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Stroke Volume and Arterial Pressure Fluid Responsiveness in Patients With Elevated Stroke Volume Variation Undergoing Major Vascular Surgery: A Prospective Intervention Study
Division of Cardiac Thoracic Vascular Anesthesia and Intensive Care Medicine, Department of Anesthesia, Intensive Care Medicine and Pain Medicine, Medical University Vienna, Vienna, Austria
Division of Cardiac Thoracic Vascular Anesthesia and Intensive Care Medicine, Department of Anesthesia, Intensive Care Medicine and Pain Medicine, Medical University Vienna, Vienna, AustriaDepartment of Anesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital of Würzburg, Würzburg, Germany
Division of Cardiac Thoracic Vascular Anesthesia and Intensive Care Medicine, Department of Anesthesia, Intensive Care Medicine and Pain Medicine, Medical University Vienna, Vienna, Austria
Division of Cardiac Thoracic Vascular Anesthesia and Intensive Care Medicine, Department of Anesthesia, Intensive Care Medicine and Pain Medicine, Medical University Vienna, Vienna, Austria
Division of Cardiac Thoracic Vascular Anesthesia and Intensive Care Medicine, Department of Anesthesia, Intensive Care Medicine and Pain Medicine, Medical University Vienna, Vienna, Austria
Division of Cardiac Thoracic Vascular Anesthesia and Intensive Care Medicine, Department of Anesthesia, Intensive Care Medicine and Pain Medicine, Medical University Vienna, Vienna, Austria
Address correspondence to Martin Dworschak, MD, MBA, Division of Cardiac Thoracic Vascular Anesthesia and Intensive Care Medicine, Department of Anesthesia, Intensive Care Medicine and Pain Medicine, General Hospital Vienna, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
Division of Cardiac Thoracic Vascular Anesthesia and Intensive Care Medicine, Department of Anesthesia, Intensive Care Medicine and Pain Medicine, Medical University Vienna, Vienna, Austria
The identification of potential hemodynamic indicators to increase the predictive power of stroke-volume variation (SVV) for mean arterial pressure (MAP) and stroke volume (SV) fluid responsiveness.
Design
A prospective intervention study.
Setting
At a single-center university hospital.
Participants
Nineteen patients during major vascular surgery with 125 fluid interventions.
Interventions
When SVV ≥13% occurred for >30 seconds, 250 mL of Ringer's lactate were given within 2 minutes.
Measurements and Main Results
Hemodynamic variables, such as pulse-pressure variation (PPV) and dynamic arterial elastance (Edyn), were measured by pulse power-wave analysis. The outcomes were MAP and SV responsiveness, defined as an increase of at least 10% of MAP and SV within 5 minutes of the fluid intervention. Of the fluid interventions, 48% were MAP-responsive, and 66% were SV-responsive. The addition of PPV and Edyn cut-off values to the SVV cut-off decreased sensitivity from 1-to-0.66 to-0.82, and concomitantly increased specificity from 0-to- 0.65-to-0.93 for the prediction of MAP and SV responsiveness in the authors’ study setting. The areas under the receiver operating characteristic curves of PPV and Edyn for the prediction of MAP responsiveness were 0.79 and 0.75, respectively. The areas under the receiver operating characteristic curves for PPV and Edyn to predict SV responsiveness were 0.85 and 0.77, respectively.
Conclusions
The PPV and Edyn showed good accuracy for the prediction of MAP and SV responsiveness in patients with elevated SVV during vascular surgery. Either PPV or Edyn may be used in conjunction with SVV to better predict MAP and SV fluid responsiveness in patients undergoing vascular surgery.
ADEQUATE ORGAN PERFUSION PRESSURE and, consequently, organ blood flow, are crucial to prevent tissue hypoperfusion, organ injury, and mitigate inflammatory response.
