Myocardial infarction and type II diabetes - preferential treatment for high risk patients?

Objectives: To compare variability of blood glucose concentration in patients with type II diabetes with (cases) and without (controls) myocardial infarction. A secondary objective was identification of predictive factors for higher blood glucose on discharge from hospital. Design: A retrospective matched case-control study. Participants: Medical notes of 101 type II diabetic patients admitted with a myocardial infarction (MI) and 101 type II diabetic patients (controls) matched on gender and age with no MI were reviewed. Blood glucose concentrations over two consecutive 48-h periods were collected. Demographic data and therapy on admission/discharge were also collected. Results: Patient characteristics were comparable on recruitment excluding family history of cardiovascular disease (P =0.003), dyslipidaemia (P =0.004) and previous history of MI (P =0.007). Variability of blood glucose in cases was greater over the first 48 h compared with the second 48 h (P =0.03), and greater when compared with controls over the first 48 h (P =0.01). Cases with blood glucose on discharge >8.2 mmol / L (n =45) were less likely to have a history of previous MI (P =0.04), ischaemic heart disease (P =0.03) or hypertension (P =0.02). Conclusions: Type II diabetics with an MI have higher and more variable blood glucose concentrations during the first 48 h of admission. Only cardiovascular 'high risk' patients had target blood glucose set on discharge. The desirability of all MI patients with diabetes, having standardized-glucose infusions to reduce variability of blood glucose, should be evaluated in a randomized controlled trial.

Robust model-order reduction of complex biological processes

This paper addresses robust model-order reduction of a high dimensional nonlinear partial differential equation (PDE) model of a complex biological process. Based on a nonlinear, distributed parameter model of the same process which was validated against experimental data of an existing, pilot-scale BNR activated sludge plant, we developed a state-space model with 154 state variables in this work. A general algorithm for robustly reducing the nonlinear PDE model is presented and based on an investigation of five state-of-the-art model-order reduction techniques, we are able to reduce the original model to a model with only 30 states without incurring pronounced modelling errors. The Singular perturbation approximation balanced truncating technique is found to give the lowest modelling errors in low frequency ranges and hence is deemed most suitable for controller design and other real-time applications. (C) 2002 Elsevier Science Ltd. All rights reserved.

Impact numbers: measures of risk factor impact on the whole population from case-control and cohort studies

Objective: To describe new measures of risk from case-control and cohort studies, which are simple to understand and relate to numbers of the population at risk. Design: Theoretical development of new measures of risk. Setting: Review of literature and previously described measures. Main results: The new measures are: (1) the population impact number (PIN), the number of those in the whole population among whom one case is attributable to the exposure or risk factor (this is equivalent to the reciprocal of the population attributable risk),- (2) the case impact number (CIN) the number of people with the disease or outcome for whom one case will be attributable to the exposure or risk factor (this is equivalent to the reciprocal of the population attributable fraction); (3) the exposure impact number (EIN) the number of people with the exposure among whom one excess case is attributable to the exposure (this is equivalent to the reciprocal of the attributable risk); (4) the exposed cases impact number (ECIN) the number of exposed cases among whom one case is attributable to the exposure (this is equivalent to the reciprocal of the aetiological fraction). The impact number reflects the number of people in each population (the whole population, the cases, all those exposed, and the exposed cases) among whom one case is attributable to the particular risk factor. Conclusions: These new measures should help communicate the impact on a population, of estimates of risk derived from cohort or case-control studies.

Risk and protective factors for depressive symptomatology among a community sample of adolescents and young adults

Objectives: The study explores the risk and protective factors for current depressive symptomatology in a large community sample of 15-to-24-year-olds. Methods: The study was designed as a cross-sectional household survey, which used telephone recruitment followed by an anonymous self-report postal questionnaire. The final sample included 3,082 adolescents and young adults from Queensland, Australia. Results: The vast majority of measured risk and protective factors were associated with current depressive symptomatology. Key risk factors included high levels of neuroticism, perceived problems with parents, sexual abuse, relationship breakups, educational failure and sexual identity conflict. A different profile of protective factors was evident for each of these high-risk groups. Of particular note was the importance of well-developed intrapersonal skills as protective for both males and females. The significance of social connectedness as a protective factor for the males overall and across a range of high-risk groups was a central finding. Conclusions and implications: The implications of these findings in relation to a range of mental health promotion and mental illness prevention and early intervention initiatives are discussed. Supported initiatives include parenting programs that consider the realities of modern families, increasing community awareness of the impact on young people of the breakdown of their intimate relationships, initiatives in educational settings and workplaces to increase tolerance of gay/lesbian and bisexual lifestyles and the enhancement of social connectedness.