Laël C. Gatewood

Test of effect of lipid lowering by diet on cardiovascular risk. The Minnesota Coronary Survey.

The Minnesota Coronary Survey was a 4.5-year, open enrollment, single end-time, double-blind, randomized clinical trial that was conducted In six Minnesota state mental hospitals and one nursing home. It Involved 4393 Institutionalized men and 4664 Institutionalized women. The trial compared the effects of a 39% fat control diet (18% saturated fat, 5% polyunsaturated fat, 16% monounsaturated fat, 446 mg dietary cholesterol per day) with a 38% fat treatment diet (9% saturated fat, 15% polyunsaturated fat, 14% monounsaturated fat, 166 mg dietary cholesterol per day) on serum cholesterol levels and the Incidence of myocardlal Infarctions, sudden deaths, and all-cause mortality. The mean duration of time on the diets was 384 days, with 1568 subjects consuming the diet for over 2 years. The mean serum cholesterol level In the pre-admission period was 207 mg/dl, falling to 175 mg/dl in the treatment group and 203 mg/dl In the control group. For the entire study population, no differences between the treatment and control groups were observed for cardiovascular events, cardiovascular deaths, or total mortality. A favorable trend for all these end-points occurred In some younger age groups.

PREDICT: A Simple Risk Score for Clinical Severity and Long-Term Prognosis After Hospitalization for Acute Myocardial Infarction or Unstable Angina The Minnesota Heart Survey

BACKGROUND We evaluated short- and long-term mortality risks in 30- to 74-year-old patients hospitalized for acute myocardial infarction or unstable angina and developed a new score called PREDICT. METHODS AND RESULTS PREDICT was based on information routinely collected in hospital. Predictors abstracted from hospital record items pertaining to the admission day, including shock, heart failure, ECG findings, cardiovascular disease history, kidney function, and age. Comorbidity was assessed from discharge diagnoses, and mortality was determined from death certificates. For 1985 and 1990 hospitalizations, the 6-year death rate in 6134 patients with 0 to 1 score points was 4%, increasing stepwise to 89% for >/=16 points. Score validity was established by only slightly attenuated mortality prediction in 3570 admissions in 1970 and 1980. When case severity was controlled for, 6-year risk declined 32% between 1970 and 1990. When PREDICT was held constant, 24% of those treated with thrombolysis died in 6 years compared with 31% of those not treated. CONCLUSIONS The simple PREDICT risk score was a powerful prognosticator of 6-year mortality after hospitalization.

Preventing heart disease: is treating the high risk sufficient?

Monte Carlo simulation was used to assess the effects of several intervention strategies on coronary heart disease mortality rates in a Finnish and a North American cohort. Lowering total serum cholesterol by 4%, smoking by 15%, and diastolic blood pressure by 3% for the whole cohort would be expected to reduce the incidence of non-fatal myocardial infarction by at least 13% and coronary heart disease deaths by at least 18%. Lowering serum cholesterol by 34%, diastolic blood pressure to 90 mmHg, and reducing smoking by 20% in the subset of the population with all three risk factors in the highest quartile would result in a 6-8% reduction in non-fatal myocardial infarction and a 2-9% reduction in deaths from coronary heart disease in these cohorts. These data demonstrate that in populations with a relatively high incidence of heart disease, treating the entire population will produce larger effects than focusing only on high-risk populations.

A national agenda for public health informatics.

The American Medical Informatics Association 2001 Spring Congress brought together the public health and informatics communities to develop a national agenda for public health informatics. Discussions on funding and governance; architecture and infrastructure; standards and vocabulary; research, evaluation, and best practices; privacy, confidentiality, and security; and training and workforce resulted in 74 recommendations with two key themes: (1) all stakeholders need to be engaged in coordinated activities related to public health information architecture, standards, confidentiality, best practices, and research and (2) informatics training is needed throughout the public health workforce. Implementation of this consensus agenda will help promote progress in the application of information technology to improve public health.

Evaluation and optimization of fMRI single-subject processing pipelines with NPAIRS and second-level CVA

