Analysis of clonality and antibiotic resistance among early clinical isolates of Enterococcus faecium in the United States.

BACKGROUND: The Enterococcus faecium genogroup, referred to as clonal complex 17 (CC17), seems to possess multiple determinants that increase its ability to survive and cause disease in nosocomial environments. METHODS: Using 53 clinical and geographically diverse US E. faecium isolates dating from 1971 to 1994, we determined the multilocus sequence type; the presence of 16 putative virulence genes (hyl(Efm), esp(Efm), and fms genes); resistance to ampicillin (AMP) and vancomycin (VAN); and high-level resistance to gentamicin and streptomycin. RESULTS: Overall, 16 different sequence types (STs), mostly CC17 isolates, were identified in 9 different regions of the United States. The earliest CC17 isolates were part of an outbreak that occurred in 1982 in Richmond, Virginia. The characteristics of CC17 isolates included increases in resistance to AMP, the presence of hyl(Efm) and esp(Efm), emergence of resistance to VAN, and the presence of at least 13 of 14 fms genes. Eight of 41 of the early isolates with resistance to AMP, however, were not in CC17. CONCLUSIONS: Although not all early US AMP isolates were clonally related, E. faecium CC17 isolates have been circulating in the United States since at least 1982 and appear to have progressively acquired additional virulence and antibiotic resistance determinants, perhaps explaining the recent success of this species in the hospital environment.

Distribution of genes encoding MSCRAMMs and Pili in clinical and natural populations of Enterococcus faecium.

Enterococcus faecium has recently emerged as an important cause of nosocomial infections. We previously identified 15 predicted surface proteins with characteristics of MSCRAMMs and/or pili and demonstrated that their genes were frequently present in 30 clinical E. faecium isolates studied; one of these, acm, has been studied in further detail. To determine the prevalence of the other 14 genes among various E. faecium populations, we have now assessed 433 E. faecium isolates, including 264 isolates from human clinical infections, 69 isolates from stools of hospitalized patients, 70 isolates from stools of community volunteers, and 30 isolates from animal-related sources. A variable distribution of the 14 genes was detected, with their presence ranging from 51% to 98% of isolates. While 81% of clinical isolates carried 13 or 14 of the 14 genes tested, none of the community group isolates and only 13% of animal isolates carried 13 or 14 genes. The presence of these genes was most frequent in endocarditis isolates, with 11 genes present in all isolates, followed by isolates from other clinical sources. The number of genes significantly associated with clinical versus fecal or animal origin (P = 0.04 to <0.0001) varied from 10 to 13, depending on whether comparisons were made against individual clinical subgroups (endocarditis, blood, and other clinical isolates) or against all clinical isolates combined as one group. The strong association of these genes with clinical isolates raises the possibility that their preservation/acquisition has favored the adaptation of E. faecium to nosocomial environments and/or patients.

Clinical and microbiological aspects of linezolid resistance mediated by the cfr gene encoding a 23S rRNA methyltransferase.

The cfr (chloramphenicol-florfenicol resistance) gene encodes a 23S rRNA methyltransferase that confers resistance to linezolid. Detection of linezolid resistance was evaluated in the first cfr-carrying human hospital isolate of linezolid and methicillin-resistant Staphylococcus aureus (designated MRSA CM-05) by dilution and diffusion methods (including Etest). The presence of cfr was investigated in isolates of staphylococci colonizing the patient's household contacts and clinical isolates recovered from patients in the same unit where MRSA CM-05 was isolated. Additionally, 68 chloramphenicol-resistant Colombian MRSA isolates recovered from hospitals between 2001 and 2004 were screened for the presence of the cfr gene. In addition to erm(B), the erm(A) gene was also detected in CM-05. The isolate belonged to sequence type 5 and carried staphylococcal chromosomal cassette mec type I. We were unable to detect the cfr gene in any of the human staphylococci screened (either clinical or colonizing isolates). Agar and broth dilution methods detected linezolid resistance in CM-05. However, the Etest and disk diffusion methods failed to detect resistance after 24 h of incubation. Oxazolidinone resistance mediated by the cfr gene is rare, and acquisition by a human isolate appears to be a recent event in Colombia. The detection of cfr-mediated linezolid resistance might be compromised by the use of the disk diffusion or Etest method.

