Non-Invasive Hemodynamic Monitoring and Incidence of Perioperative Hypotension in Gynaecologic Oncologic Surgery: Hypotension Prediction Index Working With ClearSight Versus Arterial Waveform Analysis Alone

INTRODUCTION Intraoperative hypotension (IOH) is a common adverse event during noncardiac surgery [1,2]. It is associated with an increased incidence of acute kidney injury, myocardial injury, neurological deficiencies, as well as an increased 30-day operative mortality [3-7]. Organ injuries were shown to be associated with the depth, frequency, and duration of hypotensive episodes [4,8]. Data indicate that a mean arterial pressure of 65 mmHg serves as a threshold to predict myocardial and kidney injury [9-11].

Recently developed minimally invasive methods of the arterial waveform analysis allow calculation of cardiac output, stroke volume, stroke volume variation, and systemic vascular resistance. This deeper insight into hemodynamics enables goal-directed therapy approaches; however, the given information only allows to react to events instead of preventing them [12,13].

Interestingly, recent data indicate a significant reduction in postoperative organ dysfunction by preventing IOH, suggesting a potential benefit of early intervention [14].

Surgical interventions for cancer mass reduction are among the most invasive operations in gynaecology. Extensive surgical trauma results in severe intra- and perioperative volume shifts and unstable hemodynamics [15]. Major abdomino-pelvic surgery for gynaecological oncologic surgery (GOS) may include hysterectomy, oophorectomy, omentectomy, colectomy, removal of the lymph nodes and peritoneal stripping. Surgery is carried out through laparoscopy or a laparotomic incision and is often associated with significant blood loss [16]. Hypotension during GOS is common, and as it is associated with the potential for harm, it requires prompt evaluation and treatment [16]. Extensive fluid resuscitation in peritoneal cancer patients is associated with a poor postoperative outcome independent from the underlying malignant disease and avoiding fluid overload is recommended [15].

Therefore, the prediction of intraoperative hypotension and consequently its prevention by proactive treatment may show beneficial effects for patients. Machine learning, a discipline within computer science used to analyse large data sets and to develop predictive models, has evident applications in health care [17,18]. Several attempts to use algorithms as an aid in anaesthesiology practice recently received renewed attention, with the aim of optimizing patients' perioperative status, primarily focusing on detection of early hemodynamic instability and prediction of hypotension [19].