Creating learning health systems and the emerging role of biomedical informatics.

INTRODUCTION: The nature of information used in medicine has changed. In the past, we were limited to routine clinical data and published clinical trials. Today, we deal with massive, multiple data streams and easy access to new tests, ideas, and capabilities to process them. Whereas in the past getting information for decision-making was a challenge, now, it is how to analyze, evaluate and prioritize all that is readily available through the multitude of data-collecting devices. Clinicians must become adept with the tools needed to deal with the era of big data, requiring a major change in how we learn to make decisions. Major change is often met with resistance and questions about value. A Learning Health System is an enabler to encourage the development of such tools and demonstrate value in improved decision-making. METHODS: We describe how we are developing a Biomedical Informatics program to help our medical institution's evolution as an academic Learning Health System, including strategy, training for house staff and examples of the role of informatics from operations to research. RESULTS: We described an array of learning health system implementations and educational programs to improve healthcare and prepare a cadre of physicians with basic information technology skills. The programs have been well accepted with, for example, increasing interest and enrollment in the educational programs. CONCLUSIONS: We are now in an era when large volumes of a wide variety of data are readily available. The challenge is not so much in the acquisition of data, but in assessing the quality, relevance and value of the data. The data we can get may not be the data we need. In the past, sources of data were limited, and trial results published in journals were the major source of evidence for decision making. The advent of powerful analytics systems has changed the concept of evidence. Clinicians will have to develop the skills necessary to work in the era of big data. It is not reasonable to expect that all clinicians will also be data scientists. However, understanding the role of AI and predictive analytics, and how to apply them, will become progressively more important. Programs such as the one being implemented at Wake Forest fill that need.

Chlamydia trachomatis Cross-Serovar Protection during Experimental Lung Reinfection in Mice.

Chlamydia trachomatis causes most bacterial sexually transmitted diseases worldwide. Different major outer membrane proteins (MOMPs) define various serovars of this intracellular pathogen: In women, D to L3 can cause urethritis, cervicitis, salpingitis, and oophoritis, and, thus, infertility. Protective immunity might be serovar-specific since chlamydial infection does not appear to induce an effective acquired immunity and reinfections occur. A better understanding of induced cross-serovar protection is essential for the selection of suitable antigens in vaccine development. In our mouse lung infection screening model, we evaluated the urogenital serovars D, E, and L2 in this regard. Seven weeks after primary infection or mock-infection, respectively, mice were infected a second time with the identical or one of the other serovars. Body weight and clinical score were monitored for 7 days. Near the peak of the second lung infection, bacterial load, myeloperoxidase, IFN-γ, and TNF-α in lung homogenate, as well as chlamydia-specific IgG levels in blood were determined. Surprisingly, compared with mice that were infected then for the first time, almost independent of the serovar combination used, all acquired parameters of disease were similarly diminished. Our reinfection study suggests that efficient cross-serovar protection could be achieved by a vaccine combining chlamydial antigens that do not include nonconserved MOMP regions.

Prophylactic Multi-Subunit Vaccine against Chlamydia trachomatis: In Vivo Evaluation in Mice.

Chlamydia trachomatis is the most frequent sexually-transmitted disease-causing bacterium. Urogenital serovars of this intracellular pathogen lead to urethritis and cervicitis. Ascending infections result in pelvic inflammatory disease, salpingitis, and oophoritis. One of 200 urogenital infections leads to tubal infertility. Serovars A-C cause trachoma with visual impairment. There is an urgent need for a vaccine. We characterized a new five-component subunit vaccine in a mouse vaccination-lung challenge infection model. Four recombinant Pmp family-members and Ctad1 from C. trachomatis serovar E, all of which participate in adhesion and binding of chlamydial elementary bodies to host cells, were combined with the mucosal adjuvant cyclic-di-adenosine monophosphate. Intranasal application led to a high degree of cross-serovar protection against urogenital and ocular strains of C. trachomatis, which lasted at least five months. Critical evaluated parameters were body weight, clinical score, chlamydial load, a granulocyte marker and the cytokines IFN-γ/TNF-α in lung homogenate. Vaccine antigen-specific antibodies and a mixed Th1/Th2/Th17 T cell response with multi-functional CD4+ and CD8+ T cells correlate with protection. However, serum-transfer did not protect the recipients suggesting that circulating antibodies play only a minor role. In the long run, our new vaccine might help to prevent the feared consequences of human C. trachomatis infections.

Complement and Chlamydia psittaci: Early Complement-Dependent Events Are Important for DC Migration and Protection During Mouse Lung Infection.

