Personalized Physiology Analytics Engine software

The Personalized Physiology Analytics Engine is a software tool that establishes an individual's unique physiological baseline by analyzing heart rate, respiration rate, and activity data collected during daily life. It then calculates a Multivariate Change Index (MCI) to indicate how current interrelationships among these vital signs deviate from this established baseline, offering clinicians supplementary, retrospective information for patient assessment.

The Personalized Physiology Analytics Engine (PPA Engine) is a computerized analysis software program designed to detect changes in the relationships among an ambulatory patient's vital signs, using data from already cleared multi-parameter physiological sensors. It processes inputs of Heart Rate (HR), Respiration Rate (RR), and Activity (ACT). The core methodology involves the PPA Engine first 'learning' a patient-specific baseline, defined by the typical interrelationships among these vital signs as measured during the patient's routine activities of daily living. Once this personalized baseline is established, the software analyzes subsequent incoming data to assess how the current relationships among these vital signs compare to this learned baseline, providing a continuous assessment reflecting the patient's activities. The primary output of the PPA Engine is the Multivariate Change Index (MCI), a scalar value ranging from 0 to 1. An MCI value closer to 0 indicates that the monitored relationships among the vital signs are similar to the patient's learned baseline, whereas a value closer to 1 suggests that these relationships are likely different. The MCI is presented as a time series intended for daily, intermittent, retrospective review by a qualified practitioner. It serves as an adjunct to, and is not intended to replace, standard vital signs monitoring or be used for direct clinical decision-making or diagnostic purposes. Bench testing verified the device meets its specifications, and clinical testing on healthy volunteers, including a study involving altitude changes, demonstrated that the MCI effectively correlates with changes in the monitored relationships among vital signs compared to the subject's established baseline.

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• Q1. How sensitive is the Multivariate Change Index (MCI) to subtle physiological changes, and what is the typical timeframe required to establish a reliable personalized baseline for different patient populations?; What specific algorithms or computational methods are used to 'learn' the patient's personalized baseline and then calculate the MCI from heart rate, respiration rate, and activity data?; In the clinical validation studies, what was the magnitude of altitude change, and were specific thresholds for MCI established that correlated with clinically observable physiological stress or adaptation in the healthy volunteers?; How does the PPA Engine account for normal diurnal variations or expected physiological responses to non-pathological stimuli (e.g., moderate exercise, emotional stress, sleep stages) when establishing the baseline and calculating subsequent MCI values?; What are the potential applications or benefits of using a retrospective MCI in managing ambulatory patients, and are there specific clinical scenarios where this supplementary information has been shown to be particularly insightful?