Effects of tidal volume challenge on the reliability of plethysmography variability index in hepatobiliary and pancreatic surgeries: a prospective interventional study

doi:10.21203/rs.3.rs-2114015/v1

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Effects of tidal volume challenge on the reliability of plethysmography variability index in hepatobiliary and pancreatic surgeries: a prospective interventional study

https://doi.org/10.21203/rs.3.rs-2114015/v1

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published 17 Mar, 2023

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The plethysmography variability index (PVI) is a safe, non-invasive, and useful parameter for evaluating fluid responsiveness but it does not reliably predict fluid responsiveness during low tidal volume (V_T) ventilation. We hypothesized that in a ‘tidal volume challenge’ with a transient increase in tidal volume from 6 to 8 ml/kg, the changes in PVI will predict fluid responsiveness. We performed a prospective interventional study in adult patients undergoing hepatobiliary pancreatic tumor resections, having continuous cardiac output monitoring, and receiving controlled low V_T ventilation. We recorded the values for PVI, perfusion index, corrected flow time (FTc), index of contractility (ICON), stroke volume variation, and stroke volume index at V_T of 6 ml/kg and 1 min after the V_T challenge. The V_T was reduced to 6 ml/kg and a fluid bolus was given to identify fluid responders. The area under the receiver operating characteristic curve (AUC) for absolute change in PVI after increasing V_T from 6 to 8 ml/kg was 0.86 (p-value < 0.001) with best cut-off value 2.5% with 95% sensitivity and 68% specificity after doing the tidal volume challenge. AUC for ICON and FTc at V_T 8 ml/kg were 0.72 and 0.70 with p-value 0.008 and 0.01 and best cut-off values 40.7% and 332ms respectively. Changes in PVI value obtained by transiently increasing V_T are superior to PVI value measured alone for predicting fluid responsiveness during low-V_T ventilation. Also, ICON and FTc can be used as good and reliable predictors of fluid responsiveness.

Fluid responsiveness

low tidal volume

plethysmography variability index

tidal volume challenge

Hepatobiliary and pancreatic surgeries have been associated with high mortality and morbidity rates, but recent advances in anesthetic and surgical management have significantly reduced the operative risk [1]. Taking care of patients undergoing these major surgeries in the perioperative period is often challenging due to coexisting medical problems and debilitation found in many patients as well as due to the potential for significant operative blood loss [2].

Studies have shown that the intraoperative optimization of cardiac output by repeated volume loading reduces postoperative morbidity and shortens hospital stay following abdominal surgery [3]. However, unnecessary intravenous fluids may be deleterious and intraoperative fluid restriction has been shown to improve clinical outcomes [4]. During major hepatobiliary and pancreatic surgeries, intravascular volume expansion is constantly required, but the safety margin for fluid management is quite narrow [5].

The plethysmography variability index (PVI) is a safe, useful, and dynamic variation parameter for evaluating the fluid responsiveness of perioperative patients who have undergone major abdominal surgery and for critically ill patients. PVI automatically and continuously calculates the respiratory variations in the plethysmographic waveform acquired by a pulse oximeter [6, 7]. PVI is based on the respiratory variations in the pulse oximetry plethysmographic waveform amplitude and on various factors that affect lung mechanics, including tidal volume (V_T). These factors can influence PVI values [8] and it has been presumed that the effect of V_T reaches significance at > 8 ml kg^− 1 [9]. However, there is currently a growing trend towards a reduction in V_T, not only in intensive care unit (ICU) patients with acute lung injury but also in patients with healthy lungs undergoing surgery [10].

The ‘tidal volume challenge’ test is a novel test in which there is a transient increase in V_T from 6 to 8 ml kg^− 1 of predicted body weight (PBW) and this test has been proposed to improve the reliability of dynamic indexes in predicting fluid responsiveness in patients receiving low tidal volume (V_T) ventilation [11]. However, no studies have used this new test to determine the reliability of PVI in predicting fluid responsiveness in patients receiving low V_T ventilation. Therefore, in this study, we investigated whether a temporary increase in V_T can predict fluid responsiveness through the use of PVI in patients receiving low V_T ventilation during hepatobiliary and pancreatic surgeries.

