Outcomes associated with chemotherapy in elderly patients with early stage operable breast cancer

Background. Various clinical trials have proved the efficacy of adjuvant chemotherapy in women with breast cancer. Chemotherapy efficacy and guidelines for its use differ by stage of tumor and age of the patient with no clear recommendations for patients aged 70 and above. Objective. To examine the clinical and economic outcomes associated with chemotherapy use in and to examine the disparities in treatment and survival in elderly patients with early stage operable breast cancer by age and axillary node status. Methods. We studied a cohort of 23,110 node positive and 31,572 node negative women aged 65 and over diagnosed with incident American Joint Committee on Cancer (AJCC) stage I, II or IIIa breast cancer between January 1, 1991 and December 31, 2002 using SEER-Medicare data. Total patient costs were estimated using the phase of care approach and adjusted cost estimates were obtained from regression analysis using a 3% discount rate. Cox proportional hazard ratio of mortality was used to determine the effectiveness of chemotherapy. Propensity score approach was also used to minimize the bias associated with receipt of chemotherapy. To assess disparity in treatment, multivariate logistic regression analyses were performed to assess the relative odds of receiving surgery, chemotherapy and radiation after BCS for African Americans compared to Whites. Results. Regression adjusted cost estimates for all node positive patients receiving chemotherapy was approximately $2,300 and was significantly higher (p<0.05) than for patients not receiving chemotherapy. Mortality was significantly lower in node positive and node negative women aged 65-74 years receiving chemotherapy. There was a significant difference between African American and White women in receiving BCS and radiation after BCS; however this difference was explained by patient demographics, tumor characteristics and socioeconomic status (SES). African American node positive women were 21% less likely to receive chemotherapy than White women (OR, 0.79; CI, 0.68-0.92) in multivariate analysis. Conclusion. Chemotherapy is associated with increased survival in patients aged 65-74 and total costs attributable to chemotherapy differ by phase and age of the patient. Underutilization of systemic adjuvant chemotherapy in African American women requires attention and may serve as potential areas for appropriate intervention.^

A systematic review of the evidence for use of herbal medicine for the treatment of acute diarrhea

Background. Acute diarrhea (AD) is an important cause of morbidity and mortality among both children and adults. An ideal antidiarrheal treatment should be safe, effective, compatible with Oral Rehydration Solution, and inexpensive. Herbal medicines, if effective, should fit these criteria as well or better than standard treatment. ^ Objective. The objective of the present study was to assess the effectiveness of plant preparations in patients with AD in reports of randomized and non-randomized controlled trials. ^ Aims. The aims of the present study were to identify effective antidiarrheal herbs and to identify potential antidiarrheal herbs for future studies of efficacy through well designed clinical trials in human populations. ^ Methods. Nineteen published studies of herbal management of AD were examined to identify effective plant preparations. Ten plant preparations including Berberine (Berberis aristata), tormentil root ( Potentialla tormentilla), baohauhau (from the baobaosan plant), carob (Ceratonia siliqua), pectin (Malus domestica), wood creosote (Creosote bush), guava (Psidium guajava L.), belladonna (Atropa belladonna), white bean (Phaseolis vulgaris), and wheat (Triticum aestivum) were identified. ^ Results. Qualitative data analysis of nineteen clinical trials indicated berberine’s potentially valuable antisecretory effects against diarrhea caused by Vibrio cholerae and enterotoxigenic Escherichia coli. Tormentil root showed significant efficacy against rotavirus-induced diarrhea; carob exhibited antidiarrheal properties not only by acting to detoxify and constipate but by providing a rich source of calories; guava and belladonna are antispasmodics and have been shown to relieve the symptoms of AD. Finally, white bean and wheat yielded favorable clinical and dietary outcomes in children with diarrhea. ^ Conclusion. The present study is the first to review the evidence for use of herbal compounds for treatment of AD. Future randomized controlled trials are needed to evaluate their efficacy and safety.^

Bisphosphonates for metastatic bone disease in lung cancer

Bisphosphonates have proven effectiveness in preventing skeletal-related events (SREs) in advanced breast cancer, prostate cancer and multiple myeloma. The purpose of this study was to assess efficacy of bisphosphonates in preventing SREs, in controlling pain, and in increasing life expectancy in lung cancer patients with bone metastases.^ We performed an electronic search in MEDLINE, EMBASE, Web of Science, and Cochrane library databases up to April 4, 2010. Hand searching and searching in clinicaltrials.gov were also performed. Two independent reviewers selected all clinical trials that included lung cancer patients with bone metastases treated with bisphosphonates. We excluded articles that involved cancers other than lung, patients without bone metastasis and treatment other than bisphosphonates. Outcome questions answered were efficacy measured as overall pain control, overall improvement in survival and reduction in skeletal-related events or SREs (fracture, cord compression, radiation or surgery to the bone, hypercalcemia of malignancy). The quality of each study was evaluated using the Cochrane Back Review group questionnaire to assess risk of bias (0-worst to 11-best). Data extraction and quality assessments were independently performed by two assessors. Meta-analyses were performed where more than one study with similar outcomes were found.^ We identified eight trials that met our inclusion criteria. Three studies evaluated zoledronic acid, three pamidronate, three clodronate and two ibandronate. Two were placebocontrol trials while two had multi-group comparisons (radiotherapy, radionucleotides, and chemotherapy) and two had different bisphosphonate as active controls. Quality scores ranged from 1-4 out of 11 suggesting high risk of bias. Studies failed to report adequate explanation of randomization procedures, concealment of randomization and blinding. Metaanalysis showed that patients treated with zoledronic acid alone had lower rates of developing SREs compared to placebo at 21 months (RR=0.80, 95% CI=0.66-0.97, p=0.02). Meta-analyses also showed increased pain control when a bisphosphonate was added to the existing treatment modality like chemotherapy or radiation (RR=1.17, 95% CI=1.03-1.34, p=0.02). However, pain control was not statistically significantly different among various bisphosphonates when other treatment modalities were not present. Despite improvement in SRE and pain control, bisphosphonates failed to show improvement in overall survival (Difference in means=109.1 days, 95% CI= -51.52 – 269.71, p=0.183).^ Adding biphosphonates to standard care improved pain control and reduced SREs. Biphosphonates did not improve overall survival. Further larger studies with higher quality are required to stengthen the evidence.^ Keywords/MeSH terms Bisphosphonates/diphosphonates: generic, chemical and trade names.^