The zoonotic intracellular bacterium Chlamydia psittaci causes life-threatening pneumonia in humans. During mouse lung infection, complement factor C3 and the anaphylatoxin C3a augment protection against C. psittaci by a so far unknown mechanism. To clarify how complement contributes to the early, innate and the late, specific immune response and resulting protection, this study addresses the amount of C3, the timing when its presence is required as well as the anaphylatoxin receptor(s) mediating its effects and the complement-dependent migration of dendritic cells. Challenge experiments with C. psittaci on various complement KO mice were combined with transient decomplementation by pharmacological treatment, as well as the analysis of in vivo dendritic cells migration. Our findings reveal that a plasma concentration of C3 close to wildtype levels was required to achieve full protection. The diminished levels of C3 of heterozygote C3+/- mice permitted already relative effective protection and improved survival as compared to C3-/- mice, but overall recovery of these animals was delayed. Complement was in particular required during the first days of infection. However, additionally, it seems to support protection at later stages. Migration of CD103+ dendritic cells from the infected lung to the draining lymph node-as prerequisite of antigen presentation-depended on C3 and C3aR and/or C5aR. Our results provide unique mechanistic insight in various aspects of complement-dependent immune responses under almost identical, rather physiological experimental conditions. Our study contributes to an improved understanding of the role of complement, and C3a in particular, in infections by intracellular bacteria.

Complement and Chlamydia psittaci: Non-Myeloid-Derived C3 Predominantly Induces Protective Adaptive Immune Responses in Mouse Lung Infection.

Recent advances in complement research have revolutionized our understanding of its role in immune responses. The immunomodulatory features of complement in infections by intracellular pathogens, e.g., viruses, are attracting increasing attention. Thereby, local production and activation of complement by myeloid-derived cells seem to be crucial. We could recently show that C3, a key player of the complement cascade, is required for effective defense against the intracellular bacterium Chlamydia psittaci. Avian zoonotic strains of this pathogen cause life-threatening pneumonia with systemic spread in humans; closely related non-avian strains are responsible for less severe diseases of domestic animals with economic loss. To clarify how far myeloid- and non-myeloid cell-derived complement contributes to immune response and resulting protection against C. psittaci, adoptive bone marrow transfer experiments focusing on C3 were combined with challenge experiments using a non-avian (BSL 2) strain of this intracellular bacterium. Surprisingly, our data prove that for C. psittaci-induced pneumonia in mice, non-myeloid-derived, circulating/systemic C3 has a leading role in protection, in particular on the development of pathogen-specific T- and B- cell responses. In contrast, myeloid-derived and most likely locally produced C3 plays only a minor, mainly fine-tuning role. The work we present here describes authentic, although less pronounced, antigen directed immune responses.

Vaspin suppresses cytokine-induced inflammation in 3T3-L1 adipocytes via inhibition of NFκB pathway.

Vaspin expression is increased in white adipose tissue (WAT) of diet-induced obese mice and rats and is supposed to compensate HFD-induced inflammatory processes and insulin resistance in adipose tissue by counteracting pro-inflammatory gene expression in obesity. Multiple studies have also demonstrated strong anti-inflammatory effects in vascular and skin cells. Here, we used vaspin treated 3T3-L1 murine adipocytes as well as 3T3-L1 cells with stable vaspin expression to investigate the effect of exogenous and endogenous vaspin on inflammatory processes and insulin signaling in adipocytes. Our stably transfected cells secreted significant amounts of vaspin which was in the physiological range of ∼0.5 ng/ml in cell supernatants. Adipocyte differentiation was not affected by vaspin as expression of adipogenic marker genes as well as lipid accumulation after full differentiation was similar to control cells. We found that IL-1ß induced expression and secretion of pro-inflammatory cytokines, such as IL-6, MCP1 and TNFα was significantly blunted in vaspin expressing 3T3-L1 cells. Treatment of 3T3-L1 cells with exogenous vaspin resulted in reduced cytokine-induced activation of the intracellular and pro-inflammatory NFκB signaling cascades (IKKα/ß, IκB and NFκB). Moreover, endogenous vaspin positively affected insulin signaling by increasing insulin-stimulated phosphorylation of the key mediator protein kinase B (AKT). Together, we demonstrate anti-inflammatory effects of vaspin in 3T3-L1 adipocytes as well as increased insulin signaling by endogenous expression or exogenous treatment. The results provide evidence for potent anti-inflammatory action of vaspin not only in vascular cells but also in adipose tissue.

Implementation of a Home Monitoring System for Heart Failure Patients: A Feasibility Study.

