Thoracic post-surgical monitoring and complication prediction
Patient biomarkers may be monitored before, during, and/or after thoracic surgery to predict complications, detect complications, track recovery, and/or make pre-, in- and/or post-surgery recommendations to avoid predicted complications and/or mitigate detected complications. Complications (e.g., prolonged air leak or esophageal stricture) may be predicted based on patient parameters and/or biomarker measurements. Complications may be predicted or detected based on biomarker measurements compared to thresholds associated with a biomarker (e.g., developed from baselines) generated based on patient parameters, pre- and/or in-surgery biomarker measurements, and/or surgical details (e.g., decrease in long capacity). Recovery milestones may be tracked based on biomarker measurements compared to predicted biomarker measurements for recovery stages. A recommendation (e.g., to avoid a predicted complication and/or mitigate a detected complication) may be a patient-specific selection and/or modification of more of: surgical preparation, in-surgery procedures, surgical instrument selection, surgical and/or post-surgical instrument settings, post-surgery procedures, in-surgery and/or post-surgery monitoring, etc.
1 . A computing system comprising a processor configured to at least:
determine a first threshold associated with a first thoracic post-surgical patient biomarker associated with a patient, wherein the first thoracic post-surgical patient biomarker is associated with a thoracic post-surgical complication;
determine a second threshold associated with a second thoracic post-surgical patient biomarker associated with the patient, wherein the second thoracic post-surgical patient biomarker is associated with the thoracic post-surgical complication;
obtain a first post-surgical measurement data associated with the first thoracic post-surgical patient biomarker and a second post-surgical measurement data associated with the second thoracic post-surgical patient biomarker, wherein the first post-surgical measurement data is received from a first wearable sensing system and the second post-surgical measurement data is received from a second wearable sensing system;
monitor the first post-surgical measurement data and the second post-surgical measurement data, in real-time, for an amount of time by comparing the first post-surgical measurement data against the first threshold and comparing the second post-surgical measurement data against the second threshold;
predict the thoracic post-surgical complication based on the monitoring of the first post-surgical measurement data and the second post-surgical measurement data; and
based on the prediction of the thoracic post-surgical complication,
generate a real-time notification wherein the real-time notification is configured to indicate the predicted thoracic post-surgical complication when the first post-surgical measurement data crosses the first threshold or the second post-surgical measurement data crosses the second threshold, and
send a control program to a controllable device to alter at least one operating parameter of a chest tube system associated with the patient.
2 . The computing system of claim 1 , wherein the processor is further configured to:
determine a severity level associated with the thoracic post-surgical complication based on the first post-surgical measurement data; and
determine, based on the determined severity level, a notification type associated with the real-time notification.
3 . The computing system of claim 2 , wherein:
on a condition that the determined severity level is low, the processor is configured to send the real-time notification and the severity level to a display for displaying to the patient; and
on a condition that the determined severity level is high, the processor is configured to send the real-time notification and the severity level to a display for displaying to a health care professional (HCP).
4 . The computing system of claim 1 , wherein the processor is further configured to:
obtain a context associated with setting the first threshold or the second threshold to be used for comparing the first post-surgical measurement data or the second post-surgical measurement data, wherein the context is based on at least one of a thoracic surgery recovery timeline, at least one situational attribute, a baseline data associated with the patient, or at least one environmental attribute; and
adjust the first threshold or the second threshold based on the obtained context.
5 . The computing system of claim 4 , wherein comparing the first post-surgical measurement data against the adjusted first threshold is based on the context.
6 . The computing system of claim 4 , wherein the monitoring of the second post-surgical measurement data is based on a condition that the first post-surgical measurement data crosses the first threshold for a predetermined amount of time.
7 . The computing system of claim 1 , wherein the processor being configured to predict the thoracic post-surgical complication comprises the processor being configured to predict the thoracic post-surgical complication based on a patient prediction model.
8 . The computing system of claim 1 , wherein the thoracic post-surgical complication is a lung collapse, wherein one of the first thoracic post-surgical patient biomarker or the second thoracic post-surgical patient biomarker is a rate of change in patient's Oxygen saturation, a rate of change in patient's blood pressure, or a rate of change in patient's heart rate.
9 . The computing system of claim 1 , wherein the thoracic post-surgical complication is a prolonged air leak (PAL), wherein the PAL is determined by measuring at least one of the following: an air volume, a respiratory rate, a diaphragmatic muscle tone, or a phase of respiration.
