Prediction Of Hypotension Research Articles

Intraoperative Hypotension Prediction: Current Methods, Controversies, and Research Outlook.

Intraoperative hypotension prediction has been increasingly emphasized due to its potential clinical value in reducing organ injury and the broad availability of large-scale patient datasets and powerful machine learning tools. Hypotension prediction methods can mitigate low blood pressure exposure time. However, they have yet to be convincingly demonstrated to improve objective outcomes; furthermore, they have recently become controversial. This review presents the current state of intraoperative hypotension prediction and makes recommendations on future research. We begin by overviewing the current hypotension prediction methods, which generally rely on the prevailing mean arterial pressure as one of the important input variables and typically show good sensitivity and specificity but low positive predictive value in forecasting near-term acute hypotensive events. We make specific suggestions on improving the definition of acute hypotensive events and evaluating hypotension prediction methods, along with general proposals on extending the methods to predict reduced blood flow and treatment effects. We present a start of a risk-benefit analysis of hypotension prediction methods in clinical practice. We conclude by coalescing this analysis with the current evidence to offer an outlook on prediction methods for intraoperative hypotension. A shift in research toward tailoring hypotension prediction methods to individual patients and pursuing methods to predict appropriate treatment in response to hypotension appear most promising to improve outcomes.

A comparison between invasive and noninvasive measurement of the Hypotension Prediction Index: A post hoc analysis of a prospective cohort study.

Clinical trials and validation studies demonstrate promising hypotension prediction capability by the Hypotension Prediction Index (HPI). Most studies that evaluate HPI derive it from invasive blood pressure readings, but a direct comparison with the noninvasive alternative remains undetermined. Such a comparison could provide valuable insights for clinicians in deciding between invasive and noninvasive monitoring strategies. Evaluating predictive differences between HPI when obtained through noninvasive versus invasive blood pressure monitoring. Post hoc analysis of a prospective observational study conducted between 2018 and 2020. Single-centre study conducted in an academic hospital in the Netherlands. Adult noncardiac surgery patients scheduled for over 2 h long elective procedures. After obtaining informed consent, 91 out of the 105 patients had sufficient data for analysis. The primary outcome was the difference in area under the receiver-operating characteristics (ROC) curve (AUC) obtained for HPI predictions between the two datasets. Additionally, difference in time-to-event estimations were calculated. AUC (95% confidence interval (CI)) results revealed a nonsignificant difference between invasive and noninvasive HPI, with areas of 94.2% (90.5 to 96.8) and 95.3% (90.4 to 98.2), respectively with an estimated difference of 1.1 (-3.9 to 6.1)%; P = 0.673. However, noninvasive HPI demonstrated significantly longer time-to-event estimations for higher HPI values. Noninvasive HPI is reliably accessible to clinicians during noncardiac surgery, showing comparable accuracy in HPI probabilities and the potential for additional response time. Clinicaltrials.gov (NCT03795831) on 10 January 2019. https://clinicaltrials.gov/study/NCT03795831.

Comparing the Hypotension Prediction Index to Mean Arterial Pressure and Linear Extrapolated Mean Arterial Pressure for the Prediction of Intraoperative Hypotension: A Secondary Analysis.

Comparing the Hypotension Prediction Index to Mean Arterial Pressure and Linear Extrapolated Mean Arterial Pressure for the Prediction of Intraoperative Hypotension: A Secondary Analysis.

The Prediction And Prevention Of Spınal Anaesthesıa-Induced Hypotensıon With Perfusion İndex And The Effect Of Crystalloid

