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This paper provides guidance for researchers with some mathematical background on the conduct of time-to-event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like time axis, event definition and censoring are given. Hazard models are introduced, with special emphasis on the Cox proportional hazards regression model. We provide check lists that may be useful both when fitting the model and assessing its goodness of fit and when interpreting the results. Special attention is paid to how to avoid problems with immortal time bias by introducing time-dependent covariates. We discuss prediction based on hazard models and difficulties when attempting to draw proper causal conclusions from such models. Finally, we present a series of examples where the methods and check lists are exemplified. Computational details and implementation using the freely available R software are documented in Supplementary Material. The paper was prepared as part of the STRATOS initiative. [ABSTRACT FROM AUTHOR]

Clinical prediction models (CPMs) can predict clinically relevant outcomes or events. Typically, prognostic CPMs are derived to predict the risk of a single future outcome. However, there are many medical applications where two or more outcomes are of interest, meaning this should be more widely reflected in CPMs so they can accurately estimate the joint risk of multiple outcomes simultaneously. A potentially naïve approach to multi-outcome risk prediction is to derive a CPM for each outcome separately, then multiply the predicted risks. This approach is only valid if the outcomes are conditionally independent given the covariates, and it fails to exploit the potential relationships between the outcomes. This paper outlines several approaches that could be used to develop CPMs for multiple binary outcomes. We consider four methods, ranging in complexity and conditional independence assumptions: namely, probabilistic classifier chain, multinomial logistic regression, multivariate logistic regression, and a Bayesian probit model. These are compared with methods that rely on conditional independence: separate univariate CPMs and stacked regression. Employing a simulation study and real-world example, we illustrate that CPMs for joint risk prediction of multiple outcomes should only be derived using methods that model the residual correlation between outcomes. In such a situation, our results suggest that probabilistic classification chains, multinomial logistic regression or the Bayesian probit model are all appropriate choices. We call into question the development of CPMs for each outcome in isolation when multiple correlated or structurally related outcomes are of interest and recommend more multivariate approaches to risk prediction. [ABSTRACT FROM AUTHOR]

Home hemodialysis (HD) was first introduced in the 1960s with a rapid increase in its use due to inability of dialysis units to accommodate patient demand. A sharp decline was subsequently seen with expanding outpatient dialysis facilities and changes in reimbursement policies. In the last decade, with emerging reports of benefits with home HD and more user-friendly equipment, there has been resurgence in home HD. However, home HD remains underutilized with considerable variations between and within countries. This paper will review the history of home HD, elaborate on its established benefits, identify some of the barriers in uptake of this modality and expand on potential strategies to overcome these barriers. [ABSTRACT FROM AUTHOR]

The United States is in the midst of a multifaceted public health crisis, marked by increasing midlife mortality rates among nearly all racial and ethnic groups.[[1]] The burden of this crisis has fallen most heavily among vulnerable populations, particularly individuals with lower levels of income and education.[[2]] Patterns of rising mortality - which vary across time, space, and causes of death - suggest a complex set of underlying causes, many of which may have been operative for decades.[2] How do the findings speak to the potential consequences of other trends in urban housing, in particular gentrification, whose relationship to evictions and health outcomes has thus far been shown to be mixed? America's declining well-being, health, and life expectancy: not just a white problem. 33 Fazel S, Geddes JR, Kushel M. The health of homeless people in high-income countries: descriptive epidemiology, health consequences, and clinical and policy recommendations. [Extracted from the article]

Missing data present challenges for development and real-world application of clinical prediction models. While these challenges have received considerable attention in the development setting, there is only sparse research on the handling of missing data in applied settings. The main unique feature of handling missing data in these settings is that missing data methods have to be performed for a single new individual, precluding direct application of mainstay methods used during model development. Correspondingly, we propose that it is desirable to perform model validation using missing data methods that transfer to practice in single new patients. This article compares existing and new methods to account for missing data for a new individual in the context of prediction. These methods are based on (i) submodels based on observed data only, (ii) marginalization over the missing variables, or (iii) imputation based on fully conditional specification (also known as chained equations). They were compared in an internal validation setting to highlight the use of missing data methods that transfer to practice while validating a model. As a reference, they were compared to the use of multiple imputation by chained equations in a set of test patients, because this has been used in validation studies in the past. The methods were evaluated in a simulation study where performance was measured by means of optimism corrected C-statistic and mean squared prediction error. Furthermore, they were applied in data from a large Dutch cohort of prophylactic implantable cardioverter defibrillator patients. [ABSTRACT FROM AUTHOR]