Therefore, the maintenance of adequate mean arterial pressure (MAP) and stroke volume (SV) are 2 important goals during major surgery. Stable MAP and SV values were associated with fewer postoperative complications, such as postoperative infections, acute kidney injury, myocardial injury, and a reduced hospital length of stay, as well as lower hospital mortality.
Effect of a perioperative, cardiac output-guided hemodynamic therapy algorithm on outcomes following major gastrointestinal surgery: A randomized clinical trial and systematic review.
A systematic review and meta-analysis on the use of preemptive hemodynamic intervention to improve postoperative outcomes in moderate and high-risk surgical patients.
The prediction of MAP and SV fluid responsiveness is, therefore, crucial in order to give fluids only when they are needed.
One of two ways to determine MAP and SV fluid responsiveness is the evaluation of dynamic variations over time of SV (SVV), pulse pressure (PPV), or the ratio of PPV-SVV, also known as dynamic arterial elastance (Edyn), during mechanical ventilation.
Dynamic changes in arterial waveform derived variables and fluid responsiveness in mechanically ventilated patients: A systematic review of the literature.
Dynamic arterial elastance obtained using arterial signal does not predict an increase in arterial pressure after a volume expansion in the operating room.
Functional haemodynamic monitoring: The value of SVV as measured by the LiDCORapid in predicting fluid responsiveness in high risk vascular surgical patients.
of all fluid interventions. Edyn >0.8911 or >0.6510 could predict MAP responsiveness (area under the receiver operating characteristic curve [AUROC] 0.99) well in mechanically ventilated patients in the intensive care unit (ICU),
Therefore, it remains unclear if SVV combined with other hemodynamic variables would improve prediction of MAP and SV responsiveness, especially in patients with vascular disease. Patients with vascular disease undergoing surgery are particularly challenging because vascular disease may alter arterial load and the accuracy of hemodynamic variables to predict MAP or SV responsiveness. To the authors’ knowledge, a comparison of SVV alone versus combined with other hemodynamic variables to predict MAP and SV responsiveness has not yet been performed in patients undergoing vascular surgery.
Dynamic arterial elastance obtained using arterial signal does not predict an increase in arterial pressure after a volume expansion in the operating room.
Functional haemodynamic monitoring: The value of SVV as measured by the LiDCORapid in predicting fluid responsiveness in high risk vascular surgical patients.
Therefore, the aim of this study was to identify additional hemodynamic predictors of fluid responsiveness to increase the predictive power of SVV during major vascular surgery.
Methods
Study Design and Population
This prospective intervention study was conducted at the General Hospital of Vienna, Medical University of Vienna after approval by the authors’ local ethic committee and written informed consent by all patients. Patients undergoing major vascular surgery, such as peripheral arterial surgery (except for carotid artery surgery) and open abdominal aortic surgery, were included. The exclusion criteria were patients <18 years of age and the absence of cardiac sinus rhythm at anesthesia induction.
Intervention
A 20-G arterial catheter was placed in the radial artery prior to anesthesia induction, zeroed to atmospheric pressure, and a flush test was applied to ascertain optimal damping. The minimally invasive LiDCOrapid device (LiDCO, Lake Villa, IL) was connected to the monitoring screen (Infinity Delta, Drägerwerk AG, Lübeck, Germany) according to the manufacturer's recommendations. For each patient, weight, height, age, sex, and patient type were imputed into the LiDCOrapid device to internally scale the results. Fluid, noradrenaline, or dobutamine interventions were given according to an active decision support system from skin incision to wound closure (Fig 1).
Fluid interventions with 250 mL of Ringer's lactate solution were given under pressure within 2 minutes once SVV exceeded 13% for >30 seconds (Fig 1). No baseline fluid was applied. Volume-controlled ventilation, with a tidal volume of 8 mL/kg ideal body weight was applied. Anesthetic technique and monitoring have been previously described in detail; and feasibility, as well as adherence to the active decision support system, in this prospective intervention study have been published.
