z-logo
open-access-imgOpen Access
The Dynamics of Disease Progression in Sepsis: Markov Modeling Describing the Natural History and the Likely Impact of Effective Antisepsis Agents
Author(s) -
M. Sigfrido RangelFrausto,
Didier Pittet,
Taekyu Hwang,
Robert F. Woolson,
R P Wenzel
Publication year - 1998
Publication title -
clinical infectious diseases
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.44
H-Index - 336
eISSN - 1537-6591
pISSN - 1058-4838
DOI - 10.1086/514630
Subject(s) - medicine , natural history , sepsis , intensive care medicine , disease , markov model , markov chain , immunology , computer science , machine learning
We conducted a 9-month prospective cohort study of 2,527 patients with systemic inflammatory response syndrome in three intensive care units and three general wards in a tertiary health care institution. Markov models were developed to predict the probability of movement to and from more severe stages--sepsis, severe sepsis, or septic shock--at 1, 3, and 7 days. For patients with sepsis, severe sepsis, and septic shock, the probabilities of remaining in the same category after 1 day were .65, .68, and .61, respectively. The probability for progression after 1 day was .09 for sepsis to severe sepsis and .026 for severe sepsis to shock. The probability of patients with sepsis, severe sepsis, and septic shock dying after 1 day was .005, .009, and .079, respectively. The model can be used to predict the reduction in end organ dysfunction and mortality with use of increasingly effective antisepsis agents.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom