Improving the accuracy of radiation pneumonitis dose response models

The prognosis for lung cancer patients remains poor. Five year survival rates have been reported to be 15%. Studies have shown that dose escalation to the tumor can lead to better local control and subsequently better overall survival. However, dose to lung tumor is limited by normal tissue toxicity. The most prevalent thoracic toxicity is radiation pneumonitis. In order to determine a safe dose that can be delivered to the healthy lung, researchers have turned to mathematical models predicting the rate of radiation pneumonitis. However, these models rely on simple metrics based on the dose-volume histogram and are not yet accurate enough to be used for dose escalation trials. The purpose of this work was to improve the fit of predictive risk models for radiation pneumonitis and to show the dosimetric benefit of using the models to guide patient treatment planning. The study was divided into 3 specific aims. The first two specifics aims were focused on improving the fit of the predictive model. In Specific Aim 1 we incorporated information about the spatial location of the lung dose distribution into a predictive model. In Specific Aim 2 we incorporated ventilation-based functional information into a predictive pneumonitis model. In the third specific aim a proof of principle virtual simulation was performed where a model-determined limit was used to scale the prescription dose. The data showed that for our patient cohort, the fit of the model to the data was not improved by incorporating spatial information. Although we were not able to achieve a significant improvement in model fit using pre-treatment ventilation, we show some promising results indicating that ventilation imaging can provide useful information about lung function in lung cancer patients. The virtual simulation trial demonstrated that using a personalized lung dose limit derived from a predictive model will result in a different prescription than what was achieved with the clinically used plan; thus demonstrating the utility of a normal tissue toxicity model in personalizing the prescription dose.

A BAYESIAN APPROACH TO DOSE-RESPONSE ASSESSMENT AND DRUG-DRUG INTERACTION ANALYSIS: APPLICATION TO IN VITRO STUDIES

The considerable search for synergistic agents in cancer research is motivated by the therapeutic benefits achieved by combining anti-cancer agents. Synergistic agents make it possible to reduce dosage while maintaining or enhancing a desired effect. Other favorable outcomes of synergistic agents include reduction in toxicity and minimizing or delaying drug resistance. Dose-response assessment and drug-drug interaction analysis play an important part in the drug discovery process, however analysis are often poorly done. This dissertation is an effort to notably improve dose-response assessment and drug-drug interaction analysis. The most commonly used method in published analysis is the Median-Effect Principle/Combination Index method (Chou and Talalay, 1984). The Median-Effect Principle/Combination Index method leads to inefficiency by ignoring important sources of variation inherent in dose-response data and discarding data points that do not fit the Median-Effect Principle. Previous work has shown that the conventional method yields a high rate of false positives (Boik, Boik, Newman, 2008; Hennessey, Rosner, Bast, Chen, 2010) and, in some cases, low power to detect synergy. There is a great need for improving the current methodology. We developed a Bayesian framework for dose-response modeling and drug-drug interaction analysis. First, we developed a hierarchical meta-regression dose-response model that accounts for various sources of variation and uncertainty and allows one to incorporate knowledge from prior studies into the current analysis, thus offering a more efficient and reliable inference. Second, in the case that parametric dose-response models do not fit the data, we developed a practical and flexible nonparametric regression method for meta-analysis of independently repeated dose-response experiments. Third, and lastly, we developed a method, based on Loewe additivity that allows one to quantitatively assess interaction between two agents combined at a fixed dose ratio. The proposed method makes a comprehensive and honest account of uncertainty within drug interaction assessment. Extensive simulation studies show that the novel methodology improves the screening process of effective/synergistic agents and reduces the incidence of type I error. We consider an ovarian cancer cell line study that investigates the combined effect of DNA methylation inhibitors and histone deacetylation inhibitors in human ovarian cancer cell lines. The hypothesis is that the combination of DNA methylation inhibitors and histone deacetylation inhibitors will enhance antiproliferative activity in human ovarian cancer cell lines compared to treatment with each inhibitor alone. By applying the proposed Bayesian methodology, in vitro synergy was declared for DNA methylation inhibitor, 5-AZA-2'-deoxycytidine combined with one histone deacetylation inhibitor, suberoylanilide hydroxamic acid or trichostatin A in the cell lines HEY and SKOV3. This suggests potential new epigenetic therapies in cell growth inhibition of ovarian cancer cells.