We performed this prospective interventional study on adult patients undergoing hepatobiliary pancreatic surgeries after obtaining approval from the Ethical and Scientific Committee of Fayoum University Hospital (D154) and the National Liver Institute (0146/2018). We received written informed consent from the patients or their surrogates to participate in this study. We registered this study at ClinicalTrials.gov (NCT03546179).

We conducted the study at the National Liver Institute Hospital, Menoufia, Egypt. We studied patients aged > 18 years undergoing hepatic, pancreatic, or biliary tumor resection. Patients with preoperative cardiac arrhythmias, peripheral vascular disease, low left ventricular function (ejection fraction < 40%), significant valvular heart disease, or fulminant hepatic failure were excluded from this study.

On the day of surgery, intravenous access was obtained. Demographic and anthropometric data including age, sex, height, actual body weight, PBW, body mass index, body surface area, smoking history, and comorbid diseases were recorded. All patients were monitored using 5-lead electrocardiography, non-invasive blood pressure monitoring, and pulse oximetry. General anaesthesia was induced with propofol (2 mg.kg^− 1 IV), fentanyl (3 µg.kg^− 1 IV) and rocuronium (0.6 mg.kg^− 1 IV). Anesthesia was maintained with sevoflurane (1.5–3%).

Mechanical ventilation was given to all patients to keep end-tidal CO2 between 35 and 40 mmHg. A 7 F triple-lumen central venous catheter was inserted in the right internal jugular vein with ultrasound guidance. The left radial artery was cannulated, a pressure transducer was placed on the midaxillary line, fixed to the operating table to keep the sensor at the atrial level, and zeroed to atmospheric pressure for measuring invasive arterial blood pressure. An indwelling urinary bladder catheter was inserted to monitor urinary output. Head and extremity wraps and warmer systems in the form of forced warming system were applied to maintain body temperature. A trans-oesophageal Doppler probe (Cardio QP EDM™; Deltex Medical, Chichester, UK, Fig. 1) was greased with a lubricating gel and passed nasally into the mid-esophagus until the aortic blood flow signals were best identified. Four skin electrodes (iSense electrical cardiometry skin sensors; Osypka Medical) were placed on the neck and thorax of patients as per the manufacturer’s recommendations and an electrical cardiometry monitor (ICON Cardiotronics, Inc., La Jolla, CA USA; Osypka Medical GmbH, Berlin, Germany, Fig. 2) was connected to the sensor cable. The Masimo Pulse Co-Oximeter probe (Masimo SET Rainbow R2-25r and R225a; Masimo Corp., Irvine, CA, USA, see Fig. 3) was placed on the index finger of patients.

Throughout surgery, packed red blood cells (300 ml) were transfused when the hematocrit level was < 25%. Fresh frozen plasma (200 ml) was administrated when the fibrinogen level was < 2 g.dl^− 1 or the international normalized ratio was > 2. Patients were extubated either in the operating room or postoperatively in the ICU.

Intervention protocol (Fig. 4)

After the tumor resection phase, baseline hemodynamic variables including heart rate (HR), mean arterial pressure (MAP), central venous pressure (CVP), stroke volume index (SVI), PVI, perfusion index (PI), stroke volume variation (SVV), index of contractility (ICON) and corrected flow time (FTc) with a 6 ml/kg V_T ventilation were recorded. After baseline measurement, V_T was increased to 8 ml/kg PBW for 1 min, and the above-mentioned hemodynamic variables were recorded. The V_T has then decreased to 6 ml/kg PBW and after 1 min, measurements were recorded.

After these hemodynamic measurements, volume expansion was performed for 10 minutes using an infusion of a balanced crystalloid solution (6 ml/kg actual body weight). The same hemodynamic parameters were measured under ventilation with a V_T of 6 ml/kg 5 min after volume loading. Then, the absolute change (ΔPVI_6 − 8) (PVI after − PVI before V_T challenge) and percentage change (%ΔPVI_6 − 8) (PVI after − PVI before VT challenge / PVI before VT challenge) between the PVI at 6 ml/kg of PBW (PVI₆) and that at 8 ml kg^− 1 of PBW (PVI₈) (i.e., after performing a V_T challenge) were calculated. The change in PVI after giving the fluid bolus (ΔPVI_fb) (PVI after − PVI before fluid bolus) was also calculated. The percentage change in SVI, according to volume loading, was used as the principal indicator of fluid responsiveness. Responders or non-responders were determined when the increase in SVI was ≥ 10% or < 10% after volume loading, respectively [12].

