BENCHMARKING AND IMPLEMENTATION OF A NEW INDEPENDENT MONTE CARLO DOSE CALCULATION QUALITY ASSURANCE AUDIT TOOL FOR CLINICAL TRIALS

Introduction Commercial treatment planning systems employ a variety of dose calculation algorithms to plan and predict the dose distributions a patient receives during external beam radiation therapy. Traditionally, the Radiological Physics Center has relied on measurements to assure that institutions participating in the National Cancer Institute sponsored clinical trials administer radiation in doses that are clinically comparable to those of other participating institutions. To complement the effort of the RPC, an independent dose calculation tool needs to be developed that will enable a generic method to determine patient dose distributions in three dimensions and to perform retrospective analysis of radiation delivered to patients who enrolled in past clinical trials. Methods A multi-source model representing output for Varian 6 MV and 10 MV photon beams was developed and evaluated. The Monte Carlo algorithm, know as the Dose Planning Method (DPM), was used to perform the dose calculations. The dose calculations were compared to measurements made in a water phantom and in anthropomorphic phantoms. Intensity modulated radiation therapy and stereotactic body radiation therapy techniques were used with the anthropomorphic phantoms. Finally, past patient treatment plans were selected and recalculated using DPM and contrasted against a commercial dose calculation algorithm. Results The multi-source model was validated for the Varian 6 MV and 10 MV photon beams. The benchmark evaluations demonstrated the ability of the model to accurately calculate dose for the Varian 6 MV and the Varian 10 MV source models. The patient calculations proved that the model was reproducible in determining dose under similar conditions described by the benchmark tests. Conclusions The dose calculation tool that relied on a multi-source model approach and used the DPM code to calculate dose was developed, validated, and benchmarked for the Varian 6 MV and 10 MV photon beams. Several patient dose distributions were contrasted against a commercial algorithm to provide a proof of principal to use as an application in monitoring clinical trial activity.

Health-related quality of life and survival in liver transplant candidates.

Health-related quality of life (HRQOL) is an important measure of the effects of chronic liver disease in affected patients that helps guide interventions to improve well-being. However, the relationship between HRQOL and survival in liver transplant candidates remains unclear. We examined whether the Physical Component Summary (PCS) and Mental Component Summary (MCS) scores from the Short Form 36 (SF-36) Health Survey were associated with survival in liver transplant candidates. We administered the SF-36 questionnaire (version 2.0) to patients in the Pulmonary Vascular Complications of Liver Disease study, a multicenter prospective cohort of patients evaluated for liver transplantation in 7 academic centers in the United States between 2003 and 2006. Cox proportional hazards models were used with death as the primary outcome and adjustment for liver transplantation as a time-varying covariate. The mean age of the 252 participants was 54 +/- 10 years, 64% were male, and 94% were white. During the 422 person years of follow-up, 147 patients (58%) were listed, 75 patients (30%) underwent transplantation, 49 patients (19%) died, and 3 patients were lost to follow-up. Lower baseline PCS scores were associated with an increased mortality rate despite adjustments for age, gender, Model for End-Stage Liver Disease score, and liver transplantation (P for the trend = 0.0001). The MCS score was not associated with mortality (P for the trend = 0.53). In conclusion, PCS significantly predicts survival in liver transplant candidates, and interventions directed toward improving the physical status may be helpful in improving outcomes in liver transplant candidates.

Quality of care for uninsured type 2 diabetic patients in a financial assistance medical home model

The medically uninsured population in the United States is 16% or 42 million people and consists of a significant number of Type 2 diabetic patients which is the predominant form of diabetes with 798,000 new cases diagnosed each year. There is limited health services research on uninsured populations concerning health system measures or specific disease conditions. ^ The purpose of this investigation was to determine the impact a newly implemented health care program had on the quality of care provided to patients with Type 2 diabetes. The primary study objective was to compare the quality of care while controlling for utilization, and health status of patients in the new program to their status during the previous financial assistance program. The research design was a retrospective matched-pairs design. The study population consisted of 225 patients who received medical care during 1996 and 1997 at the University Health System in San Antonio, Texas. ^ Six quality of care measures individually failed to demonstrate a statistically significant difference when compared between the two periods. However, an index measure reflecting the number of patients who received all six of the quality of care measures demonstrated a statistically significant increase in 1997 (p-value < 0.05). In 1996, 8 patients (2.6%) received all six medical management components. In 1997, 38 patients (16.8%) received all six medical management components. Four regression models were analyzed; two out of the four models demonstrated inconsistent results based on the program membership variable. ^ It is concluded that there has been a small effect of the Carelink program demonstrated by an increase from 8 to 38 patients receiving all quality of care components for Type 2 diabetics at the UHS. It is recommended that additional research be conducted in order to evaluate the quality of care provided to Type 2 diabetic patients. ^

