Ida Sim

Clinical decision support systems for the practice of evidence-based medicine.

Background: The use of clinical decision support systems to facilitate the practice of evidence-based medicine promises to substantially improve health care quality. Objective: To describe, on the basis of the proceedings of the Evidence and Decision Support track at the 2000 AMIA Spring Symposium, the research and policy challenges for capturing research and practice-based evidence in machine-interpretable repositories, and to present recommendations for accelerating the development and adoption of clinical decision support systems for evidence-based medicine.

Open mHealth Architecture: An Engine for Health Care Innovation

Standardized interfaces and shared components are critical for realizing the potential of mobile-device–enabled health care delivery and research. Chronic diseases like diabetes, asthma, and obesity account for 46% of global disease burden (1). The traditional model of episodic care in clinic and hospital-based settings is suboptimal for improving chronic disease outcomes (2). Mobile communication devices, in conjunction with Internet and social media, present opportunities to enhance disease prevention and management by extending health interventions beyond the reach of traditional care—an approach referred to as mHealth (3). However, mHealth is emerging as a patchwork of incompatible applications (“apps”) serving narrow, albeit valuable, needs, and thus could benefit from more coordinated development (4). A public-private partnership to define and instantiate an “open” mHealth architecture (described below), in the context of economic incentives and enabling policies, could support medical discovery and evidence-based practice about managing and preventing chronic disease.

Publication of Clinical Trials Supporting Successful New Drug Applications: A Literature Analysis

Background The United States (US) Food and Drug Administration (FDA) approves new drugs based on sponsor-submitted clinical trials. The publication status of these trials in the medical literature and factors associated with publication have not been evaluated. We sought to determine the proportion of trials submitted to the FDA in support of newly approved drugs that are published in biomedical journals that a typical clinician, consumer, or policy maker living in the US would reasonably search. Methods and Findings We conducted a cohort study of trials supporting new drugs approved between 1998 and 2000, as described in FDA medical and statistical review documents and the FDA approved drug label. We determined publication status and time from approval to full publication in the medical literature at 2 and 5 y by searching PubMed and other databases through 01 August 2006. We then evaluated trial characteristics associated with publication. We identified 909 trials supporting 90 approved drugs in the FDA reviews, of which 43% (394/909) were published. Among the subset of trials described in the FDA-approved drug label and classified as “pivotal trials” for our analysis, 76% (257/340) were published. In multivariable logistic regression for all trials 5 y postapproval, likelihood of publication correlated with statistically significant results (odds ratio [OR] 3.03, 95% confidence interval [CI] 1.78–5.17); larger sample sizes (OR 1.33 per 2-fold increase in sample size, 95% CI 1.17–1.52); and pivotal status (OR 5.31, 95% CI 3.30–8.55). In multivariable logistic regression for only the pivotal trials 5 y postapproval, likelihood of publication correlated with statistically significant results (OR 2.96, 95% CI 1.24–7.06) and larger sample sizes (OR 1.47 per 2-fold increase in sample size, 95% CI 1.15–1.88). Statistically significant results and larger sample sizes were also predictive of publication at 2 y postapproval and in multivariable Cox proportional models for all trials and the subset of pivotal trials. Conclusions Over half of all supporting trials for FDA-approved drugs remained unpublished ≥ 5 y after approval. Pivotal trials and trials with statistically significant results and larger sample sizes are more likely to be published. Selective reporting of trial results exists for commonly marketed drugs. Our data provide a baseline for evaluating publication bias as the new FDA Amendments Act comes into force mandating basic results reporting of clinical trials.

