Falls are the second leading cause of accidental or unintentional injury deaths worldwide. Many factors contribute to an increased risk of falling, such as age, disease state, and medication use. The purpose of the current investigation was to compare an electronic medical record (EMR) fall risk calculator, the theoretical Timed Up and Go (T-TUG), which utilizes gender, age, BMI, and prescription and OTC drug counts as variables, to other established performance- and paper-based assessments of fall risk. The National Social Life, Health, and Aging Project (NSHAP) Database was used to develop the T-TUG. Data was analyzed from participants in Wave 1 of the Irish Longitudinal Study on Ageing (TILDA) to validate the T-TUG. Performance-based assessments included mean grip force for both dominant and nondominant hands, Timed Up and Go (TUG), and a paper-based assessment titled the Steadiness Index. The latter is a series of 3 questions assessing steadiness when walking, standing, or getting up from a chair. Those participants of the TILDA cohort passing the inclusion criteria were divided into those who reported a fall in the previous year (N=1159) and those reporting no falls (N=4746). Two group comparisons were analyzed by Mann-Whitney U Test (p<0.05) and a Receiver Operator Characteristics (ROC) curve analysis was used to detect separation of fall and non-fall groups. For the Mann-Whitney U test the fall and no fall groups were statistically different for the T-TUG (p<0.001), TUG (p<0.001), dominant and nondominant grip forces (p<0.001), and the steadiness index (p< 0.001). In the fall group, the grip forces were weaker, T-TUG and TUG time longer, and the steadiness index scores lower. For the grip force assessments and steadiness index, lower scores are more likely to be associated with a higher fall risk. In the T-TUG and TUG, longer times are more likely to be associated with a higher fall risk. In the ROC curve analyses, the T-TUG (0.567, p<0.001) demonstrated similar outcomes compared to dominant (AUC=547, p<0.09) and non-dominant (AUC=0.550, p<0.01) grip forces, and the TUG (AUC=0.558, p<0.001). The steadiness index ROC analysis was slightly better than the T-TUG (AUC=0.579, p<0.001). Sensitivity (52-58%) and specificity (50-57%) ranges were equivalent for all performance-based assessments, whereas for the Steadiness Index, the sensitivity (40%) was lower than the specificity (75%). The EMR fall-risk calculator (T-TUG) is a valid triage tool to estimate fall risk in older community dwellers. The EMR calculator has the potential for real-time assessment of patients using current data compared to other performance- and paper-based assessments, which would allow the healthcare team to spend more time with higher fall risk patients.

Abstract: Cardiovascular disease (CVD) is the leading cause of death in the United States (US). One of the most important things primary care providers (PCP) can do to prevent CVD is using primary prevention treatments. In the practice where the project was implemented, a standardized process was not in place for identifying at-risk patients. Without this, there is no way to identify if providers were adequately assessing patients for atherosclerotic cardiovascular disease (ASCVD) risk by considering their risk-enhancing factors. One way to identify appropriate patients is by completing ASCVD risk calculation using the ASCVD Risk Estimator Plus from the American College of Cardiology and the American Heart Association. In addition, 2018 Guidelines for Cholesterol Management recommend ASCVD risk calculation on all patients 40-79. The use of this tool is free to both patients and providers through a website or mobile app. The calculator can be integrated into the Electronic Health Record (EHR) to improve ease of use however, that does not come standard. Therefore, ASCVD risk calculation was performed on all patients aged 40-79 presenting for a fasting lab visit (FLV) at a primary care practice comprised of 3 clinics in East Tennessee between January 17, 2022 and February 28, 2022. Excluded patients included: patients outside of the age range, who did not have a lipid level done at their FLV, or who had a total cholesterol (TC) level greater than 320mg/dL. Once calculation was performed, results were given to the patient’s PCP for medical decision making on primary prevention treatment. After providers were given the results, chart reviews were completed to assess for primary prevention treatment initiations or increases within three months of receiving the results. Preliminary results show that a total of 443 patients presented for a FLV during the timeframe. A total of 132 patients were ineligible due to age (n=70), not having a lipid level completed (n=61) or having a TC level greater than 320 mg/dL (n=1). A total of 133 patients did not show or rescheduled their FLV. Chart reviews are just beginning, and insufficient data is currently available regarding intervention results. Limitations to this project include: all participants were Caucasian therefore, result may not be applicable to a more diverse population, the project was completed during a pandemic where patients were hesitant to come into the office, even for FLV, and a considerable number of patients who risk calculation could not be completed on. Having ASCVD calculation integrated within the EHR could promote use by providers. Future long-term research is needed to identify the accuracy of this calculator. This calculator has been modified based on research. However, research to identify the accuracy could lead to modification of the calculation to provide the most accurate result possible. One way this can be done is through use of the calculator by providers across the US.