The EQ-5D, a widely used multiattribute utility instrument, is commonly used in health economic evaluations where the goal is to decide on which treatments to reimburse. Like other instruments, value sets of the EQ-5D are constructed using valuation studies typically valuing a subset of the health states and using predicted values from a regression model for the unvalued health states. In current practice the prediction errors associated with the value sets are substantial. The goal of this work is 2-fold. First, derive a formula of the mean squared error (MSE) of a value set assuming that the value set is estimated using a linear mixed model with either an independent or a Gaussian spatial correlation on the model misspecification error. Second, explore the effect of the number of health states directly valued, the number of participants and the correlation structure on the MSE. Keeping the total number of participants and the total number of valuations fixed, valuing all 242 health states of the EQ-5D-3L was found to reduce the MSE considerably compared with the common practice of valuing only 42 health states. Furthermore, an independent correlation structure with 3773 participants valuing 42 health states produced the MSE that can be achieved with less than 600 participants valuing all 242 health states under a Gaussian spatial correlation structure. Based on the comparison of the MSE values of some of the well-known designs our suggestion is to value more health states and to use a model with spatially correlated misspecification errors. [ABSTRACT FROM AUTHOR]

Human immunodeficiency virus (HIV) pre-exposure prophylaxis (PrEP) protects high risk patients from becoming infected with HIV. Clinicians need help to identify candidates for PrEP based on information routinely collected in electronic health records (EHRs). The greatest statistical challenge in developing a risk prediction model is that acquisition is extremely rare.Methods: Data consisted of 180 covariates (demographic, diagnoses, treatments, prescriptions) extracted from records on 399 385 patient (150 cases) seen at Atrius Health (2007-2015), a clinical network in Massachusetts. Super learner is an ensemble machine learning algorithm that uses k-fold cross validation to evaluate and combine predictions from a collection of algorithms. We trained 42 variants of sophisticated algorithms, using different sampling schemes that more evenly balanced the ratio of cases to controls. We compared super learner's cross validated area under the receiver operating curve (cv-AUC) with that of each individual algorithm.Results: The least absolute shrinkage and selection operator (lasso) using a 1:20 class ratio outperformed the super learner (cv-AUC = 0.86 vs 0.84). A traditional logistic regression model restricted to 23 clinician-selected main terms was slightly inferior (cv-AUC = 0.81).Conclusion: Machine learning was successful at developing a model to predict 1-year risk of acquiring HIV based on a physician-curated set of predictors extracted from EHRs. [ABSTRACT FROM AUTHOR]

Background: Red blood cells are essential for modern medicine but managing their collection and supply to cope with fluctuating demands represents a major challenge. As deterministic models based on predicted population changes have been problematic, there remains a need for more precise and reliable prediction of use. Here, we develop three new time-series methods to predict red cell use 4 to 52 weeks ahead.Study Design and Methods: From daily aggregates of red blood cell (RBC) units issued from 2005 to 2011 from the NHS Blood and Transplant, we generated a new set of non-overlapping weekly data by summing the daily data over 7 days and derived the average blood use per week over 4-week and 52-week periods. We used three new methods for linear prediction of blood use by computing the coefficients using Minimum Mean Squared Error (MMSE) algorithm.Results: We optimized the time-window size, order of the prediction, and order of the polynomial fit for our data set. By exploiting the annual periodicity of the data, we achieved significant improvements in long-term predictions, as well as modest improvements in short-term predictions. The new methods predicted mean RBC use with a standard deviation of the percentage error of 2.5% for 4 weeks ahead and 3.4% for 52 weeks ahead.Conclusion: This paradigm allows short- and long-term prediction of RBC use and could provide reliable and precise prediction up to 52 weeks ahead to improve the efficiency of blood services and sufficiency of blood supply with reduced costs. [ABSTRACT FROM AUTHOR]