Hypotension is a dangerous and common problem for patients under spinal anesthesia. Spinal anesthesia-induced hypotension(SIH) is often arisen from heart rate decrease and venous blood pooling under the spinal block level because of sympathetic blockage. Prediction and prevention of hypotension will be beneficial for the patients' health under spinal blockage. For prediction, many parameters like perfusion index(PI) have been using. This study aims to research the correlation between the patients' hemodynamic parameters, perfusion indices before spinal block and SIH, and the effectiveness of crystalloid preload before spinal block to the usage of ephedrine. Records from 101 patients between 18-100 years age, who underwent surgery with spinal anesthesia were studied. We enroll patients demografical characteristics, ASA-score, sensory-blockage level, opereation period, the patients' position for spinal block, local anethesic form-dosage, blockage level, patients' hemodynamic and PI parameters in the surgery, hypotension time, efedrin first usage time and dosage from records. These results analyzed with t-test, Pearson-Chi-Square, Wilcoxon-signed-rank and Mann-Whitney-U test. SIH were seen in 25,7% of patients. The incidence of hypotension increases with age and block level. Any corelation between SIH and PI degrees before spinal block was not found, but MAP degrees before spinal block were significantly different in hypotension group. 10ml/kg cristaloid-preload was used 37,6% of patients. SIH was not prevented by cristaloid-preload and cristaloid-preload didn’t affect efedrine dosage-timing. Cristaloid-preload changed significantly patients heart rate and PI. In this study, we found that PI was not predictive factor for the development of SIH. Besides cristaloid-preload don’t avoid development of SIH.

Diagnostic accuracy of subclavian vein versus inferior vena cava collapsibility index for predicting postinduction hypotension: An observational study

Background: Hypotension following induction of general anesthesia (GA) is commonly observed. Ultrasound (US) measurement of collapsibility index (CI) of the inferior vena cava (IVC) for predicting postinduction hypotension has been studied. As there is limited data available comparing the diagnostic accuracy of subclavian vein (SCV) versus IVC-CI, we performed this observational study. Methods: A total of 132 adult patients scheduled for elective surgery under GA were enrolled. US measurements of three readings of maximum and minimum diameters of SCV and IVC were recorded during both quiet and deep breathing, and the mean of three values was calculated. CI was derived using the formula: (dmax – dmin) × 100/dmax. Subsequently, GA was administered using standard technique, irrespective of the findings of SCV and IVC measurements. The administered drugs and dosage were recorded. Hemodynamic parameters were collected at baseline and then at every minute for the first 20 min. The primary objective was to compare the diagnostic accuracies of SCV-CI and IVC-CI for prediction of postinduction hypotension during quiet breathing. The secondary objectives were to compare the diagnostic accuracies during deep breathing and find the correlation between IVC-CI and SC-CI during quiet and deep breathing, incidence of hypotension, and time required to acquire US images. Results: Fifty-seven patients developed postinduction hypotension. During quiet breathing, SCV-CI ≥10% had a sensitivity of 68% and specificity of 56% (area under curve [AUC] [95% confidence interval {CI}] of 0.659 [0.56–0.75]; P = 0.002), while IVC-CI ≥34% had a sensitivity of 70% and specificity of 59% (AUC [95% CI] of 0.672 [0.58–0.76]; P = 0.001) for prediction of postinduction hypotension. During deep breathing, both SCV-CI and IVC-CI had moderate accuracy (P = 0.001 for both). Pearson’s correlation showed a significant positive correlation between SCV-CI and IVC-CI with a correlation coefficient (r) of 0.313 during quiet breathing and 0.379 during deep breathing (P < 0.001). The time required for acquiring US images was significantly less for SCV compared to IVC during both quiet and deep breathing (P < 0.001 for both). Conclusion: Both SCV-CI and IVC-CI were found to have good and comparable diagnostic accuracy for the prediction of postinduction hypotension. We also found a significant positive correlation between SCV-CI and IVC-CI. In comparison to IVC, US scanning of SCV took lesser time to acquire the images.

Intraoperative Hypotension Prediction Based on Features Automatically Generated Within an Interpretable Deep Learning Model.

The monitoring of arterial blood pressure (ABP) in anesthetized patients is crucial for preventing hypotension, which can lead to adverse clinical outcomes. Several efforts have been devoted to develop artificial intelligence-based hypotension prediction indices. However, the use of such indices is limited because they may not provide a compelling interpretation of the association between the predictors and hypotension. Herein, an interpretable deep learning model is developed that forecasts hypotension occurrence 10 min before a given 90-s ABP record. Internal and external validations of the model performance show the area under the receiver operating characteristic curves of 0.9145 and 0.9035, respectively. Furthermore, the hypotension prediction mechanism can be physiologically interpreted using the predictors automatically generated from the proposed model for representing ABP trends. Finally, the applicability of a deep learning model with high accuracy is demonstrated, thus providing an interpretation of the association between ABP trends and hypotension in clinical practice.

