Document Detail


Dynamic data during hypotensive episode improves mortality predictions among patients with sepsis and hypotension.
MedLine Citation:
PMID:  23385106     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
OBJECTIVES: To determine if a prediction rule for hospital mortality using dynamic variables in response to treatment of hypotension in patients with sepsis performs better than current models.
DESIGN: Retrospective cohort study.
SETTING: All ICUs at a tertiary care hospital.
PATIENTS: Adult patients admitted to ICUs between 2001 and 2007 of whom 2,113 met inclusion criteria and had sufficient data.
INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: We developed a prediction algorithm for hospital mortality in patients with sepsis and hypotension requiring medical intervention using data from the Multiparameter Intelligent Monitoring in Intensive Care II. We extracted 189 candidate variables, including treatments, physiologic variables and laboratory values collected before, during, and after a hypotensive episode. Thirty predictors were identified using a genetic algorithm on a training set (n=1500) and validated with a logistic regression model on an independent validation set (n=613). The final prediction algorithm used included dynamic information and had good discrimination (area under the receiver operating curve=82.0%) and calibration (Hosmer-Lemeshow C statistic=10.43, p=0.06). This model was compared with Acute Physiology and Chronic Health Evaluation IV using reclassification indices and was found to be superior with an Net Reclassification Improvement of 0.19 (p<0.001) and an Integrated Discrimination Improvement of 0.09 (p<0.001).
CONCLUSIONS: Hospital mortality predictions based on dynamic variables surrounding a hypotensive event is a new approach to predicting prognosis. A model using these variables has good discrimination and calibration and offers additional predictive prognostic information beyond established ones.
Authors:
Louis Mayaud; Peggy S Lai; Gari D Clifford; Lionel Tarassenko; Leo Anthony Celi; Djillali Annane
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Critical care medicine     Volume:  41     ISSN:  1530-0293     ISO Abbreviation:  Crit. Care Med.     Publication Date:  2013 Apr 
Date Detail:
Created Date:  2013-03-26     Completed Date:  2013-05-30     Revised Date:  2014-04-02    
Medline Journal Info:
Nlm Unique ID:  0355501     Medline TA:  Crit Care Med     Country:  United States    
Other Details:
Languages:  eng     Pagination:  954-62     Citation Subset:  AIM; IM    
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MeSH Terms
Descriptor/Qualifier:
Adult
Aged
Aged, 80 and over
Algorithms
Cohort Studies
Comorbidity
Critical Illness / mortality*
Female
Great Britain
Hospital Mortality / trends*
Humans
Hypotension / mortality*
Intensive Care Units*
Male
Middle Aged
Outcome Assessment (Health Care)
Predictive Value of Tests
Prognosis
Retrospective Studies
Sepsis / mortality*
Grant Support
ID/Acronym/Agency:
2R01 EB001659/EB/NIBIB NIH HHS; F32 ES020082/ES/NIEHS NIH HHS; R01 EB001659/EB/NIBIB NIH HHS
Comments/Corrections
Comment In:
Crit Care Med. 2013 Apr;41(4):1136-8   [PMID:  23528758 ]

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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