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Biomedical subjects

Harry B Burke

Publications and source records attributed to Harry B Burke.

10 recordsLinked to original sources

Neighborhood racial composition and availability of asthma drugs in retail pharmacies.

This study investigated whether there may be differences in the availability of asthma drugs and equipment in retail pharmacies in nonwhite and white neighborhoods in the District of Columbia. We conducted a telephone survey of a random sample of 38 retail pharmacies in predominately black or white neighborhoods. No differences in the reported availability of asthma drugs and equipment by neighborhood were found, although there was variability in availability of certain asthma drugs and limited availability of asthma equipment. Low demand was the most frequent reason cited by pharmacists regarding why asthma drugs or equipment were not in stock.

Anti-Asthmatic Agents↗

Epidemiology and statistical methods in prediction of patient outcome.

Substantial gaps exist in the data of the assessment of risk and prognosis that limit our understanding of the complex mechanisms that contribute to the greatest cancer epidemic, prostate cancer, of our time. This report was prepared by an international multidisciplinary committee of the World Health Organization to address contemporary issues of epidemiology and statistical methods in prostate cancer, including a summary of current risk assessment methods and prognostic factors. Emphasis was placed on the relative merits of each of the statistical methods available. We concluded that: 1. An international committee should be created to guide the assessment and validation of molecular biomarkers. The goal is to achieve more precise identification of those who would benefit from treatment. 2. Prostate cancer is a predictable disease despite its biologic heterogeneity. However, the accuracy of predicting it must be improved. We expect that more precise statistical methods will supplant the current staging system. The simplicity and intuitive ease of using the current staging system must be balanced against the serious compromise in accuracy for the individual patient. 3. The most useful new statistical approaches will integrate molecular biomarkers with existing prognostic factors to predict conditional life expectancy (i.e. the expected remaining years of a patient's life) and take into account all-cause mortality.

Adult↗

Identifying critically ill patients at high risk for developing acute renal failure: a pilot study.

BACKGROUND: Acute renal failure (ARF) occurs commonly in the intensive care unit (ICU), but predicting which patients will develop ARF is difficult. We set out to determine which risk factors would predict the development of ARF in critically ill patients who are admitted to the ICU without ARF. METHODS: From August 2002 to April 2003, we enrolled medical-surgical ICU admissions into a cohort using a sampling tool based on their risk factor (RF) profile. The risk factors we identified were separated into 3 categories: chronic major, chronic minor, and acute RFs. Combinations of these RFs were used to create a sampling tool and identify patients to enroll into our cohort. Patients with end-stage renal disease and ARF upon admission to the ICU were excluded. RESULTS: We enrolled 194 patients over a 14-month period. The mean age of the cohort was 64.6 +/- 14.7 years. The percentage of Caucasians, African Americans, and Hispanics was 40.7%, 50.5%, and 3.6%, respectively. In a univariate analysis of the entire cohort, increasing APACHE II quartile, increased A-a gradient, presence of systemic inflammatory response syndrome (SIRS), decreased levels of serum albumin, and presence of active cancer predicted ARF. In a multiple logistic regression analysis, decreased serum albumin (high levels of serum albumin were protective), increased A-a gradient, and cancer were associated with development of ARF (OR 2.17, 1.04, and 2.86, respectively). CONCLUSION: Decreased levels of serum albumin concentration, increased A-a gradient, and presence of active cancer predict which patients who are admitted to the ICU will develop ARF.

APACHE↗

Prostate cancer outcome: epidemiology and biostatistics.

Substantial gaps exist in our ability to accurately predict prognosis, and these gaps limit our understanding of the complex mechanisms that contribute to the greatest cancer epidemic of our time, prostate cancer. This review addresses contemporary epidemiologic and biostatistical issues in prostate cancer. It covers the science of outcome prediction and biomarker evaluation, recognition of the need to combine biomarkers to improve the accuracy of our outcome estimates and an analysis of current outcome assessment methods, including the TNM staging system and multivariate regression models. The simplicity and intuitive ease of the current TNM staging system must be balanced against its serious limitations in predictive accuracy and its loss of clinical utility. Statistical regression methods are required as we move to the new era of personalized medicine. We must implement statistical approaches that integrate the new molecular biomarkers with existing prognostic biomarkers to accurately predict which patients require treatment and to determine the optimal therapy.

Biomarkers, Tumor↗

Human prostate cancer risk factors.

Prostate cancer has the highest prevalence of any nonskin cancer in the human body, with similar likelihood of neoplastic foci found within the prostates of men around the world regardless of diet, occupation, lifestyle, or other factors. Essentially all men with circulating androgens will develop microscopic prostate cancer if they live long enough. This review is a contemporary and comprehensive, literature-based analysis of the putative risk factors for human prostate cancer, and the results were presented at a multidisciplinary consensus conference held in Crystal City, Virginia, in the fall of 2002. The objectives were to evaluate known environmental factors and mechanisms of prostatic carcinogenesis and to identify existing data gaps and future research needs. The review is divided into four sections, including 1) epidemiology (endogenous factors [family history, hormones, race, aging and oxidative stress] and exogenous factors [diet, environmental agents, occupation and other factors, including lifestyle factors]); 2) animal and cell culture models for prediction of human risk (rodent models, transgenic models, mouse reconstitution models, severe combined immunodeficiency syndrome mouse models, canine models, xenograft models, and cell culture models); 3) biomarkers in prostate cancer, most of which have been tested only as predictive factors for patient outcome after treatment rather than as risk factors; and 4) genotoxic and nongenotoxic mechanisms of carcinogenesis. The authors conclude that most of the data regarding risk relies, of necessity, on epidemiologic studies, but animal and cell culture models offer promise in confirming some important findings. The current understanding of biomarkers of disease and risk factors is limited. An understanding of the risk factors for prostate cancer has practical importance for public health research and policy, genetic and nutritional education and chemoprevention, and prevention strategies.

Animals↗

Anthropometric measures, cytokines and survival in haemodialysis patients.

BACKGROUND: Lower serum albumin concentration (sAlb) and higher levels of pro-inflammatory cytokines have been reported to predict death in patients treated with haemodialysis (HD). SAlb, along with anthropometric measures, has been used as a surrogate marker for nutritional status in patients with chronic disease. Though adequate nutrition has been considered an important factor for patients treated with HD, it has not been established if any nutritional markers other than lower serum albumin and lower body mass index (BMI) predict death. Furthermore, it has not been shown whether anthropometric measures other than BMI are associated with predictors of mortality. METHODS: At the outset of the study, patients were assessed using demographic and anthropometric indices including arm fat area (AFA), arm muscle area (AMA), BMI, per cent ideal weight (PIW), pre-dialysis sAlb, and circulating levels of tumour necrosis factor-alpha (TNF-alpha), IL-1 and IL-6. A severity index, previously demonstrated to be a mortality marker, was used to grade medical co-morbidity. RESULTS: Two-hundred and forty patients entered the study. The mean age was 55.1+/-14.3 years, mean sAlb 3.76+/-0.60 mg/dl, mean AFA 1742+/-1225 mm(2), mean AMA 5464+/-1817 mm(2), mean PIW 101.0+/-21.3% and mean BMI 24.9+/-5.6 kg/m(2). PIW, BMI, AFA and AMA were, as expected, all highly correlated with one another. SAlb correlated with serum transferrin; however, neither sAlb nor serum transferrin concentration correlated with circulating cytokine levels. Circulating cytokines and sAlb did not correlate with PIW, BMI, AFA or AMA. In Cox regression analyses using multiple control variables, IL-6 predicted survival, while the anthropometric measures did not. CONCLUSIONS: Pro-inflammatory cytokines and sAlb are robust predictors of death in patients treated with HD. PIW and BMI correlate well with other anthropometric measures in patients treated with HD, but these measures do not correlate with markers of inflammation. Anthropometric measures are poor predictors of survival compared with measures linked to the acute-phase response.

Adipose Tissue↗

Challenges in dyslipidemia.

Dyslipidemia is recognized as a significant risk factor for atherosclerotic disease. There has been a great deal of progress in the detection and management of dyslipidemia, but challenges remain, including whether to treat children, adolescents, and the elderly. Challenges include convincing physicians who tend to manage an outpatient disease with a single therapy that the management of cardiovascular risk and disease often requires multiple therapies, describing how we can combine therapies to provide an additive benefit without adding side effects or increasing morbidity, and determining whether a patient with one or more cardiovascular risk factors but a normal lipid should receive lipid-lowering therapy. Finally, there is the challenge of the Human Genome Project and predictive medicine. How will genetic information be integrated into the practice of medicine for disease prevention and management?

Age Factors↗