No more than two V_T challenges can be performed in any patient. Doses of vasoactive medications, if used, and positive end-expiratory pressure were kept constant.

We defined the primary outcome as the best cut-off value of ΔPVI_6 − 8 in hepato–pancreatico–biliary surgeries using a V_T challenge. The secondary outcomes were the evaluation of hemodynamic variables in fluid responders and non-responders and the detection of sensitivity and specificity of PVI₆, PVI₈, ΔPVI_6 − 8, and other measured dynamic variables of fluid responsiveness.

The Statstodo online computer program (www.statstodo.com) was used to calculate the sample size requirement for comparing two receiver operating characteristics (ROC) curves with expected areas under the curves (AUC) of 0.65 (PVI₆) and 0.90 (ΔPVI_6 − 8), assuming an α error of 0.05 and a power of 90%. A minimum of 40 patients were needed to detect an AUC difference of 0.25 when supposing that the number of responders was similar to that of non-responders [13]. Since our expected data loss was 20%, 48 patients had to be enrolled in this study.

The SPSS software Version 21.0 (IBM, Armonk, NY, USA) was used to perform statistical analysis. Data were presented as mean (standard deviation), median [interquartile range (range)], or percentage (%). Distribution normality was assessed using the Shapiro–Wilk test. Changes in continuous variables from 6 to 8 ml/kg PBW were compared using the paired t-test or Wilcoxon signed rank-sum test. Group comparisons between responders and non-responders were performed using the independent t-test or Mann–Whitney U test. Categorical variables were analyzed using the chi-square test or Fisher exact test. To test the abilities of the dynamic preload indices to predict fluid responsiveness, the AUCs of responders were calculated and compared using the Hanley–McNeil test (AUC = 0.5, a useless test with no prediction possible; AUC = 0.6–0.69, a test with poor predictability; AUC = 0.7–0.79, a fair test; AUC = 0.8–0.89, a test with good predictability; AUC = 0.9–0.99, an excellent test; AUC = 1.0, a perfect test with the best possible prediction) [14]. An optimal threshold value was determined for each variable to maximize the Youden index [sensitivity + (specificity − 1)] [15].

The patients in this study were enrolled between August 2018 and February 2020. Figure 5 shows the study flowchart diagram. Our final analysis included 48 patients. 20 patients were fluid responders while 28 patients were non-fluid responders. The physical characteristics of the patients are shown in Table 1.

Table 1

demographics and operative characteristics of the 48 included patients undergoing tidal volume challenge.
Characteristic	Overall (n = 48)	Responder (n = 20)	Non-responder (n = 28)
Age (years)	53.1 (12.5)	53.9 (11.8)	52.5 (13.1)
Sex Male Female	38 (79%) 10 (21%)	17 (85%) 3 (15%)	21 (75%) 7 (25%)
Height (cm)	170.7 (5.6)	171.4 (6.2)	170.2 (5.2)
Predicted body weight (Kg)	65.6 (6)	66.6 (6.2)	64.9 (5.8)
Body mass index (Kg.m^− 2)	26.2 (3.4)	25.4 (2.3)	26.8 (3.9)
Body surface area (m²)	1.9 (0.17)	1.8 (0.16)	1.9 (0.18)
Smoking history Yes No	32 (67%) 16 (33%)	16 (80%) 4 (20%)	16 (57%) 12 (43%)
Comorbid diseases No Yes Hypertension Diabetes Combined Others	29 (60%) 19 (40%) 6 (13%) 4 (8%) 4 (8%) 5 (11%)	12 (60%) 8 (40%) 3 (15%) 2 (10%) 1 (5%) 2 (10%)	17 (61%) 11 (39%) 3 (11%) 2 (6%) 3 (11%) 3 (11%)
Type of operation Hepatic tumor resection Pancreatic tumor resection Biliary tumor resection	24 (50%) 23 (48%) 1 (2%)	9 (45%) 10 (50%) 1 (5%)	15 (54%) 13 (46%) 0 (0%)
Use of vasopressors Yes No	11 (23%) 37 (77%)	4 (20%) 16 (80%)	7 (25%) 21 (75%)
Values are expressed as mean (SD) or number (proportion).

