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The Ability of Pulse Pressure Variations Obtained with CNAP™ Device to Predict Fluid Responsiveness in the Operating Room

Biais, Matthieu, MD*,†; Stecken, Laurent, MD*; Ottolenghi, Laetitia, MD*; Roullet, Stéphanie, MD*; Quinart, Alice, MD*; Masson, Françoise, MD*; Sztark, François, MD, PhD*,†

doi: 10.1213/ANE.0b013e3182240054
Technology, Computing, and Simulation: Research Reports
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BACKGROUND: Respiratory-induced pulse pressure variations obtained with an arterial line (ΔPPART) indicate fluid responsiveness in mechanically ventilated patients. The Infinity® CNAP™ SmartPod® (Dräger Medical AG & Co. KG, Lübeck, Germany) provides noninvasive continuous beat-to-beat arterial blood pressure measurements and a near real-time pressure waveform. We hypothesized that respiratory-induced pulse pressure variations obtained with the CNAP system (ΔPPCNAP) predict fluid responsiveness as well as ΔPPART predicts fluid responsiveness in mechanically ventilated patients during general anesthesia.

METHODS: Thirty-five patients undergoing vascular surgery were studied after induction of general anesthesia. Stroke volume (SV) measured with the Vigileo™/FloTrac™ (Edwards Lifesciences, Irvine, CA), ΔPPART, and ΔPPCNAP were recorded before and after intravascular volume expansion (VE) (500 mL of 6% hydroxyethyl starch 130/0.4). Subjects were defined as responders if SV increased by ≥15% after VE.

RESULTS: Twenty patients responded to VE and 15 did not. The correlation coefficient between ΔPPART and ΔPPCNAP before VE was r = 0.90 (95% confidence interval [CI] = 0.84–0.96; P < 0.0001). Before VE, ΔPPART and ΔPPCNAP were significantly higher in responders than in nonresponders (P < 0.0001). The values of ΔPPART and ΔPPCNAP before VE were significantly correlated with the percent increase in SV induced by VE (respectively, r2 = 0.50; P < 0.0001 and r2 = 0.57; P < 0.0001). Before VE, a ΔPPART >10% discriminated between responders and nonresponders with a sensitivity of 90% (95% CI = 69%–99%) and a specificity of 87% (95% CI = 60%–98%). The area under the receiver operating characteristic (ROC) curve was 0.957 ± 0.035 for ΔPPART. Before VE, a ΔPPCNAP >11% discriminated between responders and nonresponders with a sensitivity of 85% (95% CI = 62%–97%) and a specificity of 100% (95% CI = 78%–100%). The area under the ROC curve was 0.942 ± 0.040 for ΔPPCNAP. There was no significant difference between the area under the ROC curve for ΔPPART and ΔPPCNAP.

CONCLUSIONS: A value of ΔPPCNAP >11% has a sensitivity of at least 62% in predicting preload-dependent responders to VE in mechanically ventilated patients during general anesthesia.

Published ahead of print June 3, 2011

From the *Service d'Anesthésie et de Réanimation 1, Hôpital Pellegrin, CHU de Bordeaux; and Université Victor Segalen Bordeaux 2, Bordeaux, France.

Supported solely by departmental funds.

The authors declare no conflicts of interest.

Reprints will not be available from the authors.

Address correspondence to Matthieu Biais, MD, Service d'Anesthésie et de Réanimation 1, Hôpital Pellegrin, CHU Bordeaux, 33076 Bordeaux Cedex, France. Address e-mail to matthieu.biais@chu-bordeaux.fr.

Accepted April 22, 2011

Published ahead of print June 3, 2011

Optimal monitoring of cardiac preload in the critically ill is paramount for precise hemodynamic management, particularly during major vascular surgery. Recent published studies have shown that intraoperative optimization of cardiac output (CO) using intravascular volume expansion reduces the length of hospital stay, critical care admissions, and mortality after major surgery in various settings.15 In contrast, inappropriate fluid administration can have deleterious effects. Several clinical studies demonstrated the usefulness of dynamic indices based on heart–lung interactions for guiding intravascular volume resuscitation in patients under mechanical ventilation.610 Mechanical ventilation induces cyclic changes in intrathoracic and transpulmonary pressures that transiently affect left ventricular preload, resulting in cyclic changes in stroke volume (SV) in preload-dependent, but not in preload-independent patients.11,12 These cyclic changes in SV can be evaluated by the cyclic changes in arterial pulse pressure. Several studies have shown that pulse pressure variation is able to predict fluid responsiveness in patients in the operating room and intensive care unit.13 However, this technique requires percutaneous arterial catheterization, which is associated with several rare but serious complications (thrombosis, infections, pseudoaneurysm, hematoma, and bleeding).1418