Logistic regression models for ordinal response: A study of self-efficacy in colorectal cancer screening

The ordinal logistic regression models are used to analyze the dependant variable with multiple outcomes that can be ranked, but have been underutilized. In this study, we describe four logistic regression models for analyzing the ordinal response variable. ^ In this methodological study, the four regression models are proposed. The first model uses the multinomial logistic model. The second is adjacent-category logit model. The third is the proportional odds model and the fourth model is the continuation-ratio model. We illustrate and compare the fit of these models using data from the survey designed by the University of Texas, School of Public Health research project PCCaSO (Promoting Colon Cancer Screening in people 50 and Over), to study the patient’s confidence in the completion colorectal cancer screening (CRCS). ^ The purpose of this study is two fold: first, to provide a synthesized review of models for analyzing data with ordinal response, and second, to evaluate their usefulness in epidemiological research, with particular emphasis on model formulation, interpretation of model coefficients, and their implications. Four ordinal logistic models that are used in this study include (1) Multinomial logistic model, (2) Adjacent-category logistic model [9], (3) Continuation-ratio logistic model [10], (4) Proportional logistic model [11]. We recommend that the analyst performs (1) goodness-of-fit tests, (2) sensitivity analysis by fitting and comparing different models.^

Dynamics of a minimal model of interlocked positive and negative feedback loops of transcriptional regulation by cAMP-response element binding proteins.

cAMP-response element binding (CREB) proteins are involved in transcriptional regulation in a number of cellular processes (e.g., neural plasticity and circadian rhythms). The CREB family contains activators and repressors that may interact through positive and negative feedback loops. These loops can be generated by auto- and cross-regulation of expression of CREB proteins, via CRE elements in or near their genes. Experiments suggest that such feedback loops may operate in several systems (e.g., Aplysia and rat). To understand the functional implications of such feedback loops, which are interlocked via cross-regulation of transcription, a minimal model with a positive and negative loop was developed and investigated using bifurcation analysis. Bifurcation analysis revealed diverse nonlinear dynamics (e.g., bistability and oscillations). The stability of steady states or oscillations could be changed by time delays in the synthesis of the activator (CREB1) or the repressor (CREB2). Investigation of stochastic fluctuations due to small numbers of molecules of CREB1 and CREB2 revealed a bimodal distribution of CREB molecules in the bistability region. The robustness of the stable HIGH and LOW states of CREB expression to stochastic noise differs, and a critical number of molecules was required to sustain the HIGH state for days or longer. Increasing positive feedback or decreasing negative feedback also increased the lifetime of the HIGH state, and persistence of this state may correlate with long-term memory formation. A critical number of molecules was also required to sustain robust oscillations of CREB expression. If a steady state was near a deterministic Hopf bifurcation point, stochastic resonance could induce oscillations. This comparative analysis of deterministic and stochastic dynamics not only provides insights into the possible dynamics of CREB regulatory motifs, but also demonstrates a framework for understanding other regulatory processes with similar network architecture.

Differential dynamic properties of scleroderma fibroblasts in response to perturbation of environmental stimuli.

