OBJECTIVE: Intraoperative hypotension is associated with adverse outcomes. Predicting and proactively managing hypotension can reduce its incidence. Previously, hypotension prediction algorithms using artificial intelligence were developed for invasive arterial blood pressure monitors. This study tested whether routine non-invasive monitors could also predict intraoperative hypotension using deep learning algorithms.
METHODS: An open-source database of non-cardiac surgery patients ( https://vitadb.net/dataset ) was used to develop the deep learning algorithm. The algorithm was validated using external data obtained from a tertiary Korean hospital. Intraoperative hypotension was defined as a systolic blood pressure less than 90 mmHg. The input data included five monitors: non-invasive blood pressure, electrocardiography, photoplethysmography, capnography, and bispectral index. The primary outcome was the performance of the deep learning model as assessed by the area under the receiver operating characteristic curve (AUROC).
RESULTS: Data from 4754 and 421 patients were used for algorithm development and external validation, respectively. The fully connected model of Multi-head Attention architecture and the Globally Attentive Locally Recurrent model with Focal Loss function were able to predict intraoperative hypotension 5 min before its occurrence. The AUROC of the algorithm was 0.917 (95% confidence interval [CI], 0.915-0.918) for the original data and 0.833 (95% CI, 0.830-0.836) for the external validation data. Attention map, which quantified the contributions of each monitor, showed that our algorithm utilized data from each monitor with weights ranging from 8 to 22% for determining hypotension.
CONCLUSIONS: A deep learning model utilizing multi-channel non-invasive monitors could predict intraoperative hypotension with high accuracy. Future prospective studies are needed to determine whether this model can assist clinicians in preventing hypotension in patients undergoing surgery with non-invasive monitoring.

BACKGROUND: 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.
METHODS: 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).
CONCLUSIONS: 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.

Intraoperative hypotension (IOH) is a common issue associated with acute kidney injury, myocardial injury, stroke, and death. IOH may be avoided with the incorporation of newer advanced hemodynamic monitoring technologies. This case study examines the use of advanced hemodynamic monitoring with an early warning system for the intraoperative hemodynamic management of a patient presenting for pancreaticoduodenectomy. Incorporating the hypotension prediction index and other hemodynamic parameters to anticipate impending hypotension and treat potential causative factors is an emerging technological advancement. Understanding and embracing the potential for new advanced hemodynamic technology to reduce intraoperative hypotension\'s severity, duration, and occurrence is key to reducing negative patient outcomes.

UNASSIGNED: Acute kidney injury (AKI) is a common surgical complication and is associated with intraoperative hypotension. However, the total duration and magnitude of intraoperative hypotension associated with AKI remains unknown. In this study, the causal relationship between the intraoperative arterial pressure and postoperative AKI was investigated among chronic hypertension patients undergoing non-cardiac surgery.
UNASSIGNED: A retrospective cohort study of 6552 hypertension patients undergoing non-cardiac surgery (2011 to 2019) was conducted. The primary outcome was AKI as diagnosed with the Kidney Disease-Improving Global Outcomes criteria and the primary exposure was intraoperative hypotension. Patients\' baseline demographics, pre- and post-operative data were harvested and then analyzed with multivariable logistic regression to assess the exposure-outcome relationship.
UNASSIGNED: Among 6552 hypertension patients, 579 (8.84%) had postoperative AKI after non-cardiac surgery. The proportions of patients admitted to ICU (3.97 vs. 1.24%, p < 0.001) and experiencing all-cause death (2.76 vs. 0.80%, p < 0.001) were higher in the patients with postoperative AKI. Moreover, the patients with postoperative AKI had longer hospital stays (13.50 vs. 12.00 days, p < 0.001). Intraoperative mean arterial pressure (MAP) < 60 mmHg for >20 min was an independent risk factor of postoperative AKI. Furthermore, MAP <60 mmHg for >10 min was also an independent risk factor of postoperative AKI in patients whose MAP was measured invasively in the subgroup analysis.
UNASSIGNED: Our work suggested that MAP < 60 mmHg for >10 min measured invasively or 20 min measured non-invasively during non-cardiac surgery may be the threshold of postoperative AKI development in hypertension patients. This work may serve as a perioperative management guide for chronic hypertension patients.
UNASSIGNED: clinical trial number: ChiCTR2100050209 (8/22/2021). http://www.chictr.org.cn/showproj.aspx?proj=132277.