Background and Purpose: Reliable prediction of pro-arrhythmic side effects of novel drug candidates is still a major challenge. Although drug-induced pro-arrhythmia occurs primarily in patients with pre-existing repolarisation disturbances, healthy animals are employed for pro-arrhythmia testing. To improve current safety screening, transgenic long QT (LQTS) rabbit models with impaired repolarisation reserve were generated by overexpressing loss-of-function mutations of human HERG (HERG-G628S, loss of IKr ; LQT2), KCNE1 (KCNE1-G52R, decreased IKs ; LQT5), or both transgenes (LQT2-5) in the heart.Experimental Approach: Effects of K+ channel blockers on cardiac repolarisation and arrhythmia susceptibility were assessed in healthy wild-type (WT) and LQTS rabbits using in vivo ECG and ex vivo monophasic action potential and ECG recordings in Langendorff-perfused hearts.Key Results: LQTS models reflect patients with clinically "silent" (LQT5) or "manifest" (LQT2 and LQT2-5) impairment in cardiac repolarisation reserve: they were more sensitive in detecting IKr -blocking (LQT5) or IK1 /IKs -blocking (LQT2 and LQT2-5) properties of drugs compared to healthy WT animals. Impaired QT-shortening capacity at fast heart rates was observed due to disturbed IKs function in LQT5 and LQT2-5. Importantly, LQTS models exhibited higher incidence, longer duration, and more malignant types of ex vivo arrhythmias than WT.Conclusion and Implications: LQTS models represent patients with reduced repolarisation reserve due to different pathomechanisms. As they demonstrate increased sensitivity to different specific ion channel blockers (IKr blockade in LQT5 and IK1 and IKs blockade in LQT2 and LQT2-5), their combined use could provide more reliable and more thorough prediction of (multichannel-based) pro-arrhythmic potential of novel drug candidates. [ABSTRACT FROM AUTHOR]

Linear mixed models (LMMs) and their extensions have been widely used for high-dimensional genomic data analyses. While LMMs hold great promise for risk prediction research, the high dimensionality of the data and different effect sizes of genomic regions bring great analytical and computational challenges. In this work, we present a multikernel linear mixed model with adaptive lasso (KLMM-AL) to predict phenotypes using high-dimensional genomic data. We develop two algorithms for estimating parameters from our model and also establish the asymptotic properties of LMM with adaptive lasso when only one dependent observation is available. The proposed KLMM-AL can account for heterogeneous effect sizes from different genomic regions, capture both additive and nonadditive genetic effects, and adaptively and efficiently select predictive genomic regions and their corresponding effects. Through simulation studies, we demonstrate that KLMM-AL outperforms most of existing methods. Moreover, KLMM-AL achieves high sensitivity and specificity of selecting predictive genomic regions. KLMM-AL is further illustrated by an application to the sequencing dataset obtained from the Alzheimer's disease neuroimaging initiative. [ABSTRACT FROM AUTHOR]

Objective: To assess the impact of provider incentive policy on smoking status documentation.Data Sources: Primary data were extracted from structured electronic medical records (EMRs) from 15 community health centers (CHCs).Study Design: This was an observational study of data from 2006 to 2013, assessing changes in documentation of smoking status over time.Data Extraction Methods: We extracted structured EMR data for patients age 18 and older with at least one primary care visit.Principal Findings: Rates of documented smoking status rose from 30 percent in 2006 to 90 percent in 2013; the largest increase occurred from 2011 to 2012 following policy changes (21.3% [95% CI, 8.2%, 34.4%] from the overall trend). Rates varied by clinic and across patient subgroups.Conclusions: Documentation of smoking status improved markedly after introduction of new federal standards. Further improvement in documentation is still needed, especially for males, nonwhite patients, those using opioids, and HIV + patients. More research is needed to study whether changes in documentation lead to improvements in counseling, cessation, and patient outcomes. [ABSTRACT FROM AUTHOR]