Impact of a modified dietary plan on the health related quality of life of minority participants in the Women's Healthy Eating and Living (WHEL) Study

Advances in medical technology, in genetics, and in clinical research have led to early detection of cancer, precise diagnosis, and effective treatment modalities. Decline in cancer incidence and mortality due to cancer has led to increased number of long-term survivors. However, the ethnic minority population has not experienced this decline and still continues to carry a disparate proportion of the cancer burden. Majority of the clinical research including survivorship studies have recruited and continue to recruit a convenient sample of middle- to upper-class Caucasian survivors. Thus, minorities are underrepresented in cancer research in terms of both clinical studies and in health related quality of life (HRQOL) studies. ^ Life style and diet have been associated with increased risk of breast cancer. High vegetable low fat diet has been shown to reduce recurrence of breast cancer and early death. The Women's Healthy Eating and Living Study is an ongoing multi-site randomized controlled trial that is evaluating the high-vegetable low fat diet in reducing the recurrence of breast cancer and early death. The purpose of this dissertation was to (1) compare the impact of the modified diet on the HRQOL during the first 12-month period on specific Minorities and matched Caucasians; (2) identify predictors that significantly impact the HRQOL of the study participants; and (3) using the structural equation modeling assess the impact of nutrition on the HRQOL of the intervention group participants. Findings suggest that there are no significant differences in change in HRQOL between Minorities and Caucasians; between Minorities in the intervention group and those in the comparison group; and between women in the intervention group and those in the comparison group. Minority indicator variable and Intervention/Comparison group indicator variable were not found to be good predictors of HRQOL. Although the structural equation models suggested viable representation of the relationship between the antecedent variables, the mediating variables and the two outcome variables, the impact of nutrition was not statistically significant to be included in the model. This dissertation, by analyzing the HRQOL of minorities in the WHEL Study, attempted to add to the knowledge base specific to minority cancer survivors. ^

Interrelationships between the structural and functional aspects of social relationships, comorbidity and health related quality of life in breast cancer survivors

It is widely acknowledged in theoretical and empirical literature that social relationships, comprising of structural measures (social networks) and functional measures (perceived social support) have an undeniable effect on health outcomes. However, the actual mechanism of this effect has yet to be clearly understood or explicated. In addition, comorbidity is found to adversely affect social relationships and health related quality of life (a valued outcome measure in cancer patients and survivors). ^ This cross sectional study uses selected baseline data (N=3088) from the Women's Healthy Eating and Living (WHEL) study. Lisrel 8.72 was used for the latent variable structural equation modeling. Due to the ordinal nature of the data, Weighted Least Squares (WLS) method of estimation using Asymptotic Distribution Free covariance matrices was chosen for this analysis. The primary exogenous predictor variables are Social Networks and Comorbidity; Perceived Social Support is the endogenous predictor variable. Three dimensions of HRQoL, physical, mental and satisfaction with current quality of life were the outcome variables. ^ This study hypothesizes and tests the mechanism and pathways between comorbidity, social relationships and HRQoL using latent variable structural equation modeling. After testing the measurement models of social networks and perceived social support, a structural model hypothesizing associations between the latent exogenous and endogenous variables was tested. The results of the study after listwise deletion (N=2131) mostly confirmed the hypothesized relationships (TLI, CFI >0.95, RMSEA = 0.05, p=0.15). Comorbidity was adversely associated with all three HRQoL outcomes. Strong ties were negatively associated with perceived social support; social network had a strong positive association with perceived social support, which served as a mediator between social networks and HRQoL. Mental health quality of life was the most adversely affected by the predictor variables. ^ This study is a preliminary look at the integration of structural and functional measures of social relationships, comorbidity and three HRQoL indicators using LVSEM. Developing stronger social networks and forming supportive relationships is beneficial for health outcomes such as HRQoL of cancer survivors. Thus, the medical community treating cancer survivors as well as the survivor's social networks need to be informed and cognizant of these possible relationships. ^

