Smart Alerts: Development of Software to Optimize Data Monitoring

Dherte PM, Negrao MPG, Mori Neto S, Holzhacker R, Shimada V, Taberner P, Carmona MJC - Smart Alerts: Development of a Software to Optimize Data Monitoring. Background and objectives: Monitoring is useful for vital follow-ups and prevention, diagnosis, and treatment of several events in anesthesia. Although alarms can be useful in monitoring they can cause dangerous user`s desensitization. The objective of this study was to describe the development of specific software to integrate intraoperative monitoring parameters generating ""smart alerts"" that can help decision making, besides indicating possible diagnosis and treatment. Methods: A system that allowed flexibility in the definition of alerts, combining individual alarms of the parameters monitored to generate a more elaborated alert system was designed. After investigating a set of smart alerts, considered relevant in the surgical environment, a prototype was designed and evaluated, and additional suggestions were implemented in the final product. To verify the occurrence of smart alerts, the system underwent testing with data previously obtained during intraoperative monitoring of 64 patients. The system allows continuous analysis of monitored parameters, verifying the occurrence of smart alerts defined in the user interface. Results: With this system a potential 92% reduction in alarms was observed. We observed that in most situations that did not generate alerts individual alarms did not represent risk to the patient. Conclusions: Implementation of software can allow integration of the data monitored and generate information, such as possible diagnosis or interventions. An expressive potential reduction in the amount of alarms during surgery was observed. Information displayed by the system can be oftentimes more useful than analysis of isolated parameters.

Mild myocardial infarction - A classification problem in epidemiologic studies

In studies assessing the trends in coronary events, such as the World Health Organization (WHO) MONICA Project (multinational MONItoring of trends and determinants of CArdiovascular disease), the main emphasis has been on coronary deaths and non-fatal definite myocardial infarctions (MI). It is, however, possible that the proportion of milder MIs may be increasing because of improvements in treatment and reductions in levels of risk factors. We used the MI register data of the WHO MONICA Project to investigate several definitions for mild non-fatal MIs that would be applicable in various settings and could be used to assess trends in milder coronary events. Of 38 populations participating in the WHO MONICA MI register study, more than half registered a sufficiently wide spectrum of events that it was possible to identify subsets of milder cases. The event rates and case fatality rates of MI are clearly dependent on the spectrum of non-fatal MIs, which are included. On clinical grounds we propose that the original MONICA category ''non-fatal possible MI'' could bt:divided into two groups: ''non fatal probable MI'' and ''prolonged chest pain.'' Non-fatal probable MIs are cases, which in addition to ''typical symptoms'' have electrocardiogram (EGG) or enzyme changes suggesting cardiac ischemia, but not severe enough to fulfil the criteria for non-fatal definite MI In more than half of the MONICA Collaborating Centers, the registration of MI covers these milder events reasonably well. Proportions of non-fatal probable MIs vary less between populations than do proportions of non fatal possible MIs. Also rates of non-fatal probable MI are somewhat more highly correlated with rates of fatal events and non-fatal definite MI. These findings support the validity of the category of non-fatal probable MI. In each center the increase in event rates and the decrease in case-fatality due to the inclusion of non-fatal probable MI was lar er for women than men. For the WHO MONICA Project and other epidemiological studies the proposed category of non-fatal probable MIs can be used for assessing trends in rates of milder MI. Copyright (C) 1997 Elsevier Science Inc.