Table 2 shows hemodynamic variables in responders and non-responders. At baseline 1 and 2 (at tidal volume 6 ml.kg^− 1), there was a non-statistical significance in all variables and parameters between fluid responders and non-responders except for the index of contractility variable at baseline 1 (p-value = 0.021).

Table 2

Hemodynamic variables in responders (n = 20) and non-responders (n = 28).
	Baseline 1 (Tidal volume 6 ml.kg^− 1)	After tidal volume challenge (Tidal volume 8 ml.kg^− 1)	p value	Baseline 2 (Tidal volume 6 ml.kg^− 1)	After fluid bolus	p value
HR (beats.min^− 1) Responder Non-responder p value	84 (16) 90 (18) 0.279	89 (19) 91 (17) 0.673	0.056 0.286	86 (16) 89 (16) 0.533	87 (13) 91 (16) 0.353	0.662 0.073
MAP (mmHg) Responder Non-responder p value	83 (14) 78 (11) 0.197	83 (19) 80 (10) 0.463	0.946 0.374	81 (12) 81 (9) 0.797	83 (12) 84 (10) 0.792	0.545 0.092
CVP (cmH₂O) Responder Non-responder p value	6.5 (2.5) 8 (2.5) 0.140	7.5 (3.5) 8.5 (2.5) 0.415	0.063 0.076	7.5 (3) 9 (2.5) 0.099	8 (3.5) 9.5 (2.5) 0.091	0.548 0.002
SVI (ml. beat^− 1.m^− 2) Responder Non-responder p value	43 (11.2) 41.6 (9.7) 0.663	42.5 (11.2) 42 (10.2) 0.865	0.721 0.568	41.3 (9.9) 42.5 (9.5) 0.668	49.6 (13) 42 (10) 0.028	< 0.001 0.516
PVI (%) Responder Non-responder p value	9.5 [9–12 (4–29)] 10 [8–13.7 (4–26)] 0.992	18 [12–19 (7–35)] 10 [7.2–13 (5–32)] 0.001	< 0.001 0.552	11.5 [9.2–16 (5–29)] 10 [6–12 (5–20)] 0.050	9 [5.5–10 (3–17)] 8 [6.2–11.7 (3–20)] 0.950	< 0.001 0.124
PI (%) Responder Non-responder p value	1.5 [0.84–3.5 (0.37–6.8)] 1.4 [0.55–2.8 (0.15–4.9)] 0.315	1.6 [0.71–3 (0.48–5.9)] 0.82 [0.46–2.4 (0.12–6.3)] 0.137	0.224 0.059	1.5 [0.67–3.2 (0.27–8.4)] 0.78 [0.52–2.5 (0.13–5.4)] 0.135	1.9 [1–3.5 (0.24–9.6)] 0.97 [0.38–2.6 (0.31–7.3)] 0.114	0.025 0.012
SVV (%) Responder Non-responder p value	13 [9–16.7 (5–26)] 11.5 [8.2–14.7 (5–23)] 0.413	12.7 [10.2–17.7 (8–21)] 11.5 [8–17.5 (7–21)] 0.216	0.965 0.688	11.9 [10.2–14.2 (6–25)] 11 [9–13.7 (5–20)] 0.395	12.5 [9.7–14 (6–28)] 11.5 [8–14.7 (6–21)] 0.378	0.295 0.566
ICON (%) Responder Non-responder p value	48 [45.9–53 (34.1–110)] 41.9 [29.8–50.6 (21.7–58.9)] 0.021	46.7 [41.5–59 (31–78.1)] 40.1 [30.1–49.2 (20.7–58.9)] 0.008	0.940 0.051	45.2 [39.4–61.2 (29.9–80.5)] 41.6 [32–49.7 (19.6–60)] 0.054	56 [47.8–70.4 (33.2–97.6)] 41.2 [30.9–47.8 (21.8–57.8)] < 0.001	0.002 0.445
FTc (ms) Responder Non-responder p value	330.3 (22.6) 316.6 (26.7) 0.069	336.2 (22.8) 318.9 (25.7) 0.021	0.020 0.512	339 [313.7–344.5 (290–393)] 326.5 [298.7–341.2 (279–369)] 0.146	338.5 [326.2–339.7 (290–415)] 324.5 [304–336.7 (271–385)] 0.032	0.481 0.758
Values are expressed as mean (SD) or median [IQR (range)].
HR: heart rate, MAP: mean arterial pressure, CVP: central venous pressure, SVI: Stroke volume index, PVI: plethysmography variability index, PI: perfusion index, SVV: stroke volume variation, ICON: index of contractility, FTc: corrected flow time.

