Efficacy of the compensatory reserve measurement in an emergency department trauma population

Abstract

Background The Compensatory Reserve Measurement (CRM) is a novel method used to provide early assessment of shock based on arterial wave form morphology changes. We hypothesized that (1) CRM would be significantly lower in those trauma patients who received life‐saving interventions compared with those not receiving interventions, and (2) CRM in patients who received interventions would recover after the intervention was performed. Study design and methods We captured vital signs along with analog arterial waveform data from trauma patients meeting major activation criteria using a prospective study design. Study team members tracked interventions throughout their emergency department stay. Results Ninety subjects met inclusion with 13 receiving a blood product and 10 a major airway intervention. Most trauma was blunt (69%) with motor vehicle collisions making up the largest proportion (37%) of injury mechanism. Patients receiving blood products had lower CRM values just prior to administration versus those who did not (50% versus 58%, p = .045), and lower systolic pressure (SBP; 95 versus 123 mmHg, p = .005), diastolic (DBP; 62 versus 79, p = .007), and mean arterial pressure (MAP; 75 versus 95, p = .006), and a higher pulse rate (HR; 101 versus 89 bpm, p = .039). Patients receiving an airway intervention had lower CRM values just prior to administration versus those who did not (48% versus 58%, p = .062); however, SBP, DBP, MAP, and HR were not statistically distinguishable (p ≥ .645). Conclusions Our results support our hypotheses that the CRM distinguished those patients who received blood or an airway intervention from those who did not, and increased appropriately after interventions were performed.

Compensatory reserve and pulse character: Enhanced potential to predict urgency for transfusion and other life‐saving interventions after traumatic injury

“…15,28 The application of CRM at predicting non-hemorrhagic LSI has been previously demonstrated in the emergency department setting, where CRM was able to predict both blood product requirements and need for intubation, which was redemonstrated in our study. 34 This further supports the idea that CRM can be used to predict need for hemorrhage-related interventions (e.g., blood transfusion), LSI that is non-hemorrhagic in nature (e.g., intubation), and suggests its validity to use prospectively for the need of other non-hemorrhagic LSI in the prehospital setting. Given the retrospective nature of this study, there is some confounding in that the results show prediction of what happened, not necessarily what decision a provider would have made based on the CRM reading.…”

AI-Enabled Advanced Development for Assessing Low Circulating Blood Volume for Emergency Medical Care: Comparison of Compensatory Reserve Machine-Learning Algorithms

“…We are unaware of any other advanced technology applying ML that provides such a capability for translation of precision medicine to patient monitoring. The performance results from both CRI and CRM algorithms with high sensitivity, specificity, and accuracy presented in this paper underscore the importance of accumulating data sets from healthy subjects exposed to an experimental protocol designed to elicit hemodynamic decompensation in all subjects prior to validating the algorithms with an application in patients [ 14 , 15 , 17 , 18 , 19 , 20 , 34 ]. Simply put, consistent success in accurately assessing the clinical status of patients with compromised circulating blood volume reflects the importance of generating algorithms that are based on the physiology of healthy individuals.…”

“…This physiological phenomenon has been defined as the compensatory reserve, with its measurement relying on a machine learning (ML) approach that incorporates the interrogation of arterial blood pressure waveform features [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. Reported measurements of the compensatory reserve have consistently proven to provide greater sensitivity in time (i.e., early) and specificity for identifying individual patient status when compared to traditional standard vital signs in both human experimental [ 2 , 6 , 8 , 9 , 10 , 11 , 12 , 13 ] and clinical [ 14 , 15 , 16 , 17 , 18 , 19 , 20 ] settings. In this regard, the measurement of the compensatory reserve has proven to be “the most informative ‘vital sign’ to be captured in emergency medical care settings” because of its ability to provide earlier and individualized status of patients with hypovolemia [ 2 ].…”

Identifying critical DO2 with compensatory reserve during simulated hemorrhage in humans

“…[3][4][5][6][7][8] The CRM has proven to demonstrate greater sensitivity as an earlier indicator of progressive central hypovolemia, and greater specificity in providing a capability to distinguish "good" compensators from "poor" compensators when compared with standard clinical metrics used to assess circulatory shock in trauma patients. [9][10][11][12][13][14][15] Using a model of progressive controlled hemorrhage and whole blood resuscitation in nonhuman primates (NHP), we recently reported that CRM accurately tracks DO 2 and were able to calculate a critical DO 2 (DO 2crit ) at approximately 5.3 ml O 2 Ákg À1 min À1 . 4,16 We also showed that a progressive reduction in CRM caused by experimentally induced central hypovolemia resulting from application of lower body negative pressure (LBNP) was similar in the NHP compared with humans.…”

“…The CRM is based on a machine‐learning algorithmic approach that interrogates subtle changes in features of the arterial waveform to provide a measure of integrated compensatory mechanisms 3–8 . The CRM has proven to demonstrate greater sensitivity as an earlier indicator of progressive central hypovolemia, and greater specificity in providing a capability to distinguish “good” compensators from “poor” compensators when compared with standard clinical metrics used to assess circulatory shock in trauma patients 9–15 …”