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Relationship between ΔSOFA and mortality in patients admitted to the intensive care unit

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Prognostic scores are important for clinical and administrative management of intensive care units (ICU). SOFA score was designed to describe the sequence of complications during the patient´s stay in the ICU, not to predict outcomes. However, it has been shown that high scores on the SOFA score are associated with high mortality. ΔSOFA is a measure derived from the SOFA score defined as the difference between the SOFA score at admission and the achieved in a determined point of time.


To evaluate the relationship between ΔSOFA and mortality in patients admitted to the ICU.


Adult patients of both sexes with stay in the ICU >48 hours were analyzed. Demographic and clinical variables were recorded. The SOFA score was computed on admission and every 48 hours during the stay in the ICU. ΔSOFA was calculated and evaluated as a predictor of mortality through its discriminative capacity and calibration. Logistic regression analysis was performed to identify independent risk factors of death. A p value < 0.05 was considered statistically significant. SPSS 20.0 was used for data analysis.


Eighty patients, mean age 46 years, 53.8% men were included. The mean length of stay in ICU was 5.11 days and the mortality of 16.3%. The mean SOFA score at admission, maximum and ΔSOFA were 6.01, 6.27 and 0.26, respectively. ΔSOFA showed good discriminative capacity and calibration for predicting mortality (area under the ROC curve 0.914, 95%CI 0.881-1.0, p < 0.05; Hosmer-Lemeshow Chi2 0.371, p = 0.96) and is a risk factor for death.


ΔSOFA is useful in predicting mortality in patients admitted to the ICU.


  1. 1.

    Knaus WA, Draper EA, Wagner DP: APACHE II: a severity of disease classification system. Crit Care Med. 1985, 13: 818-29.

  2. 2.

    Poole D, Rossi C, Anghileri A: External validation of the simplified acute physiology score (SAPS) 3 in a cohort of 28,0357 patients from 147 italian intensive care units. Intensive Care Med. 2009, 35: 1916-

  3. 3.

    Lemeshow S, Teres D, Klar J: Mortality Probability Models (MPM-II) based on an international cohort of intensive care unit patients. JAMA. 1993, 270: 2478-

  4. 4.

    Vincent JL, Moreno R, Takala J: The SOFA (sepsis-related organ failure assessment) score to describe organ dysfunction/ failure. Intensive Care Med. 1996, 22: 707-10.

  5. 5.

    Moreno R, Vincent JL, Matos A: The use of maximum SOFA score to quantify organ dysfunction/failure in intensive care: results of a prospective, multicentre study. Intensive Care Med. 1999, 25: 686-96.

  6. 6.

    Lopes F: Serial evaluation of the SOFA score to predict outcome in critically Ill patients. JAMA. 2001, 286: 1754-8.

  7. 7.

    Minne L, Abu-Hanna A, De Jonge E: Evaluation of SOFA-based models for predicting mortality in the ICU: A systematic review. Crit Care Med. 2008, 12: 1-13.

  8. 8.

    Ñamendys-Silva SA, Silva-Medina MA, Vasquez-Barahona GM: Application of a modified sequential organ failure assessment score to critically ill patients. Braz J Med Biol Res. 2013, 46: 186-93.

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Correspondence to PC Arvizu-Tachiquín.

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Arvizu-Tachiquín, P., Baltazar-Torres, J., Cano-Oviedo, A. et al. Relationship between ΔSOFA and mortality in patients admitted to the intensive care unit. ICMx 3, A339 (2015) doi:10.1186/2197-425X-3-S1-A339

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  • Public Health
  • Data Analysis
  • Regression Analysis
  • Logistic Regression
  • Intensive Care Unit