Fig. 1Protocol of the active decision support system for fluid, noradrenaline, and dobutamine interventions. For this exploratory analysis only fluid interventions performed because of SVV ≥13% (red part of algorithm) were included. Reprinted and adapted from Minerva Anestesiologica 85:288-97, Menger J, Fischer A, Mouhieddine M, Seidel M, Edlinger-Stanger M, Bevilacqua M, et al. Evaluation of an active decision support system for hemodynamic optimization during elective major vascular surgery, Copyright (2019), with permission from Minerva Medica. BIS, bispectral index; CO, cardiac output; COBL, CO at baseline (before induction of anesthesia); MAP, mean arterial pressure; MAPBL, MAP at baseline (before induction of anesthesia); RL, Ringer's lactate; SV, stroke volume; SVV, stroke volume variation.
For this exploratory analysis, the study authors screened all fluid interventions because of SVV ≥13% according to the active decision support system as documented by the study anesthesiologist (Fig 1, red part). The authors exported the beat-by-beat continuous hemodynamic data from the stored database of the LiDCOrapid device. Performed fluid interventions only were included if the database verified a median SVV ≥13% at the start of the fluid intervention (t0). Fluid interventions, followed by any other interventions within 5 minutes, were excluded. Moreover, fluid interventions not performed with Ringer's lactate were excluded (Fig 2).
All hemodynamic variables listed in Table 1 were assessed as potential additional predictors for MAP and SV responsiveness in fluid interventions performed if SVV ≥13%. The LiDCOrapid device automatically averages dynamic variables over 10 seconds and updates calculations every 4 heart beats (Table 1). Such dynamic variables were SVV, PPV, systolic pressure variation and heart rate variation. The LiDCOrapid device determines SVV as (SVmax – SVmin) × 100 / ([SVmax + SVmin] / 2). In addition, the LiDCOrapid device stores MAP, systolic, and diastolic arterial pressures, heart rate, SV, cardiac index, heart rate variation, as well as systemic vascular resistance index. Finally, the authors calculated arterial compliance, Edyn, arterial elastance, and arterial resistance (Table 1). The median value of each hemodynamic variable in Table 1 for every minute from the beat-by-beat continuous hemodynamic data was calculated and paired with the fluid intervention data. The start of every fluid intervention was coded as time 0 (t0). The authors determined median values at t0 and at minutes 1-to-5 (t1-5) for the hemodynamic data shown in Table 1. For every listed hemodynamic variable, the change in relation to t0 was calculated at t1-to-5. If the fluid intervention (at t0, for example, at 8:00) was followed by the next intervention exactly after 5 minutes (at t5, for example, at 8:05), the hemodynamic values at t5 were considered both as effect at minute 5 of the previous fluid intervention and as baseline at minute 0 (t0) for the next fluid intervention. This was done because the authors did not expect to see an immediate effect of the starting fluid intervention at t0.