A method for assessing noninvasive arterial blood pressure using an electropneumatic control loop was introduced by Penáz in 1973.19 Briefly, the blood volume in a finger is measured and kept constant by applying corresponding external pressure. The continuously changing external pressure needed to keep the volume constant directly corresponds to the arterial pressure and, therefore, is an instantaneous, continuous measure of arterial blood pressure. Numerous studies evaluating the accuracy of this technology, e.g., Finapres™ (Ohmeda Monitoring Systems, Englewood, CO), found that it was not a reliable substitute for invasive radial or brachial intraarterial pressure monitoring in anesthetized patients.2023 More recently, the Infinity® CNAP™ SmartPod® (Dräger Medical AG & Co. KG, Lübeck, Germany) has become commercially available. The basic operating principle of the CNAP is similar to the Finapres, but CNAP uses multiple control loops. It has recently been shown that CNAP provides real-time estimates of mean arterial blood pressure (MAP) comparable with those measured by an invasive intraarterial catheter system during general anesthesia.24,25

The respiratory variations in pulse pressure obtained with the CNAP system have not yet been studied. We hypothesized that pulse pressure variation obtained with the CNAP device (ΔPPCNAP) is closely correlated with pulse pressure variation obtained with the invasive arterial line (ΔPPART), and that ΔPPCNAP will be as effective as ΔPPART in predicting fluid responsiveness. To test this hypothesis, ΔPPCNAP, ΔPPART, and SV were measured before and after intravascular volume expansion.

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METHODS

Patients

After obtaining approval from the local ethics committee (Comité de Protection des Personnes Sud-Ouest et Outre Mer III, Bordeaux, France) and written informed consent, 35 patients scheduled for major vascular surgery were enrolled in the study. Exclusion criteria were the following: body mass index >40 kg/m2 or <15 kg/m2, no palpable radial artery pulses, history of, or current, arteriovenous shunt, propensity to hand ischemia in the presence of radial arterial obstruction as evidenced by a positive Allen test (defined as lack of return of color within 7 seconds after release of the ulnar artery compression), hypothermia, Raynaud syndrome and related diseases, collagenosis, valvular heart disease, left ventricular ejection fraction <50%, arrhythmia, or history of lung disease.

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Anesthesia

All patients were premedicated orally with hydroxyzine (1 mg/kg) 1 hour before the induction of anesthesia. Monitoring included electrocardiogram, pulse oximetry, noninvasive arterial blood pressure, end-tidal carbon dioxide and oxygen concentration, body temperature and Bispectral Index monitoring (BIS-XP™, A2000 monitor; Aspect Medical Systems, Natick, MA). An 18-gauge IV catheter was inserted in a radial artery. After oxygen administration, anesthesia was induced and maintained with target-controlled infusion of propofol and remifentanil at an initial target concentration of 4 μg/mL and 4 ng/mL, respectively.2628 Tracheal intubation was facilitated by a nondepolarizing neuromuscular blocking drug (atracurium 0.5 mg/kg), and mechanical ventilation was started using volume-controlled ventilation. Oxygen-air mixture and ventilation variables were adjusted to maintain pulse oximeter saturation >96% and end-tidal carbon dioxide partial pressure between 33 and 38 mm Hg. Target concentrations of propofol and remifentanil were adjusted to maintain the Bispectral Index between 40 and 50.

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Hemodynamic Monitoring

The Infinity CNAP SmartPod was started as recommended by the manufacturer before the induction of anesthesia. Two appropriately sized cuff sensors were connected to the CNAP and placed on the patient's index and middle fingers and fixed with the arm strap provided. Only 1 sensor cuff is inflated at any given time. To provide optimal patient comfort and measurement reliability, the sensor cuff alternates between the 2 fingers every 30 minutes. The CNAP was connected to the Infinity® Delta monitor (Dräger Medical) to display the systolic blood pressure, diastolic blood pressure, and MAP. The Delta monitor measured oscillometric blood pressure in the brachial artery and used this measurement to recalibrate the CNAP every 15 minutes.

After the induction of anesthesia, an 8-cm 3.0F tipped catheter (Vygon, Ecouen, France) was inserted into the radial artery in the same limb as the CNAP device. A dedicated pressure transducer (FloTrac™; Edwards Lifesciences, Irvine, CA) was connected to the Delta monitor and to the catheter via a radial arterial line. A Vigileo™ system (Edwards Lifesciences) was connected to a second branch of the arterial line to measure SV.