Diseases are believed to arise from dysregulation of biological systems (pathways) perturbed by environmental triggers. Biological systems as a whole are not just the sum of their components, rather ever-changing, complex and dynamic systems over time in response to internal and external perturbation. In the past, biologists have mainly focused on studying either functions of isolated genes or steady-states of small biological pathways. However, it is systems dynamics that play an essential role in giving rise to cellular function/dysfunction which cause diseases, such as growth, differentiation, division and apoptosis. Biological phenomena of the entire organism are not only determined by steady-state characteristics of the biological systems, but also by intrinsic dynamic properties of biological systems, including stability, transient-response, and controllability, which determine how the systems maintain their functions and performance under a broad range of random internal and external perturbations. As a proof of principle, we examine signal transduction pathways and genetic regulatory pathways as biological systems. We employ widely used state-space equations in systems science to model biological systems, and use expectation-maximization (EM) algorithms and Kalman filter to estimate the parameters in the models. We apply the developed state-space models to human fibroblasts obtained from the autoimmune fibrosing disease, scleroderma, and then perform dynamic analysis of partial TGF-beta pathway in both normal and scleroderma fibroblasts stimulated by silica. We find that TGF-beta pathway under perturbation of silica shows significant differences in dynamic properties between normal and scleroderma fibroblasts. Our findings may open a new avenue in exploring the functions of cells and mechanism operative in disease development.

Heart rate variability in response to pain stimulus in VLBW infants followed longitudinally during NICU stay.

The objective of this longitudinal study, conducted in a neonatal intensive care unit, was to characterize the response to pain of high-risk very low birth weight infants (<1,500 g) from 23 to 38 weeks post-menstrual age (PMA) by measuring heart rate variability (HRV). Heart period data were recorded before, during, and after a heel lanced or wrist venipunctured blood draw for routine clinical evaluation. Pain response to the blood draw procedure and age-related changes of HRV in low-frequency and high-frequency bands were modeled with linear mixed-effects models. HRV in both bands decreased during pain, followed by a recovery to near-baseline levels. Venipuncture and mechanical ventilation were factors that attenuated the HRV response to pain. HRV at the baseline increased with post-menstrual age but the growth rate of high-frequency power was reduced in mechanically ventilated infants. There was some evidence that low-frequency HRV response to pain improved with advancing PMA.

Acute and chronic methylphenidate dose-response assessment on three adolescent male rat strains.

Methylphenidate (MPD), commonly known as Ritalin, is the most frequently prescribed drug to treat children and adults with attention deficit hyperactivity disorder (ADHD). Adolescence is a period of development involving numerous neuroplasticities throughout the central nervous system (CNS). Exposure to a psychostimulant such as MPD during this crucial period of neurodevelopment may cause transient or permanent changes in the CNS. Genetic variability may also influence these differences. Thus, the objective of the present study was to determine whether acute and chronic administration of MPD (0.6, 2.5, or 10.0mg/kg, i.p.) elicit effects among adolescent WKY, SHR, and SD rats and to compare whether there were strain differences. An automated, computerized, open-field activity monitoring system was used to study the dose-response characteristics of acute and repeated MPD administration throughout the 11-day experimental protocol. Results showed that all three adolescent rat groups exhibited dose-response characteristics following acute and chronic MPD administration, as well as strain differences. These strain differences depended on the MPD dose and locomotor index. Chronic treatment of MPD in these animals did not elicit behavioral sensitization, a phenomenon described in adult rats that is characterized by the progressive augmentation of the locomotor response to repeated administration of the drug. These results suggest that the animal's age at time of drug treatment and strain/genetic variability play a crucial role in the acute and chronic effect of MPD and in the development of behavioral sensitization.

Differential dynamic properties of scleroderma fibroblasts in response to perturbation of environmental stimuli.

