1,738 research outputs found

    Prediction of Critical Illness During Out-of-Hospital Emergency Care

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    CONTEXT: Early identification of nontrauma patients in need of critical care services in the emergency setting may improve triage decisions and facilitate regionalization of critical care. OBJECTIVES: To determine the out-of-hospital clinical predictors of critical illness and to characterize the performance of a simple score for out-of-hospital prediction of development of critical illness during hospitalization. DESIGN AND SETTING: Population-based cohort study of an emergency medical services (EMS) system in greater King County, Washington (excluding metropolitan Seattle), that transports to 16 receiving facilities. PATIENTS: Nontrauma, non-cardiac arrest adult patients transported to a hospital by King County EMS from 2002 through 2006. Eligible records with complete data (N = 144,913) were linked to hospital discharge data and randomly split into development (n = 87,266 [60%]) and validation (n = 57,647 [40%]) cohorts. MAIN OUTCOME MEASURE: Development of critical illness, defined as severe sepsis, delivery of mechanical ventilation, or death during hospitalization. RESULTS: Critical illness occurred during hospitalization in 5% of the development (n = 4835) and validation (n = 3121) cohorts. Multivariable predictors of critical illness included older age, lower systolic blood pressure, abnormal respiratory rate, lower Glasgow Coma Scale score, lower pulse oximetry, and nursing home residence during out-of-hospital care (P < .01 for all). When applying a summary critical illness prediction score to the validation cohort (range, 0-8), the area under the receiver operating characteristic curve was 0.77 (95% confidence interval [CI], 0.76-0.78), with satisfactory calibration slope (1.0). Using a score threshold of 4 or higher, sensitivity was 0.22 (95% CI, 0.20-0.23), specificity was 0.98 (95% CI, 0.98-0.98), positive likelihood ratio was 9.8 (95% CI, 8.9-10.6), and negative likelihood ratio was 0.80 (95% CI, 0.79- 0.82). A threshold of 1 or greater for critical illness improved sensitivity (0.98; 95% CI, 0.97-0.98) but reduced specificity (0.17; 95% CI, 0.17-0.17). CONCLUSIONS: In a population-based cohort, the score on a prediction rule using out-of-hospital factors was significantly associated with the development of critical illness during hospitalization. This score requires external validation in an independent populationPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85143/1/Seymour - JAMA-2010-747-54.pdf11

    Modifiable predictors of ventricular ectopy in the community

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    Background Premature ventricular contractions (PVCs) predict heart failure and death. Data regarding modifiable risk factors for PVCs are scarce. Methods and Results We studied 1424 Cardiovascular Health Study participants randomly assigned to 24-hour Holter monitoring. Demographics, comorbidities, habits, and echocardiographic measurements were examined as predictors of PVC frequency and, among 845 participants, change in PVC frequency 5 years later. Participants exhibited a median of 0.6 (interquartile range, 0.1-7.1) PVCs per hour. Of the more directly modifiable characteristics and after multivariable adjustment, every SD increase in systolic blood pressure was associated with 9% more PVCs (95% confidence interval [CI], 2%-17%; P=0.01), regularly performing no or low-intensity exercise compared with more physical activity was associated with ≈15% more PVCs (95% CI, 3-25%; P=0.02), and those with a history of smoking exhibited an average of 18% more PVCs (95% CI, 3-36%; P=0.02) than did never smokers. After 5 years, PVC frequency increased from a median of 0.5 (IQR, 0.1-4.7) to 1.2 (IQR, 0.1-13.8) per hour ( P&lt;0.0001). Directly modifiable predictors of 5-year increase in PVCs, described as the odds per each quintile increase in PVCs, included increased diastolic blood pressure (odds ratio per SD increase, 1.16; 95% CI, 1.02-1.31; P=0.02) and a history of smoking (OR, 1.31; 95% CI, 1.02-1.68; P=0.04). Conclusions Enhancing physical activity, smoking cessation, and aggressive control of blood pressure may represent fruitful strategies to mitigate PVC frequency and PVC-associated adverse outcomes

    Lung function, respiratory symptoms and venous thromboembolism risk: the Atherosclerosis Risk in Communities Study

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    The evidence of the association between chronic obstructive pulmonary disease (COPD) and venous thromboembolism (VTE) is limited. There is no study investigating the association of restrictive lung disease (RLD) and respiratory symptoms with VTE

    Alcohol consumption and leukocyte telomere length.