Arterial and CNAP pressure waveforms were recorded using the printer module of the Delta monitor and the paper recordings were scanned. The scanned images were analyzed using ImageJ software (National Institutes of Health, Bethesda, MD). Pulse pressure variability was calculated from the curve derived from the arterial line (ΔPPART) and from the curve derived from the CNAP device (ΔPPCNAP). Pulse pressure variability was calculated from the difference between systolic and diastolic blood pressure. Maximal (Pulse Pressure max) and minimal (Pulse Pressure min) differences were determined during 3 consecutive respiratory cycles. The mean values of the 3 measurements were used to calculate arterial pulse pressure variability: ΔPP = (Pulse Pressure max − Pulse Pressure min)/[(Pulse Pressure max + Pulse Pressure min)/2] × 100, as previously described.9

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Study Protocol

After the induction of anesthesia, patients received 500 mL of 6% hydroxyethyl starch 130/0.4 (Voluven®; Fresenius Kabi, Uppsala, Sweden) given over 10 minutes. The following variables were recorded before and 3 minutes after the fluid was given: MAP, heart rate, CO, SV, ΔPPART, and ΔPPCNAP. The Delta monitor used an oscillometric blood pressure measurement to recalibrate the CNAP device immediately before each set of variables was recorded. The ventilatory setting and anesthetic drug infusion were kept constant during the study period. Vasopressor administration and patient stimulation were avoided.

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Statistical Analysis

Results were expressed as mean ± SD if data were normally distributed or median (25%–75% interquartile range) if not. The effects of intravascular volume expansion on hemodynamic variables were assessed using a nonparametric Wilcoxon rank sum test. Assuming that a 15% change in SV was required for clinical significance, patients were separated into responders and nonresponders by changes in SV ≥15% and <15% after the volume expansion, respectively.9,29 Hemodynamic variables before volume expansion were compared in responders and nonresponders with a nonparametric Mann– Whitney test. Using the small sample size, all tests were performed with permutation tests and P values were calculated using 10,000 Monte Carlo simulations30,31 (StatXact®8 [Cytel Statistical Software and Services, Cambridge, MA]). The relationships between changes in SV and ΔPPART orΔPPCNAP were evaluated using a Spearman rank test. ΔPPART and ΔPPCNAP were compared using the Bland and Altman method32 and Pearson test.

Receiver operating characteristic (ROC) curves were generated for ΔPPART and ΔPPCNAP varying the discriminating threshold, and areas under the ROC curves (95% confidence interval [CI]) were calculated and compared.33 Values for each area can be between 0 and 1. A value of 0.5 indicates that the screening measure is no better than chance, whereas a value of 1 implies perfect performance. In our study, the area under the ROC curve represented the probability that a random pair of responders and nonresponders would be correctly ranked by ΔPPART and ΔPPCNAP after volume expansion. Considering previously published results,34 power analysis showed that 35 patients were necessary to detect differences of 0.15 between ΔPPART and ΔPPCNAP areas under the ROC curves (5% type I error rate, 80% power, 2-tailed test).

A P value of 0.05 was considered to be statistically significant. Statistical analysis was performed using StatView software 5.0 (SAS Institute, Inc., Cary, NC), MedCalc software 8.1.1.0 (Mariakerke, Belgium), and StatXact 8.

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RESULTS

The characteristics of the 35 patients are reported in Table 1. Hemodynamic measurements in responders and nonresponders at baseline and after intravascular volume expansion are given in Table 2. ΔPPART and ΔPPCNAP decreased significantly after volume expansion in both responders and nonresponders. Before volume expansion, ΔPPART and ΔPPCNAP were significantly higher and CO and SV were significantly lower in responders than in nonresponders (Fig. 1).

Table 1

Table 1

Table 2

Table 2

Figure 1

Figure 1

Before volume expansion, ΔPPART and ΔPPCNAP showed a significant correlation (r = 0.90; 95% CI = 0.84–0.96; P < 0.0001), a bias of 0.6% (95% CI = −0.3% to 1.5%), and limits of agreement from −4.7% (95% CI = −6.3% to −3.2%) to 5.9% (95% CI = 4.4%–7.5%) (Fig. 2A).

Figure 2

Figure 2

After volume expansion, ΔPPART and ΔPPCNAP showed a significant correlation (r = 0.74; 95% CI = 0.54–0.86; P < 0.0001), a bias of −0.9% (95% CI = −1.5% to −0.4%), and limits of agreement of −4.1% (95% CI = −5.0% to −3.1%) to 2.2% (95% CI = 1.3%–3.1%) (Fig. 2B).

Baseline ΔPPART and ΔPPCNAP correlated significantly with the percent change in SV induced by volume expansion (respectively, r2 = 0.50; P < 0.0001 and r2 = 0.57; P < 0.0001).

A 10% change in ΔPPART discriminated between responders and nonresponders with a sensitivity of 90% (95% CI = 69%–99%) and a specificity of 87% (95% CI = 60%–98%). The area under the ROC curve was 0.957 ± 0.035 (Fig. 3). An 11% change in ΔPPCNAP discriminated between responders and nonresponders with a sensitivity of 85% (95% CI = 62%–97%) and a specificity of 100% (95% CI = 78% to −100%). The area under the ROC curve was 0.942 ± 0.040 (Fig. 3). There was no significant difference between the area under the ROC curve for ΔPPART and ΔPPCNAP.

