The Critical Care Resuscitation Unit Transfers More Patients From Emergency Departments Faster and Is Associated With Improved Outcomes.

BACKGROUND: Transfer delays of critically ill patients from other hospitals' emergency departments (EDs) to an appropriate referral hospital's intensive care unit (ICU) are associated with poor outcomes. OBJECTIVES: We hypothesized that an innovative Critical Care Resuscitation Unit (CCRU) would be associated with improved outcomes by reducing transfer times to a quaternary care center and times to interventions for ED patients with critical illnesses. METHODS: This pre-post analysis compared 3 groups of patients: a CCRU group (patients transferred to the CCRU during its first year [July 2013 to June 2014]), a 2011-Control group (patients transferred to any ICU between July 2011 and June 2012), and a 2013-Control group (patients transferred to other ICUs between July 2013 and June 2014). The primary outcome was time from transfer request to ICU arrival. Secondary outcomes were the interval between ICU arrival to the operating room and in-hospital mortality. RESULTS: We analyzed 1565 patients (644 in the CCRU, 574 in the 2011-Control, and 347 in 2013-Control groups). The median time from transfer request to ICU arrival for CCRU patients was 108 min (interquartile range [IQR] 74-166 min) compared with 158 min (IQR 111-252 min) for the 2011-Control and 185 min (IQR 122-283 min) for the 2013-Control groups (p < 0.01). The median arrival-to-urgent operation for the CCRU group was 220 min (IQR 120-429 min) versus 439 min (IQR 290-645 min) and 356 min (IQR 268-575 min; p < 0.026) for the 2011-Control and 2013-Control groups, respectively. After adjustment with clinical factors, transfer to the CCRU was associated with lower mortality (odds ratio 0.64 [95% confidence interval 0.44-0.93], p = 0.019) in multivariable logistic regression. CONCLUSION: The CCRU, which decreased time from outside ED's transfer request to referral ICU arrival, was associated with lower mortality likelihood. Resuscitation units analogous to the CCRU, which transfer resource-intensive patients from EDs faster, may improve patient outcomes.

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%.

Comparison of Decision-Assist and Clinical Judgment of Experts for Prediction of Lifesaving Interventions.

Early recognition of hemorrhage during the initial resuscitation of injured patients is associated with improved survival in both civilian and military casualties. We tested a transfusion and lifesaving intervention (LSI) prediction algorithm in comparison with clinical judgment of expert trauma care providers. We collected 15 min of pulse oximeter photopletysmograph waveforms and extracted features to predict LSIs. We compared this with clinical judgment of LSIs by individual categories of prehospital providers, nurses, and physicians and a combined judgment of all three providers using the Area Under Receiver Operating Curve (AUROC). We obtained clinical judgment of need for LSI from 405 expert clinicians in135 trauma patients. The pulse oximeter algorithm predicted transfusion within 6 h (AUROC, 0.92; P < 0.003) more accurately than either physicians or prehospital providers and as accurately as nurses (AUROC, 0.76; P = 0.07). For prediction of surgical procedures, the algorithm was as accurate as the three categories of clinicians. For prediction of fluid bolus, the diagnostic algorithm (AUROC, 0.9) was significantly more accurate than prehospital providers (AUROC, 0.62; P = 0.02) and nurses (AUROC, 0.57; P = 0.04) and as accurate as physicians (AUROC, 0.71; P = 0.06). Prediction of intubation by the algorithm (AUROC, 0.92) was as accurate as each of the three categories of clinicians. The algorithm was more accurate (P < 0.03) for blood and fluid prediction than the combined clinical judgment of all three providers but no different from the clinicians in the prediction of surgery (P = 0.7) or intubation (P = 0.8). Automated analysis of 15 min of pulse oximeter waveforms predicts the need for LSIs during initial trauma resuscitation as accurately as judgment of expert trauma clinicians. For prediction of emergency transfusion and fluid bolus, pulse oximetry features were more accurate than these experts. Such automated decision support could assist resuscitation decisions, trauma team, and operating room and blood bank preparations.

Assessing trauma care provider judgement in the prediction of need for life-saving interventions.

INTRODUCTION: Human judgement on the need for life-saving interventions (LSI) in trauma is poorly studied, especially during initial casualty management. We prospectively examined early clinical judgement and compared clinical experts' predictions of LSI to their later occurrence. PATIENTS AND METHODS: Within 10-15 min of direct trauma admission, we surveyed the predictions of pre-hospital care providers (PHP, 92% paramedics), trauma centre nurses (RN), and attending or fellow trauma physicians (MD) on the need for LSI. The actual outcomes including fluid bolus, intubation, transfusion (<1h and 1-6h), and emergent surgical interventions were observed. Cohen's kappa statistic (K) and percentage agreement were used to measure agreement among provider responses. Sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) were calculated to compare clinical judgement to actual patient interventions. RESULTS: Among 325 eligible trauma patient admissions, 209 clinical judgement of LSIs were obtained from all three providers. Cohen's kappa statistic for agreement between pairs of provider groups demonstrated no "disagreement" (K<0) between groups, "fair" agreement for fluid bolus (K=0.12-0.19) and blood transfusion 0-6h (K=0.22-0.39), and "moderate" (K=0.45-0.49) agreement between PHP and RN regarding intubation and surgical interventions, but no "excellent" (K ≥ 0.81) agreement between any pair of provider groups for any intervention. The percentage agreement across the different clinician groups ranged from 50% to 83%. NPV was 90-99% across providers for all interventions except fluid bolus. CONCLUSIONS: Expert clinical judgement provides a benchmark for the prediction of major LSI use in unstable trauma patients. No excellent agreement exists across providers on LSI predictions. It is possible that quality improvement measures and computer modelling-based decision-support could reduce errors of LSI commission and omission found in resuscitation at major trauma centres and enhance decision-making in austere trauma settings by less well-trained providers than those surveyed.