Sleep quality and quantity and associated factors among high school students from south Texas

Background. Insufficient and poor quality sleep among adolescents affects not only the cognitive functioning, but overall health of the individual. Existing research suggests that adolescents from varying ethnic groups exhibit differing sleep patterns. However, little research focuses on sleep patterns and associated factors (i.e. tobacco use, mental health indicators) among Hispanic youth. ^ Methods. The study population (n=2,536) included students in grades 9-12 who attended one of the three public high schools along the Texas-Mexico border in 2003. This was a cross sectional study using secondary data collected via a web-based, confidential, self-administered survey. Separate logistic regression models were estimated to identify factors associated with reduced (<9 hours/night) and poor quality sleep on average during weeknights. ^ Results. Of participants, 49.5% reported reduced sleep while 12.8% reported poor quality sleep. Factors significantly (p<0.05) associated with poor quality sleep were: often feeling stressed or anxious (OR=5.49), being born in Mexico (OR=0.65), using a computer/playing video games 15+ hours per week (OR=2.29), working (OR=1.37), being a current smoker (OR=2.16), and being a current alcohol user (OR=1.64). Factors significantly associated with reduced quantity of sleep were: often feeling stressed or anxious (OR=2.74), often having headaches/stomachaches (OR=1.77), being a current marijuana user (OR=1.70), being a current methamphetamine user (OR=4.92), and being a current alcohol user (OR=1.27). ^ Discussion. Previous research suggests that there are several factors that can influence sleep quality and quantity in adolescents. This paper discusses these factors (i.e. work, smoking, alcohol, etc.) found to be associated with poor sleep quality and reduced sleep quantity in the Hispanic adolescent population. A reduced quantity of sleep (81.20% of the participants) and a poor quality of sleep (12.80% of the participants) were also found in high school students from South Texas. ^

QUALITY OF MATERNITY CARE IN RURAL TEXAS (MEXICAN-AMERICAN)

The purpose of the study was to describe regionalized systems of perinatal care serving predominantly low income Mexican-American women in rural underserved areas of Texas. The study focused upon ambulatory care; however, it provided a vehicle for examination of the health care system. The questions posed at the onset of the study included: (1) How well do regional organizations with various patterns of staffing and funding levels perform basic functions essential to ambulatory perinatal care? (2) Is there a relationship between the type of organization, its performance, and pregnancy outcome? (3) Are there specific recommendations which might improve an organization's future performance?^ A number of factors--including maldistribution of resources and providers, economic barriers, inadequate means of transportation, and physician resistance to transfer of patients between levels of care--have impeded the development of regionalized systems of perinatal health care, particularly in rural areas. However, studies have consistently emphasized the role of prenatal care in the early detection of risk and treatment of complications of pregnancy and childbirth, with subsequent improvement in pregnancy outcomes.^ This study has examined the "system" of perinatal care in rural areas, utilizing three basic regional models--preventive care, limited primary care, and fully primary care. Information documented in patient clinical records was utilized to compare the quality of ambulatory care provided in the three regional models.^ The study population included 390 women who received prenatal care in one of the seven study clinics. They were predominantly hispanic, married, of low income, with a high proportion of teenagers and women over 35. Twenty-eight percent of the women qualified as migrants.^ The major findings of the study are listed below: (1) Almost half of the women initiated care in the first trimester. (2) Three-fourths of the women had or exceeded the recommended number of prenatal visits. (3) There was a low rate of clinical problem recognition. Additional follow-up is needed to determine the reasons. (4) Cases with a tracer condition had significantly more visits with monitoring of the clinical condition. (5) Almost 90% of all referrals were completed. (6) Only 60% of mothers had postpartum follow-up, while almost 90% of their newborns received care. (7) The incidence of infants weighing 2500 grams or less was 4.2%. ^

Data quality in trauma transfusion studies and the impact of missing data on predicting massive transfusion