Dietary trends: Estimates from food supply and survey data

Objective: To illustrate methodological issues involved in estimating dietary trends in populations using data obtained from various sources in Australia in the 1980s and 1990s. Methods: Estimates of absolute and relative change in consumption of selected food items were calculated using national data published annually on the national food supply for 1982-83 to 1992-93 and responses to food frequency questions in two population based risk factor surveys in 1983 and 1994 in the Hunter Region of New South Wales, Australia. The validity of estimated food quantities obtained from these inexpensive sources at the beginning of the period was assessed by comparison with data from a national dietary survey conducted in 1983 using 24 h recall. Results: Trend estimates from the food supply data and risk factor survey data were in good agreement for increases in consumption of fresh fruit, vegetables and breakfast food and decreases in butter, margarine, sugar and alcohol. Estimates for trends in milk, eggs and bread consumption, however, were inconsistent. Conclusions: Both data sources can be used for monitoring progress towards national nutrition goals based on selected food items provided that some limitations are recognized. While data collection methods should be consistent over time they also need to allow for changes in the food supply (for example the introduction of new varieties such as low-fat dairy products). From time to time the trends derived from these inexpensive data sources should be compared with data derived from more detailed and quantitative estimates of dietary intake.

Evaluation of serum trace element, biochemical and hematological data of a healthy elderly group residing in So Paulo city, Brazil

In this study, blood serum trace elements, biochemical and hematological parameters were obtained to assess the health status of an elderly population residing in So Paulo city, SP, Brazil. Results obtained showed that more than 93% of the studied individuals presented most of the serum trace element concentrations and of the hematological and biochemical data within the reference values used in clinical laboratories. However, the percentage of elderly presenting recommended low density lipoprotein (LDL) cholesterol concentrations was low (70%). The study indicated positive correlation between the concentrations of Zn and LDL-cholesterol (p < 0.06).

Deaths from cirrhosis in Poland and Hungary: The impact of different alcohol policies during the 1980s

Objective - To compare patterns of deaths from cirrhosis in Poland and Hungary in the context of differing alcohol policies in the 1980s. Design - Cohort analysis of deaths from chronic Liver disease and cirrhosis between 1959 and 1992 using mortality data from the World Health Organization database. Results - The pattern of alcohol related mortality in these countries is quite different. In both countries, death rates increased in the 1960s and 1970s. In Poland, this increase was arrested in 1980 and death rates have levelled out, with the exception of those in young females. In Hungary, rates have continued to climb, although the rate of increase decreased in the 1980s. This change coincides with the introduction of a policy, following the introduction of martial law, to reduce alcohol consumption. Conclusions - The countries of central and eastern Europe display many similarities in both political history and measures of health such as overall life expectancy. When examined more closely, substantial differences emerge. Policy makers must be cautious about adopting global solutions to health challenges that fail to take into account national variations.

A stepwise approach to stroke surveillance in Brazil: the EMMA (Estudo de Mortalidade e Morbidade do Acidente Vascular Cerebral) study

Background Stroke mortality rates in Brazil are the highest in the Americas. Deaths from cerebrovascular disease surpass coronary heart disease. Aim To verify stroke mortality rates and morbidity in an area of Sao Paulo, Brazil, using the World Health Organization Stepwise Approach to Stroke Surveillance. Methods We used the World Health Organization Stepwise Approach to Stroke Surveillance structure of stroke surveillance. The hospital-based data comprised fatal and nonfatal stroke (Step 1). We gathered stroke-related mortality data in the community using World Health Organization questionnaires (Step 2). The questionnaire determining stroke prevalence was activated door to door in a family-health-programme neighbourhood (Step 3). Results A total of 682 patients 18 years and above, including 472 incident cases, presented with cerebrovascular disease and were enrolled in Step 1 during April-May 2009. Cerebral infarction (84 center dot 3%) and first-ever stroke (85 center dot 2%) were the most frequent. In Step 2, 256 deaths from stroke were identified during 2006-2007. Forty-four per cent of deaths were classified as unspecified stroke, 1/3 as ischaemic stroke, and 1/4 due to haemorrhagic subtype. In Step 3, 577 subjects over 35 years old were evaluated at home, and 244 cases of stroke survival were diagnosed via a questionnaire, validated by a board-certified neurologist. The population demographic characteristics were similar in the three steps, except in terms of age and gender. Conclusion By including data from all settings, World Health Organization stroke surveillance can provide data to help plan future resources that meet the needs of the public-health system.