Forecasting intraoperative hypotension during hepatobiliary surgery.

Prediction and avoidance of intraoperative hypotension (IOH) can lead to less postoperative morbidity. Machine learning (ML) is increasingly being applied to predict IOH. We hypothesize that incorporating demographic and physiological features in an ML model will improve the performance of IOH prediction. In addition, we added a "dial" feature to alter prediction performance. An ML prediction model was built based on a multivariate random forest (RF) trained algorithm using 13 physiologic time series and patient demographic data (age, sex, and BMI) for adult patients undergoing hepatobiliary surgery. A novel implementation was developed with an adjustable, multi-model voting (MMV) approach to improve performance in the challenging context of a dynamic, sliding window for which the propensity of data is normal (negative for IOH). The study cohort included 85% of subjects exhibiting at least one IOH event. Males constituted 70% of the cohort, median age was 55.8years, and median BMI was 27.7. The multivariate model yielded average AUC = 0.97 in the static context of a single prediction made up to 8min before a possible IOH event, and it outperformed a univariate model based on MAP-only (average AUC = 0.83). The MMV model demonstrated AUC = 0.96, PPV = 0.89, and NPV = 0.98 within the challenging context of a dynamic sliding window across 40min prior to a possible IOH event. We present a novel ML model to predict IOH with a distinctive "dial" on sensitivity and specificity to predict first IOH episode during liver resection surgeries.

Development and external validation of temporal fusion transformer models for continuous intraoperative blood pressure forecasting

During surgery, intraoperative hypotension is associated with postoperative morbidity and should therefore be avoided. Predicting the occurrence of hypotension in advance may allow timely interventions to prevent hypotension. Previous prediction models mostly use high-resolution waveform data, which is often not available. We utilised a novel temporal fusion transformer (TFT) algorithm to predict intraoperative blood pressure trajectories 7min in advance. We trained the model with low-resolution data (sampled every 15s) from 73,009 patients who were undergoing general anaesthesia for non-cardiothoracic surgery between January 1, 2017, and December 30, 2020, at the General Hospital of Vienna, Austria. The data set contained information on patient demographics, vital signs, medication, and ventilation. The model was evaluated using an internal (n=8113) and external test set (n=5065) obtained from the openly accessible Vital Signs Database. In the internal test set, the mean absolute error for predicting mean arterial blood pressure was 0.376 standard deviations-or 4mmHg-and 0.622 standard deviations-or 7mmHg-in the external test set. We also adapted the TFT model to binarily predict the occurrence of hypotension as defined by mean arterial blood pressure < 65mmHg in the next one, three, five, and 7min. Here, model discrimination was excellent, with a mean area under the receiver operating characteristic curve (AUROC) of 0.933 in the internal test set and 0.919 in the external test set. Our TFT model is capable of accurately forecasting intraoperative arterial blood pressure using only low-resolution data showing a low prediction error. When used for binary prediction of hypotension, we obtained excellent performance. No external funding.

Hypotension prediction index decision support system: a new model for decision support in hemodynamic management

Hypotension prediction index decision support system: a new model for decision support in hemodynamic management

Prediction of intraoperative hypotension using deep learning models based on non-invasive monitoring devices.

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. 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). 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 5min 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. 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.

Shedding Needed Light on a Black Box Approach to Prediction of Hypotension.

Shedding Needed Light on a Black Box Approach to Prediction of Hypotension.

A retrospective study of the effects of a vasopressor bolus on systolic slope (dP/dt) and dynamic arterial elastance (Eadyn)