Clinical outcomes of patients with diabetes, foot ulceration, and peripheral artery disease (PAD) are difficult to predict. The prediction of important clinical outcomes, such as wound healing and major amputation, would be a valuable tool to help guide management and target interventions for limb salvage. Despite the existence of a number of classification tools, no consensus exists as to the most useful bedside tests with which to predict outcome. We here present an updated systematic review from the International Working Group of the Diabetic Foot, comprising 15 studies published between 1980 and 2018 describing almost 6800 patients with diabetes and foot ulceration. Clinical examination findings as well as six non-invasive bedside tests were evaluated for their ability to predict wound healing and amputation. The most useful tests to inform on the probability of healing were skin perfusion pressure ≥ 40 mmHg, toe pressure ≥ 30 mmHg, or TcPO2  ≥ 25 mmHg. With these thresholds, all of these tests increased the probability of healing by greater than 25% in at least one study. To predict major amputation, the most useful tests were ankle pressure < 50 mmHg, ABI < 0.5, toe pressure < 30 mmHg, and TcPO2  < 25 mmHg, which increased the probability of major amputation by greater than 25%. These indicative values may be used as a guide when deciding which patients are at highest risk for poor outcomes and should therefore be evaluated for revascularization at an early stage. However, this should always be considered within the wider context of important co-existing factors such as infection, wound characteristics, and other comorbidities. [ABSTRACT FROM AUTHOR]

Background: The World Health Organization (WHO) recommends that women wait at least 24 months after a livebirth before attempting a subsequent pregnancy to reduce the risk of adverse maternal, perinatal, and infant health outcomes. However, the applicability of the WHO recommendations for women in the United States is unclear, as breast feeding, nutrition, maternal age at first birth, and total fertility rate differs substantially between the United States and the low- and middle-resource countries upon which most of the evidence is based.Methods: To inform guideline development for birth spacing specific to women in the United States, the Office of Population Affairs (OPA) convened an expert work group meeting in Washington, DC, on 14-15 September 2017 among reproductive, perinatal, paediatric, social, and public health epidemiologists; obstetrician-gynaecologists; biostatisticians; and experts in evidence synthesis related to women's health.Results: Presentations and discussion topics included the methodological quality of existing studies, evaluation of the evidence for causal effects of short interpregnancy intervals on adverse perinatal and maternal health outcomes, good practices for future research, and identification of research gaps and priorities for future work.Conclusions: This report provides an overview of the presentations, discussions, and conclusions from the expert work group meeting. [ABSTRACT FROM AUTHOR]

Objective: The Affordable Care Act and Medicare Access and CHIP Reauthorization Act are changing access to, and delivery of, health care in the United States, with potential implications for the field of health services research (HSR). We therefore investigated employers' perceptions of demand for individuals to conduct HSR, the competencies required for success, and implications for HSR training programs.Data Source: Interviews conducted in August 2016 with 21 key informants at a range of U.S.-based HSR organizations.Study Design: We conducted a semistructured, qualitative, telephone interview study to explore relevant topics.Data Collection/extraction Methods: Interviews with respondents were transcribed from recordings and then synthesized by respondent organization type and topic area.Principal Findings: Most respondents reported recently hiring health services researchers, and most anticipated hiring additional such researchers in the future. Most respondents emphasized the abilities to analyze data, work in teams, and engage with stakeholders. Finally, most respondents recommended that potential recruits gain real-world experience during their academic training.Conclusions: Our interviews indicated that current and future demand for health services researchers is strong. They also suggested that, as a field, HSR will continue to draw together individuals from a variety of backgrounds to inform a diverse array of decision makers. [ABSTRACT FROM AUTHOR]