Quantitative Overview of Randomized Trials of Amiodarone to Prevent Sudden Cardiac Death

BACKGROUND Some randomized clinical trials of amiodarone therapy to prevent sudden cardiac death have had positive results and others have had negative results, but all were relatively small. This meta-analysis aimed to pool all trials to assess the effect of amiodarone on mortality and the impact of differences in patient population and study design on trial outcomes. METHODS AND RESULTS Fifteen randomized trials were identified, and outcome measures were combined by use of a random effects model. The effect of patient population and study design on total mortality was assessed by use of a hierarchical Bayes model. Amiodarone reduced total mortality by 19% (confidence limits, 6% to 31%; P<.01), with somewhat greater reductions in cardiac mortality (23%, P<.001) and sudden death (30%, P<.001). Mortality reductions were similar in trials enrolling patients after myocardial infarction (21%), with left ventricular dysfunction (22%), and after cardiac arrest (25%). There was a trend toward greater risk reduction in trials requiring evidence of ventricular ectopy (25%) than in the remaining trials (10%). The trials using placebo controls had considerably less risk reduction (10%) than trials with active controls (27%) or usual care controls (42%, posterior odds <0.02). CONCLUSIONS Amiodarone reduced total mortality by 10% to 19% in patients at risk of sudden cardiac death. Amiodarone reduced risk similarly in patients after myocardial infarction, with heart failure, or with clinically evident arrhythmia. The apparent inconsistencies among results of randomized trials appear to be due to small sample sizes and the type of control group used, not the type of patient enrolled.

Formal representation of eligibility criteria

Standards-based, computable knowledge representations for eligibility criteria are increasingly needed to provide computer-based decision support for automated research participant screening, clinical evidence application, and clinical research knowledge management. We surveyed the literature and identified five aspects of eligibility criteria knowledge representation that contribute to the various research and clinical applications: the intended use of computable eligibility criteria, the classification of eligibility criteria, the expression language for representing eligibility rules, the encoding of eligibility concepts, and the modeling of patient data. We consider three of these aspects (expression language, codification of eligibility concepts, and patient data modeling) to be essential constructs of a formal knowledge representation for eligibility criteria. The requirements for each of the three knowledge constructs vary for different use cases, which therefore should inform the development and choice of the constructs toward cost-effective knowledge representation efforts. We discuss the implications of our findings for standardization efforts toward knowledge representation for sharable and computable eligibility criteria.

A meta-analysis of randomized trials comparing coronary artery bypass grafting with percutaneous transluminal coronary angioplasty in multivessel coronary artery disease☆

We performed a meta-analysis of randomized trials that compared percutaneous transluminal coronary angioplasty (PTCA) with coronary artery bypass graft (CABG) surgery in patients with multivessel coronary artery disease. The outcomes of death, combined death, and nonfatal myocardial infarction (MI), repeat revascularization, and freedom from angina were analyzed. The overall risk of death and nonfatal MI was not different over a follow-up of 1 to 3 years (CABG:PTCA odds ratio [OR] 1.03, 95% confidence interval 0.81 to 1.32, p = 0.81). Patients randomized to CABG tended to have a higher risk of death or MI in the early, periprocedural period (OR 1.33, p = 0.091), but a lower risk in subsequent follow-up (OR 0.74, p = 0.093). CABG patients were much less likely to undergo another revascularization procedure (p < 0.00001), and were more likely to be angina free (OR 1.57, p < 0.00001). Thus, CABG and PTCA patients have similar overall risks of death and nonfatal MI at 1 to 3 years of follow-up, but relative risk differences in mortality of up to 25% cannot be excluded. CABG patients have significantly less angina and less repeat revascularization than PTCA patients.

A practical method for transforming free-text eligibility criteria into computable criteria

Formalizing eligibility criteria in a computer-interpretable language would facilitate eligibility determination for study subjects and the identification of studies on similar patient populations. Because such formalization is extremely labor intensive, we transform the problem from one of fully capturing the semantics of criteria directly in a formal expression language to one of annotating free-text criteria in a format called ERGO annotation. The annotation can be done manually, or it can be partially automated using natural-language processing techniques. We evaluated our approach in three ways. First, we assessed the extent to which ERGO annotations capture the semantics of 1000 eligibility criteria randomly drawn from ClinicalTrials.gov. Second, we demonstrated the practicality of the annotation process in a feasibility study. Finally, we demonstrate the computability of ERGO annotation by using it to (1) structure a library of eligibility criteria, (2) search for studies enrolling specified study populations, and (3) screen patients for potential eligibility for a study. We therefore demonstrate a new and practical method for incrementally capturing the semantics of free-text eligibility criteria into computable form.