Risk factors of pneumonia in traumatic spinal-cord-injured patients

Pneumonia is a well-documented and common respiratory infection in patients with acute traumatic spinal cord injuries, and may recur during the course of acute care. Using data from the North American Clinical Trials Network (NACTN) for Spinal Cord Injury, the incidence, timing, and recurrence of pneumonia were analyzed. The two main objectives were (1) to investigate the time and potential risk factors for the first occurrence of pneumonia using the Cox Proportional Hazards model, and (2) to investigate pneumonia recurrence and its risk factors using a Counting Process model that is a generalization of the Cox Proportional Hazards model. The results from survival analysis suggested that surgery, intubation, American Spinal Injury Association (ASIA) grade, direct admission to a NACTN site and age (older than 65 or not) were significant risks for first event of pneumonia and multiple events of pneumonia. The significance of this research is that it has the potential to identify patients at the time of admission who are at high risk for the incidence and recurrence of pneumonia. Knowledge and the time of occurrence of pneumonias are important factors for the development of prevention strategies and may also provide some insights into the selection of emerging therapies that compromise the immune system. ^

Geographic variation in the utilization and survival associated with oxaliplatin chemotherapy in patients with stage III colon cancer

Background: Overall objectives of this dissertation are to examine the geographic variation and socio-demographic disparities (by age, race and gender) in the utilization and survival of newly FDA-approved chemotherapy agents (Oxaliplatin-containing regimens) as well as to determine the cost-effectiveness of Oxaliplatin in a large nationwide and population-based cohort of Medicare patients with resected stage-III colon cancer. Methods: A retrospective cohort of 7,654 Medicare patients was identified from the Surveillance, Epidemiology and End Results – Medicare linked database. Multiple logistic regression was performed to examine the relationship between receipt of Oxaliplatin-containing chemotherapy and geographic regions while adjusting for other patient characteristics. Cox proportional hazard model was used to estimate the effect of Oxaliplatin-containing chemotherapy on the survival variation across regions using 2004-2005 data. Propensity score adjustments were also made to control for potential bias related to non-random allocation of the treatment group. We used Kaplan-Meier sample average estimator to calculate the cost of disease after cancer-specific surgery to death, loss-to follow-up or censorship. Results: Only 51% of the stage-III patients received adjuvant chemotherapy within three to six months of colon-cancer specific surgery. Patients in the rural regions were approximately 30% less likely to receive Oxaliplatin chemotherapy than those residing in a big metro region (OR=0.69, p=0.033). The hazard ratio for patients residing in metro region was comparable to those residing in big metro region (HR: 1.05, 95% CI: 0.49-2.28). Patients who received Oxalipaltin chemotherapy were 33% less likely to die than those received 5-FU only chemotherapy (adjusted HR=0.67, 95% CI: 0.41-1.11). KMSA-adjusted mean payments were almost 2.5 times higher in the Oxaliplatin-containing group compared to 5-FU only group ($45,378 versus $17,856). When compared to no chemotherapy group, ICER of 5-FU based regimen was $12,767 per LYG, and ICER of Oxaliplatin-chemotherapy was $60,863 per LYG. Oxaliplatin was found economically dominated by 5-FU only chemotherapy in this study population. Conclusion: Chemotherapy use varies across geographic regions. We also observed considerable survival differences across geographic regions; the difference remained even after adjusting for socio-demographic characteristics. The cost-effectiveness of Oxaliplatin in Medicare patients may be over-estimated in the clinical trials. Our study found 5-FU only chemotherapy cost-effective in adjuvant settings in patients with stage-III colon cancer.^

A hybrid method in combining treatment effects from matched and unmatched studies