10 . The computing system of claim 1 , wherein the first patient thoracic post-surgical biomarker is a phase of respiration and the second thoracic post-surgical patient biomarker is a diaphragmatic muscle tone, wherein the processor is further configured to send the control program to modulate a suction level of the chest tube system, wherein the suction level is modulated based at least one or more of the first post-surgical measurement data associated with the phase of respiration or the second post-surgical measurement data associated with the diaphragmatic muscle tone.
11 . The computing system of claim 1 , wherein the comparing is performed by accounting for an amount of lung volume reduction.
12 . The computing system of claim 1 , wherein the processor is further configured to:
generate an actionable notification associated with the prediction of the thoracic post-surgical complication, wherein the actionable notification enables the patient or a healthcare provider (HCP) to perform at least one recommended action.
13 . A sensing system comprising a processor configured to at least:
obtain a first threshold associated with a first thoracic post-surgical patient biomarker, wherein the first thoracic post-surgical patient biomarker is associated with a thoracic post-surgical complication;
obtain a second threshold associated with a second thoracic post-surgical patient biomarker, wherein the second thoracic post-surgical patient biomarker is associated with the thoracic post-surgical complication;
obtain a first post-surgical measurement data associated with the first thoracic post-surgical patient biomarker and a second post-surgical measurement data associated with the second thoracic post-surgical patient biomarker;
monitor the first post-surgical measurement data and the second post-surgical measurement data, in real-time, by comparing the first post-surgical measurement data against the first threshold and comparing the second post-surgical measurement data against the second threshold;
predict the thoracic post-surgical complication based on the monitoring; and
based on the prediction of the thoracic post-surgical complication, generate a real-time notification wherein the real-time notification is configured to indicate the predicted thoracic post-surgical complication, when one or more of the first post-surgical measurement data crosses the first threshold or the second post-surgical measurement data crosses the second threshold, and send a control program to a controllable device to alter at least one operating parameter of a chest tube system associated with a patient.
14 . The sensing system of claim 13 , wherein the processor is further configured to:
receive a request for predicting the thoracic post-surgical complication based on the first post-surgical measurement data associated with the first thoracic post-surgical patient biomarker crossing the first threshold for a first predetermined amount of time or the second post-surgical measurement data associated with the second thoracic post-surgical patient biomarker crossing the second threshold for a second predetermined amount of time; and
generate the real-time notification indicating the predicted thoracic post-surgical complication when the first post-surgical measurement data crosses the first threshold for the first predetermined amount of time or the second post-surgical measurement data crosses the second threshold for the second predetermined amount of time.
15 . The sensing system of claim 13 , wherein the processor is further being configured to predict the thoracic post-surgical complication comprises the processor being configured to predict the thoracic post-surgical complication based on a patient prediction model.
16 . The sensing system of claim 13 , wherein the processor is further configured to:
determine a severity level associated with the thoracic post-surgical complication based on at least one of the first post-surgical measurement data or the second post-surgical measurement data; and
determine, based on the determined severity level, a notification type associated with the real-time notification.
17 . The sensing system of claim 16 , wherein;
on a condition that the determined severity level is low, the processor is configured to send the real-time notification and the severity level to a display for displaying to the patient; and
on a condition that the determined severity level is high, the processor is configured to send the real-time notification and the severity level to a display for displaying to a health care professional (HCP).
18 . The sensing system of claim 13 , wherein the processor is further configured to:
determine a context associated with setting the first threshold or the second threshold to be used for comparing the first post-surgical measurement data or the second post-surgical measurement data, wherein the context is based on at least one of a thoracic surgery recovery timeline, at least one situational attribute, a baseline data associated with the patient, or at least one environmental attribute; and
adjust the first threshold or the second threshold based on the determined context.
19 . The sensing system of claim 18 , wherein the at least one situational attribute comprises at least one of a physical mobility state or a sleeping state.
20 . The sensing system of claim 13 , wherein the thoracic post-surgical complication is a prolonged air leak (PAL), and wherein the first thoracic post-surgical patient biomarker or the second thoracic post-surgical patient biomarker is a rate of change in patient's Oxygen saturation, a rate of change in patient's blood pressure, a rate of change in patient's heart rate, a rate of air flow, or a rate of air volume.