986 resultados para Rainfall event classification


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c. 2

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Text classification, information filtering, semi-supervised learning, quality control

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Fuzzy classification, semi-supervised learning, data mining

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East Germany, men, fertility, first births, event history analysis, problem-centered interviews, methodical integration, triangulation, social psychology, gender

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Background: The classification or index of heart failure severity in patients with acute myocardial infarction (AMI) was proposed by Killip and Kimball aiming at assessing the risk of in-hospital death and the potential benefit of specific management of care provided in Coronary Care Units (CCU) during the decade of 60. Objective: To validate the risk stratification of Killip classification in the long-term mortality and compare the prognostic value in patients with non-ST-segment elevation MI (NSTEMI) relative to patients with ST-segment elevation MI (STEMI), in the era of reperfusion and modern antithrombotic therapies. Methods: We evaluated 1906 patients with documented AMI and admitted to the CCU, from 1995 to 2011, with a mean follow-up of 05 years to assess total mortality. Kaplan-Meier (KM) curves were developed for comparison between survival distributions according to Killip class and NSTEMI versus STEMI. Cox proportional regression models were developed to determine the independent association between Killip class and mortality, with sensitivity analyses based on type of AMI. Results: The proportions of deaths and the KM survival distributions were significantly different across Killip class >1 (p <0.001) and with a similar pattern between patients with NSTEMI and STEMI. Cox models identified the Killip classification as a significant, sustained, consistent predictor and independent of relevant covariables (Wald χ2 16.5 [p = 0.001], NSTEMI) and (Wald χ2 11.9 [p = 0.008], STEMI). Conclusion: The Killip and Kimball classification performs relevant prognostic role in mortality at mean follow-up of 05 years post-AMI, with a similar pattern between NSTEMI and STEMI patients.

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Magdeburg, Univ., Fak. für Informatik, Habil.-Schr., 2006

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Background:Information about post-acute coronary syndrome (ACS) survival have been mostly short-term findings or based on specialized, cardiology referral centers.Objectives:To describe one-year case-fatality rates in the Strategy of Registry of Acute Coronary Syndrome (ERICO) cohort, and to study baseline characteristics as predictors.Methods:We analyzed data from 964 ERICO participants enrolled from February 2009 to December 2012. We assessed vital status by telephone contact and official death certificate searches. The cause of death was determined according to the official death certificates. We used log-rank tests to compare the probabilities of survival across subgroups. We built crude and adjusted (for age, sex and ACS subtype) Cox regression models to study if the ACS subtype or baseline characteristics were independent predictors of all-cause or cardiovascular mortality.Results:We identified 110 deaths in the cohort (case-fatality rate, 12.0%). Age [Hazard ratio (HR) = 2.04 per 10 year increase; 95% confidence interval (95%CI) = 1.75–2.38], non-ST elevation myocardial infarction (HR = 3.82 ; 95%CI = 2.21–6.60) or ST elevation myocardial infarction (HR = 2.59; 95%CI = 1.38–4.89) diagnoses, and diabetes (HR = 1.78; 95%CI = 1.20‑2.63) were significant risk factors for all-cause mortality in the adjusted models. We found similar results for cardiovascular mortality. A previous coronary artery disease diagnosis was also an independent predictor of all-cause mortality (HR = 1.61; 95%CI = 1.04–2.50), but not for cardiovascular mortality.Conclusion:We found an overall one-year mortality rate of 12.0% in a sample of post-ACS patients in a community, non-specialized hospital in São Paulo, Brazil. Age, ACS subtype, and diabetes were independent predictors of poor one‑year survival for overall and cardiovascular-related causes.

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Magdeburg, Univ., Fak. für Inf., Diss., 2014

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v.24:no.11(1940)

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v.36:no.6(1958)

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v.28:no.2(1944)