Predicting Post-Induction Hypotension

Hypotension during surgery is associated with increased morbidity and mortality. The majority of patients will have post-induction hypotension (PIH), a mean arterial blood pressure below 65 mmHg for at least one minute and occurring during the first 20 min after anesthesia induction. PIH is highly prevalent and probably occurs more often than intraoperative hypotension.
PIH is very likely to have an equally negative effect on outcome as any other type of intra-operative hypotension. Even short periods of hypotension are known to contribute to the occurrence of postoperative renal failure, myocardial injury, stroke and length of hospital stay.
The early identification and treatment of hypotension is clinically relevant. Current therapies are reactive and are started after hypotension occurs. Post-induction hypotension (PIH) is likely to occur in the majority of cases in the face of boluses of anesthetic agents causing severe vasodilation and even temporary cardiac depression as a surgical stimulus is missing. Since any type of hypotension is likely to have negative effects, prevention is warranted. A machine learning algorithm based on the arterial pressure signal for the prediction of PIH, in analogy of the recently FDA-approved intra-operative Hypotension Prediction Index, would eventually allow preemptive treatment and prevention of post-induction hypotension altogether.

The primary aim of this study is data collection of continuous noninvasive arterial pressure waveform signals with the ClearSight finger cuff and clinical data from patients’ electronic medical record in surgical patients and Intensive Care Unit patients who need intubation or elective tracheostomy, to be used to predict the likelihood of derangement of physiologic parameters in awake patients before induction of anesthesia and to predict the occurrence of post-induction hypotension and intra-operative hypotension using machine learning.

Post-induction (and intra-operative) hypotension is a frequent finding in patients scheduled for surgery or intubation/tracheostomy in the ICU, and with the development of a machine learning model, we will be able to predict hypotensvie episodes following administration of anesthetic agents.

Patients will be connected to the ClearSight system 30 minutes prior to start of induction of anesthesia. Data will be continuously collected until at least 30 minutes after start of surgery, or until 20 minutes after start induction (for ICU patients).

Not applicable.