In the biomedical studies, the general data structures have been the matched (paired) and unmatched designs. Recently, many researchers are interested in Meta-Analysis to obtain a better understanding from several clinical data of a medical treatment. The hybrid design, which is combined two data structures, may create the fundamental question for statistical methods and the challenges for statistical inferences. The applied methods are depending on the underlying distribution. If the outcomes are normally distributed, we would use the classic paired and two independent sample T-tests on the matched and unmatched cases. If not, we can apply Wilcoxon signed rank and rank sum test on each case. ^ To assess an overall treatment effect on a hybrid design, we can apply the inverse variance weight method used in Meta-Analysis. On the nonparametric case, we can use a test statistic which is combined on two Wilcoxon test statistics. However, these two test statistics are not in same scale. We propose the Hybrid Test Statistic based on the Hodges-Lehmann estimates of the treatment effects, which are medians in the same scale.^ To compare the proposed method, we use the classic meta-analysis T-test statistic on the combined the estimates of the treatment effects from two T-test statistics. Theoretically, the efficiency of two unbiased estimators of a parameter is the ratio of their variances. With the concept of Asymptotic Relative Efficiency (ARE) developed by Pitman, we show ARE of the hybrid test statistic relative to classic meta-analysis T-test statistic using the Hodges-Lemann estimators associated with two test statistics.^ From several simulation studies, we calculate the empirical type I error rate and power of the test statistics. The proposed statistic would provide effective tool to evaluate and understand the treatment effect in various public health studies as well as clinical trials.^

Association of ALLHAT investigator characteristics with participant recruitment

Clinical trials are often not successful because of the inability to recruit a sufficient number of patients. The Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT), the largest antihypertensive trial ever conducted, provided highly generalized results and successful recruitment of over 42,000 participants. The overall purpose of this study was to examine the association of investigator characteristics with anti-hypertensive (AHT) participant recruitment in ALLHAT. This secondary data analyses collected data from the ALLHAT investigator profile survey and related investigator characteristics to recruitment success. The sample size was 502 investigators, with recruitment data from 37,947AHT participants. Recruitment was dichotomized by categorizing all sites with recruitment numbers at or above the overall median recruitment number of 46 as "Successful Recruitment". Frequency distributions and univariate and multivariate logistic regression were conducted. When adjusting for all other factors, Hispanic ethnicity, suburban setting, Department of Veterans Affairs Medical Centers (VAMC) site type, number of clinical site staff working on the trial, study coordinator hours per week, medical conference sessions attended, the investigator's primary goal and the likelihood that a physician will convince a patient to continue on randomized treatment, have significant impacts on the recruitment success of ALLHAT investigators. Most of the ALLHAT investigators described their primary commitment as being towards their patients and not to scientific knowledge alone. However, investigators that distinguished themselves as leaders in research had greater recruitment success than investigators who were leaders in clinical practice. ALLHAT was a highly successful trial that proved that community based cardiovascular trials can be implemented on a large scale. Exploring characteristics of ALLHAT investigators provides data that can be generalized to sponsors, sites, and others interested in maximizing clinical trial recruitment numbers. Future studies should further evaluate investigator and study coordinator factors that impact cardiovascular clinical trial recruitment success.^

On Bayesian seamless phase I-II designs

Early phase clinical trial designs have long been the focus of interest for clinicians and statisticians working in oncology field. There are several standard phse I and phase II designs that have been widely-implemented in medical practice. For phase I design, the most commonly used methods are 3+3 and CRM. A newly-developed Bayesian model-based mTPI design has now been used by an increasing number of hospitals and pharmaceutical companies. The advantages and disadvantages of these three top phase I designs have been discussed in my work here and their performances were compared using simulated data. It was shown that mTPI design exhibited superior performance in most scenarios in comparison with 3+3 and CRM designs. ^ The next major part of my work is proposing an innovative seamless phase I/II design that allows clinicians to conduct phase I and phase II clinical trials simultaneously. Bayesian framework was implemented throughout the whole design. The phase I portion of the design adopts mTPI method, with the addition of futility rule which monitors the efficacy performance of the tested drugs. Dose graduation rules were proposed in this design to allow doses move forward from phase I portion of the study to phase II portion without interrupting the ongoing phase I dose-finding schema. Once a dose graduated to phase II, adaptive randomization was used to randomly allocated patients into different treatment arms, with the intention of more patients being assigned to receive more promising dose(s). Again simulations were performed to compare the performance of this innovative phase I/II design with a recently published phase I/II design, together with the conventional phase I and phase II designs. The simulation results indicated that the seamless phase I/II design outperform the other two competing methods in most scenarios, with superior trial power and the fact that it requires smaller sample size. It also significantly reduces the overall study time. ^ Similar to other early phase clinical trial designs, the proposed seamless phase I/II design requires that the efficacy and safety outcomes being able to be observed in a short time frame. This limitation can be overcome by using validated surrogate marker for the efficacy and safety endpoints.^

OXIDATIVE STRESS BASED STRATEGIES FOR ENHANCING THE EFFICACY OF HISTONE DEACETYLASE INHIBITORS (HDACi)