Diseases are believed to arise from dysregulation of biological systems (pathways) perturbed by environmental triggers. Biological systems as a whole are not just the sum of their components, rather ever-changing, complex and dynamic systems over time in response to internal and external perturbation. In the past, biologists have mainly focused on studying either functions of isolated genes or steady-states of small biological pathways. However, it is systems dynamics that play an essential role in giving rise to cellular function/dysfunction which cause diseases, such as growth, differentiation, division and apoptosis. Biological phenomena of the entire organism are not only determined by steady-state characteristics of the biological systems, but also by intrinsic dynamic properties of biological systems, including stability, transient-response, and controllability, which determine how the systems maintain their functions and performance under a broad range of random internal and external perturbations. As a proof of principle, we examine signal transduction pathways and genetic regulatory pathways as biological systems. We employ widely used state-space equations in systems science to model biological systems, and use expectation-maximization (EM) algorithms and Kalman filter to estimate the parameters in the models. We apply the developed state-space models to human fibroblasts obtained from the autoimmune fibrosing disease, scleroderma, and then perform dynamic analysis of partial TGF-beta pathway in both normal and scleroderma fibroblasts stimulated by silica. We find that TGF-beta pathway under perturbation of silica shows significant differences in dynamic properties between normal and scleroderma fibroblasts. Our findings may open a new avenue in exploring the functions of cells and mechanism operative in disease development.

Dynamics of a minimal model of interlocked positive and negative feedback loops of transcriptional regulation by cAMP-response element binding proteins.

cAMP-response element binding (CREB) proteins are involved in transcriptional regulation in a number of cellular processes (e.g., neural plasticity and circadian rhythms). The CREB family contains activators and repressors that may interact through positive and negative feedback loops. These loops can be generated by auto- and cross-regulation of expression of CREB proteins, via CRE elements in or near their genes. Experiments suggest that such feedback loops may operate in several systems (e.g., Aplysia and rat). To understand the functional implications of such feedback loops, which are interlocked via cross-regulation of transcription, a minimal model with a positive and negative loop was developed and investigated using bifurcation analysis. Bifurcation analysis revealed diverse nonlinear dynamics (e.g., bistability and oscillations). The stability of steady states or oscillations could be changed by time delays in the synthesis of the activator (CREB1) or the repressor (CREB2). Investigation of stochastic fluctuations due to small numbers of molecules of CREB1 and CREB2 revealed a bimodal distribution of CREB molecules in the bistability region. The robustness of the stable HIGH and LOW states of CREB expression to stochastic noise differs, and a critical number of molecules was required to sustain the HIGH state for days or longer. Increasing positive feedback or decreasing negative feedback also increased the lifetime of the HIGH state, and persistence of this state may correlate with long-term memory formation. A critical number of molecules was also required to sustain robust oscillations of CREB expression. If a steady state was near a deterministic Hopf bifurcation point, stochastic resonance could induce oscillations. This comparative analysis of deterministic and stochastic dynamics not only provides insights into the possible dynamics of CREB regulatory motifs, but also demonstrates a framework for understanding other regulatory processes with similar network architecture.

Nonviolent factors: Variables associated with ethnic violence, and factors which are associated with nonviolent response to grievances toward members of other ethnic groups

Ethnic violence appears to be the major source of violence in the world. Ethnic hostilities are potentially all-pervasive because most countries in the world are multi-ethnic. Public health's focus on violence documents its increasing role in this issue.^ The present study is based on a secondary analysis of a dataset of responses by 272 individuals from four ethnic groups (Anglo, African, Mexican, and Vietnamese Americans) who answered questions regarding variables related to ethnic violence from a general questionnaire which was distributed to ethnically diverse purposive, nonprobability, self-selected groups of individuals in Houston, Texas, in 1993.^ One goal was psychometric: learning about issues in analysis of datasets with modest numbers, comparison of two approaches to dealing with missing observations not missing at random (conducting analysis on two datasets), transformation analysis of continuous variables for logistic regression, and logistic regression diagnostics.^ Regarding the psychometric goal, it was concluded that measurement model analysis was not possible with a relatively small dataset with nonnormal variables, such as Likert-scaled variables; therefore, exploratory factor analysis was used. The two approaches to dealing with missing values resulted in comparable findings. Transformation analysis suggested that the continuous variables were in the correct scale, and diagnostics that the model fit was adequate.^ The substantive portion of the analysis included the testing of four hypotheses. Hypothesis One proposed that attitudes/efficacy regarding alternative approaches to resolving grievances from the general questionnaire represented underlying factors: nonpunitive social norms and strategies for addressing grievances--using the political system, organizing protests, using the system to punish offenders, and personal mediation. Evidence was found to support all but one factor, nonpunitive social norms.^ Hypothesis Two proposed that the factor variables and the other independent variables--jail, grievance, male, young, and membership in a particular ethnic group--were associated with (non)violence. Jail, grievance, and not using the political system to address grievances were associated with a greater likelihood of intergroup violence.^ No evidence was found to support Hypotheses Three and Four, which proposed that grievance and ethnic group membership would interact with other variables (i.e., age, gender, etc.) to produce variant levels of subgroup (non)violence.^ The generalizability of the results of this study are constrained by the purposive self-selected nature of the sample and small sample size (n = 272).^ Suggestions for future research include incorporating other possible variables or factors predictive of intergroup violence in models of the kind tested here, and the development and evaluation of interventions that promote electoral and nonelectoral political participation as means of reducing interethnic conflict. ^

