US 12,144,645 B2
Therapy scoring for hemodynamic conditions
Rachel June Smith, Irvine, CA (US); Zhongping Jian, Irvine, CA (US); Feras Al Hatib, Irvine, CA (US); Andrew Marino, Rowland Heights, CA (US); and Sai Prasad Buddi, Costa Mesa, CA (US)
Filed by Edwards Lifesciences Corporation, Irvine, CA (US)
Filed on Aug. 5, 2020, as Appl. No. 16/985,937.
Claims priority of provisional application 62/980,585, filed on Feb. 24, 2020.
Prior Publication US 2021/0259629 A1, Aug. 26, 2021
Int. Cl. A61B 5/00 (2006.01); A61B 5/021 (2006.01); A61B 5/024 (2006.01); A61B 5/029 (2006.01); G16H 20/17 (2018.01); G16H 40/67 (2018.01); G16H 50/30 (2018.01)
CPC A61B 5/4848 (2013.01) [A61B 5/021 (2013.01); A61B 5/02405 (2013.01); A61B 5/029 (2013.01); A61B 5/4839 (2013.01); A61B 5/742 (2013.01); G16H 20/17 (2018.01); G16H 40/67 (2018.01); G16H 50/30 (2018.01)] 17 Claims
OG exemplary drawing
 
1. A system for monitoring arterial pressure of a patient and determining or more scores that are predictive of responsiveness of the patient to a corresponding therapy, the system comprising:
a hemodynamic sensor that produces sensed hemodynamic data representative of an arterial pressure waveform of the patient;
a system memory that stores hemodynamic therapy scoring software code;
a user interface; and
a hardware processor that is configured to execute the hemodynamic therapy scoring software code to:
derive, from the sensed hemodynamic data representative of the arterial pressure waveform of the patient, first magnitude data of a first hemodynamic parameter selected from the group consisting of a stroke volume variation (SVV), a pulse pressure variation (PPV), a stroke volume (SV), and a stroke volume index (SVI); wherein the first hemodynamic parameter is derived from the sensed hemodynamic data and indicative of a condition treatable by an intravenous delivery of saline fluid to the patient; and wherein the first magnitude data includes values of the first hemodynamic parameter over time;
derive first trend data of the first hemodynamic parameter, wherein the first trend data is based on one or more first best-fit lines of the first hemodynamic parameter, each of the one or more first best-fit lines corresponding to a respective time interval defined for the values of the first hemodynamic parameter over time;
derive, from the sensed hemodynamic data, second magnitude data of a second hemodynamic parameter selected from the group consisting of a systemic vascular resistance (SVR) and a systemic vascular resistance index (SVRI); wherein the second hemodynamic parameter is derived from the sensed hemodynamic data and indicative of a condition treatable by an intravenous delivery of a vasopressor to the patient; and wherein the second magnitude data includes values of the second hemodynamic parameter over time;
derive second trend data of the second hemodynamic parameter, wherein the second trend data is based on one or more second best-fit lines of the second hemodynamic parameter, each of the one or more second best-fit lines corresponding to a respective time interval defined for the values of the second hemodynamic parameter over time;
derive, from the sensed hemodynamic data, third magnitude data of a third hemodynamic parameter selected from the group consisting of a maximal rate of rise of arterial pressure (dP/dtmax), a cardiac output (CO), and a cardiac index (CI); wherein the third hemodynamic parameter is derived from the sensed hemodynamic data and indicative of a condition treatable by an intravenous delivery of an inotrope to the patient; and wherein the third magnitude data includes values of the third hemodynamic parameter over time;
derive third trend data of the third hemodynamic parameter, wherein the third trend data is based on one or more third best-fit lines of the third hemodynamic parameter, each of the one or more third best-fit lines corresponding to a respective time interval defined for the values of the third hemodynamic parameter over time;
determine a first magnitude sub-score based on the first magnitude data, a second magnitude sub-score based on the second magnitude data, and a third magnitude sub-score based on the third magnitude data;
determine a first trend sub-score based on the first trend data, a second trend sub-score based on the second trend data, and a third trend sub-score based on the third trend data;
determine a first score as a combination of the first magnitude sub-score and the first trend sub-score, a second score as a combination of the second magnitude sub-score and the second trend sub-score, and a third score as a combination of the third magnitude sub-score and the third trend sub-score; and
output, via the user interface, a representation of the first score that is predictive of responsiveness of the patient to the intravenous delivery of the saline fluid, a representation of the second score that is predictive of responsiveness of the patient to the intravenous delivery of the vasopressor, and a representation of the third score that is predictive of responsiveness of the patient to the intravenous delivery of the inotrope.