Histone deacetylase inhibitors (HDACi) are anti-cancer drugs that primarily act upon acetylation of histones, however they also increase levels of intracellular reactive oxygen species (ROS). We hypothesized that agents that cause oxidative stress might enhance the efficacy of HDACi. To test this hypothesis, we treated acute lymphocytic leukemia cells (ALL) with HDACi and adaphostin (ROS generating agent). The combination of two different HDACi (vorinostat or entinostat) with adaphostin synergistically induced apoptosis in ALL. This synergistic effect was blocked when cells were pre-treated with the caspase-9 inhibitor, LEHD. In addition, we showed that loss of the mitochondrial membrane potential is the earliest event observed starting at 12 h. Following this event, we observed increased levels of superoxide at 16 h, and ultimately caspase-3 activation. Pre-treatment with the antioxidant N-acetylcysteine (NAC) blocked ROS generation and reversed the loss of mitochondrial membrane potential for both combinations. Interestingly, DNA fragmentation and caspase-3 activity was only blocked by NAC in cells treated with vorinostat-adaphostin; but not with entinostat-adaphostin. These results suggest that different redox mechanisms are involved in the induction of ROS-mediated apoptosis. To further understand these events, we studied the role of the antioxidants glutathione (GSH) and thioredoxin (Trx). We found that the combination of entinostat-adaphostin induced acetylation of the antioxidant thioredoxin (Trx) and decreased intracellular levels of GSH. However, no effect on Trx activity was observed in either combination. In addition, pre-treatment with GSH ethyl ester, a soluble form of GSH, did not block DNA fragmentation. Together these results suggested that GSH and Trx are not major players in the induction of oxidative stress. Array data examining the expression of genes involved in oxidative stress demonstrated a differential regulation between cells treated with vorinostat-adaphostin and entinostat-adaphostin. Some of the genes differentially expressed between the combinations include aldehyde oxidase 1, glutathione peroxidase-5, -6, peroxiredoxin 6 and myeloperoxidase. Taken together, these experimental results indicate that the synergistic activity of two different HDACi with adaphostin is mediated by distinct redox mechanisms in ALL cells. Understanding the mechanism involved in these combinations will advance scientific knowledge of how the action of HDACi could be augmented in leukemia models. Moreover, this information could be used for the development of effective clinical trials combining HDACi with other anticancer agents.

Data Accuracy in Medical Record Abstraction

Clinical Research Data Quality Literature Review and Pooled Analysis We present a literature review and secondary analysis of data accuracy in clinical research and related secondary data uses. A total of 93 papers meeting our inclusion criteria were categorized according to the data processing methods. Quantitative data accuracy information was abstracted from the articles and pooled. Our analysis demonstrates that the accuracy associated with data processing methods varies widely, with error rates ranging from 2 errors per 10,000 files to 5019 errors per 10,000 fields. Medical record abstraction was associated with the highest error rates (70–5019 errors per 10,000 fields). Data entered and processed at healthcare facilities had comparable error rates to data processed at central data processing centers. Error rates for data processed with single entry in the presence of on-screen checks were comparable to double entered data. While data processing and cleaning methods may explain a significant amount of the variability in data accuracy, additional factors not resolvable here likely exist. Defining Data Quality for Clinical Research: A Concept Analysis Despite notable previous attempts by experts to define data quality, the concept remains ambiguous and subject to the vagaries of natural language. This current lack of clarity continues to hamper research related to data quality issues. We present a formal concept analysis of data quality, which builds on and synthesizes previously published work. We further posit that discipline-level specificity may be required to achieve the desired definitional clarity. To this end, we combine work from the clinical research domain with findings from the general data quality literature to produce a discipline-specific definition and operationalization for data quality in clinical research. While the results are helpful to clinical research, the methodology of concept analysis may be useful in other fields to clarify data quality attributes and to achieve operational definitions. Medical Record Abstractor’s Perceptions of Factors Impacting the Accuracy of Abstracted Data Medical record abstraction (MRA) is known to be a significant source of data errors in secondary data uses. Factors impacting the accuracy of abstracted data are not reported consistently in the literature. Two Delphi processes were conducted with experienced medical record abstractors to assess abstractor’s perceptions about the factors. The Delphi process identified 9 factors that were not found in the literature, and differed with the literature by 5 factors in the top 25%. The Delphi results refuted seven factors reported in the literature as impacting the quality of abstracted data. The results provide insight into and indicate content validity of a significant number of the factors reported in the literature. Further, the results indicate general consistency between the perceptions of clinical research medical record abstractors and registry and quality improvement abstractors. Distributed Cognition Artifacts on Clinical Research Data Collection Forms Medical record abstraction, a primary mode of data collection in secondary data use, is associated with high error rates. Distributed cognition in medical record abstraction has not been studied as a possible explanation for abstraction errors. We employed the theory of distributed representation and representational analysis to systematically evaluate cognitive demands in medical record abstraction and the extent of external cognitive support employed in a sample of clinical research data collection forms. We show that the cognitive load required for abstraction in 61% of the sampled data elements was high, exceedingly so in 9%. Further, the data collection forms did not support external cognition for the most complex data elements. High working memory demands are a possible explanation for the association of data errors with data elements requiring abstractor interpretation, comparison, mapping or calculation. The representational analysis used here can be used to identify data elements with high cognitive demands.