Table 1Additional Hemodynamic Variables Assessed as Potential Predictors for MAP and SV Responsiveness in Fluid Interventions Performed if SVV ≥13%
Hemodynamic Variable
Definition
Unit
Source
MAP
mmHg
LiDCOrapid device
Sys
mmHg
LiDCOrapid device
Dia
mmHg
LiDCOrapid device
HR
L/min
LiDCOrapid device
SV
mL
LiDCOrapid device
CI
= (stroke volume × HR) / body surface area
L/min/m2
LiDCOrapid device
HRV
= SD [HR] × 100 / mean [HR]
%
averaged over 10 seconds and updated every 4 beats by LiDCOrapid device
SVRI
= 80 × (MAP – right arterial pressure) / CI)
dynes * s /cm5/m2
LiDCOrapid device
SPV
= maximum systolic pressure-minimum systolic pressure
mmHg
averaged over 10 seconds and updated every 4 beats by LiDCOrapid device
The study outcomes were MAP and SV responsiveness, defined as an increase of at least 10% of MAP or SV, respectively, within 5 minutes of the fluid intervention. Hemodynamic data were clustered for each patient. Differences in hemodynamic data between MAP or SV responders and nonresponders were assessed using clustered Wilcoxon rank sum test. Differences in numbers between MAP or SV responsive versus nonresponsive fluid interventions were assessed with chi-square tests. Hemodynamic measures at t0 were correlated to MAP and SV fluid responsiveness in multivariate logistic regression models, with MAP or SV responsiveness being the outcome variable. For every model, 1 hemodynamic value at t0 (Table 1) was chosen as a predictor for MAP or SV responsiveness in fluid interventions performed if SVV ≥13%. Odds-ratio and p values were computed for every hemodynamic value at t0. In addition, MAP or SV at t0, administration of noradrenaline and dobutamine, and a random effect for the patient were accounted for in every model. Both PPV and Edyn were the hemodynamic variables, showing the lowest p values in the multivariate logistic regression models, and were, therefore, chosen for further AUROC analysis; univariate logistic regression models were fitted, and the optimal cutoff values for PPV and Edyn, yielding the highest Youden-Index, were calculated to predict MAP or SV responsiveness. Sensitivity and specificity of the optimal cutoff were calculated. The first goal was to compare sensitivity and specificity when using SVV alone or when combining it with PPV and Edyn to predict MAP or SV responsiveness. The authors analyzed to what extent specificity would improve and sensitivity would decrease when adding PPV and Edyn. The second goal was to assess whether PPV and Edyn attained good accuracy with an AUROC ≥0.75
for the prediction of MAP and SV responsiveness. Therefore, ROC curves were drawn, and the AUROC was calculated. Calculations were performed using R, version 3.6.1 or higher (R Foundation for Statistical Computing). Normally distributed data were reported as mean (± SD), and nonnormally distributed data as median (IQR). The categorical data were presented as counts and percentages. No correction for multiple testing was applied; therefore, all p values were of descriptive character.
Results
Number of Fluid Interventions
The entire active decision support system (Fig 1) was evaluated in detail and published previously.
In short, 734 fluid, noradrenaline, and dobutamine interventions were carried out according to an operating active decision support system in 32 patients during major vascular surgery. Among the 734 interventions, 200 fluid interventions because of SVV ≥13% were performed, as documented by the study anesthesiologist (Fig 1, red part). Among the 200 fluid interventions, 125 fluid interventions of 19 patients were included in this analysis (Fig 2). The median (IQR) [range] number of fluid interventions per patient was 6 (2-10) (1-16). The baseline characteristics of patients are shown in Table 2.
Table 2Baseline Characteristics
Patients (n = 19)
Sex, n (%)
Male
15 (79)
Female
4 (21)
Age, y
64 ± 11
BMI, kg/m2
26 ± 5
ASA
II
7
III
12
Procedure, n (%)
Femoral endarterectomy with patch angioplasty
2 (11)
Aortobifemoral bypass surgery
11 (58)
Femoro-politeal bypass surgery
4 (21)
Femoro-tibial bypass surgery
1 (5)
Femoro-femoral bypass surgery
1 (5)
Anesthesia, n (%)
General anesthesia with sevoflurane
18 (95)
Total intravenous anesthesia
1 (5)
Abbreviations: ASA, American Society of Anesthesiologists; BMI, body mass index.
Sixty (48%) fluid interventions were MAP-responsive, and 65 (52%) interventions were MAP non-responsive (Table 3). The maximum change in MAP within 5 minutes was 16% (12-21%) and 5% (2-8%) in MAP-responsive and MAP-nonresponsive interventions, respectively (p < 0.001) (Table 3). The MAP-responsive and MAP-nonresponsive interventions had similar MAP values at t0 (Table 3).
