| SimpleSense (Nanowear Technologies, Brooklyn, NY) measured parameter . | Derived biomarker . | Clinical significance . |
|---|---|---|
| ECG vectors | Heart rate and heart rhythm (including occurrences of ectopic beats and frequency of occurrence) | Pain Anxiety Pulmonary embolism Fat embolism |
| Impedance cardiograph | Used to estimate changes in cardiac output over time (this is a trending metric that can be used to observe changes over time) | Anemia Pulmonary embolism Fat embolism |
| Thoracic impedance | Respiration rate and respiration relative tidal volume (this is a metric used to trend the changes in respiratory effort over each breath and over time) | Pain Pulmonary embolism Fat embolism Lidocaine toxicity |
| Activity and posture | Absolute orientation of the chest is measured continuously. Activity as effective movement along x, y, and z axes: in other words, up-down, forward-backward, and side-to-side of the chest is measured continuouslya | Ambulatory level Adherence to post-operative positioning instructions |
| Heart sounds | The presence of distinct S1 and S2 heart sounds and potential presence of S3 or S4 sounds, The loudness of the characteristic heart sounds have been shown to be relevant to estimating blood pressure in SimpleSense-BP (Nanowear Technologies, Brooklyn, NY) | Vasovagal episode Pain Hypertension leading to hematoma |
| Blood pressure | A SimpleSense (Nanowear Technologies, Brooklyn, NY) platform algorithm combines the above vital signs to derive a systolic and diastolic blood pressure measurementb | Vasovagal episode Pain Hypertension leading to hematoma |
| SimpleSense (Nanowear Technologies, Brooklyn, NY) measured parameter . | Derived biomarker . | Clinical significance . |
|---|---|---|
| ECG vectors | Heart rate and heart rhythm (including occurrences of ectopic beats and frequency of occurrence) | Pain Anxiety Pulmonary embolism Fat embolism |
| Impedance cardiograph | Used to estimate changes in cardiac output over time (this is a trending metric that can be used to observe changes over time) | Anemia Pulmonary embolism Fat embolism |
| Thoracic impedance | Respiration rate and respiration relative tidal volume (this is a metric used to trend the changes in respiratory effort over each breath and over time) | Pain Pulmonary embolism Fat embolism Lidocaine toxicity |
| Activity and posture | Absolute orientation of the chest is measured continuously. Activity as effective movement along x, y, and z axes: in other words, up-down, forward-backward, and side-to-side of the chest is measured continuouslya | Ambulatory level Adherence to post-operative positioning instructions |
| Heart sounds | The presence of distinct S1 and S2 heart sounds and potential presence of S3 or S4 sounds, The loudness of the characteristic heart sounds have been shown to be relevant to estimating blood pressure in SimpleSense-BP (Nanowear Technologies, Brooklyn, NY) | Vasovagal episode Pain Hypertension leading to hematoma |
| Blood pressure | A SimpleSense (Nanowear Technologies, Brooklyn, NY) platform algorithm combines the above vital signs to derive a systolic and diastolic blood pressure measurementb | Vasovagal episode Pain Hypertension leading to hematoma |
ECG, electrocardiogram. aThe differentiation of postures between sitting upright and standing upright based only on the data acquired by SimpleSense (Nanowear Technologies, Brooklyn, NY) during that time interval is not currently possible, given that the posture is sensed on the chest. However, the device measures activity and posture, and activity reflects the transition between postures. bIn an observational multisite study by Nanowear, 120 subjects (female (N = 61, 52%) between 18 and 83 years of age were recruited with the following stratification: normal, 20%; prehypertensive, 37%; Stage 1, 26%; and Stage 2, 18%. From these subjects, 1686 measurements of blood pressure from a sphygmomanometer were associated with simultaneously acquired vital signs from the SimpleSense device (Nanowear Technologies, Brooklyn, NY). A proprietary machine-learning–based algorithm called SimpleSense-BP (Nanowear Technologies, Brooklyn, NY) was developed with inputs as metrics derived from the multiparametric data, such as intensity and duration of S1 and S2 heart sounds, the time elapsed between ECG waveforms such as R peaks and these characteristic heart sounds, and patient demographic data, to calculate systolic and diastolic blood pressure.1