Design and analysis for three level hierarchical structures with count response

In numerous intervention studies and education field trials, random assignment to treatment occurs in clusters rather than at the level of observation. This departure of random assignment of units may be due to logistics, political feasibility, or ecological validity. Data within the same cluster or grouping are often correlated. Application of traditional regression techniques, which assume independence between observations, to clustered data produce consistent parameter estimates. However such estimators are often inefficient as compared to methods which incorporate the clustered nature of the data into the estimation procedure (Neuhaus 1993).1 Multilevel models, also known as random effects or random components models, can be used to account for the clustering of data by estimating higher level, or group, as well as lower level, or individual variation. Designing a study, in which the unit of observation is nested within higher level groupings, requires the determination of sample sizes at each level. This study investigates the design and analysis of various sampling strategies for a 3-level repeated measures design on the parameter estimates when the outcome variable of interest follows a Poisson distribution. ^ Results study suggest that second order PQL estimation produces the least biased estimates in the 3-level multilevel Poisson model followed by first order PQL and then second and first order MQL. The MQL estimates of both fixed and random parameters are generally satisfactory when the level 2 and level 3 variation is less than 0.10. However, as the higher level error variance increases, the MQL estimates become increasingly biased. If convergence of the estimation algorithm is not obtained by PQL procedure and higher level error variance is large, the estimates may be significantly biased. In this case bias correction techniques such as bootstrapping should be considered as an alternative procedure. For larger sample sizes, those structures with 20 or more units sampled at levels with normally distributed random errors produced more stable estimates with less sampling variance than structures with an increased number of level 1 units. For small sample sizes, sampling fewer units at the level with Poisson variation produces less sampling variation, however this criterion is no longer important when sample sizes are large. ^ 1Neuhaus J (1993). “Estimation efficiency and Tests of Covariate Effects with Clustered Binary Data”. Biometrics , 49, 989–996^

Response to chemotherapy, recurrence and survival in advanced-stage ovarian, fallopian tube and primary peritoneal cancer patients with non-Ashkenazi Jewish BRCA mutations, compared to those without

