inform the patient of the likely outcome and to set realistic expectations.

Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^

Depressive symptoms and sexual risk behaviors in adolescents

The purpose of this study was to determine whether depression is a factor in explaining the difference in sex behaviors among adolescents with different ethnic backgrounds, family and school contexts. We hypothesize that adolescents with a higher number of depressive symptoms are more likely to engage in sexual risk behaviors than adolescents with fewer depressive symptoms. Further, adolescent depression and sexual behaviors are mediated or moderated by individual characteristics, family and school contexts. ^ Background. large ethnic disparities exist in adolescent engagement in risky sexual behaviors, yet, there is little in the literature that explains these disparities. Studies of sexual behavior of youths abound; yet, there is little literature on the prevalence and correlates of depression or the association between depression and sexual behaviors among different ethnic groups. Objectives. (1) To determine ethnic differences in the prevalence of depressive symptoms using data collected through the National Longitudinal Study of Adolescent Health (Add Health). (2) To determine predictors of sex risk behaviors among adolescents, including the role of depression. (3) To identify predictors of depression among these adolescents. Methods. Add Health data from wave 1 and wave 2 interviews of 7th–12th graders were analyzed using multivariate models constructed with both depression and sexual behavior as outcome variables. Logistic regression models determined whether and to what extent the independent variables, including depression, sex behaviors, demographic factors, individual and family characteristics, and school context were related to the probability of engaging in risky sexual behaviors. Results. Ethnic differences in depressive symptoms did not persist after demographic and contextual variables were included in the model. Sex behaviors all shared the hypothesized relationship with depressive symptoms. The odds of risky sex behaviors increased as number of depressive symptoms increased. Depression was predicted by marijuana use and having a serious argument with father for males at Wave 1 and by age and future orientation for females. Wave 2 depression was predicted by Wave 1 depression. ^

The effects of binge drinking on military readiness: The inability to deploy

The negative outcomes from alcohol misuse have been chronicled for decades in epidemiological studies. Recent research has focused on patterns of drinking. Binge and heavy drinking have been associated with multiple negative outcomes, to include surrogate outcomes designed to measure decrements to military readiness. This study is perhaps the first to examine whether binge or heavy drinking patterns are associated with the U.S. military’s overall inability to deploy rate or the individual reasons unable to deploy. ^ The prevalence of binge and heavy drinking and the inability to deploy rates were assessed from responses to the 2005 Department of Defense Survey of Health Related Behaviors Among Military Personnel. A secondary analysis of extant data resulted in a final sample size of 13,619 respondents who represented 847,253 active-duty military personnel. Multivariate models were fitted to examine the association between patterns of drinking and individual reasons for the inability to deploy. ^ Logistic regression showed no association of binge or heavy drinking to greater inability to deploy. Interestingly, individual reasons for the inability to deploy did show an association to include: Training, Dental Issue, No HIV Test, and Family Situation. There was no association noted for the individual reasons: Injury, Illness, Leave/Temporary Duty, or Other. Binge and heavy drinkers appear to be more susceptible to the psychosocial determinants than physical determinants as reasons for the inability to deploy. ^

The performance of interferon-gamma release assays in tuberculosis patients with type II diabetes

The sensitivity of Interferon-γ release assays for detection of Mycobacterium tuberculosis (MTB) infection or disease is affected by conditions that depress host immunity (such as HIV). It is critical to determine whether these assays are affected by diabetes and related conditions (i.e. hyperglycemia, chronic hyperglycemia, or being overweight/obese) given that immune impairment is thought to underline susceptibility to tuberculosis (TB) in people with diabetes. This is important for tuberculosis control due to the millions of type 2 diabetes patients at risk for tuberculosis worldwide.^ The objective of this study was to identify host characteristics, including diabetes, that may affect the sensitivity of two commercially available Interferon-γ (IFN-γ) release assays (IGRA), the QuantiFERON®-TB Gold (QFT-G) and the T-SPOT®.TB in active TB patients. We further explored whether IFN-γ secretion in response to MTB antigens (ESAT-6 and CFP-10) is associated with diabetes and its defining characteristics (high blood glucose, high HbA1c, high BMI). To achieve these objectives, the sensitivity of QFT-G and T-SPOT. TB assays were evaluated in newly diagnosed, tuberculosis confirmed (by positive smear for acid fast bacilli and/or positive culture for MTB) adults enrolled at Texas and Mexico study sites between March 2006 and April 2009. Univariate and multivariate models were constructed to identify host characteristics associated with IGRA result and level of IFN-γ secretion.^ QFT-G was positive in 68% of tuberculosis patients. Those with diabetes, chronic hyperglycemia or obesity were more likely to have a positive QFT-G result, and to secrete higher levels of IFN-γ in response to the mycobacterial antigens (p<0.05). Previous history of BCG vaccination was the only other host characteristic associated with QFT-G result, whereby a higher proportion of non-BCG vaccinated persons were QFT-G positive, in comparison to vaccinated persons. In a separate group of patients, the T-SPOT.TB was 94% sensitive, with similar performance in all tuberculosis patients, regardless of host characteristics.^ In summary, we have demonstrated the validity of QFT-G and T-SPOT. TB to support the diagnosis of TB in patients with a range of host characteristics, but most notably in patients with diabetes. We also confirmed that TB patients with diabetes and associated characteristics (chronic hyperglycemia or BMI) secreted higher titers of IFN-γ when stimulated with MTB specific antigens, in comparison to patients without these characteristics. Together, these findings suggest that the mechanism by which diabetes increases risk to TB may not be explained by the inability to secrete IFN-γ, a key cytokine for TB control.^

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.