OBJECTIVE: To evaluate the sensitivity, specificity and overall accuracy of a model based on the presence or absence of organ dysfunctions and/or infection (ODIN) to predict the outcome for intensive care unit patients. DESIGN: Prospective study. SETTING: General intensive care unit in a university teaching hospital. PATIENTS: 1070 consecutive, unselected patients. INTERVENTIONS: There were no interventions. MEASUREMENTS AND MAIN RESULTS: We recorded within the first 24 h of admission the presence or absence of dysfunction in 6 organ systems: respiratory, cardiovascular, renal, hematologic, hepatic and neurologic, and/or infection (ODIN) in all patients admitted to our ICU, thus establishing a profile of organ dysfunctions in each patient. Using univariate analysis, a strong correlation was found between the number of ODIN and the death rate (2.6, 9.7, 16.7, 32.3, 64.9, 75.9, 94.4 and 100% for 0, 1, 2, 3, 4, 5, 6 and 7 ODIN, respectively; (p < 0.001). In addition, the highest mortality rates were associated with hepatic (60.8%), hematologic (58.1%) and renal (54.8%) dysfunctions, and the lowest with respiratory dysfunction (36.5%) and infection (38.3%). For taking into account both the number and the type of organ dysfunction, a logistic regression model was then used to calculate individual probabilities of death that depended upon the statistical weight assigned to each ODIN (in the following order of descending severity: cardiovascular, renal, respiratory, neurologic, hematologic, hepatic dysfunctions and infection). The ability of this severity-of-disease classification system to stratify a wide variety of patients prognostically (sensitivity 51.4%, specificity 93.4%, overall accuracy 82.1%) was not different from that of currently used scoring systems. CONCLUSIONS: These findings suggest that determination of the number and the type of organ dysfunctions and infection offers a clear and reliable method for characterizing ICU patients. Before a widespread use, this model requires to be validated in other institutions.
OBJECTIVE: To evaluate the sensitivity, specificity and overall accuracy of a model based on the presence or absence of organ dysfunctions and/or infection (ODIN) to predict the outcome for intensive care unit patients. DESIGN: Prospective study. SETTING: General intensive care unit in a university teaching hospital. PATIENTS: 1070 consecutive, unselected patients. INTERVENTIONS: There were no interventions. MEASUREMENTS AND MAIN RESULTS: We recorded within the first 24 h of admission the presence or absence of dysfunction in 6 organ systems: respiratory, cardiovascular, renal, hematologic, hepatic and neurologic, and/or infection (ODIN) in all patients admitted to our ICU, thus establishing a profile of organ dysfunctions in each patient. Using univariate analysis, a strong correlation was found between the number of ODIN and the death rate (2.6, 9.7, 16.7, 32.3, 64.9, 75.9, 94.4 and 100% for 0, 1, 2, 3, 4, 5, 6 and 7 ODIN, respectively; (p < 0.001). In addition, the highest mortality rates were associated with hepatic (60.8%), hematologic (58.1%) and renal (54.8%) dysfunctions, and the lowest with respiratory dysfunction (36.5%) and infection (38.3%). For taking into account both the number and the type of organ dysfunction, a logistic regression model was then used to calculate individual probabilities of death that depended upon the statistical weight assigned to each ODIN (in the following order of descending severity: cardiovascular, renal, respiratory, neurologic, hematologic, hepatic dysfunctions and infection). The ability of this severity-of-disease classification system to stratify a wide variety of patients prognostically (sensitivity 51.4%, specificity 93.4%, overall accuracy 82.1%) was not different from that of currently used scoring systems. CONCLUSIONS: These findings suggest that determination of the number and the type of organ dysfunctions and infection offers a clear and reliable method for characterizing ICU patients. Before a widespread use, this model requires to be validated in other institutions.
Authors: Tarek Sharshar; Sylvie Bastuji-Garin; Bernard De Jonghe; Robert D Stevens; Andrea Polito; Virginie Maxime; Pablo Rodriguez; Charles Cerf; Hervé Outin; Philippe Touraine; Kathleen Laborde Journal: Intensive Care Med Date: 2010-03-24 Impact factor: 17.440
Authors: J L Vincent; R Moreno; J Takala; S Willatts; A De Mendonça; H Bruining; C K Reinhart; P M Suter; L G Thijs Journal: Intensive Care Med Date: 1996-07 Impact factor: 17.440
Authors: B Lamia; M-F Hellot; C Girault; F Tamion; F Dachraoui; P Lenain; G Bonmarchand Journal: Intensive Care Med Date: 2006-08-01 Impact factor: 17.440
Authors: Chaker Ben Hamida; Jean-Yves Lauzet; Saida Rézaiguia-Delclaux; Christophe Duvoux; Daniel Cherqui; Philippe Duvaldestin; François Stéphan Journal: Intensive Care Med Date: 2003-04-03 Impact factor: 17.440
Authors: Nicolas Lerolle; Dominique Nochy; Emmanuel Guérot; Patrick Bruneval; Jean-Yves Fagon; Jean-Luc Diehl; Gary Hill Journal: Intensive Care Med Date: 2010-03 Impact factor: 17.440