Table 3MAP and SV at the Start of Fluid Intervention (t0) and Their Maximum Change Within 5 Minutes (n = 125 Fluid Interventions)
Eighty-two (66%) fluid interventions were SV-responsive, and 43 (34%) interventions were SV–non-responsive (Table 3). Maximum change in SV within 5 minutes was 17% (13-24%) and 6% (3-7%) in SV-responsive and SV–nonresponsive interventions, respectively (p < 0.001) (Table 3). The SV-responsive and SV–nonresponsive interventions had similar SV values at t0 (Table 3). Supplemental Table S1 shows all hemodynamic data at t0 in MAP or SV-responsive and SV–nonresponsive interventions. The greatest increases in MAP and SV were observed at minute 4 (3-5) (median [IQR]) in MAP- or SV-responsive interventions (Supplemental Figs S1 and S2). Fifty-three (65%) of 82 SV-responsive interventions showed an increase in MAP ≥10% (Fig 3).
Fig. 3Stroke volume (SV) and mean arterial pressure (MAP) responsiveness in 125 fluid interventions. Each arrow starts at the baseline SV and MAP value and ends at the maximum SV and MAP value within 5 minutes after the start of the fluid intervention. MAP, mean arterial pressure; SV, stroke volume.
Seven (16%) of 43 SV–nonresponsive interventions showed an increase in MAP ≥10% (Fig 3).
Additional Hemodynamic Predictors for MAP and SV Responsiveness in Fluid Interventions Performed if SVV ≥13%
Among the hemodynamic variables assessed as potential predictors (Table 1), PPV and Edyn showed the lowest p values to predict both MAP and SV responsiveness (Supplemental Table S2).
Comparison of Sensitivity and Specificity for Predicting MAP and SV Responsiveness When Using SVV Alone or Combined With PPV and Edyn
As fluid interventions only were performed if SVV ≥13%, sensitivity of SVV alone was 1, and specificity was 0 in the authors’ study setting (Table 4). An additional PPV cut-off ≥17.1% would predict MAP responsiveness, with a sensitivity of 0.82 and specificity of 0.65, whereas an additional Edyn cut-off ≥1.1 would predict MAP responsiveness, with a sensitivity of 0.75 and a specificity of 0.71 (Table 4). Further increasing the PPV cut-off to ≥18.6% would predict SV responsiveness, with a sensitivity of 0.66 and specificity of 0.93, whereas an Edyn cut-off ≥1.0 would predict SV responsiveness, with a sensitivity of 0.71 and specificity of 0.74 (Table 4). If the PPV or Edyn cut-offs were additional triggers for fluid interventions, the number of non-responsive (marked in red, Table 4) fluid interventions would decrease, but some responsive interventions (marked in blue, Table 4) would not be performed anymore. The total number of non-responsive (marked in red, Table 4) and responsive but not-performed interventions (marked in blue, Table 4) when using PPV or Edyn in addition to SVV would be smaller than the number of non-responsive interventions when only using SVV (marked in red, Table 4).
Table 4Number of Fluid Interventions Performed if SVV ≥13%, Number of Fluid Interventions That Would Have Been Performed if SVV ≥13% and PPV ≥ Cut-Off Value, and Number of Fluid Interventions that would have been performed if SVV ≥13% and Edyn ≥ Cut-Off Value (n = 125 fluid interventions)
ΔMAP ≥10%
ΔSV ≥10%
Yes
No
Yes
No
Fluid given if SVV ≥13%
Yes
60 (48%)
65 (52%)
Fluid given if SVV ≥13%
Yes
82 (66%)
43 (34%)
No
-
-
No
-
-
Sens: 1
Spec: 0
Sens: 1
Spec: 0
Fluid given if SVV ≥13% and PPV ≥17%
Yes
49 (39%)
23 (18%)
Fluid given if SVV ≥13% and PPV ≥19%
Yes
54 (43%)
3 (2%)
No
11 (9%)
42 (34%)
No
28 (22%)
40 (32%)
Sens: 0.82
Spec: 0.65
Sens: 0.66
Spec: 0.93
Fluid given if SVV ≥13% and Edyn ≥1.1
Yes
45 (36%)
19 (15%)
Fluid given if SVV ≥13% and Edyn ≥1.0
Yes
58 (46%)
11 (9%)
No
15 (12%)
46 (37%)
No
24 (19%)
32 (26%)
Sens: 0.75
Spec: 0.71
Sens: 0.71
Spec: 0.74
NOTE. Non-responsive fluid interventions (would have been performed if SVV/PPV/Edyn ≥ cut-off, but no ΔMAP/SV ≥10%) are marked in red. Responsive, but not performed fluid interventions (would not have been performed if SVV/PPV/Edyn < cut-off, but ΔMAP/SV ≥10%) are marked in blue.