Objective: This study examines the stock and supply including educational pipeline of health services researchers (HSRers) in the United States.Data Sources: National Institutes of Health Research Portfolio Online Reporting Tools, Agency for Healthcare Research and Quality Grants On-Line Database, Health Services Research Projects in Progress, PubMed, Integrated Postsecondary Education Data System, AcademyHealth membership database, and social networks.Study Design: Exploratory descriptive analysis of individuals involved in health services research (HSR) in the United States as of 2015/16.Principal Findings: The HSRer stock grew by 25 percent to 45 percent between 2007 and 2015/16, which was slower than the previous measurement period. The growth in the number of doctoral and master's degrees conferred in core HSR fields has been slowing in recent years. Minorities are underrepresented among HSRers, but this trend may improve over time given the diversity of the educational pipeline. Hispanics, however, were generally underrepresented in the educational pipeline.Conclusions: The average annual growth rate of HSRers appears to be within range of national recommendations. Additional work is needed to assess whether supply of HSRers is meeting demand and to ensure a competent and diverse educational pipeline that meets the needs of an evolving health system. [ABSTRACT FROM AUTHOR]

Aims: Cardiac dysfunction is a severe complication of anthracycline-containing anticancer therapy. The outcome of anthracycline-induced cardiomyopathy (AICM) compared with other non-ischaemic causes of heart failure (HF), such as idiopathic dilated cardiomyopathy (IDCM), is unresolved. The aim of this study was to compare the survival of AICM patients with an IDCM cohort followed at our centre from 1990 to 2016.Methods and Results: We included 67 patients (67% female, 50 ± 15 years) with AICM, defined as onset of otherwise unexplained left ventricular ejection fraction (LVEF) ≤50% following anthracycline therapy, and 488 IDCM patients (28% female, 55 ± 12 years). Patients were followed with constantly optimized HF therapy, for 7.6 ± 5.5 and 8.1 ± 5.5 years, respectively. In both cohorts, 25% of patients reached the combined endpoint of death/heart transplantation. Overall survival rates at 5 and 10 years were similar (AICM: 86% and 61%, IDCM: 88% and 75%; P = 0.61), and so was cardiovascular survival (AICM: 91% and 76%, IDCM: 91% and 80%; P = 0.373), also after 1:1 propensity matching (P = 0.27) and adjusting for age, LVEF and left ventricular size. A trend toward higher all-cause mortality was present in AICM patients [hazard ratio (HR) 1.67, 95% confidence interval (CI) 0.95-2.92, P = 0.076]. No differences were observed between AICM and IDCM with regard to pharmacological HF therapy, but AICM patients were less likely to receive devices (13% vs. 41.8% in IDCM, P < 0.001).Conclusion: Cardiovascular mortality in patients with AICM did not differ from that of a matched IDCM cohort, despite cancer-related morbidity and less prevalent use of devices. These data suggest that patients with AICM should be treated with appropriate guideline-directed medical therapies similar to other non-ischaemic dilated cardiomyopathies. [ABSTRACT FROM AUTHOR]

Aim: We aimed to compare the academic outcomes of a cohort of children born very preterm (VPT, <32 weeks of gestation) and children born at term at age 7 years and assess the ability of a pre-academic skill screen at age five to predict later academic impairment in children born VPT at age seven.Methods: One hundred ninety-four children born VPT (born with either gestational age <30 weeks or birthweight <1250 g) and 70 controls born at term from a prospective birth cohort were compared on academic outcomes (Wide Range Achievement Test, WRAT4) at age seven using regression analyses. Receiver-operating characteristic curves were used to determine whether pre-academic skills (Kaufman Survey of Early Academic and Language Skills, K-SEALS) at age five predicted academic impairment at age seven in 174 of the VPT cohort.Results: At the age of 7 years, children born VPT had lower mean word reading (-9.7, 95% CI: -14.7 to -4.6), spelling (-8.3, 95% CI: -13.3 to -3.3) and math computation (-10.9, 95% CI: -15.3 to -6.5) scores (all P-values ≤0.001) compared with controls born at term, even after adjusting for social risk and time since school commencement. In terms of pre-academic screening, the Numbers, Letters and Words subtest of the K-SEALS had adequate sensitivity and specificity (70-80%) for predicting children with academic impairment at age seven.Conclusions: Children born VPT underperformed in academic outcomes at age seven compared with controls born at term. A pre-academic screening tool used at school entry can predict children born VPT at risk of academic impairment at age seven who could benefit from targeted early intervention. [ABSTRACT FROM AUTHOR]