Novel Imaging-Based Techniques Reveal a Role for PD-1/PD-L1 in Tumor Immune Surveillance in the Lung

The binding of immune inhibitory receptor Programmed Death 1 (PD-1) on T cells to its ligand PD-L1 has been implicated as a major contributor to tumor induced immune suppression. Clinical trials of PD-L1 blockade have proven effective in unleashing therapeutic anti-tumor immune responses in a subset of patients with advanced melanoma, yet current response rates are low for reasons that remain unclear. Hypothesizing that the PD-1/PD-L1 pathway regulates T cell surveillance within the tumor microenvironment, we employed intravital microscopy to investigate the in vivo impact of PD-L1 blocking antibody upon tumor-associated immune cell migration. However, current analytical methods of intravital dynamic microscopy data lack the ability to identify cellular targets of T cell interactions in vivo, a crucial means for discovering which interactions are modulated by therapeutic intervention. By developing novel imaging techniques that allowed us to better analyze tumor progression and T cell dynamics in the microenvironment; we were able to explore the impact of PD-L1 blockade upon the migratory properties of tumor-associated immune cells, including T cells and antigen presenting cells, in lung tumor progression. Our results demonstrate that early changes in tumor morphology may be indicative of responsiveness to anti-PD-L1 therapy. We show that immune cells in the tumor microenvironment as well as tumors themselves express PD-L1, but immune phenotype alone is not a predictive marker of effective anti-tumor responses. Through a novel method in which we quantify T cell interactions, we show that T cells are largely engaged in interactions with dendritic cells in the tumor microenvironment. Additionally, we show that during PD-L1 blockade, non-activated T cells are recruited in greater numbers into the tumor microenvironment and engage more preferentially with dendritic cells. We further show that during PD-L1 blockade, activated T cells engage in more confined, immune synapse-like interactions with dendritic cells, as opposed to more dynamic, kinapse-like interactions with dendritic cells when PD-L1 is free to bind its receptor. By advancing the contextual analysis of anti-tumor immune surveillance in vivo, this study implicates the interaction between T cells and tumor-associated dendritic cells as a possible modulator in targeting PD-L1 for anti-tumor immunotherapy.

A New Multidisciplinary Home Care Telemedicine System to Monitor Stable Chronic Human Immunodeficiency Virus-Infected Patients: A Randomized Study

BACKGROUND: Antiretroviral therapy has changed the natural history of human immunodeficiency virus (HIV) infection in developed countries, where it has become a chronic disease. This clinical scenario requires a new approach to simplify follow-up appointments and facilitate access to healthcare professionals. METHODOLOGY: We developed a new internet-based home care model covering the entire management of chronic HIV-infected patients. This was called Virtual Hospital. We report the results of a prospective randomised study performed over two years, comparing standard care received by HIV-infected patients with Virtual Hospital care. HIV-infected patients with access to a computer and broadband were randomised to be monitored either through Virtual Hospital (Arm I) or through standard care at the day hospital (Arm II). After one year of follow up, patients switched their care to the other arm. Virtual Hospital offered four main services: Virtual Consultations, Telepharmacy, Virtual Library and Virtual Community. A technical and clinical evaluation of Virtual Hospital was carried out. FINDINGS: Of the 83 randomised patients, 42 were monitored during the first year through Virtual Hospital (Arm I) and 41 through standard care (Arm II). Baseline characteristics of patients were similar in the two arms. The level of technical satisfaction with the virtual system was high: 85% of patients considered that Virtual Hospital improved their access to clinical data and they felt comfortable with the videoconference system. Neither clinical parameters [level of CD4+ T lymphocytes, proportion of patients with an undetectable level of viral load (p = 0.21) and compliance levels >90% (p = 0.58)] nor the evaluation of quality of life or psychological questionnaires changed significantly between the two types of care. CONCLUSIONS: Virtual Hospital is a feasible and safe tool for the multidisciplinary home care of chronic HIV patients. Telemedicine should be considered as an appropriate support service for the management of chronic HIV infection. TRIAL REGISTRATION: Clinical-Trials.gov: NCT01117675.

Modelos de métrica para sistemas electrónicos de adquisición de datos y de procesamiento para investigación clínica con dispositivos cardiacos implantables