Maximizing data quality may be especially difficult in trauma-related clinical research. Strategies are needed to improve data quality and assess the impact of data quality on clinical predictive models. This study had two objectives. The first was to compare missing data between two multi-center trauma transfusion studies: a retrospective study (RS) using medical chart data with minimal data quality review and the PRospective Observational Multi-center Major Trauma Transfusion (PROMMTT) study with standardized quality assurance. The second objective was to assess the impact of missing data on clinical prediction algorithms by evaluating blood transfusion prediction models using PROMMTT data. RS (2005-06) and PROMMTT (2009-10) investigated trauma patients receiving ≥ 1 unit of red blood cells (RBC) from ten Level I trauma centers. Missing data were compared for 33 variables collected in both studies using mixed effects logistic regression (including random intercepts for study site). Massive transfusion (MT) patients received ≥ 10 RBC units within 24h of admission. Correct classification percentages for three MT prediction models were evaluated using complete case analysis and multiple imputation based on the multivariate normal distribution. A sensitivity analysis for missing data was conducted to estimate the upper and lower bounds of correct classification using assumptions about missing data under best and worst case scenarios. Most variables (17/33=52%) had <1% missing data in RS and PROMMTT. Of the remaining variables, 50% demonstrated less missingness in PROMMTT, 25% had less missingness in RS, and 25% were similar between studies. Missing percentages for MT prediction variables in PROMMTT ranged from 2.2% (heart rate) to 45% (respiratory rate). For variables missing >1%, study site was associated with missingness (all p≤0.021). Survival time predicted missingness for 50% of RS and 60% of PROMMTT variables. MT models complete case proportions ranged from 41% to 88%. Complete case analysis and multiple imputation demonstrated similar correct classification results. Sensitivity analysis upper-lower bound ranges for the three MT models were 59-63%, 36-46%, and 46-58%. Prospective collection of ten-fold more variables with data quality assurance reduced overall missing data. Study site and patient survival were associated with missingness, suggesting that data were not missing completely at random, and complete case analysis may lead to biased results. Evaluating clinical prediction model accuracy may be misleading in the presence of missing data, especially with many predictor variables. The proposed sensitivity analysis estimating correct classification under upper (best case scenario)/lower (worst case scenario) bounds may be more informative than multiple imputation, which provided results similar to complete case analysis.^

Health outcomes, quality improvement and cost-effectiveness of the Community Diabetes Education (CoDE) program: Results of a randomized controlled trial

The objectives of this dissertation were to evaluate health outcomes, quality improvement measures, and the long-term cost-effectiveness and impact on diabetes-related microvascular and macrovascular complications of a community health worker-led culturally tailored diabetes education and management intervention provided to uninsured Mexican Americans in an urban faith-based clinic. A prospective, randomized controlled repeated measures design was employed to compare the intervention effects between: (1) an intervention group (n=90) that participated in the Community Diabetes Education (CoDE) program along with usual medical care; and (2) a wait-listed comparison group (n=90) that received only usual medical care. Changes in hemoglobin A1c (HbA1c) and secondary outcomes (lipid status, blood pressure and body mass index) were assessed using linear mixed-models and an intention-to-treat approach. The CoDE group experienced greater reduction in HbA1c (-1.6%, p<.001) than the control group (-.9%, p<.001) over the 12 month study period. After adjusting for group-by-time interaction, antidiabetic medication use at baseline, changes made to the antidiabetic regime over the study period, duration of diabetes and baseline HbA1c, a statistically significant intervention effect on HbA1c (-.7%, p=.02) was observed for CoDE participants. Process and outcome quality measures were evaluated using multiple mixed-effects logistic regression models. Assessment of quality indicators revealed that the CoDE intervention group was significantly more likely to have received a dilated retinal examination than the control group, and 53% achieved a HbA1c below 7% compared with 38% of control group subjects. Long-term cost-effectiveness and impact on diabetes-related health outcomes were estimated through simulation modeling using the rigorously validated Archimedes Model. Over a 20 year time horizon, CoDE participants were forecasted to have less proliferative diabetic retinopathy, fewer foot ulcers, and reduced numbers of foot amputations than control group subjects who received usual medical care. An incremental cost-effectiveness ratio of $355 per quality-adjusted life-year gained was estimated for CoDE intervention participants over the same time period. The results from the three areas of program evaluation: impact on short-term health outcomes, quantification of improvement in quality of diabetes care, and projection of long-term cost-effectiveness and impact on diabetes-related health outcomes provide evidence that a community health worker can be a valuable resource to reduce diabetes disparities for uninsured Mexican Americans. This evidence supports formal integration of community health workers as members of the diabetes care team.^