Keywords
Pregnancy-induced hypertension
Neuraxial anesthesia
Hypotension
Submitted : 2023-04-30
Accepted : 2023-05-15
Published : 2023-05-30
Abstract
Objective: To explore and evaluate the predictive value of subclavian vein collapsibility index (SCV-CI) on hypotension during neuraxial anesthesia in patients with pregnancy-induced hypertension (PIH). Methods: Pregnant women with PIH who underwent elective cesarean section in our hospital from January to July 2021 were selected as the research subjects. Patients who experienced hypotension during anesthesia were included into the hypotension group, whereas patients who had a normal blood pressure during anesthesia were included in the normotensive group. The SCV-CI was then calculated for three respiratory cycles, the average value was taken as the base value, and the patient was monitored for another 20 minutes. The blood pressure, heart rate, blood oxygen saturation, and SCV-CI of the patients were measured, and the incidence of maternal nausea and vomiting and cord blood gas were recorded. Then, a correlation analysis was conducted on the relationship between subclavian vein collapsibility index and hypotension. A receiver operating characteristic curve was drawn to seek the threshold value of subclavian vein collapsibility index for post-anesthesia hypotension. Results: There was no significant difference in systolic blood pressure (SBP), diastolic blood pressure (DBP), and heart rate (HR) between the two groups before anesthesia (P > 0.05). After anesthesia, the above indexes (SBP, 103.25 ± 12.48 mmHg; DBP, 58.94 ± 7.46 mmHg; and HR, 52.96 ± 6.48 beats/min) were significantly lower than those of the normal blood pressure group, and the difference was statistically significant (P < 0.05). In comparison, the SCV-CI in the hypotension group was 35.82 ± 4.93% greater than that in the normal blood pressure group (23.85 ± 5.27%), and the incidence of nausea and vomiting in the hypotension group (40.0%) was significantly higher than that in the normotensive group (10.53%), and the difference was statistically significant (P < 0.05). The area under the curve of SCV-CI prediction against hypotension in patients with PIH under neuraxial anesthesia was 0.825 (95% CI: 0.762–0.893, P < 0.001), the cut-off value was 25.68%, the predictive sensitivity was 92.68%, and the specificity was 81.24%. Conclusion: SCV-CI has a good predictive value for the occurrence of hypotension in patients with PIH during neuraxial anesthesia.
References
Asrari A, Ansari M, Khazaei J, et al., 2021, The Impacts of a Decision Making Framework on Distribution Network Reconfiguration. IEEE Transactions on Sustainable Energy, 16(11): 821–824.
Masoumi-Amiri SM, Shahabi M, Barforoushi T, 2021, Interactive Framework Development for Microgrid Expansion Strategy and Distribution Network Expansion Planning. Sustainable Energy Grids and Networks, 29(11): 1005-1008.
Dash SK, Mishra S, Abdelaziz A Y, et al. 2022, Optimal Planning of Multitype DGs and D-STATCOMs in Power Distribution Network Using an Efficient Parameter Free Metaheuristic Algorithm. Energies, 15(4): 216–218.
Liu X, 2021, Automatic Routing of Medium Voltage Distribution Network Based on Load Complementary Characteristics and Power Supply Unit Division. International Journal of Electrical Power & Energy Systems, 33(2): 106–109.
Ketjoy N, 2021, The Analysis Framework for High Penetration PV Rooftop in LV Distribution Network: Case Study Provincial Electricity Authority. 32(1): 473–476.
Aygun NK, Bulut O, Byk E, 2021, A Framework for Capacity Expansion Planning in Failure-Prone Flow-Networks via Systemic Risk Analysis. IEEE Systems Journal, 21(09): 9–12.
Nasri A, Abdollahi A, Rashidinejad M, 2022, Multi-Stage and Resilience-Based Distribution Network Expansion Planning Against Hurricanes Based on Vulnerability and Resiliency Metrics. International Journal of Electrical Power & Energy Systems, 136(12): 1076–1079.
Alobaidi AH, Khodayar M, Vafamehr A, et al., 2021, Stochastic Expansion Planning of Battery Energy Storage for the Interconnected Distribution and Data Networks. International Journal of Electrical Power & Energy Systems, 13(2): 1072–1078.
Ali ZM, Diaaeldin IM, El-Rafei A, et al., 2021, A Novel Distributed Generation Planning Algorithm Via Graphically-Based Network Reconfiguration and Soft Open Points Placement Using Archimedes Optimization Algorithm. Ain Shams Engineering Journal, 31(2): 175–178.
Sabzehgar R, Amirhosseini DZ, Manshadi SD, et al., 2021, Stochastic Expansion Planning of Various Energy Storage Technologies in Active Power Distribution Networks. Sustainability, 13(2): 87–93.
Ashoornezhad A, Asadi Q, Falaghi H, et al., 2021, Private Investors Participation in Long-Term Distribution Network Planning. Proceedings of Power Electronics, Drive Systems, and Technologies Conference, 154–159.
Shahbazi A, Aghaei J, Pirouzi S, et al., 2021, Holistic Approach to Resilient Electrical Energy Distribution Network Planning. International Journal of Electrical Power & Energy Systems, 132(5): 1072–1075.
Wu Z, Xu Z, Gu W, et al., 2021, Decentralized Game-Based Robustly Planning Scheme for Distribution Network and Microgrids Considering Bilateral Energy Trading. IEEE Transactions on Sustainable Energy, 25(9): 628–635.
Zhang Y, Tao Y, Zhang S, et al. 2021, Optimal Sensing Task Distribution Algorithm for Mobile Sensor Networks with Agent Cooperation Relationship. IEEE Internet of Things Journal, 25(10): 275–279.
Paul S, Sharma A, Padhy NP, 2021, Risk Constrained Energy Efficient Optimal Operation of a Converter Governed AC/DC Hybrid Distribution Network with Distributed Energy Resources and Volt-VAR Controlling Devices. IEEE Transactions on Industry Applications, 24(08): 318–324.
Zhang Y, Qian T, Tang W, 2022, Buildings-to-Distribution-Network Integration Considering Power Transformer Loading Capability and Distribution Network Reconfiguration. Energy, 24(6): 176–179.
Li P, Zhang Z, Grosu R, et al., 2022, An End-to-End Neural Network Framework for State-of-Health Estimation and Remaining Useful Life Prediction of Electric Vehicle Lithium Batteries. Renewable and Sustainable Energy Reviews, 15(4): 1184–1187.
Zhao H, Jin J, Liu Y, et al., 2022, AdaBoost-MICNN: A New Network Framework for Pulsar Candidate Selection. Monthly Notices of the Royal Astronomical Society, 24(2): 264–268.
Mohamed A, Morrow DJ, Best RJ, et al., 2021, Distributed Battery Energy Storage Systems Operation Framework for Grid Power Levelling in the Distribution Networks. IET Smart Grid, 19(6): 75–79.
Che TC, Wang X, Ghidaoui MS, 2022, Leak Localization in Looped Pipe Networks Based on a Factorized Transient Wave Model: Theoretical Framework[J]. Water Resources Research, 26(4): 258-264.