Ashima Das

Validation of a pediatric bedside tool to predict time to death after withdrawal of life support.

AIM To evaluate the accuracy of a tool developed to predict timing of death following withdrawal of life support in children. METHODS Pertinent variables for all pediatric deaths (age ≤ 21 years) from 1/2009 to 6/2014 in our pediatric intensive care unit (PICU) were extracted through a detailed review of the medical records. As originally described, a recently developed tool that predicts timing of death in children following withdrawal of life support (dallas predictor tool [DPT]) was used to calculate individual scores for each patient. Individual scores were calculated for prediction of death within 30 min (DPT30) and within 60 min (DPT60). For various resulting DPT30 and DPT60 scores, sensitivity, specificity and area under the receiver operating characteristic curve were calculated. RESULTS There were 8829 PICU admissions resulting in 132 (1.5%) deaths. Death followed withdrawal of life support in 70 patients (53%). After excluding subjects with insufficient data to calculate DPT scores, 62 subjects were analyzed. Average age of patients was 5.3 years (SD: 6.9), median time to death after withdrawal of life support was 25 min (range; 7 min to 16 h 54 min). Respiratory failure, shock and sepsis were the most common diagnoses. Thirty-seven patients (59.6%) died within 30 min of withdrawal of life support and 52 (83.8%) died within 60 min. DPT30 scores ranged from -17 to 16. A DPT30 score ≥ -3 was most predictive of death within that time period, with sensitivity = 0.76, specificity = 0.52, AUC = 0.69 and an overall classification accuracy = 66.1%. DPT60 scores ranged from -21 to 28. A DPT60 score ≥ -9 was most predictive of death within that time period, with sensitivity = 0.75, specificity = 0.80, AUC = 0.85 and an overall classification accuracy = 75.8%. CONCLUSION In this external cohort, the DPT is clinically relevant in predicting time from withdrawal of life support to death. In our patients, the DPT is more useful in predicting death within 60 min of withdrawal of life support than within 30 min. Furthermore, our analysis suggests optimal cut-off scores. Additional calibration and modifications of this important tool could help guide the intensive care team and families considering DCD.

43: PREVALENCE, PREDICTORS AND OUTCOMES OF MRSA INFECTIONS IN PATIENTS HAVING MAJOR SURGICAL PROCEDURES

patients. Methods: We performed a two center observational study of patients treated in medical and surgical intensive care units in Boston, Massachusetts. All data was obtained from the Research Patient Data Registry at Partners HealthCare. We studied 4,703 patients, age ≥ 18 years, who received critical care between 1997 and 2011 who had serum iron levels measured within a year prior to hospital admission. Patients with end stage renal disease prior to critical care initiation were identified by submitting the administrative data to the United States Renal Data System and excluded due to high prevalence of both intravenous iron supplementation and bloodstream infection in this population The exposure of interest was serum iron categorized a priori as 0–59.9 μg/dL, 60–169.9 μg/dL (referent) and > 170 μg/dL. The primary outcome was bloodstream infection determined by positive blood culture in the 48 hours prior to ICU admission to 48 hours after ICU admission. Secondary outcomes included sepsis and 30-day mortality. Sepsis was defined by ICD-9-CM and validated by the 2001 SCCM/ ESICM, ACCP, ATS, SIS international sepsis definitions conference guidelines. Information on vital status for the study cohort was obtained from the Social Security Administration Death Master File which has high sensitivity and specificity for mortality. Adjusted odds ratios were estimated by multivariable logistic regression models with inclusion of covariate terms thought to plausibly interact with both serum iron and bloodstream infection. Adjustment included age, race, gender, comorbidities (Deyo-Charlson Index) and patient type (medical versus surgical). Results: The majority of patients were men (55%), white (78%) and had medical related DRGs (71%). The mean (SD) age at ICU admission was 63.9 (16.1) years. 243 (5%) patients had pre-hospital iron levels > 170 μg/dL. The cohort bloodstream infection rate was 18.4%, with 35.2% of patients diagnosed with sepsis, and an all cause 30-day mortality rate of 23.5%. Pre-hospital serum iron was a robust predictor of bloodstream infection and remained so following multivariable adjustment. Patients with pre-hospital serum iron > 170 μg/ dL have an OR for bloodstream infection of 1.38 (95%CI, 1.00, 1.92, p=0.050) and an adjusted OR of 1.41 (95%CI, 1.01, 1.97, p=0.041) relative to patients with serum iron 60–169.9 μg/dL. Pre-hospital serum iron 0–59.9 μg/dL was not associated with increased odds of bloodstream infection relative to patients with serum iron 60–169.9 μg/dL. Further, patients with pre-hospital serum iron > 170 μg/dL have an OR for sepsis of 1.39 (95%CI, 1.05, 1.84, p=0.022) following adjustment for age, gender, race, patient type and Deyo-Charson index, relative to patients with serum iron 60–169.9 μg/dL. Patients with pre-hospital serum iron > 170 μg/dL have an OR for 30-day mortality of OR of 1.42 (95%CI, 1.02, 1.96, p=0.035) following adjustment for age, gender, race, patient type, Deyo-Charson index and acute organ failure, relative to patients with serum iron 60–169.9 μg/dL. Conclusions: In critically ill patients, an elevated serum iron >170 μg/dL prior to hospitalization is a robust predictor of the development of bloodstream infection, sepsis and 30-day mortality following ICU admission. If our findings are corroborated, information regarding iron metabolism and its relationship to infection in the critically ill may potentially help guide infection management efforts in the ICU.