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The Personalized Physiology Engine (PPA Engine) is intended to be used with data from already cleared sensors measuring physiological parameters, including heart rate, respiratory rate, and activity in ambulatory patients being monitored in a healthcare facility or at home. The device provides a time series Multivariate Change Index (MCI) which indicates whether the relationships among the patient's monitored vital signs change from those measured at baseline, which has been derived from measurements previously obtained during routine activities of daily living. The MCI is based on an integrated computation evaluating changes in the parameters and their relationships to each other.

The PPA Engine is an adjunct to and is not intended to replace vital signs monitoring. The MCI is intended for daily intermittent, retrospective review by a qualified practitioner. The PPA Engine is intended to provide additional information for use during routine patient monitoring. The MCI is not intended for making clinical decisions regarding patient treatment or for diagnostic purposes.

The PhysIQ Personalized Physiology Analytics Engine ("PPA Engine") is a computerized analysis software program that is designed for detecting change in the relationships among the patient's vital signs throughout dynamic physical activity, based on data input from multi-parameter vital sign monitoring devices. The PPA Engine first "learns" a patient's personalized baseline, defined by the relationship among the vital signs derived from measurements obtained during routine activities of daily living. Once the baseline vital sign relationships are established, it analyzes the subsequent data to assess how the relationships among the vital signs incoming during the monitoring period compare to the established baseline. The PPA Engine can analyze data collected wherever the patient is monitored, reflecting a patient's activities of daily living. The device is intended for monitoring ambulatory patients.

The PPA Engine requires vital sign inputs of Heart Rate (HR), Respiration Rate (RR) and Activity (ACT) (body motion). The PPA Engine can accept input from commercial vital sign monitors or combinations of monitors that can provide multivariate observations of these vital signs.

The PPA Engine calculates the Multivariate Change Index (MCI), a scalar index between 0 and 1, which represents the likelihood that the relationships among the patient's vital signs are different from those at baseline, which was established during routine activities of daily living. An MCI value closer to zero (0) indicates that the monitored relationships among the vital signs are similar to the learned baseline. An MCI value closer to one (1) indicates that the patient's monitored relationships among the vital signs are likely to be different from the learned baseline.

The MCI is also presented as a time series (MCI over time) and it is intended to for retrospective review by the clinician The MCI is not intended to replace standard patient monitoring. Rather, it was designed to supplement standard monitoring of ambulatory patients.

The provided 510(k) summary for the Personalized Physiology Analytics Engine (PPA Engine) indicates a substantial equivalence determination based on various testing. However, it does not explicitly provide a table of acceptance criteria with corresponding performance metrics in the format requested. The document describes the types of testing performed and the conclusions drawn from those tests, but not specific quantifiable targets or results.

Here's a breakdown of the available information based on your request:

1. A table of acceptance criteria and the reported device performance

The provided document does not contain a table with specific acceptance criteria (e.g., sensitivity, specificity, accuracy thresholds) and their corresponding reported device performance metrics. Instead, it states that the device "performs as intended per its specifications" and that its output "correlates with changes in the relationships among vital signs compared with baseline."

Summary of Device Performance (as reported implicitly):

• Bench Testing:
  • Verification testing confirmed the device meets its specifications.
  • Validation testing showed correlation of MCI with changes in vital sign relationships compared to baseline.
• Clinical Testing (Healthy Volunteers):
  • Demonstrated that the MCI correlates with changes in monitored vital sign relationships compared to the subject's baseline, fulfilling its intended use.

2. Sample sizes used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

• Test Set Sample Size: Not explicitly stated. The document refers to "human physiological data collected" from a "perturbed clinical data study" and a "simulator data study" for bench testing validation, and "healthy volunteer studies" for clinical testing. The number of participants in these studies is not provided.
• Data Provenance:
  • Bench Testing: "Perturbed clinical data study" (unspecified origin) and "Simulator data study."
  • Clinical Testing: "Healthy volunteer studies were conducted under an IRB-approved non-significant risk protocol" (suggests prospective data collection). The country of origin is not specified but given the FDA submission, it's likely within the US or compliant with US standards.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

Not provided. The document describes "changes in the relationships among vital signs compared with baseline" as the ground truth concept, often influenced by physiological events rather than expert interpretation of an image or signal.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set

Not applicable. The ground truth for this device appears to be based on physiological changes, not expert interpretation requiring adjudication.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

No MRMC comparative effectiveness study is mentioned. The device is described as providing a "Multivariate Change Index (MCI)" for "daily intermittent, retrospective review by a qualified practitioner" as an adjunct to vital signs monitoring. It explicitly states it is not intended for making clinical decisions regarding patient treatment or for diagnostic purposes, which suggests it's not a primary diagnostic tool to be compared in an MRMC study.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done

Yes, the testing described appears to be a standalone performance evaluation of the algorithm. The "Perturbed clinical data study" and "Simulator data study" for bench testing and the "healthy volunteer studies" for clinical testing assessed how the MCI output correlated with physiological changes, thus evaluating the algorithm's performance in generating the MCI.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

The ground truth used appears to be objective physiological changes or perturbations.

• For bench testing, this involved validating correlation of MCI "with changes in the relationships among vital signs compared to baseline."
• For clinical testing, "healthy volunteer studies... during a trip with a substantial altitude change (causing natural perturbation in relationships among vital signs)" were used, and results demonstrated the MCI correlates with "change in the monitored relationships among the vital signs compared to the subject's baseline."

8. The sample size for the training set

Not provided. The document mentions the PPA Engine "learns" a patient's personalized baseline from "measurements previously obtained during routine activities of daily living," but it does not specify the sample size or duration of this "learning" phase for general model development, nor does it distinguish between training and testing sets in a conventional machine learning sense for the submitted evidence. The learning described is patient-specific baseline establishment.

9. How the ground truth for the training set was established

The document describes a personalized baseline for each patient, established from "measurements previously obtained during routine activities of daily living." This implies that the system identifies a "normal" or baseline state for an individual based on their own physiological data during typical activities. There is no mention of external expert labeling or a separate "ground truth" for a training set in the typical sense of a supervised learning model, as the device's normality is patient-specific and learned from their own data.

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