Patient Characteristics and Outcomes of Gastrointestinal Stromal Tumor Patients Undergoing Imatinib Plasma Level Testing

Although gastrointestinal stromal tumor (GIST) is effectively treated with imatinib, there are a number of clinical challenges in the optimal treatment of these patients. The plasma steady-state trough level of imatinib has been proposed to correlate with clinical outcome. Plasma imatinib level may be affected by a number of patient characteristics. Additionally, the ideal plasma trough concentration of imatinib is likely to vary based on the KIT genotype (genotype determines imatinib binding affinity) of the individual patient. Patients’ genotype or plasma imatinib level may influence the type and duration of response that is appreciable by clinical evaluation. The objectives of this study were to determine effects of genotype on the type of response appreciable by current imaging criteria, to determine the distribution of plasma imatinib levels in patients with GIST, to determine factors that correlate with plasma imatinib level, to determine the incremental effects of imatinib dose escalation; and to explore the median plasma levels and outcomes of patients with various KIT mutations. We therefore obtained KIT mutation information and analyzed CT response for size and density measurement of GISTs at baseline and within the first four moths of imatinib treatment. In 126 patients with metastatic/unresectable disease, the KIT genotype of patients’ tumor was significantly associated with unique response characteristics measurable by CT. Furthermore, hepatic and peritoneal metastases differed in their response characteristics. A subgroup of patients with KIT exon 9 mutation, who received higher doses of imatinib and experienced higher trough imatinib levels, experienced improved progression-free survival similar to that of KIT exon 11 patients. Therefore, we have found that imatinib plasma levels were higher in patients with elevated Aspartate amino transferase, were women, were older, or were being treated concomitantly with CYP450 substrate drugs. As expected, CYP450 inducers correlated with a lower plasma imatinib levels in GIST patients. Renal metabolism of imatinib accounts for <10%, so it was not included in the analysis but may affect covariates. Interestingly, there was a trend for low imatinib levels and inferior progression-free survival in patients who had undergone complete gastrectomy. Patients with KIT exon 9 mutation in our cohort received higher imatinib doses, experienced higher trough imatinib levels, and experienced a PFS similar to that of KIT exon 11 patients. In conclusion, imatinib plasma levels are influenced by a number of patient characteristics. The optimal imatinib plasma level for individual patients is not known but is an area of intense investigation. Our study confirms patients with KIT exon 9 mutations benefit from high-dose imatinib and higher trough imatinib levels.