| SimpleSense (Nanowear Technologies, Brooklyn, NY) measured parameter . | Derived biomarker . | Clinical significance . |
|---|---|---|
| ECG vectors | Heart rate and heart rhythm (including occurrences of ectopic beats and frequency of occurrence) | Pain Anxiety Pulmonary embolism Fat embolism |
| Impedance cardiograph | Used to estimate changes in cardiac output over time (this is a trending metric that can be used to observe changes over time) | Anemia Pulmonary embolism Fat embolism |
| Thoracic impedance | Respiration rate and respiration relative tidal volume (this is a metric used to trend the changes in respiratory effort over each breath and over time) | Pain Pulmonary embolism Fat embolism Lidocaine toxicity |
| Activity and posture | Absolute orientation of the chest is measured continuously. Activity as effective movement along x, y, and z axes: in other words, up-down, forward-backward, and side-to-side of the chest is measured continuouslya | Ambulatory level Adherence to post-operative positioning instructions |
| Heart sounds | The presence of distinct S1 and S2 heart sounds and potential presence of S3 or S4 sounds, The loudness of the characteristic heart sounds have been shown to be relevant to estimating blood pressure in SimpleSense-BP (Nanowear Technologies, Brooklyn, NY) | Vasovagal episode Pain Hypertension leading to hematoma |
| Blood pressure | A SimpleSense (Nanowear Technologies, Brooklyn, NY) platform algorithm combines the above vital signs to derive a systolic and diastolic blood pressure measurementb | Vasovagal episode Pain Hypertension leading to hematoma |
| SimpleSense (Nanowear Technologies, Brooklyn, NY) measured parameter . | Derived biomarker . | Clinical significance . |
|---|---|---|
| ECG vectors | Heart rate and heart rhythm (including occurrences of ectopic beats and frequency of occurrence) | Pain Anxiety Pulmonary embolism Fat embolism |
| Impedance cardiograph | Used to estimate changes in cardiac output over time (this is a trending metric that can be used to observe changes over time) | Anemia Pulmonary embolism Fat embolism |
| Thoracic impedance | Respiration rate and respiration relative tidal volume (this is a metric used to trend the changes in respiratory effort over each breath and over time) | Pain Pulmonary embolism Fat embolism Lidocaine toxicity |
| Activity and posture | Absolute orientation of the chest is measured continuously. Activity as effective movement along x, y, and z axes: in other words, up-down, forward-backward, and side-to-side of the chest is measured continuouslya | Ambulatory level Adherence to post-operative positioning instructions |
| Heart sounds | The presence of distinct S1 and S2 heart sounds and potential presence of S3 or S4 sounds, The loudness of the characteristic heart sounds have been shown to be relevant to estimating blood pressure in SimpleSense-BP (Nanowear Technologies, Brooklyn, NY) | Vasovagal episode Pain Hypertension leading to hematoma |
| Blood pressure | A SimpleSense (Nanowear Technologies, Brooklyn, NY) platform algorithm combines the above vital signs to derive a systolic and diastolic blood pressure measurementb | Vasovagal episode Pain Hypertension leading to hematoma |
ECG, electrocardiogram. aThe differentiation of postures between sitting upright and standing upright based only on the data acquired by SimpleSense (Nanowear Technologies, Brooklyn, NY) during that time interval is not currently possible, given that the posture is sensed on the chest. However, the device measures activity and posture, and activity reflects the transition between postures. bIn an observational multisite study by Nanowear, 120 subjects (female (N = 61, 52%) between 18 and 83 years of age were recruited with the following stratification: normal, 20%; prehypertensive, 37%; Stage 1, 26%; and Stage 2, 18%. From these subjects, 1686 measurements of blood pressure from a sphygmomanometer were associated with simultaneously acquired vital signs from the SimpleSense device (Nanowear Technologies, Brooklyn, NY). A proprietary machine-learning–based algorithm called SimpleSense-BP (Nanowear Technologies, Brooklyn, NY) was developed with inputs as metrics derived from the multiparametric data, such as intensity and duration of S1 and S2 heart sounds, the time elapsed between ECG waveforms such as R peaks and these characteristic heart sounds, and patient demographic data, to calculate systolic and diastolic blood pressure.1
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