Effect of machine learning-guided haemodynamic optimization on postoperative free flap perfusion in reconstructive maxillofacial surgery: A study protocol.

AIMS: Intraoperative hypotension and liberal fluid haemodynamic therapy are associated with postoperative medical and surgical complications in maxillofacial free flap surgery. The novel haemodynamic parameter hypotension prediction index (HPI) has shown good performance in predicting hypotension by analysing arterial pressure waveform in various types of surgery. HPI-based haemodynamic protocols were able to reduce the duration and depth of hypotension. We will try to determine whether haemodynamic therapy based on HPI can improve postoperative flap perfusion and tissue oxygenation by improving intraoperative mean arterial pressure and reducing fluid infusion. METHODS: We present here a study protocol for a single centre, randomized, controlled trial (n = 42) in maxillofacial patients undergoing free flap surgery. Patients will be randomized into an intervention or a control group. In the intervention, group haemodynamic optimization will be guided by machine learning algorithm and functional haemodynamic parameters presented by the HemoSphere platform (Edwards Lifesciences, Irvine, CA, USA), most importantly, HPI. Tissue oxygen saturation of the free flap will be monitored noninvasively by near-infrared spectroscopy during the first 24 h postoperatively. The primary outcome will be the average value of tissue oxygen saturation in the first 24 h postoperatively.

Hypotension prediction index guided versus conventional goal directed therapy to reduce intraoperative hypotension during thoracic surgery: a randomized trial.

PURPOSE: Intraoperative hypotension is linked to increased incidence of perioperative adverse events such as myocardial and cerebrovascular infarction and acute kidney injury. Hypotension prediction index (HPI) is a novel machine learning guided algorithm which can predict hypotensive events using high fidelity analysis of pulse-wave contour. Goal of this trial is to determine whether use of HPI can reduce the number and duration of hypotensive events in patients undergoing major thoracic procedures. METHODS: Thirty four patients undergoing esophageal or lung resection were randomized into 2 groups -"machine learning algorithm" (AcumenIQ) and "conventional pulse contour analysis" (Flotrac). Analyzed variables were occurrence, severity and duration of hypotensive events (defined as a period of at least one minute of MAP below 65 mmHg), hemodynamic parameters at 9 different timepoints interesting from a hemodynamics viewpoint and laboratory (serum lactate levels, arterial blood gas) and clinical outcomes (duration of mechanical ventilation, ICU and hospital stay, occurrence of adverse events and in-hospital and 28-day mortality). RESULTS: Patients in the AcumenIQ group had significantly lower area below the hypotensive threshold (AUT, 2 vs 16.7 mmHg x minutes) and time-weighted AUT (TWA, 0.01 vs 0.08 mmHg). Also, there were less patients with hypotensive events and cumulative duration of hypotension in the AcumenIQ group. No significant difference between groups was found in terms of laboratory and clinical outcomes. CONCLUSIONS: Hemodynamic optimization guided by machine learning algorithm leads to a significant decrease in number and duration of hypotensive events compared to traditional goal directed therapy using pulse-contour analysis hemodynamic monitoring in patients undergoing major thoracic procedures. Further, larger studies are needed to determine true clinical utility of HPI guided hemodynamic monitoring. TRIAL REGISTRATION: Date of first registration: 14/11/2022 Registration number: 04729481-3a96-4763-a9d5-23fc45fb722d.