Objective: Early childhood psychosocial experiences determine future health and health-care use. Identifying psychosocial predictors in cystic fibrosis may inform intervention strategies that can reduce health-care utilization.Design: The study was designed as a prospective cohort study.Setting: The study was set in the only cystic fibrosis clinic in Western Australia.Patients: The patients were children up to 6 years diagnosed with cystic fibrosis in Western Australia between 2005 and 2011.Main Outcome Measures: Psychosocial data collected for each year of life were compared with Australian population data and analysed as predictors of annual hospital, emergency and outpatient visits.Results: Compared with the Australian population, cystic fibrosis families demonstrated lower socio-economic status and labour supply (P < 0.001), increased residential mobility (P < 0.001) and trends towards increased rates of parental separation (P = 0.066). Marital discord and maternal and child psychological stress significantly predicted increased hospital admissions, emergency and outpatient visits.Conclusions: Social gradients may exist for families of young children with cystic fibrosis in Western Australia with potential implications for child health. Family psychological and relationship stress predicted increased child cystic fibrosis-related health-care use. [ABSTRACT FROM AUTHOR]

Dental educators have traditionally prioritized cognitive indicators (especially undergraduate grade point average and Dental Admission Test scores) in choosing students for admission to dental school. These indicators' role in predicting academic outcomes, including coursework and examination success, is well documented. However, noncognitive predictors including conscientiousness, self-discipline, achievement-striving, task orientation, deliberation, resilience, and situational judgment have also been identified. This study's aims were to assess the significance of noncognitive indicators measured by the version of the Neuroticism-Extraversion-Openness Personality Inventory (NEO PI) known as the NEO-PI-3, determine the place in the curriculum when these indicators' impact was most influential, and compare their influence with that of the cognitive indicators. Analysis was performed on stored data for three classes of dental students from admission through clinical exams at one U.S. dental school. Significant associations were found between NEO-PI-3 domains and facets (especially Conscientiousness) and the outcomes of coursework grades, standardized exam scores, and clinical behavior scores. Multiple regression analyses identified that the noncognitive indicators enhanced the prediction of students' academic and clinical performance early in the curriculum and then equaled or surpassed the predictive impact of cognitive indicators as they progressed through the curriculum sequence. The implications of noncognitive predictors for dental education are discussed including the challenge to identify and then weight the indicators, whether to include them as admissions criteria, how to assess their impact as compared with cognitive measures, the necessity of standardization of assessment, and if and when to evaluate their relevance to professional practice. [ABSTRACT FROM AUTHOR]

Background and Purpose: Ligand affinity has been a fundamental concept in the field of pharmacology and has traditionally been considered to be constant for a given receptor-ligand interaction. Recent studies have demonstrated that this is not true for all three members of the G(s)-coupled beta-adrenoceptor family. This study evaluated antagonist affinity measurements at a different G(s)-coupled receptor, the histamine H(2) receptor, to determine whether antagonist affinity measurements made at a different family of GPCRs were constant.Experimental Approach: CHO cells stably expressing the human histamine H(2) receptor and a CRE-SPAP reporter were used and antagonist affinity was assessed in short-term cAMP assays and longer term CRE gene transcription assays.Key Results: Nine agonists and seven antagonists, of sufficient potency at the H(2) receptor to examine in detail, were identified. Measurements of antagonist affinity were the same regardless of the efficacy of the competing agonist, time of agonist incubation, cellular response measured or presence of a PDE inhibitor.Conclusions and Implications: Antagonist affinity at the G(s)-coupled histamine H(2) receptor obeys the accepted dogma for antagonism at GPCRs. This study further confirms that something unusual is indeed happening with the beta-adrenoceptors and is not an artefact related to the transfected cell system used. As the human histamine H(2) receptor does not behave in a similar manner to any of the human beta-adrenoceptors, it is clear that information gathered from one GPCR cannot be simply extrapolated to predict the behaviour of another GPCR. Each GPCR therefore requires careful and detailed evaluation on its own. [ABSTRACT FROM AUTHOR]

