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Critical Care |

A Predictive Model for Acute Respiratory Distress Syndrome (ARDS) Mortality Using Red Cell Distribution Width (RDW)

Ala Alkhatib, MD; Rania Esteitie, MD; Lori Lyn Price, MS; Peter LaCamera, MD
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St Elizabeth Medical Center, Boston, MA


Chest. 2015;148(4_MeetingAbstracts):182A. doi:10.1378/chest.2243168
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Abstract

SESSION TITLE: ARDS Posters

SESSION TYPE: Original Investigation Poster

PRESENTED ON: Wednesday, October 28, 2015 at 01:30 PM - 02:30 PM

PURPOSE: RDW is associated with mortality in patients with coronary disease, heart failure, and kidney injury. ARDS is characterized by inflammatory lung injury that may be fatal. There are clinical and research related advantages to early predictions of mortality in this population. The purpose of this study is to create a predictive model for mortality in ARDS using clinical factors and to determine whether the addition of RDW to the model improves its accuracy.

METHODS: This observational retrospective cohort study includes 318 ARDS patients extracted from an ICU database (Multiparameter Intelligent Monitoring in Intensive Care-MIMIC II) created by Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center between the years of 2001-08. ICU mortality was used as the outcome in logistic regression for the prediction models. Clinical factors including age, comorbidity score, Sequential Organ Failure Assessment (SOFA) score, and PaO2/FiO2 ratio were chosen for the base model. RDW value at time of ARDS diagnosis was added to the base model to determine if it improved prediction of mortality. The benefit of adding RDW to the base model was evaluated by: 1) change in discrimination measured by area under the ROC curve (AUC); 2) net reclassification improvement (NRI, percent of patients with improved prediction); and 3) integrated discrimination improvement (IDI, mean improvement in predicted probabilities). Improvement for 2) and 3) was measured by increased probabilities for patients who died, and decreased probabilities for participants who survived.

RESULTS: 318 subjects were included; 113 (36%) died in the ICU; 52% were male and the mean (SD) for age was 58.6 (18.3) years. AUC for the base model without RDW was 0.76, and 0.78 when RDW was added [difference of 0.0261, p-value of 0.05]. The NRI was 0.46 (p-value <0.0001), indicating that 46% of the patients had improved predicted probabilities in the model using RDW. The IDI was 0.0317 (p-value of 0.005) corresponding to a 16% improvement in the predicted probabilities of ICU mortality with the addition of RDW.

CONCLUSIONS: A model using 4 clinical factors to predict ICU mortality developed in a cohort of ARDS patients was improved with the addition of RDW.

CLINICAL IMPLICATIONS: An accurate prediction of mortality at the time of ARDS diagnosis could enhance initial management and communication and may provide a useful tool for clinical research.

DISCLOSURE: The following authors have nothing to disclose: Ala Alkhatib, Rania Esteitie, Lori Lyn Price, Peter LaCamera

No Product/Research Disclosure Information


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