The impact of diabetes on survival among patients with first myocardial infarction

OBJECTIVE - The purpose of this paper is to estimate the impact of diabetes on survival among patients with first acute myocardial infarction, using data from the World Health Organization (WHO) Monitoring Trends and Determinants of Cardiovascular Disease (MONICA) Project in Newcastle, New South Wales, Australia. RESEARCH DESIGN AND METHODS - The WHO MONICA Project is a community-based surveillance system that monitors coronary heart disease morbidity and mortality. All patients with suspected coronary events were observed for 28 days after the onset of symptoms. RESULTS - Of 5,322 patients with acute myocardial infarction and no previous history of ischemic heart disease (3,643 men and 1,679 women), 333 men (9%) and 224 women (13%) had a history of diabetes. The age-adjusted 28-day case fatality for women with diabetes (25%) was significantly higher than for women without diabetes (16%); relative risk 1.56 (95% CI: 1.19-2.04). The difference for men was also significant (25% with diabetes and 20% without diabetes); relative risk 1.25 (95% CI: 1.02-1.53). Age-specific case fatality increased significantly with age in both men and women without diabetes, but systematic age effects were not so apparent in patients with diabetes. Case fatality significantly decreased over the study period in patients without diabetes, but not among the diabetic patients. CONCLUSIONS - The increased risk of death in the diabetic patients remained after accounting for their poorer risk factor profiles; even if they reached the hospital alive, diabetic patients were also less likely to survive than nondiabetic patients. The relative impact of diabetes on survival is greater in women than in men.

Self-organization with traveling waves: A case for a convective torus

A traveling wave of BaSO4 in the chlorite-thiourea reaction has shown concentric precipitation patterns upon being triggered by the autocatalyst HOCl. The precipitation patterns show circular rings of alternate null and full precipitation regions. This self-organization appears to be the result of the formation of a convective torus. The formation of the convective torus can be described as a Benard-Marangoni instability with lateral heating.

Wavelet correlation between subjects: A time-scale data driven analysis for brain mapping using fMRI

Functional magnetic resonance imaging (fMRI) based on BOLD signal has been used to indirectly measure the local neural activity induced by cognitive tasks or stimulation. Most fMRI data analysis is carried out using the general linear model (GLM), a statistical approach which predicts the changes in the observed BOLD response based on an expected hemodynamic response function (HRF). In cases when the task is cognitively complex or in cases of diseases, variations in shape and/or delay may reduce the reliability of results. A novel exploratory method using fMRI data, which attempts to discriminate between neurophysiological signals induced by the stimulation protocol from artifacts or other confounding factors, is introduced in this paper. This new method is based on the fusion between correlation analysis and the discrete wavelet transform, to identify similarities in the time course of the BOLD signal in a group of volunteers. We illustrate the usefulness of this approach by analyzing fMRI data from normal subjects presented with standardized human face pictures expressing different degrees of sadness. The results show that the proposed wavelet correlation analysis has greater statistical power than conventional GLM or time domain intersubject correlation analysis. (C) 2010 Elsevier B.V. All rights reserved.

Analyzing the connectivity between regions of interest: An approach based on cluster Granger causality for NRI data analysis

The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single ""representative"" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI. (c) 2010 Elsevier Inc. All rights reserved.

The impact of functional connectivity changes on support vector machines mapping of fMRI data

Functional magnetic resonance imaging (fMRI) is currently one of the most widely used methods for studying human brain function in vivo. Although many different approaches to fMRI analysis are available, the most widely used methods employ so called ""mass-univariate"" modeling of responses in a voxel-by-voxel fashion to construct activation maps. However, it is well known that many brain processes involve networks of interacting regions and for this reason multivariate analyses might seem to be attractive alternatives to univariate approaches. The current paper focuses on one multivariate application of statistical learning theory: the statistical discrimination maps (SDM) based on support vector machine, and seeks to establish some possible interpretations when the results differ from univariate `approaches. In fact, when there are changes not only on the activation level of two conditions but also on functional connectivity, SDM seems more informative. We addressed this question using both simulations and applications to real data. We have shown that the combined use of univariate approaches and SDM yields significant new insights into brain activations not available using univariate methods alone. In the application to a visual working memory fMRI data, we demonstrated that the interaction among brain regions play a role in SDM`s power to detect discriminative voxels. (C) 2008 Elsevier B.V. All rights reserved.