Objectives. Previous studies have shown a survival advantage in ovarian cancer patients with Ashkenazi-Jewish (AJ) BRCA founder mutations, compared to sporadic ovarian cancer patients. The purpose of this study was to determine if this association exists in ovarian cancer patients with non-Ashkenazi Jewish BRCA mutations. In addition, we sought to account for possible "survival bias" by minimizing any lead time that may exist between diagnosis and genetic testing. ^ Methods. Patients with stage III/IV ovarian, fallopian tube, or primary peritoneal cancer and a non-Ashkenazi Jewish BRCA1 or 2 mutation, seen for genetic testing January 1996-July 2007, were identified from genetics and institutional databases. Medical records were reviewed for clinical factors, including response to initial chemotherapy. Patients with sporadic (non-hereditary) ovarian, fallopian tube, or primary peritoneal cancer, without family history of breast or ovarian cancer, were compared to similar cases, matched by age, stage, year of diagnosis, and vital status at time interval to BRCA testing. When possible, 2 sporadic patients were matched to each BRCA patient. An additional group of unmatched, sporadic ovarian, fallopian tube and primary peritoneal cancer patients was included for a separate analysis. Progression-free (PFS) & overall survival (OS) were calculated by the Kaplan-Meier method. Multivariate Cox proportional hazards models were calculated for variables of interest. Matched pairs were treated as clusters. Stratified log rank test was used to calculate survival data for matched pairs using paired event times. Fisher's exact test, chi-square, and univariate logistic regression were also used for analysis. ^ Results. Forty five advanced-stage ovarian, fallopian tube and primary peritoneal cancer patients with non-Ashkenazi Jewish (non-AJ) BRCA mutations, 86 sporadic-matched and 414 sporadic-unmatched patients were analyzed. Compared to the sporadic-matched and sporadic-unmatched ovarian cancer patients, non-AJ BRCA mutation carriers had longer PFS (17.9 & 13.8 mos. vs. 32.0 mos., HR 1.76 [95% CI 1.13–2.75] & 2.61 [95% CI 1.70–4.00]). In relation to the sporadic- unmatched patients, non-AJ BRCA patients had greater odds of complete response to initial chemotherapy (OR 2.25 [95% CI 1.17–5.41]) and improved OS (37.6 mos. vs. 101.4 mos., HR 2.64 [95% CI 1.49–4.67]). ^ Conclusions. This study demonstrates a significant survival advantage in advanced-stage ovarian cancer patients with non-AJ BRCA mutations, confirming the previous studies in the Jewish population. Our efforts to account for "survival bias," by matching, will continue with collaborative studies. ^

11beta-hydroxysteroid dehydrogenases as regulators of pulmonary corticosterone during the granulomatous response to trehalose 6,6'-dimycolate

Protection against Mycobacterium tuberculosis requires development and maintenance of granulomatous lesions, a feature considered to be the pathological hallmark of Tuberculosis (TB) disease. Upon encountering Mtb or mycobacterial antigens, specifically trehalose 6,6'-dimycolate (TDM), a strong local pro-inflammatory response is initiated. Systemic production of anti-inflammatory glucocorticoids (GCs) is also induced. Emergence of these antagonists at the inflammatory foci is counterproductive to development of the granulomatous structure and detrimental to host protection against TB. Therefore, it was hypothesized that local enzymatic regulation of GCs occurs locally at the site of granulomatous inflammation. The experiments described here strongly suggest that 11β-hydroxysteroid dehydrogenases (11βHSDs) shuttle GCs between active and inert forms during the acute granulomatous response, supporting the net reduction of corticosterone. The patterns of GC and 11βHSD regulation were specific to the lung (the site of inflammation) and were not observed in other tissues. Furthermore, 11βHSD2, which decreases corticosterone concentrations, was not expressed in models of dysregulated granulomatous inflammation. These findings suggest that cellular exposure to local active GC concentrations is restricted via 11βHSDs as a mechanism to initiate and maintain granuloma formation. The information derived from the experiments outlined in this dissertation provides a better understanding of the events required for establishment and maintenance of the protective granulomatous response. As a practical consequence, exploiting 11βHSD2 modulation of GCs at the site of Mtb infection may lead to improvement of Tuberculosis treatment strategies.^

Characterization of cytochrome P450 4F subfamily: Response in traumatic brain injury and gene regulation

