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 discrete-feature 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.

Keywords: Arterial stiffness; Cesarean section; Finger cuff; Hypotension; Neural network; Non-invasive; Predictive algorithm.

Conflict of interest statement

The following authors declare no competing interests: IG, MT, ED, IA, JS, and BM. MB declares MB is fully employed by Caretaker Medical LLC and has been involved in the development of both the CareTaker hardware as well as the PDA formalism. Since the CareTaker is a commercial device he stands to gain financially from the PDA formalism’s validation and acceptance.

Figures

Fig. 1

a. Time evolution of the AS response of patient 04, 60 min prior to induction, who subsequently required minimal phenylephrine intervention, 200 ml. b. Time evolution of the AS response of patient 07, 60 min prior to induction, who subsequently required significant phenylephrine intervention, 1400 ml. The graph of patient 07 also shows an inset with an expansion and overlay of about 15 s of the original beat-by-beat AS data as well as the AS data re-sampled at 10 Hz

Fig. 2

Black trace (a): Normalized AS autocorrelation spectrum of patient 04 (Fig. 1a, 200 mcg) suggests minimal coherence in the AS signal due to highly unequal and low-amplitude positive and negative correlations. Gray trace (b): Normalized AS autocorrelation spectrum of patient 07 (Fig. 1b, 1400 mcg) suggests high coherence in the AS signal. Spectra are offset from each other for clarity

Fig. 3

Graph of the result of the absolute autocorrelation value integration, for each patient, as a function of the total phenylephrine dosage administered to the respective patient

Fig. 4

Youden index (open circles) and AUC values (solid squares) as a function of phenylephrine dosage threshold for the single-feature analysis using the absolute autocorrelation value integration as a metric

Fig. 5

Light gray trace: ROC analysis based on phenylephrine dosage  400 mcg. AUC = 0.87 for autocorrelation area. Solid black line: ROC analysis based on average of 500 runs of 12 node NN based on phenylephrine dosage  450 mcg, AUC = 0.89

Fig. 6

Evolution of absolute error (a) and mean AUC (b) as a function of the number of nodes of the single-layer network as well as the phenylephrine dosage. The network node axis is logarithmic to better reveal the dependence of the classification error and classification accuracy (AUC) for single-digit network nodes

Fig. 7

Evolution of the difference of the absolute error (a) and mean AUC (b) between the single-layer network and the two-layer network as a function of the number of nodes of the network as well as the phenylephrine dosage. Note that the error difference (A) is negative for nodes< 12, indicating that the two-layer error is larger. For nodes< 12 the AUC is smaller for the two-layer network, as indicated by the positive AUC difference. For larger node numbers there is no difference in classification performance

Fig. 8

Evolution of the difference of the absolute error (a) and mean AUC (b) between the single-layer network and the three-layer network as a function of the number of nodes of the network as well as the phenylephrine dosage. Note that the error difference (A) is negative for nodes< 12, indicating that the three-layer error is larger. For nodes< 12 the AUC is smaller for the three-layer network, as indicated by the positive AUC difference. For larger node numbers there is no difference in classification performance