Catriona Miller

Intraosseous infusion rates under high pressure: a cadaveric comparison of anatomic sites.

BACKGROUND When traditional vascular access methods fail, emergency access through the intraosseous (IO) route can be lifesaving. Fluids, medications, and blood components have all been delivered through these devices. We sought to compare the performance of IO devices placed in the sternum, humeral head, and proximal tibia using a fresh human cadaver model. METHODS Commercially available IO infusion devices were placed into fresh human cadavers: sternum (FAST-1), humeral head (EZ-IO), and proximal tibia (EZ-IO). Sequentially, the volume of 0.9% saline infused into each site under 300 mm Hg pressure over 5 minutes was measured. Rates of successful initial IO device placement and subjective observations related to the devices were also recorded. RESULTS For 16 cadavers over a 5-minute bolus infusion, the total volume of fluid infused at the three IO access sites was 469 (190) mL for the sternum, 286 (218) mL for the humerus, and 154 (94) mL for the tibia. Thus, the mean (SD) flow rate infused at each site was as follows: (1) sternum, 93.7 (37.9) mL/min; (2) humerus, 57.1 (43.5) mL/min; and (3) tibia, 30.7 (18.7) mL/min. The tibial site had the greatest number of insertion difficulties. CONCLUSION This is the first study comparing the rate of flow at the three most clinically used adult IO infusion sites in an adult human cadaver model. Our results showed that the sternal site for IO access provided the most consistent and highest flow rate compared with the humeral and tibial insertion sites. The average flow rate in the sternum was 1.6 times greater than in the humerus and 3.1 times greater than in the tibia.

Trends of Hemoglobin Oximetry: Do They Help Predict Blood Transfusion During Trauma Patient Resuscitation?

BACKGROUND:A noninvasive decision support tool for emergency transfusion would benefit triage and resuscitation. We tested whether 15 minutes of continuous pulse oximetry–derived hemoglobin measurements (SpHb) predict emergency blood transfusion better than conventional oximetry, vital signs, and invasive point-of-admission (POA) laboratory testing. We hypothesized that the trends in noninvasive SpHb features monitored for 15 minutes predict emergency transfusion better than pulse oximetry, shock index (SI = heart rate/systolic blood pressure), or routine POA laboratory measures. METHODS:We enrolled direct trauma patient admissions ≥18 years with prehospital SI ≥0.62, collected vital signs (continuous SpHb and conventional pulse oximetry, heart rate, and blood pressure) for 15 minutes after admission, and recorded transfusion (packed red blood cells [pRBCs]) within 1 to 3, 1 to 6, and 1 to 12 hours of admission. One blood sample was drawn during the first 15 minutes. The laboratory Hb was compared with its corresponding SpHb reading for numerical, clinical, and prediction difference. Ten prediction models for transfusion, including combinations of prehospital vital signs, SpHb, conventional oximetry, and routine POA, were selected by stepwise logistic regression. Predictions were compared via area under the receiver operating characteristic curve by the DeLong method. RESULTS:A total of 677 trauma patients were enrolled in the study. The prediction performance of the models, including POA laboratory values and SI (and the need for blood pressure), was better than those without POA values or SI. In predicting pRBC 1- to 3-hour transfusion, adding SpHb features (receiver operating characteristic curve [ROC] = 0.65; 95% confidence interval [CI], 0.53–0.77) does not improve ROC from the base model (ROC = 0.64; 95% CI, 0.52–0.76) with P = 0.48. Adding POA laboratory Hb features (ROC = 0.72; 95% CI, 0.60–0.84) also does not improve prediction performance (P = 0.18). Other POA laboratory testing predicted emergency blood use with ROC of 0.88 (95% CI, 0.81–0.96), significantly better than the use of SpHb (P = 0.00084) and laboratory Hb (P = 0.0068). CONCLUSIONS:SpHb added no benefit over conventional oximetry to predict urgent pRBC transfusion for trauma patients. Both models containing POA laboratory test features performed better at predicting pRBC use than prehospital SI, the current best noninvasive vital signs transfusion predictor.

Predicting blood transfusion using automated analysis of pulse oximetry signals and laboratory values

BACKGROUND Identification of hemorrhaging trauma patients and prediction of blood transfusion needs in near real time will expedite care of the critically injured. We hypothesized that automated analysis of pulse oximetry signals in combination with laboratory values and vital signs obtained at the time of triage would predict the need for blood transfusion with accuracy greater than that of triage vital signs or pulse oximetry analysis alone. METHODS Continuous pulse oximetry signals were recorded for directly admitted trauma patients with abnormal prehospital shock index (heart rate [HR] / systolic blood pressure) of 0.62 or greater. Predictions of blood transfusion within 24 hours were compared using Delong’s method for area under the receiver operating characteristic (AUROC) curves to determine the optimal combination of triage vital signs (prehospital HR + systolic blood pressure), pulse oximetry features (40 waveform features, O2 saturation, HR), and laboratory values (hematocrit, electrolytes, bicarbonate, prothrombin time, international normalization ratio, lactate) in multivariate logistic regression models. RESULTS We enrolled 1,191 patients; 339 were excluded because of incomplete data; 40 received blood within 3 hours; and 14 received massive transfusion. Triage vital signs predicted need for transfusion within 3 hours (AUROC, 0.59) and massive transfusion (AUROC, 0.70). Pulse oximetry for 15 minutes predicted transfusion more accurately than triage vital signs for both time frames (3-hour AUROC, 0.74; p = 0.004) (massive transfusion AUROC, 0.88; p < 0.001). An algorithm including triage vital signs, pulse oximetry features, and laboratory values improved accuracy of transfusion prediction (3-hour AUROC, 0.84; p < 0.001) (massive transfusion AUROC, 0.91; p < 0.001). CONCLUSION Automated analysis of triage vital signs, 15 minutes of pulse oximetry signals, and laboratory values predicted use of blood transfusion during trauma resuscitation more accurately than triage vital signs or pulse oximetry analysis alone. Results suggest automated calculations from a noninvasive vital sign monitor interfaced with a point-of-care laboratory device may support clinical decisions by recognizing patients with hemorrhage sufficient to need transfusion. LEVEL OF EVIDENCE Epidemiologic/prognostic study, level III.