Figure 3

Figure 3

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DISCUSSION

Our results suggest that in the operating room during vascular surgery, respiratory-induced variations in the pulse pressure measured noninvasively in the finger using the CNAP system correlate closely with pulse pressure variations measured invasively with an arterial catheter. Both methods of measuring pulse pressure variability predict fluid responsiveness.

Solus-Biguenet et al.35 evaluated the respiratory-induced variations in pulse pressure obtained with a finger blood pressure monitor (Finapres [ΔPPFINA]) in 8 patients undergoing hepatic resection. Their comparison between ΔPPFINA and ΔPPART showed a bias of 0.1% and a limit of agreement of −6% to 6%. We found a larger bias of 0.6% (95% CI = −1.5 to 0.3) and relatively similar limits of agreement of −4.7% (95% CI = −6.3 to −3.2) to 5.9% (95% CI = 4.4–7.5). Solus-Biguenet et al. found that a 14% change in ΔPPFINA predicted fluid responsiveness well; the area under their ROC curves was 0.81 (0.70–0.93). We used a 10% change; the area under our ROC curve was 0.942 ± 0.040.

There may be several reasons that explain why our results appear to be more positive than those of Solus-Biguenet et al. We used the CNAP device, successor of their Finapres device. We delivered a larger volume of fluid, 500 mL of 6% hydroxyethyl starch, instead of their 250 mL of colloid. Our responders were defined as those patients who had a 15% increase in SV rather than their 10%. They measured SV invasively whereas our estimate came from a noninvasive FloTrac device. Because our FloTrac reference method uses a peripheral arterial pressure waveform, our reference measurement of SV may contain errors introduced by changes in vascular resistance. Our ability to discriminate between responders and nonresponders may be overstated. Solus-Biguenet et al. identified responders after measuring SV invasively using a pulmonary artery catheter. Their more conservative estimations for specificity and sensitivity may be more accurate. However, both studies confirm that pulse pressure variations measured noninvasively in the finger predict fluid responsiveness in patients.

Several research groups have identified conditions in which changes in pulse pressure variation do not accurately predict fluid responsiveness. The tidal volume needs to be >8 mL · kg−1, the ratio of heart rate to respiratory rate >3.6,36 and there needs to be absence of arrhythmia, spontaneous breathing, and right ventricular dysfunction.37

Our study has some limitations. The accuracy with which the Vigileo device measures SV has been tested in numerous settings with various results.38,39 However, the device is able to track changes in SV and CO induced by intravascular volume expansion with accuracy.7,34,40,41 Our testing was done just after the induction of general anesthesia and before the beginning of surgery. Changes in vascular tone that occur during surgery may alter the ability to predict fluid responsiveness. We did not study the ability of the device to predict fluid responsiveness during hypotension, septic shock, hypothermia, periods of rapid cold blood transfusion, and severe edema of the hand and extremities. We did not collect data concerning the satisfaction of anesthesiologists or surgeons with the device.

In conclusion, our results suggest that the amplitude of the respiratory-induced variations in the pulse pressure in the finger predict fluid responsiveness in the operating room.

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DISCLOSURES

Name: Matthieu Biais, MD.

Contribution: Matthieu Biais conceived and designed the study, performed data acquisition, participated in the data analysis and interpretation of the results, was involved in the statistical analysis and wrote the paper, and is designated as the archival author and is responsible for maintaining the study records.

Attestation: Matthieu Biais read and approved the final manuscript.

Name: Laurent Stecken, MD.

Contribution: Laurent Stecken performed data acquisition.

Attestation: Laurent Stecken read and approved the final manuscript.

Name: Laetitia Ottolenghi, MD.

Contribution: Laetitia Ottolenghi performed data acquisition.

Attestation: Laetitia Ottolenghi read and approved the final manuscript.

Name: Stéphanie Roullet, MD.

Contribution: Stéphanie Roullet performed data acquisition.

Attestation: Stéphanie Roullet read and approved the final manuscript.

Name: Alice Quinart, MD.

Contribution: Alice Quinart performed data acquisition.

Attestation: Alice Quinart read and approved the final manuscript.

Name: Françoise Masson, MD.

Contribution: Françoise Masson was involved in the statistical analysis and wrote the paper.

Attestation: Françoise Masson read and approved the final manuscript.

Name: François Sztark, MD, PhD.

Contribution: François Sztark participated in the data analysis and interpretation of the results and was involved in the statistical analysis and wrote the paper.

Attestation: François Sztark read and approved the final manuscript.

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ACKNOWLEDGMENTS

The authors thank Ray Cooke for revising the English, and Dräger Medical Systems for providing the Infinity CNAP SmartPod and the Infinity Delta monitor.

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