Browsing by Author "Bakdash, K."

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    Patient-Specific Injury Metrics Predict Early Biomarker Response in Multiply Injured Patients
    (Office of the Vice Chancellor for Research, 2016-04-08) Bakdash, K.; McCarroll, T.; Metzger, C.; Gaski, G.; McKinley, T.
    Introduction: It is important to identify multiply injured patients (MIPs) that can tolerate high-magnitude procedures and those at risk for complications. Determining how injury leads to immunologic dysfunction could identify MIPs at risk for complications. We explored a new precision medicine approach in which we determined how patientspecific injury metrics corresponded to changes in cytokines in a prospective cohort of MIPs. Methods: This was a prospective observational cohort of 40 MIPs, 18-55 yo, admitted to surgical ICU having had full axial CTs done at admission. Mechanical tissue damage was quantified by calculating volumetric measures of injuries from CT scans into the Tissue Damage Volume score (TDV). Ischemic tissue damage was calculated by calculation of all abnormal Shock Volumes (SV) (heart rate/systolic blood pressure > 0.9) in the first 24hr after injury. TIMS was calculated by combining mechanical and ischemic tissue damage: TIMS = TDV+5*SV. Linear regression was performed between TIMS and 21 cytokines including interleukin (IL)-6; IL-8; IL-10; IL-1RA; IL-2RA; MCP-1 drawn at 0hr, 8hr, and 24hr after injury. Linear regression was also performed between the cytokines, Injury Severity Score (ISS) and minimum pH (day 1). Results: Mean and median ISS was 29 (range 9 – 66). Minimum pH demonstrated best correspondence to cytokine levels measured 0hr and 8hr after injury. TIMS demonstrated the best correspondence to cytokine levels 24hr after injury. ISS demonstrated minimal predictive value of cytokines at any timepoint. Discussion: A precision medicine approach using a patient-specific quantity of injury predicted trauma-associated changes in circulating cytokines at 0hr, 8 hr, and 24 hr after surgery. This corresponds favorably with timing of orthopaedic surgical decisions regarding staged fracture interventions. While clinical significance of these findings is unknown, computational data analyses of temporal cytokine changes have been shown to be predictive of adverse outcomes after injury.
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    THE PATIENT-SPECIFIC INJURY SCORE: PRECISION MEDICINE IN TRAUMA PATIENTS PREDICTS ORGAN DYSFUNCTION AND OUTCOMES
    (Office of the Vice Chancellor for Research, 2016-04-08) Metzger, C.; McCarroll, T.; Bakdash, K.; Zarzaur, B.; Steenberg, S.; Cutshall, A.; Brown, K.; Savage, S.; McKinley, T.O.; Gaski, G.E.
    Introduction: Current injury scoring systems in polytraumatized patients are limited at predicting patient outcomes. We present a novel method that quantifies mechanical tissue damage and cumulative hypoperfusion using a precision medicine approach. We hypothesized that a Patient-Specific Injury score formulated from individualized injury indices would stratify patient risk for developing organ dysfunction after injury. We compared correspondence between PSI and the Injury Severity Score with outcomes of organ dysfunction and MOF. Methods: Fifty Multiply-injured-patients (MIPs) were studied. Tissue Damage Volume scores were measured from admission pan-axial CT scans using purpose-designed post-processing software to quantify volumetric magnitude and distribution of injuries. Ischemic injury was quantified using Shock Volumes. SV is a time-magnitude integration of shock index. Values above 0.9 were measured in the 24-hours after injury. Metabolic response was quantified by subtracting the lowest first 24 hr pH from 7.40. PSI combines these indices into the formula: PSI=[0.2TDV+SV]*MR. Correspondence coefficients from regression modeling between PSI and organ dysfunction, measured by the Marshall Multiple Organ Dysfunction score averaged from days 2-5 post-injury, were compared to similar regression models of ISS vs. day 2-5 MOD-scores. We compared PSI and ISS in patients that did or did not develop MOF. Results: PSI demonstrated better correlation to organ dysfunction (r2=0.576) in comparison to ISS (r2=0.393) using the MOD-score on days 2-5. Mean PSI increased 3.4x(58.5vs.17.0;p<0.02) and ISS scores increased 1.4x(39.0vs.28.0;p=0.10) in patients that developed MOF versus those that did not. Conclusions: This study shows that a precision medicine approach that integrates patient-specific indices of mechanical tissue damage, ischemic tissue injury, and metabolic response better corresponds to phenotypic changes including organ dysfunction and MOF compared to ISS in MIPs. The PSI-score can be calculated within 24 hours of injury, making it useful for stratifying risk and predicting the magnitude of organ dysfunction to anticipate.