Accuracy of PPV and Edyn for MAP and SV Responsiveness in Fluid Interventions Performed if SVV ≥13%
The AUROC of PPV and Edyn to predict MAP responsiveness were 0.79 and 0.75, respectively (Supplemental Fig S3). The AUROC of PPV and Edyn to predict SV responsiveness were 0.85 and 0.77, respectively (Supplemental Fig S3).
Discussion
Two-thirds of the fluid interventions performed if SVV ≥13% were SV-responsive, whereas only half of the fluid interventions were MAP-responsive. The addition of PPV and Edyn cut-off values to the SVV cut-off would decrease sensitivity from 1 to 0.66 to 0.82 and increase specificity from 0 to 0.65 to 0.93 for the prediction of MAP and SV responsiveness in the authors’ study setting. The additional predictors, PPV and Edyn, accurately predicted MAP and SV responsiveness with an AUROC between 0.75 and 0.85.
MAP and SV Responsiveness in Fluid Interventions
As mentioned previously, both SV and MAP are important targets in the hemodynamic management of anesthesiologists,
This is why the authors’ decided to consider both SV and MAP responsiveness as primary outcomes in this study.
In this study, SVV ≥13% alone predicted SV responsiveness in two-thirds and MAP responsiveness in only one-half of the fluid interventions. In other studies, SVV ≥10% alone predicted SV responsiveness in 80%
These findings highlight the need for an additional predictor for MAP responsiveness in particular.
Additional Predictors for MAP and SV Responsiveness in Fluid Interventions Performed if SVV ≥13%
Among the assessed hemodynamic variables in the authors’ study, PPV and Edyn had the lowest p values in the models predicting MAP responsiveness. Both PPV and Edyn can be regarded as dynamic measures related to SVR.
Interestingly, systemic vascular resistance or the systemic vascular resistance index could not predict MAP or SV responsiveness in this and other studies.
Indeed, PPV and Edyn better reflect the dynamic elastic properties of the vascular wall compared to SVR, as they measure the specific variation over time. Edyn is defined as the ratio of PPV to SVV (Table 1) and describes the variation in pulse pressure for a given variation in SV during a respiratory cycle. In theory, the higher the variation in pulse pressure is compared to the variation in SV during a respiratory cycle, the greater should be the increase in MAP after a fluid bolus.
Predictive performance of dynamic arterial elastance for arterial pressure response to fluid expansion in mechanically ventilated hypotensive adults: A systematic review and meta-analysis of observational studies.
Comparison of Sensitivity and Specificity for Predicting MAP and SV Responsiveness When Using SVV Alone or Combined With PPV and Edyn
The addition of PPV and Edyn cut-off values to the SVV cut-off would decrease sensitivity from 1 to 0.66 to 0.82 and would increase specificity from 0 to 0.65 to 0.93 for the prediction of MAP and SV responsiveness in the authors’ study setting. When using PPV or Edyn in combination with SVV, the sum of non-responsive and responsive but not performed interventions was lower than the number of non-responsive interventions when only using SVV (Table 4). The authors’ group previously showed that an active decision system only relying on SVV ≥13% without baseline fluid as indication for giving fluid was feasible and did not result in clinically significant fluid depletion or overload.
