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Infection Probability Score (IPS): A method to help assess the probability of infection in critically ill patients
Daliana Peres Bota1, Christian Mélot, Flavio Lopes Ferreira
1Department of Intensive Care, Erasme Hospital, Free University of Brussels, Belgium.
Objective:
To develop a simple score to help assess the presence or absence of infection in critically ill patients using routinely available variables.
Design:
Observational study of a prospective cohort of patients divided into a developmental set (n = 353) and a validation set (n = 140).
Setting:
Department of intensive care at an academic tertiary care center.
Patients:
Four hundred and ninety-three adult patients admitted to the intensive care unit for > or =24 hrs.
Interventions:
None.
Measurements And Main Results:
The presence of infection was defined using the Centers for Disease Control definitions. Body temperature, heart rate, respiratory rate, white blood cell count, and C-reactive protein concentrations were measured, and the Sequential Organ Failure Assessment score was calculated throughout the intensive care unit stay. Infection was documented in 92 of the 353 patients (26%) in the developmental set and in 41 of the 140 patients (29%) in the validation set. Univariate logistic regression was used to select significant predictors for infection. Each continuous predictor was transformed in a categorical variable using a robust locally weighted least square regression between infection and the continuous variable of interest. When more than two categories were created, the variable was separated into iso-weighted dummy variables. A multiple logistic regression model predicting infection was calculated with all the variables coded 1 or 0 allowing for relative scoring of the different predictors. The resulting Infection Probability Score consisted of six different variables and ranged from 0 to 26 points (0-2 for temperature, 0-12 for heart rate, 0-1 for respiratory rate, 0-3 for white blood cell count, 0-6 for C-reactive protein, 0-2 for Sequential Organ Failure Assessment score). The best predictors for infection were heart rate and C-reactive protein, whereas respiratory rate was found to have the poorest predictive value. The cutoff value for the Infection Probability Score was 14 points, with a positive predictive value of 53.6% and a negative predictive value of 89.5%. Model performance was very good (Hosmer-Lemeshow statistic, p =.918), and the areas under receiver operating characteristic curves were 0.820 for the developmental set and 0.873 for the validation set.
Conclusions:
The Infection Probability Score is a simple score that can help assess the probability of infection in critically ill patients. The variables used are simple, routinely available, and familiar to clinicians. Patients with a score <14 points have only a 10% risk of infection.
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