Making Sense of Mobile Health Data: An Open Architecture to Improve Individual- and Population-Level Health

Mobile phones and devices, with their constant presence, data connectivity, and multiple intrinsic sensors, can support around-the-clock chronic disease prevention and management that is integrated with daily life. These mobile health (mHealth) devices can produce tremendous amounts of location-rich, real-time, high-frequency data. Unfortunately, these data are often full of bias, noise, variability, and gaps. Robust tools and techniques have not yet been developed to make mHealth data more meaningful to patients and clinicians. To be most useful, health data should be sharable across multiple mHealth applications and connected to electronic health records. The lack of data sharing and dearth of tools and techniques for making sense of health data are critical bottlenecks limiting the impact of mHealth to improve health outcomes. We describe Open mHealth, a nonprofit organization that is building an open software architecture to address these data sharing and “sense-making” bottlenecks. Our architecture consists of open source software modules with well-defined interfaces using a minimal set of common metadata. An initial set of modules, called InfoVis, has been developed for data analysis and visualization. A second set of modules, our Personal Evidence Architecture, will support scientific inferences from mHealth data. These Personal Evidence Architecture modules will include standardized, validated clinical measures to support novel evaluation methods, such as n-of-1 studies. All of Open mHealth’s modules are designed to be reusable across multiple applications, disease conditions, and user populations to maximize impact and flexibility. We are also building an open community of developers and health innovators, modeled after the open approach taken in the initial growth of the Internet, to foster meaningful cross-disciplinary collaboration around new tools and techniques. An open mHealth community and architecture will catalyze increased mHealth efficiency, effectiveness, and innovation.

Estrous cycle variations in cholecystokinin and substance P concentrations in discrete areas of the rat brain

Cholecystokinin (CCK) and substance P (SP) were measured in discrete areas of the rat brain at different stages of the estrous cycle. Significantly higher levels of CCK were found in the lateral septum during diestrus as compared to proestrus. In the parietal cortex, CCK concentrations were significantly higher in diestrus than in proestrus. In the amygdala, estrous levels of CCK were significantly higher than proestrous levels. SP concentrations were significantly higher in diestrus than in proestrus in the medial and lateral septum, and the medial and lateral preoptic area. In the amygdala and ventral tegmental area, SP concentrations were significantly higher in estrus than in proestrus. These data suggest that certain CCK and SP neuronal systems may play a role in regulating the hypothalamo-pituitary-gonadal axis and/or be involved in steroid-dependent behavior.

Hyperthermia-induced inhibition of respiration and mitochondrial protein denaturation in CHL cells

Respiration of Chinese hamster lung V79 cells, as assayed by O2 consumption, increases linearly from 8 to 40 degrees C when plotted in the Arrhenius fashion but is strongly inhibited above 40 degrees C. The protein of mitochondria isolated from V79 cells undergoes structural transitions at 28 and 40 degrees C. This is supported by changes in the fluorescence excitation spectrum of conjugated pyrene maleimide and, to a lesser extent, intrinsic protein fluorophores. Electron spin resonance labelling studies with a derivative of tempo maleimide imply that extensive protein unfolding coincides with the 40 degrees C transition. The structural transition at 40 degrees C correlates well with inhibition of O2 consumption, is irreversible and is probably due to protein denaturation, while the change at 28 degrees C is reversible and has no effect on O2 consumption. Previous studies indicate the presence of a broad lipid transition extending from approximately 8 to 30 degrees C in mitochondrial membranes with all lipids being in the liquid-crystalline state above 30 degrees C. Thus, the onset of the lipid transition may induce the observed protein conformational change at 28 degrees C, but inhibition of respiration above 40 degrees C can be explained by protein denaturation alone. The region from 28 to 40 degrees C of stable protein conformation corresponds to the temperature range of V79 cell growth.