Introduction The reliable detection and, ultimately, prediction of hypotensive events in post-operative settings remains an unsolved problem, as patients are currently only monitored intermittently because of the lack of validated, non-invasive/non-intrusive and continuous physiological monitoring technologies. With this goal in mind, the aim of this study was to validate a next-generation platform version of the currently FDA-cleared non-invasive Caretaker (CT) physiological monitor in the hemodynamically challenging environment of abdominal surgeries in comparison with blood pressures obtained from arterial catheters, evaluated against ANSI/AAMI/ISO 81060–2:2019 standards as well as against current non-invasive standard of care measurements provided by clinical-grade automatic oscillometric cuffs. Methods Comparison data from 41 major abdominal surgery patients at Cooper Hospital (Camden NJ) were analyzed in this IRB approved study. Each patient was monitored with a radial arterial catheter and CT using a finger cuff applied to the contralateral middle finger. Systolic and diastolic blood pressures continuously collected from the arterial catheter and CT were compared using Pearson correlation coefficients and Bland-Altman analysis. In addition, a trend analysis using 4Q plots was performed. Both the CT’s continuous BP tracking and the CT’s self-calibration capability were analyzed. Results The continuous data comparisons were performed with and without taking the CT recalibrations into account. With the recalibrations the mean differences and standard deviations (STDs) for systole and diastole were, respectively, -1.14 mmHg (13.82 mmHg) and -2.49 mmHg (9.42 mmHg), while the correlations were 0.80 and 0.78. Mean differences and STDs for an initial calibration and no subsequent recalibrations were, respectively for systole and diastole, -0.42 mmHg (16.73 mmHg) and -2.57 mmHg (10.36 mmHg), while the correlations were 0.64 and 0.67. For the CT’s self-calibrations alone, correlations for systole and diastole were, respectively, 0.83 and 0.75, while corresponding mean differences (STD) were -3.19 mmHg (10.86 mmHg) and -2.41 mmHg (8.18 mmHg). For 41% of total surgery time, both systole and diastole were within 8 mmHg of the arterial catheter Gold Standard. The concordances for systolic and diastolic blood pressure changes on a 30-second time scale were 0.87 and 0.86. The same comparison analysis for the automatic cuff and the arterial catheter data yielded: correlations for systole and diastole: 0.69 and 0.61, mean differences and STDs: 2.48 mmHg (15.82 mmHg) and 0.65 mmHg (10.68 mmHg). Conclusions The results of this study are significant in that they validate the future use of the CT physiological monitor, which utilizes Pulse Decomposition Analysis (PDA), in the post-operative monitoring scenario both as a monitor to detect hypotensive events to facilitate clinical intervention as well as provide signal inputs that could enable anticipatory measures.
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.
No abstract
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.