About: In-hospital death prediction by multilevel logistic regressin in patients with acute coronary syndromes     Goto   Sponge   NotDistinct   Permalink

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Description
  • Background: The odds of death of patients with acute coronary syndromes (ACS) in non-PCI (percutaneous coronary intervention) hospitals in the Czech Republic change depending on a number of factors (age, heart rate, systolic blood pressure, creatinine, Killip class, the diagnosis, and the number of recommended medications and treatment of ACE-inhibitor or sartan). Objectives: We present a detailed description of multilevel logistic regression applied in the derivation of the conclusion described in the Background, namely we compare multilevel logistic regression with logistic regression. Methods: The above mentioned clinical findings have been derived on the basis of data from the three-year (7/2008-6/2011) registry of acute coronary syndromes ALERT-CZ (Acute coronary syndromes - Longitudinal Evaluation of Real-life Treatment in non-PCI hospitals in the Czech Republic). A total of 32 hospitals contributed into the registry. The number of patients with ACS (n=6013) in the hospitals varied from 15 to 827. Results: The likelihood ratio test showed that the independence of medical outcomes across hospitals cannot be assumed (p<0.001, the variance partition coefficient VPC=8.9%). For this reason, we chose multilevel logistic regression to analyse data, specifically logistic mixed regression (the hospital identity was a random effect). The calibration properties of this model were very good (Hosmer-Lemeshow test, p=0.989). The total discriminant ability of the model was 91.8%. Conclusions: Considering some differences among hospitals, it was appropriate to take into account patient affiliation to various hospitals and to use multilevel logistic regression instead of logistic regression.
  • Background: The odds of death of patients with acute coronary syndromes (ACS) in non-PCI (percutaneous coronary intervention) hospitals in the Czech Republic change depending on a number of factors (age, heart rate, systolic blood pressure, creatinine, Killip class, the diagnosis, and the number of recommended medications and treatment of ACE-inhibitor or sartan). Objectives: We present a detailed description of multilevel logistic regression applied in the derivation of the conclusion described in the Background, namely we compare multilevel logistic regression with logistic regression. Methods: The above mentioned clinical findings have been derived on the basis of data from the three-year (7/2008-6/2011) registry of acute coronary syndromes ALERT-CZ (Acute coronary syndromes - Longitudinal Evaluation of Real-life Treatment in non-PCI hospitals in the Czech Republic). A total of 32 hospitals contributed into the registry. The number of patients with ACS (n=6013) in the hospitals varied from 15 to 827. Results: The likelihood ratio test showed that the independence of medical outcomes across hospitals cannot be assumed (p<0.001, the variance partition coefficient VPC=8.9%). For this reason, we chose multilevel logistic regression to analyse data, specifically logistic mixed regression (the hospital identity was a random effect). The calibration properties of this model were very good (Hosmer-Lemeshow test, p=0.989). The total discriminant ability of the model was 91.8%. Conclusions: Considering some differences among hospitals, it was appropriate to take into account patient affiliation to various hospitals and to use multilevel logistic regression instead of logistic regression. (en)
Title
  • In-hospital death prediction by multilevel logistic regressin in patients with acute coronary syndromes
  • In-hospital death prediction by multilevel logistic regressin in patients with acute coronary syndromes (en)
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  • In-hospital death prediction by multilevel logistic regressin in patients with acute coronary syndromes
  • In-hospital death prediction by multilevel logistic regressin in patients with acute coronary syndromes (en)
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  • RIV/00064203:_____/13:10193787!RIV14-MZ0-00064203
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
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  • I
http://linked.open...iv/cisloPeriodika
  • 1
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http://linked.open...aciTvurceVysledku
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http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 80224
http://linked.open...ai/riv/idVysledku
  • RIV/00064203:_____/13:10193787
http://linked.open...riv/jazykVysledku
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  • in-hospital death; risk factors; acute coronary syndromes; Multilevel logistic regression (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • CZ - Česká republika
http://linked.open...ontrolniKodProRIV
  • [4252A46FDD30]
http://linked.open...i/riv/nazevZdroje
  • European Journal for Biomedical Informatics
http://linked.open...in/vavai/riv/obor
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http://linked.open...v/svazekPeriodika
  • 9
http://linked.open...iv/tvurceVysledku
  • Hanzlíček, Petr
  • Reissigová, Jindra
  • Vojáček, Jan
  • Widimský, Petr
  • Zvárová, Jana
  • Jánský, Petr
  • Grünfeldová, Hana
  • Monhart, Zdeněk
issn
  • 1801-5603
number of pages
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