The application of a neural network to predict hypotension and vasopressor requirements non-invasively in obstetric patients having spinal anesthesia for elective cesarean section (C/S)

Abstract

Background: Neural networks are increasingly used to assess physiological processes or pathologies, as well as to predict the increased likelihood of an impending medical crisis, such as hypotension. Method: We compared the capabilities of a single hidden layer neural network of 12 nodes to those of a discretefeature discrimination approach with the goal being to predict the likelihood of a given patient developing significant hypotension under spinal anesthesia when undergoing a Cesarean section (C/S). Physiological input information was derived from a non-invasive blood pressure device (Caretaker [CT]) that utilizes a finger cuff to measure blood pressure and other hemodynamic parameters via pulse contour analysis. Receiver-operator-curve/ area-under-curve analyses were used to compare performance. Results: The results presented here suggest that a neural network approach (Area Under Curve [AUC] = 0.89 [p < 0.001]), at least at the implementation level of a clinically relevant prediction algorithm, may be superior to a discrete feature quantification approach (AUC = 0.87 [p < 0.001]), providing implicit access to a plurality of features and combinations thereof. In addition, the expansion of the approach to include the submission of other physiological data signals, such as heart rate variability, to the network can be readily envisioned. Conclusion: This pilot study has demonstrated that increased coherence in Arterial Stiffness (AS) variability obtained from the pulse wave analysis of a continuous non-invasive blood pressure device appears to be an effective predictor of hypotension after spinal anesthesia in the obstetrics population undergoing C/S. This allowed us to predict specific dosing thresholds of phenylephrine required to maintain systolic blood pressure above 90 mmHg.

Spinal anesthesia using 29G pencil point needle: a large single center experience in non-obstetric patients

“…Subarachnoid anesthesia has been extensively studied in the obstetric population [19], [20]. This historical cohort of patients for non-obstetric surgery describes a very high success rate with the use of the 29G pencil point needle without any reports of PDPH.…”

Artificial intelligence and anesthesia: a narrative review

“…In particular, the random forest model showed the best performance, with an AUC of 0.842 ( 32 ). In another study authors compared the capabilities of a single hidden layer NN of 12 nodes to those of a discrete-feature discrimination approach in predicting significant hypotension under spinal anesthesia during cesarean section ( 33 ). The results presented suggested that a NN approach may be superior to a discrete feature quantification approach.…”

Machine Learning-Guided Anesthesiology: A Review of Recent Advances and Clinical Applications

“…A common perioperative event is fluctuations in blood pressure, particularly hypotension, which can lead to serious complications, such as cardiovascular injury or even death. Several articles in this subcategory predicted hypotension before its occurrence to enable specialists to take necessary tasks to prevent it (27)(28)(29)(30)(31)(32)(33). Another crucial event is difficult laryngoscopy, defined as the inability to visualize part of the vocal cords during multiple laryngoscopy attempts by a trained anesthesiologist.…”