RESUMEN Las enfermedades cardiovasculares constituyen en la actualidad la principal causa de mortalidad en el mundo y se prevé que sigan siéndolo en un futuro, generando además elevados costes para los sistemas de salud. Los dispositivos cardiacos implantables constituyen una de las opciones para el diagnóstico y el tratamiento de las alteraciones del ritmo cardiaco. La investigación clínica con estos dispositivos alcanza gran relevancia para combatir estas enfermedades que tanto afectan a nuestra sociedad. Tanto la industria farmacéutica y de tecnología médica, como los propios investigadores, cada día se ven involucrados en un mayor número de proyectos de investigación clínica. No sólo el incremento en su volumen, sino el aumento de la complejidad, están generando mayores gastos en las actividades asociadas a la investigación médica. Esto está conduciendo a las compañías del sector sanitario a estudiar nuevas soluciones que les permitan reducir los costes de los estudios clínicos. Las Tecnologías de la Información y las Comunicaciones han facilitado la investigación clínica, especialmente en la última década. Los sistemas y aplicaciones electrónicos han proporcionado nuevas posibilidades en la adquisición, procesamiento y análisis de los datos. Por otro lado, la tecnología web propició la aparición de los primeros sistemas electrónicos de adquisición de datos, que han ido evolucionando a lo largo de los últimos años. Sin embargo, la mejora y perfeccionamiento de estos sistemas sigue siendo crucial para el progreso de la investigación clínica. En otro orden de cosas, la forma tradicional de realizar los estudios clínicos con dispositivos cardiacos implantables precisaba mejorar el tratamiento de los datos almacenados por estos dispositivos, así como para su fusión con los datos clínicos recopilados por investigadores y pacientes. La justificación de este trabajo de investigación se basa en la necesidad de mejorar la eficiencia en la investigación clínica con dispositivos cardiacos implantables, mediante la reducción de costes y tiempos de desarrollo de los proyectos, y el incremento de la calidad de los datos recopilados y el diseño de soluciones que permitan obtener un mayor rendimiento de los datos mediante la fusión de datos de distintas fuentes o estudios. Con este fin se proponen como objetivos específicos de este proyecto de investigación dos nuevos modelos: - Un modelo de recuperación y procesamiento de datos para los estudios clínicos con dispositivos cardiacos implantables, que permita estructurar y estandarizar estos procedimientos, con el fin de reducir tiempos de desarrollo Modelos de Métrica para Sistemas Electrónicos de Adquisición de Datos y de Procesamiento para Investigación Clínica con Dispositivos Cardiacos Implantables de estas tareas, mejorar la calidad del resultado obtenido, disminuyendo en consecuencia los costes. - Un modelo de métrica integrado en un Sistema Electrónico de Adquisición de Datos (EDC) que permita analizar los resultados del proyecto de investigación y, particularmente del rendimiento obtenido del EDC, con el fin de perfeccionar estos sistemas y reducir tiempos y costes de desarrollo del proyecto y mejorar la calidad de los datos clínicos recopilados. Como resultado de esta investigación, el modelo de procesamiento propuesto ha permitido reducir el tiempo medio de procesamiento de los datos en más de un 90%, los costes derivados del mismo en más de un 85% y todo ello, gracias a la automatización de la extracción y almacenamiento de los datos, consiguiendo una mejora de la calidad de los mismos. Por otro lado, el modelo de métrica posibilita el análisis descriptivo detallado de distintos indicadores que caracterizan el rendimiento del proyecto de investigación clínica, haciendo factible además la comparación entre distintos estudios. La conclusión de esta tesis doctoral es que los resultados obtenidos han demostrado que la utilización en estudios clínicos reales de los dos modelos desarrollados ha conducido a una mejora en la eficiencia de los proyectos, reduciendo los costes globales de los mismos, disminuyendo los tiempos de ejecución, e incrementando la calidad de los datos recopilados. Las principales aportaciones de este trabajo de investigación al conocimiento científico son la implementación de un sistema de procesamiento inteligente de los datos almacenados por los dispositivos cardiacos implantables, la integración en el mismo de una base de datos global y optimizada para todos los modelos de dispositivos, la generación automatizada de un repositorio unificado de datos clínicos y datos de dispositivos cardiacos implantables, y el diseño de una métrica aplicada e integrable en los sistemas electrónicos de adquisición de datos para el análisis de resultados de rendimiento de los proyectos de investigación clínica. ABSTRACT Cardiovascular diseases are the main cause of death worldwide and it is expected to continue in the future, generating high costs for health care systems. Implantable cardiac devices have become one of the options for diagnosis and treatment of cardiac rhythm disorders. Clinical research with these devices has acquired great importance to fight against these diseases that affect so many people in our society. Both pharmaceutical and medical technology companies, and also investigators, are involved in an increasingly number of clinical research projects. The growth in volume and the increase in medical research complexity are contributing to raise the expenditure level associated with clinical investigation. This situation is driving health care sector companies to explore new solutions to reduce clinical trial costs. Information and Communication Technologies have facilitated clinical research, mainly in the last decade. Electronic systems and software applications have provided new possibilities in the acquisition, processing and analysis of clinical studies data. On the other hand, web technology contributed to the appearance of the first electronic data capture systems that have evolved during the last years. Nevertheless, improvement of these systems is still a key aspect for the progress of clinical research. On a different matter, the traditional way to develop clinical studies with implantable cardiac devices needed an improvement in the processing of the data stored by these devices, and also in the merging of these data with the data collected by investigators and patients. The rationale of this research is based on the need to improve the efficiency in clinical investigation with implantable cardiac devices, by means of reduction in costs and time of projects development, as well as improvement in the quality of information obtained from the studies and to obtain better performance of data through the merging of data from different sources or trials. The objective of this research project is to develop the next two models: • A model for the retrieval and processing of data for clinical studies with implantable cardiac devices, enabling structure and standardization of these procedures, in order to reduce the time of development of these tasks, to improve the quality of the results, diminish therefore costs. • A model of metric integrated in an Electronic Data Capture system (EDC) that allow to analyze the results of the research project, and particularly the EDC performance, in order to improve those systems and to reduce time and costs of the project, and to get a better quality of the collected clinical data. As a result of this work, the proposed processing model has led to a reduction of the average time for data processing by more than 90 per cent, of related costs by more than 85 per cent, and all of this, through automatic data retrieval and storage, achieving an improvement of quality of data. On the other hand, the model of metrics makes possible a detailed descriptive analysis of a set of indicators that characterize the performance of each research project, allowing inter‐studies comparison. This doctoral thesis results have demonstrated that the application of the two developed models in real clinical trials has led to an improvement in projects efficiency, reducing global costs, diminishing time in execution, and increasing quality of data collected. The main contributions to scientific knowledge of this research work are the implementation of an intelligent processing system for data stored by implantable cardiac devices, the integration in this system of a global and optimized database for all models of devices, the automatic creation of an unified repository of clinical data and data stored by medical devices, and the design of a metric to be applied and integrated in electronic data capture systems to analyze the performance results of clinical research projects.