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BACKGROUND: Off-pump coronary artery bypass grafting (OPCABG) presents distinct hemodynamic characteristics, yet the relationship between intraoperative hypotension and short-term adverse outcomes remains clear. Our study aims to investigate association between intraoperative hypotension and postoperative acute kidney injury (AKI), mortality and length of stay in OPCABG patients.
METHODS: Retrospective data of 494 patients underwent OPCABG from January 2016 to July 2023 were collected. We analyzed the relationship between intraoperative various hypotension absolute values (MAP > 75, 65 < MAP ≤ 75, 55 < MAP ≤ 65, MAP ≤ 55 mmHg) and postoperative AKI, mortality and length of stay. Logistic regression assessed the impacts of exposure variable on AKI and postoperative mortality. Linear regression was used to analyze risk factors on the length of intensive care unit stay (ICU) and hospital stay.
RESULTS: The incidence of AKI was 31.8%, with in-hospital and 30-day mortality at 2.8% and 3.5%, respectively. Maintaining a MAP greater than or equal 65 mmHg [odds ratio (OR) 0.408; p = 0.008] and 75 mmHg (OR 0.479; p = 0.024) was significantly associated with a decrease risk of AKI compared to MAP less than 55 mmHg for at least 10 min. Prolonged hospital stays were linked to low MAP, while in-hospital mortality and 30-day mortality were not linked to IOH but exhibited correlation with a history of myocardial infarction. AKI showed correlation with length of ICU stay.
CONCLUSIONS: MAP > 65 mmHg emerges as a significant independent protective factor for AKI in OPCABG and IOH is related to length of hospital stay. Proactive intervention targeting intraoperative hypotension may provide a potential opportunity to reduce postoperative renal injury and hospital stay.
BACKGROUND: ChiCTR2400082518. Registered 31 March 2024. https://www.chictr.org.cn/bin/project/edit?pid=225349 .

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BACKGROUND: Intraoperative hypotension is a common side effect of general anesthesia. Here we examined whether the Hypotension Prediction Index (HPI), a novel warning system, reduces the severity and duration of intraoperative hypotension during general anesthesia.
METHODS: This randomized controlled trial was conducted in a tertiary referral hospital. We enrolled patients undergoing general anesthesia with invasive arterial monitoring. Patients were randomized 1:1 either to receive hemodynamic management with HPI guidance (intervention) or standard of care (control) treatment. Intraoperative hypotension treatment was initiated at HPI > 85 (intervention) or mean arterial pressure (MAP) < 65 mmHg (control). The primary outcome was hypotension severity, defined as a time-weighted average (TWA) MAP < 65 mmHg. Secondary outcomes were TWA MAP < 60 and < 55 mmHg.
RESULTS: Of the 60 patients who completed the study, 30 were in the intervention group and 30 in the control group. The patients\' median age was 62 years, and 48 of them were male. The median duration of surgery was 490 min. The median MAP before surgery presented no significant difference between the two groups. The intervention group showed significantly lower median TWA MAP < 65 mmHg than the control group (0.02 [0.003, 0.08] vs. 0.37 [0.20, 0.58], P < 0.001). Findings were similar for TWA MAP < 60 mmHg and < 55 mmHg. The median MAP during surgery was significantly higher in the intervention group than that in the control group (87.54 mmHg vs. 77.92 mmHg, P < 0.001).
CONCLUSIONS: HPI guidance appears to be effective in preventing intraoperative hypotension during general anesthesia. Further investigation is needed to assess the impact of HPI on patient outcomes.
BACKGROUND: ClinicalTrials.gov (NCT04966364); 202105065RINA; Date of registration: July 19, 2021; The recruitment date of the first patient: July 22, 2021.

BACKGROUND: Intraoperative goal-directed hemodynamic therapy (GDHT) is a cornerstone of enhanced recovery protocols. We hypothesized that use of an advanced noninvasive intraoperative hemodynamic monitoring system to guide GDHT may decrease intraoperative hypotension (IOH) and improve perfusion during pancreatic resection.
METHODS: The monitor uses machine learning to produce the Hypotension Prediction Index to predict hypotensive episodes. A clinical decision-making algorithm uses the Hypotension Prediction Index and hemodynamic data to guide intraoperative fluid versus pressor management. Pre-implementation (PRE), patients were placed on the monitor and managed per usual. Post-implementation (POST), anesthesia teams were educated on the algorithm and asked to use the GDHT guidelines. Hemodynamic data points were collected every 20 s (8942 PRE and 26,638 POST measurements). We compared IOH (mean arterial pressure <65 mmHg), cardiac index >2, and stroke volume variation <12 between the two groups.
RESULTS: 10 patients were in the PRE and 24 in the POST groups. In the POST group, there were fewer minimally invasive resections (4.2% versus 30.0%, P = 0.07), more pancreaticoduodenectomies (75.0% versus 20.0%, P < 0.01), and longer operative times (329.0 + 108.2 min versus 225.1 + 92.8 min, P = 0.01). After implementation, hemodynamic parameters improved. There was a 33.3% reduction in IOH (5.2% ± 0.1% versus 7.8% ± 0.3%, P < 0.01, a 31.6% increase in cardiac index >2.0 (83.7% + 0.2% versus 63.6% + 0.5%, P < 0.01), and a 37.6% increase in stroke volume variation <12 (73.2% + 0.3% versus 53.2% + 0.5%, P < 0.01).
CONCLUSIONS: Advanced intraoperative hemodynamic monitoring to predict IOH combined with a clinical decision-making tree for GDHT may improve intraoperative hemodynamic parameters during pancreatectomy. This warrants further investigation in larger studies.