BackgroundTo enhance the utility of functional hemodynamic monitoring, the variables systolic slope (dP/dt) and dynamic arterial elastance (Eadyn) are calculated by the Hypotension Prediction Index (HPI) Acumen® Software. This study was designed to characterize the effects of phenylephrine and ephedrine on dP/dt and Eadyn.MethodsThis was a retrospective, non-randomized analysis of data collected during two clinical studies. All patients required intra-operative controlled mechanical ventilation and had an indwelling radial artery catheter connected to an Acumen IQ sensor. Raw arterial pressure waveform data was downloaded from the patient monitor and all hemodynamic measurements were calculated off-line. The anesthetic record was reviewed for bolus administrations of either phenylephrine or ephedrine. Cardiovascular variables prior to drug administration were compared to those following vasopressor administrations. The primary outcome was the difference for dP/dt and Eadyn at baseline compared with the average after the bolus administration. All data sets demonstrated non-normal distributions so statistical analysis of paired and unpaired data followed the Wilcoxon matched pairs signed-rank test or Mann-Whitney U test, respectively.Results201 doses of phenylephrine and 100 doses of ephedrine were analyzed. All data sets are reported as median [95% CI]. Mean arterial pressure (MAP) increased from 62 [54,68] to 78 [76,80] mmHg following phenylephrine and from 59 [55,62] to 80 [77,83] mmHg following ephedrine. Stroke volume and cardiac output both increased. Stroke volume variation and pulse pressure variation decreased. Both drugs produced significant increases in dP/dt, from 571 [531, 645] to 767 [733, 811] mmHg/sec for phenylephrine and from 537 [509, 596] to 848 [779, 930] mmHg/sec for ephedrine. No significant changes in Eadyn were observed.ConclusionBolus administration of phenylephrine or ephedrine increases dP/dt but does not change Eadyn. dP/dt demonstrates potential for predicting the inotropic response to phenylephrine or ephedrine, providing guidance for the most efficacious vasopressor when treating hypotension.Trial registrationData was collected from two protocols. The first was deemed to not require written, informed consent by the Institutional Review Board (IRB). The second was IRB-approved (Effect of Diastolic Dysfunction on Dynamic Cardiac Monitors) and registered on ClinicalTrials.gov (NCT04177225).

Leveraging Hypotension Prediction Index to Forecast LPS-Induced Acute Lung Injury and Inflammation in a Porcine Model: Exploring the Role of Hypoxia-Inducible Factor in Circulatory Shock.

Acute respiratory distress syndrome (ARDS) is a critical illness in critically unwell patients, characterized by refractory hypoxemia and shock. This study evaluates an early detection tool and investigates the relationship between hypoxia and circulatory shock in ARDS, to improve diagnostic precision and therapy customization. We used a porcine model, inducing ARDS with mechanical ventilation and intratracheal plus intravenous lipopolysaccharide (LPS) injection. Hemodynamic changes were monitored using an Acumen IQ sensor and a ForeSight Elite sensor connected to the HemoSphere platform. We evaluated tissue damage, inflammatory response, and hypoxia-inducible factor (HIF) alterations using enzyme-linked immunosorbent assay and immunohistochemistry. The results showed severe hypotension and increased heart rates post-LPS exposure, with a notable rise in the hypotension prediction index (HPI) during acute lung injury (p = 0.024). Tissue oxygen saturation dropped considerably in the right brain region. Interestingly, post-injury HIF-2α levels were lower at the end of the experiment. Our findings imply that the HPI can effectively predict ARDS-related hypotension. HIF expression levels may serve as possible markers of rapid ARDS progression. Further research should be conducted on the clinical value of this novel approach in critical care, as well as the relationship between the HIF pathway and ARDS-associated hypotension.

Beyond the debut: unpacking six years of Hypotension Prediction Index software in intraoperative hypotension prevention - a systematic review and meta-analysis.

Intraoperative hypotension (IOH) during general anesthesia is associated with higher morbidity and mortality, although randomized trials have not established a causal relation. Historically, our approach to IOH has been reactive. The Hypotension Prediction Index (HPI) is a machine learning software that predicts hypotension minutes in advance. This systematic review and meta-analysis explores whether using HPI alongside a personalized treatment protocol decreases intraoperative hypotension. A systematic search was performed in Pubmed and Scopus to retrieve articles published from January 2018 to February 2024 regarding the impact of the HPI software on reducing IOH in adult patients undergoing non-cardio/thoracic surgery. Excluded were case series, case reports, meta-analyses, systematic reviews, and studies using non-invasive arterial waveform analysis. The risk of bias was assessed by the Cochrane risk-of-bias tool (RoB 2) and the Risk Of Bias In Non-randomised Studies (ROBINS-I). A meta-analysis was undertaken solely for outcomes where sufficient data were available from the included studies. 9 RCTs and 5 cohort studies were retrieved. The overall median differences between the HPI-guided and the control groups were - 0.21 (95% CI:-0.33, -0.09) - p < 0.001 for the Time-Weighted Average (TWA) of Mean Arterial Pressure (MAP) < 65mmHg, -3.71 (95% CI= -6.67, -0.74)-p = 0.014 for the incidence of hypotensive episodes per patient, and - 10.11 (95% CI= -15.82, -4.40)-p = 0.001 for the duration of hypotension. Notably a large amount of heterogeneity was detected among the studies. While the combination of HPI software with personalized treatment protocols may prevent intraoperative hypotension (IOH), the large heterogeneity among the studies and the lack of reliable data on its clinical significance necessitate further investigation.