Propuesta de algoritmos de predicción de glucosa en pacientes diabéticos

La diabetes mellitus es el conjunto de alteraciones provocadas por un defecto en la cantidad de insulina secretada o por un aprovechamiento deficiente de la misma. Es causa directa de complicaciones a corto, medio y largo plazo que disminuyen la calidad y las expectativas de vida de las personas con diabetes. La diabetes mellitus es en la actualidad uno de los problemas más importantes de salud. Ha triplicado su prevalencia en los últimos 20 anos y para el año 2025 se espera que existan casi 300 millones de personas con diabetes. Este aumento de la prevalencia junto con la morbi-mortalidad asociada a sus complicaciones micro y macro-vasculares convierten la diabetes en una carga para los sistemas sanitarios, sus recursos económicos y sus profesionales, haciendo de la enfermedad un problema individual y de salud pública de enormes proporciones. De momento no existe cura a esta enfermedad, de modo que el objetivo terapéutico del tratamiento de la diabetes se centra en la normalización de la glucemia intentando minimizar los eventos de hiper e hipoglucemia y evitando la aparición o al menos retrasando la evolución de las complicaciones vasculares, que constituyen la principal causa de morbi-mortalidad de las personas con diabetes. Un adecuado control diabetológico implica un tratamiento individualizado que considere multitud de factores para cada paciente (edad, actividad física, hábitos alimentarios, presencia de complicaciones asociadas o no a la diabetes, factores culturales, etc.). Sin embargo, a corto plazo, las dos variables más influyentes que el paciente ha de manejar para intervenir sobre su nivel glucémico son la insulina administrada y la dieta. Ambas presentan un retardo entre el momento de su aplicación y el comienzo de su acción, asociado a la absorción de los mismos. Por este motivo la capacidad de predecir la evolución del perfil glucémico en un futuro cercano, ayudara al paciente a tomar las decisiones adecuadas para mantener un buen control de su enfermedad y evitar situaciones de riesgo. Este es el objetivo de la predicción en diabetes: adelantar la evolución del perfil glucémico en un futuro cercano para ayudar al paciente a adaptar su estilo de vida y sus acciones correctoras, con el propósito de que sus niveles de glucemia se aproximen a los de una persona sana, evitando así los síntomas y complicaciones de un mal control. La aparición reciente de los sistemas de monitorización continua de glucosa ha proporcionado nuevas alternativas. La disponibilidad de un registro exhaustivo de las variaciones del perfil glucémico, con un periodo de muestreo de entre uno y cinco minutos, ha favorecido el planteamiento de nuevos modelos que tratan de predecir la glucemia utilizando tan solo las medidas anteriores de glucemia o al menos reduciendo significativamente la información de entrada a los algoritmos. El hecho de requerir menor intervención por parte del paciente, abre nuevas posibilidades de aplicación de los predictores de glucemia, haciéndose viable su uso en tiempo real, como sistemas de ayuda a la decisión, como detectores de situaciones de riesgo o integrados en algoritmos automáticos de control. En esta tesis doctoral se proponen diferentes algoritmos de predicción de glucemia para pacientes con diabetes, basados en la información registrada por un sistema de monitorización continua de glucosa así como incorporando la información de la insulina administrada y la ingesta de carbohidratos. Los algoritmos propuestos han sido evaluados en simulación y utilizando datos de pacientes registrados en diferentes estudios clínicos. Para ello se ha desarrollado una amplia metodología, que trata de caracterizar las prestaciones de los modelos de predicción desde todos los puntos de vista: precisión, retardo, ruido y capacidad de detección de situaciones de riesgo. Se han desarrollado las herramientas de simulación necesarias y se han analizado y preparado las bases de datos de pacientes. También se ha probado uno de los algoritmos propuestos para comprobar la validez de la predicción en tiempo real en un escenario clínico. Se han desarrollado las herramientas que han permitido llevar a cabo el protocolo experimental definido, en el que el paciente consulta la predicción bajo demanda y tiene el control sobre las variables metabólicas. Este experimento ha permitido valorar el impacto sobre el control glucémico del uso de la predicción de glucosa. ABSTRACT Diabetes mellitus is the set of alterations caused by a defect in the amount of secreted insulin or a suboptimal use of insulin. It causes complications in the short, medium and long term that affect the quality of life and reduce the life expectancy of people with diabetes. Diabetes mellitus is currently one of the most important health problems. Prevalence has tripled in the past 20 years and estimations point out that it will affect almost 300 million people by 2025. Due to this increased prevalence, as well as to morbidity and mortality associated with micro- and macrovascular complications, diabetes has become a burden on health systems, their financial resources and their professionals, thus making the disease a major individual and a public health problem. There is currently no cure for this disease, so that the therapeutic goal of diabetes treatment focuses on normalizing blood glucose events. The aim is to minimize hyper- and hypoglycemia and to avoid, or at least to delay, the appearance and development of vascular complications, which are the main cause of morbidity and mortality among people with diabetes. A suitable, individualized and controlled treatment for diabetes involves many factors that need to be considered for each patient: age, physical activity, eating habits, presence of complications related or unrelated to diabetes, cultural factors, etc. However, in the short term, the two most influential variables that the patient has available in order to manage his/her glycemic levels are administered insulin doses and diet. Both suffer from a delay between their time of application and the onset of the action associated with their absorption. Therefore, the ability to predict the evolution of the glycemic profile in the near future could help the patient to make appropriate decisions on how to maintain good control of his/her disease and to avoid risky situations. Hence, the main goal of glucose prediction in diabetes consists of advancing the evolution of glycemic profiles in the near future. This would assist the patient in adapting his/her lifestyle and in taking corrective actions in a way that blood glucose levels approach those of a healthy person, consequently avoiding the symptoms and complications of a poor glucose control. The recent emergence of continuous glucose monitoring systems has provided new alternatives in this field. The availability of continuous records of changes in glycemic profiles (with a sampling period of one or five minutes) has enabled the design of new models which seek to predict blood glucose by using automatically read glucose measurements only (or at least, reducing significantly the data input manually to the algorithms). By requiring less intervention by the patient, new possibilities are open for the application of glucose predictors, making its use feasible in real-time applications, such as: decision support systems, hypo- and hyperglycemia detectors, integration into automated control algorithms, etc. In this thesis, different glucose prediction algorithms are proposed for patients with diabetes. These are based on information recorded by a continuous glucose monitoring system and incorporate information of the administered insulin and carbohydrate intakes. The proposed algorithms have been evaluated in-silico and using patients’ data recorded in different clinical trials. A complete methodology has been developed to characterize the performance of predictive models from all points of view: accuracy, delay, noise and ability to detect hypo- and hyperglycemia. In addition, simulation tools and patient databases have been deployed. One of the proposed algorithms has additionally been evaluated in terms of real-time prediction performance in a clinical scenario in which the patient checked his/her glucose predictions on demand and he/she had control on his/her metabolic variables. This has allowed assessing the impact of using glucose prediction on glycemic control. The tools to carry out the defined experimental protocols were also developed in this thesis.