CYP4F enzymes metabolize endogenous molecules including arachidonic acid, leukotrienes and prostaglandins. The involvement of these eisosanoids in inflammation has led to the hypothesis that CYP4Fs may modulate inflammatory conditions after traumatic brain injury (TBI). In rat, TBI elicited changes in mRNA expression of CYP4Fs as a function of time in the cerebrum region. These changes in CYP4F mRNA levels inversely correlated with the cerebral leukotriene B4 (LTB4) level following injury at the same time points. TBI also resulted in changes in CYP4F protein expression and localization around the injury site, where CYP4F1 and CYP4F6 immunoreactivity increased in surrounding astrocytes and CYP4F4 immunoreactivity shifted from endothelia of cerebral vessels to astrocytes. The study with rat primary astrocytes indicated that pro-inflammatory cytokines TNFα and IL-1β could affect the transcription of CYP4Fs to a certain degree, whereas the changing pattern in the primary astrocytes appeared to be different from that in the in vivo TBI model.^ In addition, the regulation of CYP4F genes has been an unsolved issue although factors including cytokines and fatty acids appear to affect CYP4Fs expression in multiple models. In this project, HaCaT cells were used as an in vitro cellular model to define signaling pathways involved in the regulation of human CYP4F genes. Retinoic acids inhibited CYP4F11 expression, whereas cytokines TNFα and IL-1β induced transcription of CYP4F11 in HaCaT cells. The induction of CYP4F11 by both cytokines could be blocked by a JNK specific inhibitor, indicating the involvement of the JNK pathway in the up-regulation of CYP4F11. Retinoic acids are known to function in gene regulation through nuclear receptors RARs and RXRs. The RXR agonist LG268 greatly induced transcription of CYP4F11, whereas RAR agonist TTNPB obviously inhibited CYP4F11 transcription, indicating that the down-regulation of CYP4F11 by retinoic acid was mediated by RARs, and that inhibition of CYP4F11 by retinoic acid may also be related to the competition for RXR receptors. Thus, the CYP4F11 gene is regulated by signaling pathways including the RXR pathway and the JNK pathway. In contrast, the regulation mechanism of other CYP4Fs by retinoic acids appears to be different from that of CYP4F11.^

Secondary left ventricular ejection fraction response to beta blocker therapy in heart failure patients

Chronic β-blocker treatment improves survival and left ventricular ejection fraction (LVEF) in patients with systolic heart failure (HF). Data on whether the improvement in LVEF after β-blocker therapy is sustained for a long term or whether there is a loss in LVEF after an initial gain is not known. Our study sought to determine the prevalence and prognostic role of secondary decline in LVEF in chronic systolic HF patients on β-blocker therapy and characterize these patients. Retrospective chart review of HF hospitalizations fulfilling Framingham Criteria was performed at the MEDVAMC between April 2000 and June 2006. Follow up vital status and recurrent hospitalizations were ascertained until May 2010. Three groups of patients were identified based on LVEF response to beta blockers; group A with secondary decline in LVEF following an initial increase, group B with progressive increase in LVEF and group C with progressive decline in LVEF. Covariate adjusted Cox proportional hazard models were used to examine differences in heart failure re-hospitalizations and all cause mortality between the groups. Twenty five percent (n=27) of patients had a secondary decline in LVEF following an initial gain. The baseline, peak and final LVEF in this group were 27.6±12%, 40.1±14% and 27.4±13% respectively. The mean nadir LVEF after decline was 27.4±13% and this decline occurred at a mean interval of 2.8±1.9 years from the day of beta blocker initiation. These patients were older, more likely to be whites, had advanced heart failure (NYHA class III/IV) more due to a non ischemic etiology compared to groups B & C. They were also more likely to be treated with metoprolol (p=0.03) compared to the other two groups. No significant differences were observed in combined risk of all cause mortality and HF re-hospitalization [hazard ratio 0.80, 95% CI 0.47 to 1.38, p=0.42]. No significant difference was observed in survival estimates between the groups. In conclusion, a late decline in LVEF does occur in a significant proportion of heart failure patients treated with beta blockers, more so in patients treated with metoprolol.^