The bedside hemodynamic assessment of the critically ill remains challenging since blood volume, arterial–venous interaction and compliance are not measured directly. Mean circulatory filling pressure (Pmcf) is the blood pressure throughout the vascular system at zero flow. Animal studies have shown Pmcf provides information on vascular compliance, volume responsiveness and enables the calculation of stressed volume. It is now possible to measure Pmcf at the bedside. We performed a systematic review of the current Pmcf measurement techniques and compared their clinical applicability, precision, accuracy and limitations. A comprehensive search strategy was performed in PubMed, Embase and the Cochrane databases. Studies measuring Pmcf in heart-beating patients at the bedside were included. Data were extracted from the articles into predefined forms. Quality assessment was based on the Newcastle–Ottawa Scale for cohort studies. A total of 17 prospective cohort studies were included. Three techniques were described: Pmcf hold, based on inspiratory hold-derived venous return curves, Pmcf arm, based on arterial and venous pressure equilibration in the arm as a model for the entire circulation, and Pmcf analogue, based on a Guytonian mathematical model of the circulation. The included studies show Pmcf to accurately follow intravascular fluid administration and vascular compliance following drug-induced hemodynamic changes. Bedside Pmcf measures allow for more direct assessment of circulating blood volume, venous return and compliance. However, studies are needed to determine normative Pmcf values and their expected changes to therapies if they are to be used to guide clinical practice.

Background: The Hypotension Prediction Index (the index) software is a machine learning algorithm that detects physiologic changes that may lead to hypotension. The original validation used a case control (backward) analysis that has been suggested to be biased. This study therefore conducted a cohort (forward) analysis and compared this to the original validation technique. Methods: A retrospective analysis of data from previously reported studies was conducted. All data were analyzed identically with two different methodologies, and receiver operating characteristic curves were constructed. Both backward and forward analyses were performed to examine differences in area under the receiver operating characteristic curves for the Hypotension Prediction Index and other hemodynamic variables to predict a mean arterial pressure (MAP) less than 65 mmHg for at least 1 min 5, 10, and 15 min in advance. Results: The analysis included 2,022 patients, yielding 4,152,124 measurements taken at 20-s intervals. The area under the curve for the index predicting hypotension analyzed by backward and forward methodologies respectively was 0.957 (95% CI, 0.947 to 0.964) versus 0.923 (95% CI, 0.912 to 0.933) 5 min in advance, 0.933 (95% CI, 0.924 to 0.942) versus 0.923 (95% CI, 0.911 to 0.933) 10 min in advance, and 0.929 (95% CI, 0.918 to 0.938) versus 0.926 (95% CI, 0.914 to 0.937) 15 min in advance. No variable other than MAP had an area under the curve greater than 0.7. The areas under the curve using forward analysis for MAP predicting hypotension 5, 10, and 15 min in advance were 0.932 (95% CI, 0.920 to 0.940), 0.929 (95% CI, 0.918 to 0.938), and 0.932 (95% CI, 0.921 to 0.940), respectively. The R ² for the variation in the index due to MAP was 0.77. Conclusions: Using an updated methodology, the study found that the utility of the Hypotension Prediction Index to predict future hypotensive events is high, with an area under the receiver operating characteristics curve similar to that of the original validation method. Editor’s Perspective: What We Already Know about This Topic: What This Article Tells Us That Is New:

Purpose The aim of this study is to provide a summary of the existing literature on the association between hypotension during intensive care unit (ICU) stay and mortality and morbidity, and to assess whether there is an exposure-severity relationship between hypotension exposure and patient outcomes. Methods CENTRAL, Embase, and PubMed were searched up to October 2022 for articles that reported an association between hypotension during ICU stay and at least one of the 11 predefined outcomes. Two independent reviewers extracted the data and assessed the risk of bias. Results were gathered in a summary table and studies designed to investigate the hypotension-outcome relationship were included in the meta-analyses. Results A total of 122 studies (176,329 patients) were included, with the number of studies varying per outcome between 0 and 82. The majority of articles reported associations in favor of ‘no hypotension’ for the outcomes mortality and acute kidney injury (AKI), and the strength of the association was related to the severity of hypotension in the majority of studies. Using meta-analysis, a significant association was found between hypotension and mortality (odds ratio: 1.45; 95% confidence interval (CI) 1.12–1.88; based on 13 studies and 34,829 patients), but not for AKI. Conclusion Exposure to hypotension during ICU stay was associated with increased mortality and AKI in the majority of included studies, and associations for both outcomes increased with increasing hypotension severity. The meta-analysis reinforced the descriptive findings regarding mortality but did not yield similar support for AKI.