BACKGROUND: Improving the management of patients with complex chronic disease is a substantial undertaking with the simultaneous goals of improving patient outcomes and controlling costs. Reducing avoidable hospitalization for such patients is a step toward both objectives. Some of the deterioration experienced in chronic disease patients occurs outside the view of their clinicians, and before the patient becomes overtly symptomatic. Home monitoring has been used for more than 20 years to detect deterioration earlier so that the patients could be treated before they became ill enough to require hospitalization. Patient participation is an important requirement for successful home monitoring. There has been some concern that patients would be unwilling or unable to engage in a program that collected multiple measurements. The Cedars-Sinai Cardiology Center provides a high-touch, intense management program for patients with congestive heart failure (CHF). A group of their patients were chosen to join a complex, multidevice home monitoring system to see whether such patients would find value in the additional effort. OBJECTIVE: The objective of our study was to determine whether patients already actively engaged in a high-touch intensive management program for CHF would take on the additional burden of a complex home monitoring effort. METHODS: A total of 20 patients from the Cedars-Sinai group were enrolled in a monitoring program utilizing 5 different devices. Anonymous surveys were collected from the patients to assess their satisfaction with the program. RESULTS: In total, 90% (18/20) completed the program, and 61% (11/20) submitted the survey. Among the 18 patients, overall compliance with the requested measurements was 70%. It was found that 73% (8/11) felt better about their health as a result of the program, whereas another 73% (8/11) believed that the care team now had a better picture of their health. CONCLUSIONS: Substantial patient compliance and satisfaction can be achieved in a sophisticated home monitoring program.

Identifying and Investigating Unexpected Response to Treatment: A Diabetes Case Study.

The availability of electronic health records creates fertile ground for developing computational models of various medical conditions. We present a new approach for detecting and analyzing patients with unexpected responses to treatment, building on machine learning and statistical methodology. Given a specific patient, we compute a statistical score for the deviation of the patient's response from responses observed in other patients having similar characteristics and medication regimens. These scores are used to define cohorts of patients showing deviant responses. Statistical tests are then applied to identify clinical features that correlate with these cohorts. We implement this methodology in a tool that is designed to assist researchers in the pharmaceutical field to uncover new features associated with reduced response to a treatment. It can also aid physicians by flagging patients who are not responding to treatment as expected and hence deserve more attention. The tool provides comprehensive visualizations of the analysis results and the supporting data, both at the cohort level and at the level of individual patients. We demonstrate the utility of our methodology and tool in a population of type II diabetic patients, treated with antidiabetic drugs, and monitored by the HbA1C test.

A system for identifying and investigating unexpected response to treatment.

The availability of electronic health records creates fertile ground for developing computational models for various medical conditions. Using machine learning, we can detect patients with unexpected responses to treatment and provide statistical testing and visualization tools to help further analysis. The new system was developed to help researchers uncover new features associated with reduced response to treatment, and to aid physicians in identifying patients that are not responding to treatment as expected and hence deserve more attention. The solution computes a statistical score for the deviation of a given patient's response from responses observed individuals with similar characteristics and medication regimens. Statistical tests are then applied to identify clinical features that correlate with cohorts of patients showing deviant responses. The results provide comprehensive visualizations, both at the cohort and the individual patient levels. We demonstrate the utility of this system in a population of diabetic patients.

Roundtable--the changing oncology landscape: evolution or revolution?

Complex challenges face all players in the oncology landscape, from health care policy leaders and third-party payers, to practicing physicians and nurses, to patients and their families. In these unsteady economic times, possible answers proposed by some may represent part of the problem to others. A distinguished panel assembled at the NCCN 18th Annual Conference: Advancing the Standard of Cancer Care to explore the changing oncology landscape. This article is the synopsis of that discussion, with panelists shedding light on such issues as the astronomic cost of medical care, the need for clinicians to think outside the formulary, and the therapeutic decision-making process in the new world of "big data."

Leveraging medical thesauri and physician feedback for improving medical literature retrieval for case queries.

OBJECTIVE: This paper presents a study of methods for medical literature retrieval for case queries, in which the goal is to retrieve literature articles similar to a given patient case. In particular, it focuses on analyzing the performance of state-of-the-art general retrieval methods and improving them by the use of medical thesauri and physician feedback. MATERIALS AND METHODS: The Kullback-Leibler divergence retrieval model with Dirichlet smoothing is used as the state-of-the-art general retrieval method. Pseudorelevance feedback and term weighing methods are proposed by leveraging MeSH and UMLS thesauri. Evaluation is performed on a test collection recently created for the ImageCLEF medical case retrieval challenge. RESULTS: Experimental results show that a well-tuned state-of-the-art general retrieval model achieves a mean average precision of 0.2754, but the performance can be improved by over 40% to 0.3980, through the proposed methods. DISCUSSION: The results over the ImageCLEF test collection, which is currently the best collection available for the task, are encouraging. There are, however, limitations due to small evaluation set size. The analysis shows that further refinement of the methods is necessary before they can be really useful in a clinical setting. CONCLUSION: Medical case-based literature retrieval is a critical search application that presents a number of unique challenges. This analysis shows that the state-of-the-art general retrieval models are reasonably good for the task, but the performance can be significantly improved by developing new task-specific retrieval models that incorporate medical thesauri and physician feedback.

A reminiscence.

The founding of the William Carlos Williams poetry competition for medical students is recounted. A few highlights from its nearly twenty-five years of operation are offered. Gleanings from the hearts and souls of some of the winning poets are shared.