Assessment of Ultrasonographic Measurement of Inferior Vena Cava Collapsibility Index in the Prediction of Hypotension Associated with Tourniquet Release in Total Knee Replacement Surgeries under Spinal Anesthesia

Abstract This was a Prospective, observational study conducted on 60 patients with Total Knee Replacement (TKR); to characterize the collapsibility index of the IVC as a potential screening tool to identify patients who are candidate for hypotensive events after tourniquet release in TKR in an otherwise hemodynamically stable population. We found that; the mean age of all patients was (57 ± 5.6) years. Regarding gender of the patients, (58.3%) of patients were females; while (41.7%) were males. Regarding basic clinical data; the mean BMI of all patients was (25.7 ± 2.5); with (60%) of patients had DM, and (56.7%) had HTN. Regarding ASA class, (3.3%) had class I, (73.3%) had class II, and (23.3%) had class III. Regarding Baseline hemodynamic data; the average MAP was (104.6 ± 6) mmHg, the average RR was (10.5 ± 1.3) breath/min, and the average HR was (74.75 ± 7) beat/min. Regarding Data before tourniquet release; the average MAP was (102.3 ± 7.5) mmHg, the average RR was (10.7 ± 1.2) breath/min, and the average HR was (75.3 ± 6.5) beat/min, with average IVC-CI of (48.4 ± 7.7). Regarding Data 15-m after tourniquet release; the average MAP was (93.1 ± 9.3) mmHg, the average RR was (10.7 ± 1.4) breath/min, and the average HR was (76.45 ± 6.6) beat/min, with average IVC-CI of (51 ± 8.4). Regarding final outcome, (11.7%) of patients suffered Hypotension (PTRH). The 60 TKR patients were classified according to outcomes into 2 independent groups: group A IVCCI &amp;gt;55,5% (12 patients) and group B IVCCI &amp;lt;55,5% (48 patients). Regarding comparative study between the 2 groups as regards demographic data; no statistically significant differences were found except for ASA physical status of patients where group A had patients in ASA I class and most of its patients are ASA II while group B had no patients in ASA I class and a quarter of its patients were ASA III. Regarding comparative study between the 2 groups as regards hemodynamic and ultrasonographic data; no statistically different results were noted between groups in baseline readings. Results recorded before tourniquet release showed that IVC during inspiration reading was statistically lower while IVC collapsibility index was statistically higher in Group A when compared with group B . Results recorded 15 min after tourniquet release showed that IVC during inspiration reading was statistically lower while IVC collapsibility index was statistically higher in Group A when compared with group B, the IVC-CI before tourniquet release was not correlated with percent reduction in systolic blood pressure after tourniquet release in both groups with high (Group A) or low IVC-CI (Group B). Comparing patients who developed hypotension versus patients who did not, the 60 TKR patients were classified into hypotensive (7 patients) and normotensive (53 patients) groups. No statistically significant differences were found between hypotensive versus normotensive patients concerning demographic data and data recorded before tourniquet release. Baseline data showed significantly higher respiratory rate in hypotensive patients. Data recorded 15 min after tourniquet release showed statistically lower SBP, DBP and MAP, significantly higher HR and significantly lower IVC during inspiration and expiration in patients who developed hypotension. We studied different factors that may be included before tourniquet release that may predict the degree of PTRH by using ROC-curve analysis. IVC-CI before tourniquet release and BMI failed to be predictive factors for the occurrence of PTRH, while respiratory and heart rates before tourniquet release significantly predict the occurrence of PTRH. Performing multiple regression analysis using forward method showed that the increase in RR had an independent effect on increasing the probability of PTRH occurrence; with significant statistical difference (p &amp;lt; 0.039). Conclusion IVC-CI before tourniquet release is not a predictor for PTRH in patients undergoing TKR under spinal anesthesia.