To clarify whether intraoperative hypotension contributes to the development of postoperative cognitive dysfunction. A systematic review of prospective studies reporting on intraoperative hypotension and postoperative cognitive dysfunction in elective, non-cognitive impaired, adult surgical patients. PubMed, EMBASE and the Cochrane Library were searched up to the 1st of January 2021. Studies had to use a clear definition of hypotension, although differing definitions were accepted. Neurocognitive tests to determine postoperative cognitive dysfunction had to be done pre- and postoperatively, with a minimum follow-up of seven days postoperatively. Risk of bias was assessed using the Cochrane Risk of Bias Tool 2.0 for randomized controlled trials and the Newcastle-Ottawa Scale for cohort studies. Out of 941 studies screened, five randomized controlled trials and four cohort studies were included for qualitative analysis. Extensive methodological differences between studies were present hindering proper quantitive analysis. No studies reported statistically significant differences in incidence of postoperative cognitive dysfunction in hypo- compared to normotensive patients. Five studies reported exact incidences of postoperative cognitive dysfunction. This systematic review showed no conclusive association between intraoperative hypotension and the development of postoperative cognitive dysfunction. Given the vast methodological differences of the included studies, the role of intraoperative hypotension in the development of postoperative cognitive dysfunction remains uncertain. Future research into the association between intraoperative hypotension and postoperative cognitive dysfunction should be conducted in a standardized manner.

BACKGROUND: Intra-operative hypotension is associated with adverse postoperative outcomes. A machine-learning-derived algorithm developed to predict hypotension based on arterial blood pressure (ABP) waveforms significantly reduced intra-operative hypotension. The algorithm calculates the likelihood of hypotension occurring within minutes, expressed as the Hypotension Prediction Index (HPI) which ranges from 0 to 100. Currently, HPI is only available for patients monitored with invasive ABP, which is restricted to high-risk procedures and patients. In this study, the performance of HPI, employing noninvasive continuous ABP measurements, is assessed. OBJECTIVES: The first aim was to compare the performance of the HPI algorithm, using noninvasive versus invasive ABP measurements, at a mathematically optimal HPI alarm threshold (Youden index). The second aim was to assess the performance of the algorithm using a HPI alarm threshold of 85 that is currently used in clinical trials. Hypotension was defined as a mean arterial pressure (MAP) below 65 mmHg for at least 1 min. The predictive performance of the algorithm at different HPI alarm thresholds (75 and 95) was studied. DESIGN: Observational cohort study. SETTING: Tertiary academic medical centre. PATIENTS: Five hundred and seven adult patients undergoing general surgery. RESULTS: The performance of the algorithm with invasive and noninvasive ABP input was similar. A HPI alarm threshold of 85 showed a median [IQR] time from alarm to hypotension of 2.7 [1.0 to 7.0] min with a sensitivity of 92.7 (95% confidence interval [CI], 91.2 to 94.3), specificity of 87.6 (95% CI, 86.2 to 89.0), positive predictive value of 79.9 (95% CI, 77.7 to 82.1) and negative predictive value of 95.8 (95% CI, 94.9 to 96.7). A HPI alarm threshold of 75 provided a lower positive predictive value but a prolonged time from prediction to actual hypotension. CONCLUSION: This study demonstrated that the algorithm can be employed using continuous noninvasive ABP waveforms. This opens up the potential to predict and prevent hypotension in a larger patient population. TRIAL REGISTRATION: Clinical trials registration number NCT03533205.

OBJECTIVES: Cardiac surgery is associated with perioperative complications, some of which might be attributable to hypotension. The Hypotension Prediction Index (HPI), a machine-learning-derived early warning tool for hypotension, has only been evaluated in noncardiac surgery. We investigated whether using HPI with diagnostic guidance reduced hypotension during cardiac surgery and in the ICU. DESIGN: Randomized clinical trial conducted between May 2021 and February 2023. SETTING: Single-center study conducted in an academic hospital in the Netherlands. PATIENTS: Adults undergoing elective on-pump coronary artery bypass grafting, with or without single heart valve surgery, were enrolled if a mean arterial pressure (MAP) greater than or equal to 65 mm Hg was targeted during the surgical off-pump phases and ICU stay. After eligibility assessment, 142 of 162 patients approached gave informed consent for participation. INTERVENTIONS: Patients randomized 1:1 received either diagnostic guidance in addition to standard care if HPI reached greater than or equal to 75 (n = 72) or standard care alone (n = 70). MEASUREMENTS AND MAIN RESULTS: The primary outcome was the severity of hypotension, measured as time-weighted average (TWA) of MAP less than 65 mm Hg. Secondary outcomes encompassed hypertension severity and intervention disparities. Of 142 patients randomized, 130 were included in the primary analysis. The HPI group showed 63% reduction in median TWA of hypotension compared with the standard care group, with a median of differences of –0.40 mm Hg (95% CI, –0.65 to –0.27; p < 0.001). In the HPI group, patients spent a median 28 minutes (95% CI, 17–44 min) less in hypotension, with a measurement duration of 322 minutes in the HPI group and 333 minutes in the standard care group. No significant differences were observed in hypertension severity, treatment choice, or fluid, vasopressors, and inotrope amounts. CONCLUSIONS: Using HPI combined with diagnostic guidance on top of standard care significantly decreased hypotension severity in elective cardiac surgery patients compared with standard care.