Intraoperative Hypotension Prediction—A Proactive Perioperative Hemodynamic Management—A Literature Review

Tidal Volume Challenge to Assess Volume Responsiveness with Dynamic Preload Indices During Non-Cardiac Surgery: A Prospective Study

Background/Objectives: The aim of this study is to assess whether changes in Pulse Pressure Variation (PPV) and Stroke Volume Variation (SVV) following a VtC can predict the response to fluid administration in patients undergoing surgery under general anesthesia with protective mechanical ventilation. Methods: A total of 40 patients undergoing general surgery or vascular surgery without clamping the aorta were enrolled. Protective mechanical ventilation was applied, and the radial artery was catheterized in all patients. The protocol began one hour after the induction of general anesthesia and the stabilization of hemodynamic parameters. The parameters PPV6 and SVV6 were recorded during ventilation with a Vt of 6 mL/kg Ideal Body Weight (IBW) (T1). Then, the Vt was increased to 8 mL/kg IBW for 3 min without changing other respiratory parameters. After the VtC, the parameters PPV8 and SVV8 (T2) were recorded. After the stabilization of hemodynamic parameters, volume expansion (VE) was administered with colloid fluid of 6 mL/kg IBW. Parameters before (T3) and 5 min after fluid challenge (T4) were recorded. The change in the Stroke Volume Index (SVI) before and after VE was used to indicate fluid responsiveness. Patients were classified as fluid responders (SVI ≥ 10%) or non-responders (SVI < 10%). Results: The parameter ΔPPV(6-8) demonstrated good predictive ability to predict fluid responsiveness, evidenced by an Area Under the Curve (AUC) of 0.86 [95% Confidence Interval (CI) 0.74 to 0.95, p < 0.0001]. The threshold of ΔPPV(6-8) exceeding 2% identified responders with a sensitivity of 83% (95% CI 0.45 to 1.0, p < 0.0001) and a specificity of 73% (95% CI 0.48 to 1.0, p < 0.0001). The parameter ΔSVV(6-8) also revealed good predictive ability, reflected by an AUC of 0.82 (95% CI 0.67 to 0.94, p < 0.0001). The criterion ΔSVV(6-8) greater than 2% pinpointed responders with a sensitivity of 83% (95% CI 0.71 to 1.0, p < 0.001) and a specificity of 77% (95% CI 0.44 to 1.0, p < 0.001). Conclusions: This study demonstrates that VtC possesses good predictive ability for fluid responsiveness in patients undergoing general surgery.

Predictive ability of hypotension prediction index and machine learning methods in intraoperative hypotension: a systematic review and meta-analysis

INTRODUCTION

Intraoperative Hypotension (IOH) poses a substantial risk during surgical procedures. The integration of Artificial Intelligence (AI) in predicting IOH holds promise for enhancing detection capabilities, providing an opportunity to improve patient outcomes. This systematic review and meta analysis explores the intersection of AI and IOH prediction, addressing the crucial need for effective monitoring in surgical settings.

METHOD

A search of Pubmed, Scopus, Web of Science, and Embase was conducted. Screening involved two-phase assessments by independent reviewers, ensuring adherence to predefined PICOS criteria. Included studies focused on AI models predicting IOH in any type of surgery. Due to the high number of studies evaluating the hypotension prediction index (HPI), we conducted two sets of meta-analyses: one involving the HPI studies and one including non-HPI studies. In the HPI studies the following outcomes were analyzed: cumulative duration of IOH per patient, time weighted average of mean arterial pressure < 65 (TWA-MAP < 65), area under the threshold of mean arterial pressure (AUT-MAP), and area under the receiver operating characteristics curve (AUROC). In the non-HPI studies, we examined the pooled AUROC of all AI models other than HPI.

RESULTS

43 studies were included in this review. Studies showed significant reduction in IOH duration, TWA-MAP < 65 mmHg, and AUT-MAP < 65 mmHg in groups where HPI was used. AUROC for HPI algorithms demonstrated strong predictive performance (AUROC = 0.89, 95CI). Non-HPI models had a pooled AUROC of 0.79 (95CI: 0.74, 0.83).

CONCLUSION

HPI demonstrated excellent ability to predict hypotensive episodes and hence reduce the duration of hypotension. Other AI models, particularly those based on deep learning methods, also indicated a great ability to predict IOH, while their capacity to reduce IOH-related indices such as duration remains unclear.

Remote Monitoring and Artificial Intelligence: Outlook for 2050

Remote monitoring and artificial intelligence will become common and intertwined in anesthesiology by 2050. In the intraoperative period, technology will lead to the development of integrated monitoring systems that will integrate multiple data streams and allow anesthesiologists to track patients more effectively. This will free up anesthesiologists to focus on more complex tasks, such as managing risk and making value-based decisions. This will also enable the continued integration of remote monitoring and control towers having profound effects on coverage and practice models. In the PACU and ICU, the technology will lead to the development of early warning systems that can identify patients who are at risk of complications, enabling early interventions and more proactive care. The integration of augmented reality will allow for better integration of diverse types of data and better decision-making. Postoperatively, the proliferation of wearable devices that can monitor patient vital signs and track their progress will allow patients to be discharged from the hospital sooner and receive care at home. This will require increased use of telemedicine, which will allow patients to consult with doctors remotely. All of these advances will require changes to legal and regulatory frameworks that will enable new workflows that are different from those familiar to today's providers.