Full of sound and fury, signifying nothing: burden of transient noncritical monitor alarms in a trauma resuscitation unit.

We examined the types of patient monitor alarms encountered in the trauma resuscitation unit of a major level 1 trauma center. Over a 1-year period, 316688 alarms were recorded for 6701 trauma patients (47 alarms/patient). Alarms were more frequent among patients with a Glasgow Coma Scale of 8 or less. Only 2.4% of all alarms were classified as “patient crisis,” with the rest in the presumably less critical categories “patient advisory,” “patient warning,” and “system warning.” Nearly half of alarms were ⩽5 seconds in duration. In this patient population, a 2-second delay would reduce alarms by 25%, and a delay of 5 seconds would reduce all alarms by 49%.

Continuous Vital Sign Analysis to Predict Secondary Neurological Decline After Traumatic Brain Injury

Background: In the acute resuscitation period after traumatic brain injury (TBI), one of the goals is to identify those at risk for secondary neurological decline (ND), represented by a constellation of clinical signs that can be identified as objective events related to secondary brain injury and independently impact outcome. We investigated whether continuous vital sign variability and waveform analysis of the electrocardiogram (ECG) or photoplethysmogram (PPG) within the first hour of resuscitation may enhance the ability to predict ND in the initial 48 hours after traumatic brain injury (TBI). Methods: Retrospective analysis of ND in TBI patients enrolled in the prospective Oximetry and Noninvasive Predictors Of Intervention Need after Trauma (ONPOINT) study. ND was defined as any of the following occurring in the first 48 h: new asymmetric pupillary dilatation (>2 mm), 2 point GCS decline, interval worsening of CT scan as assessed by the Marshall score, or intervention for cerebral edema. Beat-to-beat variation of ECG or PPG, as well as waveform features during the first 15 and 60 min after arrival in the TRU were analyzed to determine physiologic parameters associated with future ND. Physiologic and admission clinical variables were combined in multivariable logistic regression models predicting ND and inpatient mortality. Results: There were 33 (17%) patients with ND among 191 patients (mean age 43 years old, GCS 13, ISS 12, 69% men) who met study criteria. ND was associated with ICU admission (P < 0.001) and inpatient mortality (P < 0.001). Both ECG (AUROC: 0.84, 95% CI: 0.76,0.93) and PPG (AUROC: 0.87, 95% CI: 0.80, 0.93) analyses during the first 15 min of resuscitation demonstrated a greater ability to predict ND then clinical characteristics alone (AUROC: 0.69, 95% CI: 0.59, 0.8). Age (P = 0.02), Marshall score (P = 0.001), penetrating injury (P = 0.02), and predictive probability for ND by PPG analysis at 15 min (P = 0.03) were independently associated with inpatient mortality. Conclusions: Analysis of variability and ECG or PPG waveform in the first minutes of resuscitation may represent a non-invasive early marker of future ND.

Computer Modelling Using Prehospital Vitals Predicts Transfusion and Mortality.

Abstract Objective: Test computer-assisted modeling techniques using prehospital vital signs of injured patients to predict emergency transfusion requirements, number of intensive care days, and mortality, compared to vital signs alone. Methods: This single-center retrospective analysis of 17,988 trauma patients used vital signs data collected between 2006 and 2012 to predict which patients would receive transfusion, require 3 or more days of intensive care, or die. Standard transmitted prehospital vital signs (heart rate, blood pressure, shock index, and respiratory rate) were used to create a regression model (PH-VS) that was internally validated and evaluated using area under the receiver operating curve (AUROC). Transfusion records were matched with blood bank records. Documentation of death and duration of intensive care were obtained from the trauma registry. Results: During the course of their hospital stay, 720 of the 17,988 patients in the study population died (4%), 2,266 (12.6%) required at least a 3-day stay in the intensive care unit (ICU), 1,171 (6.5%) required transfusions, and 210 (1.2%) received massive transfusions. The PH-VS model significantly outperformed any individual vital sign across all outcomes (average AUROC = 0.82), The PH-VS model correctly predicted that 512 of 777 (65.9%) and 580 of 931 (62.3%) patients in the study population would receive transfusions within the first 2 and 6 hours of admission, respectively. Conclusions: The predictive ability of individual vital signs to predict outcomes is significantly enhanced with the model. This could support prehospital triage by enhancing decision makers’ ability to match critically injured patients with appropriate resources with minimal delays.