Enhancing Hypotension Prediction in Real-time Patient Monitoring Through Deep Learning: A Novel Application of XResNet with Contrastive Learning and Value Attention Mechanisms.

The precise prediction of hypotension is vital for advancing preemptive patient care strategies. Traditional machine learning approaches, while instrumental in this field, are hampered by their dependence on structured historical data and manual feature extraction techniques. These methods often fall short of recognizing the intricate patterns present in physiological signals. Addressing this limitation, our study introduces an innovative application of deep learning technologies, utilizing a sophisticated end-to-end architecture grounded in XResNet. This architecture is further enhanced by the integration of contrastive learning and a value attention mechanism, specifically tailored to analyze arterial blood pressure (ABP) waveform signals. Our approach improves the performance of hypotension prediction over the existing state-of-theart ABP model [7]. This research represents a step towards optimizing patient care, embodying the next generation of AI-driven healthcare solutions. Through our findings, we demonstrate the promise of deep learning in overcoming the limitations of conventional prediction models, thereby offering an avenue for enhancing patient outcomes in clinical settings.

Personalized prediction of intradialytic hypotension in clinical practice: Development and evaluation of a novel AI dashboard incorporating risk factors from previous and current dialysis sessions

BackgroundIntradialytic hypotension (IDH) is one of the most common and critical complications of hemodialysis. Despite many proven factors associated with IDH, accurately predicting it before it occurs for individual patients during dialysis sessions remains a challenge. PurposeTo establish artificial intelligence (AI) predictive models for IDH, which consider risk factors from previous and ongoing dialysis to optimize model performance. We then implement a novel digital dashboard with the best model for continuous monitoring of patients’ status undergoing hemodialysis. The AI dashboard can display the real-time probability of IDH for each patient in the hemodialysis center providing an objective reference for care members for monitoring IDH and treating it in advance. MethodsEight machine learning (ML) algorithms, including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Light Gradient Boosting Machine (LightGBM), Multilayer Perception (MLP), eXtreme Gradient Boosting (XGBoost), and NaiveBayes, were used to establish the predictive model of IDH to determine if the patient will acquire IDH within 60 min. In addition to real-time features, we incorporated several features sourced from previous dialysis sessions to improve the model’s performance. The electronic medical records of patients who had undergone hemodialysis at Chi Mei Medical Center between September 1, 2020 and December 31, 2020 were included in this research. Impact evaluation of AI assistance was conducted by IDH rate. ResultsThe results showed that the XGBoost model had the best performance (accuracy: 0.858, sensitivity: 0.858, specificity: 0.858, area under the curve: 0.936) and was chosen for AI dashboard implementation. The care members were delighted with the dashboard providing real-time scientific probabilities for IDH risk and historic predictive records in a graphic style. Other valuable functions were appended in the dashboard as well. Impact evaluation indicated a significant decrease in IDH rate after the application of AI assistance. ConclusionThis AI dashboard provides high-quality results in IDH risk prediction during hemodialysis. High-risk patients for IDH will be recognized 60 min earlier, promoting individualized preventive interventions as part of the treatment plan. Our approachis believed to promise an excellent way for IDH management.

Prediction of spinal anesthesia-induced hypotension in cesarian section with focus on carotid doppler ultrasound and cardiometry

Prediction of spinal anesthesia-induced hypotension in cesarian section with focus on carotid doppler ultrasound and cardiometry

Implementation of Hemodynamic Management with the Hypotension Prediction Index during Elective Open Abdominal Aortic Surgery: A Pilot Observational Report

Implementation of Hemodynamic Management with the Hypotension Prediction Index during Elective Open Abdominal Aortic Surgery: A Pilot Observational Report

Norepinephrine weaning guided by the Hypotension Prediction Index in vasoplegic shock after cardiac surgery: protocol for a single-centre, open-label randomised controlled trial – the NORAHPI study

IntroductionNorepinephrine (NE) is the first-line recommended vasopressor for restoring mean arterial pressure (MAP) in vasoplegic syndrome (vs) following cardiac surgery with cardiopulmonary bypass. However, solely focusing on target MAP values...