Estimating correlations from epidemiological data in the presence of measurement error

Analysis of a major multi-site epidemiologic study of heart disease has required estimation of the pairwise correlation of several measurements across sub-populations. Because the measurements from each sub-population were subject to sampling variability, the Pearson product moment estimator of these correlations produces biased estimates. This paper proposes a model that takes into account within and between sub-population variation, provides algorithms for obtaining maximum likelihood estimates of these correlations and discusses several approaches for obtaining interval estimates. (C) 1997 by John Wiley & Sons, Ltd.

Antiretroviral Drug Resistance in a Respondent-Driven Sample of HIV-Infected Men Who Have Sex With Men in Brazil

Background: There are few studies on HIV subtypes and primary and secondary antiretroviral drug resistance (ADR) in community-recruited samples in Brazil. We analyzed HIV clade diversity and prevalence of mutations associated with ADR in men who have sex with men in all five regions of Brazil. Methods: Using respondent-driven sampling, we recruited 3515 men who have sex with men in nine cities: 299 (9.5%) were HIV-positive; 143 subjects had adequate genotyping and epidemiologic data. Forty-four (30.8%) subjects were antiretroviral therapy-experienced (AE) and 99 (69.2%) antiretroviral therapy-naive (AN). We sequenced the reverse transcriptase and protease regions of the virus and analyzed them for drug resistant mutations using World Health Organization guidelines. Results: The most common subtypes were B (81.8%), C (7.7%), and recombinant forms (6.9%). The overall prevalence of primary ADR resistance was 21.4% (i.e. among the AN) and secondary ADR was 35.8% (i.e. among the AE). The prevalence of resistance to protease inhibitors was 3.9% (AN) and 4.4% (AE); to nucleoside reverse transcriptase inhibitors 15.0% (AN) and 31.0% (AE) and to nonnucleoside reverse transcriptase inhibitors 5.5% (AN) and 13.2% (AE). The most common resistance mutation for nucleoside reverse transcriptase inhibitors was 184V (17 cases) and for nonnucleoside reverse transcriptase inhibitors 103N (16 cases). Conclusions: Our data suggest a high level of both primary and secondary ADR in men who have sex with men in Brazil. Additional studies are needed to identify the correlates and causes of antiretroviral therapy resistance to limit the development of resistance among those in care and the transmission of resistant strains in the wider epidemic.

The GHEP-EMPOP collaboration on mtDNA population data-A new resource for forensic casework

Mitochondrial DNA (mtDNA) population data for forensic purposes are still scarce for some populations, which may limit the evaluation of forensic evidence especially when the rarity of a haplotype needs to be determined in a database search. In order to improve the collection of mtDNA lineages from the Iberian and South American subcontinents, we here report the results of a collaborative study involving nine laboratories from the Spanish and Portuguese Speaking Working Group of the International Society for Forensic Genetics (GHEP-ISFG) and EMPOP. The individual laboratories contributed population data that were generated throughout the past 10 years, but in the majority of cases have not been made available to the scientific community. A total of 1019 haplotypes from Iberia (Basque Country, 2 general Spanish populations, 2 North and 1 Central Portugal populations), and Latin America (3 populations from Sao Paulo) were collected, reviewed and harmonized according to defined EMPOP criteria. The majority of data ambiguities that were found during the reviewing process (41 in total) were transcription errors confirming that the documentation process is still the most error-prone stage in reporting mtDNA population data, especially when performed manually. This GHEP-EMPOP collaboration has significantly improved the quality of the individual mtDNA datasets and adds mtDNA population data as valuable resource to the EMPOP database (www.empop.org). (C) 2010 Elsevier Ireland Ltd. All rights reserved.