CLINICAL TRIAL ENROLLMENT IN A MULTIDISCIPLINARY PROSTATE CANCER

Purpose: Clinical oncology trials are hampered by low accrual rates. Less than 5% of adult cancer patients are treated on a clinical trial. We aimed to evaluate clinical trial enrollment in our Multidisciplinary Prostate Cancer Clinic and to assess if a clinical trial initiative, introduced in 2006, increased our trial enrollment.Methods: Prostate cancer patients with non-metastatic disease who were seen in the clinic from 2004 to 2008 were included in the analysis. Men were categorized by whether they were seen before or after the clinical trial enrollment initiative started in 2006. The initiative included posting trial details in the clinic, educating patients about appropriate clinical trial options during the treatment recommendation discussion, and providing patients with documentation of trials offered to them. Univariate and multivariate (MVA) logistic regression analysis evaluated the impact of patient characteristics and the clinical trial initiative on clinical trial enrollment.Results: The majority of the 1,370 men were white (83%), and lived within the surrounding counties or state (69.4%). Median age was 64.2 years. Seventy-three point five percent enrolled in at least one trial and 28.5% enrolled in more than one trial. Sixty-seven percent enrolled in laboratory studies, 18% quality of life studies, 13% novel studies, and 3.7% procedural studies. On MVA, men seen in later years (p < 0.0001) were more likely to enroll in trials. The proportion of men enrolling increased from 38.9% to 84.3% (p<0.0001) after the clinical trial initiative. On MVA, older men (p < 0.0001) were less likely to enroll in clinical trials. There was a trend toward men in the high-risk group being more likely to participate in clinical trials (p = 0.056). There was a second trend for men of Hispanic, Asian, Native American and Indian decent being less likely to participate in clinical trials (p = 0.054).Conclusion: Clinical trial enrollment in the multidisciplinary clinic increased after introduction of a clinical trial initiative. Older men were less likely to enroll in trials. We speculate we achieved high enrollment rates because 1) specific trials are discussed at time of treatment recommendations, 2) we provide a letter documenting offered trials and 3) we introduce patients to the research team at the same clinic visit if they are interested in trial participation.

The influence of staff and organizational characteristics on social environment in substance abuse treatment facilities

The staff of 20 substance abuse treatment facilities were administered the Ward Atmosphere Scale, an instrument which measures treatment environment. Ten facilities were freestanding and ten were hospital based, and were drawn from a large, not-for-profit national chain using a random selection process. Controlling for several staff and facility attributes, it was found that no substantial effects on treatment environment existed due to facility type, freestanding or hospital-based. Implications of the study exist in selection of facility type for purchasers of substance abuse treatment and for the hiring and training of clinical staff for treatment facilities. Study findings also suggest that inadequate or insufficient measures exist to examine the construct 'treatment environment'. ^

Prediction of cardiovascular disease risk factors in Mexican-American children from parental risk factors, and social and demographic characteristics in the HHANES

Objectives. Cardiovascular disease (CVD) including CVD secondary to diabetes type II, a significant health problem among Mexican American populations, originates in early childhood. This study seeks to determine risk factors available to the health practitioner that can identify the child at potential risk of developing CVD, thereby enabling early intervention. ^ Design. This is a secondary analysis of cross-sectional data of matched Mexican American parents and children selected from the HHANES, 1982–1984. ^ Methods. Parents at high risk for CVD were identified based on medical history, and clinical and physical findings. Factor analysis was performed on children's skinfold thicknesses, height, weight, and systolic and diastolic blood pressures, in order to produce a limited number of uncorrelated child CVD risk factors. Multiple regression analyses were then performed to determine other CVD markers associated with these Factors, independently for mothers and fathers. ^ Results. Factor analysis of children's measurements revealed three uncorrelated latent variables summarizing the children's CVD risk: Factor1: ‘Fatness’, Factor2: ‘Size and Maturity’, and Factor3: ‘Blood Pressure’, together accounting for the bulk of variation in children's measurements (86–89%). Univariate analyses showed that children from high CVD risk families did not differ from children of low risk families in occurrence of high blood pressure, overweight, biological maturity, acculturation score, or social and economic indicators. However, multiple regression using the factor scores (from factor analysis) as dependent variables, revealed that higher CVD risk in parents, was significantly associated with increased fatness and increased blood pressure in the children. Father's CVD risk status was associated with higher levels of body fat in his children and higher levels of blood pressure in sons. Mother's CVD risk status was associated with higher blood pressure levels in children, and occurrence of obesity in the mother associated with higher fatness levels in her children. ^ Conclusion. Occurrence of cardiovascular disease and its risk factors in parents of Mexican American children, may be used to identify children at potentially higher risk for developing CV disease in the future. Obesity in mothers appears to be an important marker for the development of higher levels of body fatness in children. ^

Continuous safety screens for randomized controlled clinical trials with blinded treatment information