Introduction: The Centers for Disease Control and Prevention found that each year over three million people are treated for fall injuries, and of those three million, one in five falls causes serious injury. One clinical report stated only 37% of elderly patients are asked about falls in the primary care setting. The report found barriers to further fall-related care were due to the many factors that go into assessing if a patient is a fall-risk. Thus, assessing the fall risk for a large elderly population can be both challenging and time-consuming. The purpose of this study is to evaluate the effectiveness of assessing fall risk with the Theoretical Timed Up and Go (T-TUG), using Wave 1 of the Irish Longitudinal Database (TILDA). The validation was done by comparing the T-TUG results to that of the Impairments survey and activities of daily living (ADLs) found in the TILDA. Methods: The data used in this study were obtained from Wave 1 of the TILDA. The TILDA is a survey-designed longitudinal study on aging done on a national scale in Ireland. Study participants who passed inclusion criteria were divided into those who had reported falling in the previous year (N=1221) and those who had not (N=4857). The T-TUG is a fall-risk calculator developed from the NSHAP database, with a multiple regression function using the Timed Up and Go as the dependent variable, and age, gender, body mass index, and over the counter and prescription drugs as the predictor variables. The NSHAP regression coefficients were combined with the TILDA participant parameters defined above to calculate new T-TUG scores for the TILDA cohort. Differentiation between the fall and no fall groups for the T-TUG, ADLs and Impairments survey were done using the Mann-Whitney U Test (p < 0.05). Receiver Operator Characteristics (ROC) curve analyses were done to identify cut-off points, sensitivities, and specificities differentiating the fall and no fall groups for these assessments. Results: Mann-Whitney analysis demonstrated that the fall group scores were statistically different from the no fall group for all three assessments (p-value < 0.001). As determined by AUC, the ROC analysis indicated that the T-TUG (AUC=0.570, p Conclusion: All assessments evaluated were effective at differentiating participants within this database reporting a fall within the last year from those who had not. Whereas the T-TUG and Impairments survey were equally effective at detecting true fallers and non-fallers, the ADLs were much more effective at detecting non-fallers. The T-TUG has the potential to be an EMR based fall risk calculator and could be invaluable as an institutional triage tool.