Implementación del modelo RIM de HL7 v3 en orientación a objetos y su uso en procesos de interoperabilidad semántica

El trabajo ha sido realizado dentro del marco de los proyectos EURECA (Enabling information re-Use by linking clinical REsearch and Care) e INTEGRATE (Integrative Cancer Research Through Innovative Biomedical Infrastructures), en los que colabora el Grupo de Informática Biomédica de la UPM junto a otras universidades e instituciones sanitarias europeas. En ambos proyectos se desarrollan servicios e infraestructuras con el objetivo principal de almacenar información clínica, procedente de fuentes diversas (como por ejemplo de historiales clínicos electrónicos de hospitales, de ensayos clínicos o artículos de investigación biomédica), de una forma común y fácilmente accesible y consultable para facilitar al máximo la investigación de estos ámbitos, de manera colaborativa entre instituciones. Esta es la idea principal de la interoperabilidad semántica en la que se concentran ambos proyectos, siendo clave para el correcto funcionamiento del software del que se componen. El intercambio de datos con un modelo de representación compartido, común y sin ambigüedades, en el que cada concepto, término o dato clínico tendrá una única forma de representación. Lo cual permite la inferencia de conocimiento, y encaja perfectamente en el contexto de la investigación médica. En concreto, la herramienta a desarrollar en este trabajo también está orientada a la idea de maximizar la interoperabilidad semántica, pues se ocupa de la carga de información clínica con un formato estandarizado en un modelo común de almacenamiento de datos, implementado en bases de datos relacionales. El trabajo ha sido desarrollado en el periodo comprendido entre el 3 de Febrero y el 6 de Junio de 2014. Se ha seguido un ciclo de vida en cascada para la organización del trabajo realizado en las tareas de las que se compone el proyecto, de modo que una fase no puede iniciarse sin que se haya terminado, revisado y aceptado la fase anterior. Exceptuando la tarea de documentación del trabajo (para la elaboración de esta memoria), que se ha desarrollado paralelamente a todas las demás. ----ABSTRACT--- The project has been developed during the second semester of the 2013/2014 academic year. This Project has been done inside EURECA and INTEGRATE European biomedical research projects, where the GIB (Biomedical Informatics Group) of the UPM works as a partner. Both projects aim is to develop platforms and services with the main goal of storing clinical information (e.g. information from hospital electronic health records (EHRs), clinical trials or research articles) in a common way and easy to access and query, in order to support medical research. The whole software environment of these projects is based on the idea of semantic interoperability, which means the ability of computer systems to exchange data with unambiguous and shared meaning. This idea allows knowledge inference, which fits perfectly in medical research context. The tool to develop in this project is also "semantic operability-oriented". Its purpose is to store standardized clinical information in a common data model, implemented in relational databases. The project has been performed during the period between February 3rd and June 6th, of 2014. It has followed a "Waterfall model" of software development, in which progress is seen as flowing steadily downwards through its phases. Each phase starts when its previous phase has been completed and reviewed. The task of documenting the project‟s work is an exception; it has been performed in a parallel way to the rest of the tasks.