Standard methods for testing safety data are needed to ensure the safe conduct of clinical trials. In particular, objective rules for reliably identifying unsafe treatments need to be put into place to help protect patients from unnecessary harm. DMCs are uniquely qualified to evaluate accumulating unblinded data and make recommendations about the continuing safe conduct of a trial. However, it is the trial leadership who must make the tough ethical decision about stopping a trial, and they could benefit from objective statistical rules that help them judge the strength of evidence contained in the blinded data. We design early stopping rules for harm that act as continuous safety screens for randomized controlled clinical trials with blinded treatment information, which could be used by anyone, including trial investigators (and trial leadership). A Bayesian framework, with emphasis on the likelihood function, is used to allow for continuous monitoring without adjusting for multiple comparisons. Close collaboration between the statistician and the clinical investigators will be needed in order to design safety screens with good operating characteristics. Though the math underlying this procedure may be computationally intensive, implementation of the statistical rules will be easy and the continuous screening provided will give suitably early warning when real problems were to emerge. Trial investigators and trial leadership need these safety screens to help them to effectively monitor the ongoing safe conduct of clinical trials with blinded data.^

Clinical and economic outcomes of serious postoperative infections following resection of common respiratory and gastrointestinal solid tumors

Unlike infections occurring during periods of chemotherapy-induced neutropenia, postoperative infections in patients with solid malignancy remain largely understudied. The purpose of this population-based study was to evaluate the clinical and economic burden, as well as the relationship of hospital surgical volume and outcomes associated with serious postoperative infection (SPI) – i.e., bacteremia/sepsis, pneumonia, and wound infection – following resection of common solid tumors.^ From the Texas Discharge Data Research File, we identified all Texas residents who underwent resection of cancer of the lung, esophagus, stomach, pancreas, colon, or rectum between 2002 and 2006. From their billing records, we identified ICD-9 codes indicating SPI and also subsequent SPI-related readmissions occurring within 30 days of surgery. Random-effects logistic regression was used to calculate the impact of SPI on mortality, as well as the association between surgical volume and SPI, adjusting for case-mix, hospital characteristics, and clustering of multiple surgical admissions within the same patient and patients within the same hospital. Excess bed days and costs were calculated by subtracting values for patients without infections from those with infections computed using multilevel mixed-effects generalized linear model by fitting a gamma distribution to the data using log link.^ Serious postoperative infection occurred following 9.4% of the 37,582 eligible tumor resections and was independently associated with an 11-fold increase in the odds of in-hospital mortality (95% Confidence Interval [95% CI], 6.7-18.5, P < 0.001). Patients with SPI required 6.3 additional hospital days (95% CI, 6.1 - 6.5) at an incremental cost of $16,396 (95% CI, $15,927–$16,875). There was a significant trend toward lower overall rates of SPI with higher surgical volume (P=0.037). ^ Due to the substantial morbidity, mortality, and excess costs associated with SPI following solid tumor resections and given that, under current reimbursement practices, most of this heavy burden is borne by acute care providers, it is imperative for hospitals to identify more effective prophylactic measures, so that these potentially preventable infections and their associated expenditures can be averted. Additional volume-outcomes research is also needed to identify infection prevention processes that can be transferred from higher- to lower-volume providers.^

Procedure for the identification of population characteristics for targeting intervention services on the basis of outcome-goal priorities: Case management services for the chronic mentally ill