The authors now suggest adding PPV or Edyn as additional triggers for fluid interventions to further improve MAP and SV responsiveness.
Accuracy of PPV and Edyn for MAP and SV Responsiveness in Fluid Interventions Performed if SVV ≥13%
Both PPV and Edyn achieved good accuracy for predicting MAP responsiveness with an AUROC of 0.79 and 0.75 in the authors’ study. In other studies, Edyn >0.8911 or >0.6510 had an excellent accuracy for the prediction of MAP responsiveness (AUROC 0.99) in mechanically ventilated patients in the ICU who received fluid if SVV ≥10%.
However, it only moderately predicted MAP responsiveness (AUROC 0.71) in patients during neurosurgical or abdominal surgery who received fluid if SVV ≥10%.
The authors’ data confirmed that SVV alone could already predict SV responsiveness in two-thirds of all fluid interventions. Consequently, there may be less need for an additional predictor for SV responsiveness as opposed to MAP responsiveness. Nevertheless, PPV and Edyn accurately predicted SV responsiveness with an AUROC of 0.85 and 0.77 in the authors’ study. Their results warrant the use of PPV or Edyn in addition to SVV to better predict MAP responsiveness in particular but also SV responsiveness.
Limitations and Strengths
Vasopressors lead to a higher preload by constricting the venous system.
However, the study authors accounted for the administration of noradrenaline and dobutamine in the multivariable logistic regression models.
The estimation of an optimal cut-off may be limited due to the small sample size in the authors’ study. Yet the proof-of-concept study showed that PPV and Edyn could predict MAP or SV responsiveness in vascular surgery patients with high SVV.
To the best of the authors’ knowledge, their study was the first to compare SVV alone versus combined with Edyn or PPV to predict MAP or SV responsiveness in vascular surgery patients. Previous studies in vascular surgery patients performed fluid interventions according to clinical assessment and then assessed if SVV alone, PPV alone, or Edyn alone could predict MAP, SVI or CO fluid responsiveness.
Dynamic arterial elastance obtained using arterial signal does not predict an increase in arterial pressure after a volume expansion in the operating room.
Functional haemodynamic monitoring: The value of SVV as measured by the LiDCORapid in predicting fluid responsiveness in high risk vascular surgical patients.
The addition of PPV and Edyn cut-off values to the SVV cut-off would decrease sensitivity from 1 to 0.66 to 0.82 and increase specificity from 0 to 0.65 to 0.93 regarding the prediction of MAP and SV responsiveness in the authors’ study setting. Both PPV and Edyn achieved proper accuracy for the prediction of MAP and SV responsiveness, with an AUROC between 0.75 and 0.85. Therefore, PPV or Edyn may be used in conjunction with SVV to better predict MAP and SV fluid responsiveness in patients undergoing major vascular surgery.
Effect of a perioperative, cardiac output-guided hemodynamic therapy algorithm on outcomes following major gastrointestinal surgery: A randomized clinical trial and systematic review.
A systematic review and meta-analysis on the use of preemptive hemodynamic intervention to improve postoperative outcomes in moderate and high-risk surgical patients.
Dynamic changes in arterial waveform derived variables and fluid responsiveness in mechanically ventilated patients: A systematic review of the literature.
Dynamic arterial elastance obtained using arterial signal does not predict an increase in arterial pressure after a volume expansion in the operating room.
Functional haemodynamic monitoring: The value of SVV as measured by the LiDCORapid in predicting fluid responsiveness in high risk vascular surgical patients.
Predictive performance of dynamic arterial elastance for arterial pressure response to fluid expansion in mechanically ventilated hypotensive adults: A systematic review and meta-analysis of observational studies.
This study was supported by funds of the Division of Cardiothoracic and Vascular Anesthesia and Intensive Care Medicine, Medical University of Vienna, Austria.
Chi-square test for number of fluid interventions; clustered Wilcoxon rank-sum test for hemodynamic data.
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