TY - JOUR
T1 - Hypotension Prediction Index guided Goal Directed therapy and the amount of Hypotension during Major Gynaecologic Oncologic Surgery: a Randomized Controlled clinical Trial
AU - Frassanito, Luciano
AU - Giuri, Pietro Paolo
AU - Vassalli, Francesco
AU - Piersanti, Alessandra
AU - Garcia, Manuel Ignacio Monge
AU - Sonnino, Chiara
AU - Zanfini, Bruno Antonio
AU - Catarci, Stefano
AU - Antonelli, Massimo
AU - Draisci, Gaetano
PY - 2023
Y1 - 2023
N2 - : Intraoperative hypotension (IOH) is associated with increased morbidity and mortality. Hypotension Prediction Index (HPI) is a machine learning derived algorithm that predicts IOH shortly before it occurs. We tested the hypothesis that the application of the HPI in combination with a pre-defined Goal Directed Therapy (GDT) hemodynamic protocol reduces IOH during major gynaecologic oncologic surgery. We enrolled women scheduled for major gynaecologic oncologic surgery under general anesthesia with invasive arterial pressure monitoring. Patients were randomized to a GDT protocol aimed at optimizing stroke volume index (SVI) or hemodynamic management based on HPI guidance in addition to GDT. The primary outcome was the amount of IOH, defined as the timeweighted average (TWA) mean arterial pressure (MAP) < 65 mmHg. Secondary outcome was the TWA-MAP < 65 mmHg during the first 20 min after induction of GA. After exclusion of 10 patients the final analysis included 60 patients (30 in each group). The median (25-75th IQR) TWA-MAP < 65 mmHg was 0.14 (0.04-0.66) mmHg in HPI group versus 0.77 (0.36-1.30) mmHg in Control group, P < 0.001. During the first 20 min after induction of GA, the median TWA-MAP < 65 mmHg was 0.53 (0.06-1.8) mmHg in the HPI group and 2.15 (0.65-4.2) mmHg in the Control group, P = 0.001. Compared to a GDT protocol aimed to SVI optimization, a machine learning-derived algorithm for prediction of IOH combined with a GDT hemodynamic protocol, reduced IOH and hypotension after induction of general anesthesia in patients undergoing major gynaecologic oncologic surgery.Trial registration number: NCT04547491. Date of registration: 10/09/2020.
AB - : Intraoperative hypotension (IOH) is associated with increased morbidity and mortality. Hypotension Prediction Index (HPI) is a machine learning derived algorithm that predicts IOH shortly before it occurs. We tested the hypothesis that the application of the HPI in combination with a pre-defined Goal Directed Therapy (GDT) hemodynamic protocol reduces IOH during major gynaecologic oncologic surgery. We enrolled women scheduled for major gynaecologic oncologic surgery under general anesthesia with invasive arterial pressure monitoring. Patients were randomized to a GDT protocol aimed at optimizing stroke volume index (SVI) or hemodynamic management based on HPI guidance in addition to GDT. The primary outcome was the amount of IOH, defined as the timeweighted average (TWA) mean arterial pressure (MAP) < 65 mmHg. Secondary outcome was the TWA-MAP < 65 mmHg during the first 20 min after induction of GA. After exclusion of 10 patients the final analysis included 60 patients (30 in each group). The median (25-75th IQR) TWA-MAP < 65 mmHg was 0.14 (0.04-0.66) mmHg in HPI group versus 0.77 (0.36-1.30) mmHg in Control group, P < 0.001. During the first 20 min after induction of GA, the median TWA-MAP < 65 mmHg was 0.53 (0.06-1.8) mmHg in the HPI group and 2.15 (0.65-4.2) mmHg in the Control group, P = 0.001. Compared to a GDT protocol aimed to SVI optimization, a machine learning-derived algorithm for prediction of IOH combined with a GDT hemodynamic protocol, reduced IOH and hypotension after induction of general anesthesia in patients undergoing major gynaecologic oncologic surgery.Trial registration number: NCT04547491. Date of registration: 10/09/2020.
KW - General Anesthesia
KW - Gynecologic Neoplasm
KW - Machine Learning
KW - Intraoperative Complications/diagnosis
KW - Hypotension/prevention & control
KW - General Anesthesia
KW - Gynecologic Neoplasm
KW - Machine Learning
KW - Intraoperative Complications/diagnosis
KW - Hypotension/prevention & control
UR - http://hdl.handle.net/10807/234123
U2 - 10.1007/s10877-023-01017-1
DO - 10.1007/s10877-023-01017-1
M3 - Article
SN - 1387-1307
VL - 2023
SP - 1
EP - 13
JO - Journal of Clinical Monitoring and Computing
JF - Journal of Clinical Monitoring and Computing
ER -