VITAL SIGNS
patients, with associated demographic and clinical data. Te BHSAI obtained data from a study conducted by Memorial Hermann Life Flight in Houston, and established collabora- tive relationships with Boston MedFlight, a hospital-based air medical transportation service and the Massachusetts General Hospital Emergency Department in Boston. Over the course of several years, the team collected diastolic and systolic blood pres- sure and heart rate data from 2,688 trauma patients, a sufficiently large and varied body of data for them to work with.
Ensuring the data were reliable, consistent and of sufficiently high quality required additional time to develop automated algo- rithms, including AI algorithms, to flag data that should not be included in the development of the model. For example, the BHSAI team had to filter out-of-range vital-sign data and data artifacts induced by the stress of air-ambulance transport, such as non-physiological changes in heart rates, and confirm that data collected from different models of vital sign monitors were demonstrably consistent. Te team also ended up excluding data from 1,029 patients for various reasons. After rigorous analysis and elimination of invalid data, the team ended up with 540 hours of continuous, reliable vital-sign data on which to train the algorithm.
TRAINING THE ALGORITHM TO ASSIGN HEMORRHAGE RISK “Te training of the algorithm didn’t take a lot of time in comparison to how long it took to collect the data and develop algorithms to automatically flag invalid data,” said Reifman. “We’re not talking about billions of data points and mathemati- cal models that have a trillion parameters, like some of the more recent AI technologies.”
After the BHSAI team had developed and applied the automated algorithms to flag and discard invalid data, they started experi- menting with different AI algorithms to map vital signs into risk levels. Te researchers conducted a supervised training on the vital-sign data using a multivariate regression algorithm, which computed the hemorrhage risk-level threshold on a scale of 0-to-1, with 0 representing 100% certainty of no hemorrhage risk and 1 representing 100% certainty that hemorrhage is likely to be pres- ent. Te algorithm sorted the patients into one of three classes of hemorrhage risk: low (hemorrhage risk (HR) level I, representing a 2-fold lower risk than the average in the test population), aver- age (HR level II, representing the prevalence of hemorrhage in the test population) and high (HR level III, representing a 2-fold higher risk). Te results of the analysis showed that the algorithm was capable of stratifying risk to a high degree of reliability with
48 Army AL&T Magazine Fall 2024
no overlap. When they compared the algorithm’s risk assessments longitudinally over time, in more than 70% of the comparisons the risk level remained unchanged.
In its final form, the APPRAISE-HRI algorithm consists of a smartphone app that receives heart rate and blood pressure data via Bluetooth from a patient’s vital-sign monitor and runs it through three software modules. Te first module conducts pre-processing of the patient’s heart rate and systolic and diastolic blood pressures. Tis involves trimming out unreliable physio- logical data, making sure that the data used to analyze the state of the patient is valid, timely and of sufficient quantity. Te resulting processed heart rate and pulse pressure data are then passed along to the second module, which is the AI portion of the algorithm. It performs multivariate linear regression on the data to assess the likelihood of hemorrhage on a 0-to-1 scale. Tis assessment is then passed along to the third module, which analyzes the outputs of the AI model and places the results into one of the three risk levels, which is then provided to the first responder for action.
ANSWERING THE FDA’S QUESTIONS When MRDC develops and tests a military medical product to the point of readiness, the command’s Medical Technology Transfer Office seeks partnerships with commercial manufactur- ers to build and market the device. If the device is regulated by the FDA, it must first be granted a clearance, or an approval to proceed to market. To make the APPRAISE-HRI more appealing to potential commercialization partners, Reifman recommended that BHSAI obtain the FDA clearance rather than passing that requirement along to the manufacturer.
Prior to applying for clearance review, while the device was in the final stages of testing, BHSAI worked with MRDC’s Office of Regulated Activities to prepare a pre-submission to the FDA to help anticipate and address issues of concern. Te FDA responded with 26 questions related to the rationale for select- ing the three vital signs, model development and the need for new, never-before-used data sets to independently validate the APPRAISE-HRI. Te BHSAI team answered all of them to the FDA’s satisfaction, which Reifman believes accelerated the subse- quent final clearance review process.
Te FDA’s clearance review process takes the form of a risk and benefit analysis, in this case comparing the APPRAISE-HRI’s potential for improved quality of care, ease of use and ability to work with FDA-cleared vital-sign monitors against the poten- tial for misinterpretation of the results, algorithm error, data
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