Although the processes involved in rational patient targeting may be obvious for certain services, for others, both the appropriate sub-populations to receive services and the procedures to be used for their identification may be unclear. This project was designed to address several research questions which arise in the attempt to deliver appropriate services to specific populations. The related difficulties are particularly evident for those interventions about which findings regarding effectiveness are conflicting. When an intervention clearly is not beneficial (or is dangerous) to a large, diverse population, consensus regarding withholding the intervention from dissemination can easily be reached. When findings are ambiguous, however, conclusions may be impossible.^ When characteristics of patients likely to benefit from an intervention are not obvious, and when the intervention is not significantly invasive or dangerous, the strategy proposed herein may be used to identify specific characteristics of sub-populations which may benefit from the intervention. The identification of these populations may be used both in further informing decisions regarding distribution of the intervention and for purposes of planning implementation of the intervention by identifying specific target populations for service delivery.^ This project explores a method for identifying such sub-populations through the use of related datasets generated from clinical trials conducted to test the effectiveness of an intervention. The method is specified in detail and tested using the example intervention of case management for outpatient treatment of populations with chronic mental illness. These analyses were applied in order to identify any characteristics which distinguish specific sub-populations who are more likely to benefit from case management service, despite conflicting findings regarding its effectiveness for the aggregate population, as reported in the body of related research. However, in addition to a limited set of characteristics associated with benefit, the findings generated, a larger set of characteristics of patients likely to experience greater improvement without intervention. ^

Bayesian adaptive designs for drug combinations in early phase clinical trials

Treating patients with combined agents is a growing trend in cancer clinical trials. Evaluating the synergism of multiple drugs is often the primary motivation for such drug-combination studies. Focusing on the drug combination study in the early phase clinical trials, our research is composed of three parts: (1) We conduct a comprehensive comparison of four dose-finding designs in the two-dimensional toxicity probability space and propose using the Bayesian model averaging method to overcome the arbitrariness of the model specification and enhance the robustness of the design; (2) Motivated by a recent drug-combination trial at MD Anderson Cancer Center with a continuous-dose standard of care agent and a discrete-dose investigational agent, we propose a two-stage Bayesian adaptive dose-finding design based on an extended continual reassessment method; (3) By combining phase I and phase II clinical trials, we propose an extension of a single agent dose-finding design. We model the time-to-event toxicity and efficacy to direct dose finding in two-dimensional drug-combination studies. We conduct extensive simulation studies to examine the operating characteristics of the aforementioned designs and demonstrate the designs' good performances in various practical scenarios.^

Bayesian Adaptive Designs for Early Phase Clinical Trials

My dissertation focuses mainly on Bayesian adaptive designs for phase I and phase II clinical trials. It includes three specific topics: (1) proposing a novel two-dimensional dose-finding algorithm for biological agents, (2) developing Bayesian adaptive screening designs to provide more efficient and ethical clinical trials, and (3) incorporating missing late-onset responses to make an early stopping decision. Treating patients with novel biological agents is becoming a leading trend in oncology. Unlike cytotoxic agents, for which toxicity and efficacy monotonically increase with dose, biological agents may exhibit non-monotonic patterns in their dose-response relationships. Using a trial with two biological agents as an example, we propose a phase I/II trial design to identify the biologically optimal dose combination (BODC), which is defined as the dose combination of the two agents with the highest efficacy and tolerable toxicity. A change-point model is used to reflect the fact that the dose-toxicity surface of the combinational agents may plateau at higher dose levels, and a flexible logistic model is proposed to accommodate the possible non-monotonic pattern for the dose-efficacy relationship. During the trial, we continuously update the posterior estimates of toxicity and efficacy and assign patients to the most appropriate dose combination. We propose a novel dose-finding algorithm to encourage sufficient exploration of untried dose combinations in the two-dimensional space. Extensive simulation studies show that the proposed design has desirable operating characteristics in identifying the BODC under various patterns of dose-toxicity and dose-efficacy relationships. Trials of combination therapies for the treatment of cancer are playing an increasingly important role in the battle against this disease. To more efficiently handle the large number of combination therapies that must be tested, we propose a novel Bayesian phase II adaptive screening design to simultaneously select among possible treatment combinations involving multiple agents. Our design is based on formulating the selection procedure as a Bayesian hypothesis testing problem in which the superiority of each treatment combination is equated to a single hypothesis. During the trial conduct, we use the current values of the posterior probabilities of all hypotheses to adaptively allocate patients to treatment combinations. Simulation studies show that the proposed design substantially outperforms the conventional multi-arm balanced factorial trial design. The proposed design yields a significantly higher probability for selecting the best treatment while at the same time allocating substantially more patients to efficacious treatments. The proposed design is most appropriate for the trials combining multiple agents and screening out the efficacious combination to be further investigated. The proposed Bayesian adaptive phase II screening design substantially outperformed the conventional complete factorial design. Our design allocates more patients to better treatments while at the same time providing higher power to identify the best treatment at the end of the trial. Phase II trial studies usually are single-arm trials which are conducted to test the efficacy of experimental agents and decide whether agents are promising to be sent to phase III trials. Interim monitoring is employed to stop the trial early for futility to avoid assigning unacceptable number of patients to inferior treatments. We propose a Bayesian single-arm phase II design with continuous monitoring for estimating the response rate of the experimental drug. To address the issue of late-onset responses, we use a piece-wise exponential model to estimate the hazard function of time to response data and handle the missing responses using the multiple imputation approach. We evaluate the operating characteristics of the proposed method through extensive simulation studies. We show that the proposed method reduces the total length of the trial duration and yields desirable operating characteristics for different physician-specified lower bounds of response rate with different true response rates.