Background and Purpose: Torsade de pointes (TdP) can be induced by a reduction in cardiac repolarizing capacity. The aim of this study was to assess whether IKs blockade or enhancement of INa could potentiate TdP induced by IKr blockade and to investigate whether short-term variability (STV) or triangulation of action potentials preceded TdP.Experimental Approach: Experiments were performed in open-chest, pentobarbital-anaesthetized, alpha 1-adrenoceptor-stimulated, male New Zealand White rabbits, which received three consecutive i.v. infusions of either the IKr blocker E-4031 (1, 3 and 10 nmol kg(-1) min(-1)), the IKs blocker HMR1556 (25, 75 and 250 nmol kg(-1) min(-1)) or E-4031 and HMR1556 combined. In a second study rabbits received either the same doses of E-4031, the INa enhancer, ATX-II (0.4, 1.2 and 4.0 nmol kg(-1)) or both of these drugs. ECGs and epicardial monophasic action potentials were recorded.Key Results: HMR1556 alone did not cause TdP but increased E-4031-induced TdP from 25 to 80%. ATX-II alone caused TdP in 38% of rabbits, as did E-4031; 75% of rabbits receiving both drugs had TdP. QT intervals were prolonged by all drugs but the extent of QT prolongation was not related to the occurrence of TdP. No changes in STV were detected and triangulation was only increased after TdP occurred.Conclusions and Implications: Giving modulators of ion channels in combination substantially increased TdP but, in this model, neither STV nor triangulation of action potentials could predict TdP. [ABSTRACT FROM AUTHOR]

Although previous research provided empirical support for the main assumptions of the Addicted-Self (A-S) Model of recovery, it is not known whether the model predicts recovery for various gender, ethnic, age, and drug preference populations. It may be that the model predicts recovery only for some groups of addicts and should not be viewed as a general theory of the recovery process. Addressing this concern using data from the Los Angeles Target Cities Drug Treatment Enhancement Project, it was determined that only trivial population differences exist in the primary variables associated with the A-S Model. The A-S Model predicts abstinence with about the same degree of accuracy and parsimony for all populations. The findings indicate that the A-S Model is a general theory of drug and alcohol addictive behavior cessation. [ABSTRACT FROM AUTHOR]

Objective: To quantify uncertainty in forecasts of health expenditures.Study Design: Stochastic time series models are estimated for historical variations in fertility, mortality, and health spending per capita in the United States, and used to generate stochastic simulations of the growth of Medicare expenditures. Individual health spending is modeled to depend on the number of years until death.Data Sources/study Setting: A simple accounting model is developed for forecasting health expenditures, using the U.S. Medicare system as an example.Principal Findings: Medicare expenditures are projected to rise from 2.2 percent of GDP (gross domestic product) to about 8 percent of GDP by 2075. This increase is due in equal measure to increasing health spending per beneficiary and to population aging. The traditional projection method constructs high, medium, and low scenarios to assess uncertainty, an approach that has many problems. Using stochastic forecasting, we find a 95 percent probability that Medicare spending in 2075 will fall between 4 percent and 18 percent of GDP, indicating a wide band of uncertainty. Although there is substantial uncertainty about future mortality decline, it contributed little to uncertainty about future Medicare spending, since lower mortality both raises the number of elderly, tending to raise spending, and is associated with improved health of the elderly, tending to reduce spending. Uncertainty about fertility, by contrast, leads to great uncertainty about the future size of the labor force, and therefore adds importantly to uncertainty about the health-share of GDP. In the shorter term, the major source of uncertainty is health spending per capita.Conclusions: History is a valuable guide for quantifying our uncertainty about future health expenditures. The probabilistic model we present has several advantages over the high-low scenario approach to forecasting. It indicates great uncertainty about future Medicare expenditures relative to GDP. [ABSTRACT FROM AUTHOR]