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    The relationship between alcohol consumption and mortality generally exhibits a U-shaped curve. The longevity observed with moderate alcohol consumption may be explained by other confounding factors, and, if such a relationship is present, the mechanism is not well understood. Indeed, the optimal amount of alcohol consumption for health has yet to be determined. Leukocyte telomere length is an emerging quantifiable marker of biological age and health, and a shorter telomere length is a predictor of increased mortality. Because leukocyte telomere length is a quantifiable and objectively measurable biomarker of aging, we sought to identify the amount of alcohol consumption associated with the longest telomere length and least telomere length attrition. Among over 2,000 participants from two distinct cohort studies, we found no pattern of alcohol consumption that was associated with longer telomere length or less telomere length attrition over time. Binge drinking may reduce telomere length. Using telomere length as a marker of age and health, these data fail to demonstrate any benefits of alcohol consumption, even when consumed in moderation

    Ectopy on a single 12‐lead ECG, incident cardiac myopathy, and death in the community

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    BackgroundAtrial fibrillation and heart failure are 2 of the most common diseases, yet ready means to identify individuals at risk are lacking. The 12-lead ECG is one of the most accessible tests in medicine. Our objective was to determine whether a premature atrial contraction observed on a standard 12-lead ECG would predict atrial fibrillation and mortality and whether a premature ventricular contraction would predict heart failure and mortality.Methods and resultsWe utilized the CHS (Cardiovascular Health) Study, which followed 5577 participants for a median of 12&nbsp;years, as the primary cohort. The ARIC (Atherosclerosis Risk in Communities Study), the replication cohort, captured data from 15&nbsp;792 participants over a median of 22&nbsp;years. In the CHS, multivariable analyses revealed that a baseline 12-lead ECG premature atrial contraction predicted a 60% increased risk of atrial fibrillation (hazard ratio, 1.6; 95% CI, 1.3-2.0; P&lt;0.001) and a premature ventricular contraction predicted a 30% increased risk of heart failure (hazard ratio, 1.3; 95% CI, 1.0-1.6; P=0.021). In the negative control analyses, neither predicted incident myocardial infarction. A premature atrial contraction was associated with a 30% increased risk of death (hazard ratio, 1.3; 95% CI, 1.1-1.5; P=0.008) and a premature ventricular contraction was associated with a 20% increased risk of death (hazard ratio, 1.2; 95% CI, 1.0-1.3; P=0.044). Similarly statistically significant results for each analysis were also observed in ARIC.ConclusionsBased on a single standard ECG, a premature atrial contraction predicted incident atrial fibrillation and death and a premature ventricular contraction predicted incident heart failure and death, suggesting that this commonly used test may predict future disease

    Fast Color Quantization Using Weighted Sort-Means Clustering

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    Color quantization is an important operation with numerous applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. However, despite its popularity as a general purpose clustering algorithm, k-means has not received much respect in the color quantization literature because of its high computational requirements and sensitivity to initialization. In this paper, a fast color quantization method based on k-means is presented. The method involves several modifications to the conventional (batch) k-means algorithm including data reduction, sample weighting, and the use of triangle inequality to speed up the nearest neighbor search. Experiments on a diverse set of images demonstrate that, with the proposed modifications, k-means becomes very competitive with state-of-the-art color quantization methods in terms of both effectiveness and efficiency.Comment: 30 pages, 2 figures, 4 table
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