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.^

Bayesian adaptive designs in early phase clinical trials

There are two practical challenges in the phase I clinical trial conduct: lack of transparency to physicians, and the late onset toxicity. In my dissertation, Bayesian approaches are used to address these two problems in clinical trial designs. The proposed simple optimal designs cast the dose finding problem as a decision making process for dose escalation and deescalation. The proposed designs minimize the incorrect decision error rate to find the maximum tolerated dose (MTD). For the late onset toxicity problem, a Bayesian adaptive dose-finding design for drug combination is proposed. The dose-toxicity relationship is modeled using the Finney model. The unobserved delayed toxicity outcomes are treated as missing data and Bayesian data augment is employed to handle the resulting missing data. Extensive simulation studies have been conducted to examine the operating characteristics of the proposed designs and demonstrated the designs' good performances in various practical scenarios.^

A simulation study of the standard design, the rolling six design, the CRM, and the modified CRM in Phase I clinical trials

The Phase I clinical trial is considered the "first in human" study in medical research to examine the toxicity of a new agent. It determines the maximum tolerable dose (MTD) of a new agent, i.e., the highest dose in which toxicity is still acceptable. Several phase I clinical trial designs have been proposed in the past 30 years. The well known standard method, so called the 3+3 design, is widely accepted by clinicians since it is the easiest to implement and it does not need a statistical calculation. Continual reassessment method (CRM), a design uses Bayesian method, has been rising in popularity in the last two decades. Several variants of the CRM design have also been suggested in numerous statistical literatures. Rolling six is a new method introduced in pediatric oncology in 2008, which claims to shorten the trial duration as compared to the 3+3 design. The goal of the present research was to simulate clinical trials and compare these phase I clinical trial designs. Patient population was created by discrete event simulation (DES) method. The characteristics of the patients were generated by several distributions with the parameters derived from a historical phase I clinical trial data review. Patients were then selected and enrolled in clinical trials, each of which uses the 3+3 design, the rolling six, or the CRM design. Five scenarios of dose-toxicity relationship were used to compare the performance of the phase I clinical trial designs. One thousand trials were simulated per phase I clinical trial design per dose-toxicity scenario. The results showed the rolling six design was not superior to the 3+3 design in terms of trial duration. The time to trial completion was comparable between the rolling six and the 3+3 design. However, they both shorten the duration as compared to the two CRM designs. Both CRMs were superior to the 3+3 design and the rolling six in accuracy of MTD estimation. The 3+3 design and rolling six tended to assign more patients to undesired lower dose levels. The toxicities were slightly greater in the CRMs.^