In 2018, the United States Preventive Services Task Force (USPSTF) recommended that African American men and those with a family history of prostate cancer should discuss the pros and cons of PSA testing with their physician and engage in shared decision making. Identifying risk factors of prostate cancer, calculating individualized prediction of the risk of prostate cancer, and ensuring that the patients are well informed with knowledge to understand these risks so as to easily make decisions, are extremely important in shared decision making. The overarching goal of this dissertation is to assist in these three steps and facilitate shared decision making in a high-risk population. Aim 1 assessed the association of demographic characteristics, clinical markers and genitourinary symptoms with the diagnosis of prostate cancer (Aim 1a), as well as with the diagnosis of significant prostate cancer (Aim 1b), in a high-risk population. A nested case-control study for Aim 1a and a case-control study for Aim 1b was conducted using the Prostate Risk Assessment Program (PRAP) data, which enrolls African American men and those with a family history of prostate cancer. Multivariate conditional logistic regression was used to assess the association between the risk factors for any prostate cancer, while multivariate logistic regression was used for clinically significant prostate cancer. The risk of any prostate cancer increased with increasing age, presence of family history and increasing PSA levels, while the risk of significant prostate cancer was associated with increasing PSA level. This suggests that PSA level, as a continuum, is extremely important while predicting prostate cancer, especially significant prostate cancer within a high-risk population. Using Aim 2, we compared the performance of two prostate cancer risk calculators, Prostate Cancer Prevention Trial – Risk Calculator 2.0 (PCPT-RC 2.0) and the European Randomized Study of Screening for Prostate Cancer -Risk Calculator 3/5 (ERSPC-RC 3/5) to predict any prostate cancer and clinically significant prostate cancer in a high-risk American population. All men who underwent prostate biopsy with the PRAP data registry since 1996 were included in the study. The probability of being diagnosed with any prostate cancer and significant prostate cancer (Gleason score &gt; 6) was calculated using the online versions of PCPT-RC 2.0 and ERSPC-RC 3/5. The performance of these calculators was compared using calibration (calibration plot and calibration-in-the-large), discrimination (comparing AUC curves using DeLong’s method) and decision curve analysis (to assess clinical utility). The calibration suggested that both risk calculators under-predicted the probability of any prostate cancer while PCPT-RC 2.0 over-predicted the probability of significant prostate cancer. Analysis of the AUC curves suggested that the PCPT-RC 2.0 (AUC: 0.59, 95% CI 0.52 to 0.66) showed a trend towards better discrimination for any prostate cancer as compared to ERSPC-RC 3/5 (AUC: 0.55, 95% CI 0.48-0.63, p= 0.3819). Similarly, PCPT-RC 2.0 (AUC: 0.71, 95% CI 0.61-0.82) showed a trend towards better discrimination for significant prostate cancer as compared to ERSPC-RC 3/5 (AUC: 0.63, 95% CI 0.51 to 0.75, p= 0.2335). PCPT-RC 2.0 proved to be clinically beneficial to predict significant prostate cancer in the range of lower prediction thresholds. These results suggest that the PCPT-RC 2.0 is superior to ERSPC-RC 3/5 in a high-risk American population. Aim 3 utilized a systematic review of the decision aids used to improve prostate cancer knowledge, improve risk perception, reduce confusion, involve in shared decision making or utilize PSA tests in men at high-risk of prostate cancer, defined as those with African descent or those with a family history of prostate cancer. Data was extracted by searching MEDLINE, CINAHL, EMBASE, and PsycINFO via Ovid and EBSCOhost. After screening titles and abstracts, the resulting full-text articles were assessed for inclusion and exclusion criteria. A data extraction table was created, and the methodological quality of the studies was assessed based on three criteria – randomization, double blinding and intention-to-treat analysis. Due to the clinical heterogeneity of the studies, a descriptive analysis of all the studies was conducted and tabulated. A total of 2605 articles were retrieved after literature search, of which 8 articles met the inclusion criteria and were included in the qualitative analysis. Of these 8 articles, 6 were targeted at those who were African American or those with an African descent and 2 articles included interventions targeted at those with a family history of prostate cancer. Majority of the studies targeted at African American men demonstrated an improvement in knowledge and reduction in decisional conflict in the intervention group compared to the control group. The two studies that included men with a family history of prostate cancer did not show any change in knowledge or decisional conflict in the intervention group compared to the comparison group. All studies were of low quality, except one which was medium quality. Thus, this review unveiled that tailored decision aids would be helpful in improving knowledge and reducing decisional conflict in African American men while decision aids designed for men with family history of prostate cancer would not significantly change prostate cancer knowledge or decisional conflict compared to the standard decision aid. Thus, one of the tailored decision aids can be used to help African American men improve their knowledge of prostate cancer and reduce decisional conflict, while the standard decision aid can be used in men with a family history of prostate cancer. These conclusions can be assimilated into the USPSTF recommended shared decision-making sessions between the patients and the physicians. / Epidemiology