In functional magnetic resonance imaging (fMRI) analysis, although the univariate general linear model (GLM) is currently the dominant approach to brain activation detection, there is growing interest in multivariate approaches such as principal component analysis, canonical variate analysis (CVA), independent component analysis and cluster analysis, which have the potential to reveal neural networks and functional connectivity in the brain. To understand the effect of processing options on performance of multivariate model-based fMRI processing pipelines with real fMRI data, we investigated the impact of commonly used fMRI preprocessing steps and optimized the associated multivariate CVA-based, single-subject processing pipelines with the NPAIRS (nonparametric prediction, activation, influence and reproducibility resampling) performance metrics [prediction accuracy and statistical parametric image (SPI) reproducibility] on the Fiswidgets platform. We also compared the single-subject SPIs of univariate GLM with multivariate CVA-based processing pipelines from SPM, FSL.FEAT, NPAIRS.GLM and NPAIRS.CVA software packages (or modules) using a novel second-level CVA. We found that for the block-design data, (a) slice timing correction and global intensity normalization have little consistent impact on the fMRI processing pipeline, but spatial smoothing, temporal detrending or high-pass filtering, and motion correction significantly improved pipeline performance across all subjects; (b) the combined optimization of spatial smoothing, temporal detrending and CVA model parameters on average improved between-subject reproducibility; and (c) the most important pipeline choices include univariate or multivariate statistical models and spatial smoothing. This study suggests that considering options other than simply using GLM with a fixed spatial filter may be of critical importance in determining activation patterns in BOLD fMRI studies.

Patterns of tooth loss in older adults with and without dementia: A retrospective study based on a Minnesota cohort

OBJECTIVES: To study tooth loss patterns in older adults with dementia.

Patterns of tooth loss in older adults with and without dementia

OBJECTIVES: To study tooth loss patterns in older adults with dementia.

IPHIE: An International partnership in health informatics education

Medical informatics contributes significantly to high quality and efficient health care and medical research. The need for well educated professionals in the field of medical informatics therefore is now worldwide recognized. Students of medicine, computer science/informatics are educated in the field of medical informatics and dedicated curricula on medical informatics have emerged. To advance and further develop the beneficial role of medical informatics in the medical field, an international orientation of health and medical informatics students seems an indispensable part of their training. An international orientation and education of medical informatics students may help to accelerate the dissemination of acquired knowledge and skills in the field and the promotion of medical informatics research results on a more global level. Some years ago, the departments of medical informatics of the university of Heidelberg/university of applied sciences Heilbronn and the university of Amsterdam decided to co-operate in the field of medical informatics. Now, this co-operation has grown out to an International Partnership of Health Informatics Education (IPHIE) of 5 universities, i.e. the university of Heidelberg, the university of Heilbronn, the university of Minnesota, the university of Utah and the university of Amsterdam. This paper presents the rationale behind this international partnership, the state of the art of the co-operation and our future plans for expanding this international co-operation.

The role of glucagon in the regulation of blood glucose: Model studies

Mathematical models afford a procedure of unifying concepts and hypotheses by expressing quantitative relationships between observables. The model presented indicates the roles of both insulin and glucagon as regulators of blood glucose, albeit in different ranges of the blood glucose concentrations. Insulin secretion is induced during hyperglycemia, while glucagon secretion results during hypoglycemia. These are demonstrated by simulations of a mathematical model conformed to data from the oral glucose tolerance test and the insulin infusion test in normal control subjects and stable and unstable diabetic patients. The model studies suggest the parameters could prove of value in quantifying the diabetic condition by indicating the degree of instability.

A Java-based fMRI Processing Pipeline Evaluation System for Assessment of Univariate General Linear Model and Multivariate Canonical Variate Analysis-based Pipelines

As functional magnetic resonance imaging (fMRI) becomes widely used, the demands for evaluation of fMRI processing pipelines and validation of fMRI analysis results is increasing rapidly. The current NPAIRS package, an IDL-based fMRI processing pipeline evaluation framework, lacks system interoperability and the ability to evaluate general linear model (GLM)-based pipelines using prediction metrics. Thus, it can not fully evaluate fMRI analytical software modules such as FSL.FEAT and NPAIRS.GLM. In order to overcome these limitations, a Java-based fMRI processing pipeline evaluation system was developed. It integrated YALE (a machine learning environment) into Fiswidgets (a fMRI software environment) to obtain system interoperability and applied an algorithm to measure GLM prediction accuracy. The results demonstrated that the system can evaluate fMRI processing pipelines with univariate GLM and multivariate canonical variates analysis (CVA)-based models on real fMRI data based on prediction accuracy (classification accuracy) and statistical parametric image (SPI) reproducibility. In addition, a preliminary study was performed where four fMRI processing pipelines with GLM and CVA modules such as FSL.FEAT and NPAIRS.CVA were evaluated with the system. The results indicated that (1) the system can compare different fMRI processing pipelines with heterogeneous models (NPAIRS.GLM, NPAIRS.CVA and FSL.FEAT) and rank their performance by automatic performance scoring, and (2) the rank of pipeline performance is highly dependent on the preprocessing operations. These results suggest that the system will be of value for the comparison, validation, standardization and optimization of functional neuroimaging software packages and fMRI processing pipelines.