Figs. 6, 7, and 8 show receiver-operating characteristic curves of different parameters (PVI, ICON, and FTc respectively) values for 48 patients undergoing tidal volume challenge.

Table 3 shows the diagnostic ability of various variables to predict fluid responsiveness in patients undergoing tidal volume challenge with the best cut-off, sensitivity, and specificity values for significant variables (p < 0.05).

Table 3

Diagnostic ability of various variables to predict fluid responsiveness in the included 48 patients undergoing tidal volume challenge
	Area Under the Receiver-Operating Characteristic Curve (95% CI)	p value	Best Cut-off Value	Sensitivity (%)	Specificity (%)
HR at V_T 6 ml/kg PBW	0.41 (0.25–0.57)	0.315	-	-	-
HR at V_T 8 ml/kg PBW	0.44 (0.27–0.61)	0.537	-	-	-
MAP at V_T 6 ml/kg PBW	0.57 (0.40–0.74)	0.380	-	-	-
MAP at V_T 8 ml/kg PBW	0.53 (0.36–0.71)	0.645	-	-	-
CVP at V_T 6 ml/kg PBW	0.37 (0.20–0.53)	0.127	-	-	-
CVP at V_T 8 ml/kg PBW	0.41 (0.24–0.58)	0.301	-	-	-
SVI at V_T 6 ml/kg PBW	0.54 (0.37–0.71)	0.631	-	-	-
SVI at V_T 8 ml/kg PBW	0.53 (0.37–0.70)	0.661	-	-	-
PVI at V_T 6 ml/kg PBW	0.49 (0.33–0.66)	0.992	-	-	-
PVI at V_T 8 ml/kg PBW	0.79 (0.66–0.92)	0.001	11.5%	85	61
Change in PVI from V_T 6 to 8 ml/kg PBW	0.86 (0.76–0.96)	< 0.001	2.5%	95	68
Percentage change in PVI from V_T 6 to 8 ml/kg PBW	0.83 (0.72–0.94)	< 0.001	29%	82	75
Change in PVI after fluid bolus	0.78 (0.65–0.92)	0.001	2%	70	79
PI at V_T 6 ml/kg PBW	0.58 (0.42–0.74)	0.315	-	-	-
PI at V_T 8 ml/kg PBW	0.62 (0.46–0.78)	0.137	-	-	-
SVV at V_T 6 ml/kg PBW	0.57 (0.40–0.73)	0.413	-	-	-
SVV at V_T 8 ml/kg PBW	0.60 (0.44–0.76)	0.216	-	-	-
ICON at V_T 6 ml/kg PBW	0.69 (0.54–0.84)	0.021	45.5%	80	65
ICON at V_T 8 ml/kg PBW	0.72 (0.58–0.87)	0.008	40.7%	85	58
FTc at V_T 6 ml/kg PBW	0.61 (0.45–0.77)	0.184	-	-	-
FTc at V_T 8 ml/kg PBW	0.70 (0.55–0.85)	0.017	332 ms	70	75
HR: heart rate, V_T: tidal volume, PBW: predicted body weight, MAP: mean arterial pressure, CVP: central venous pressure, SVI: Stroke volume index, PVI: plethysmography variability index, PI: perfusion index, SVV: stroke volume variation, ICON: index of contractility, FTc: corrected flow time.
Dashes indicate the variable was not measured (p > 0.05).

The main finding of the study is that when V_T is increased from 6 to 8 ml/kg of PBW, the absolute change in PVI (ΔPVI_6 − 8) reliably predicts fluid responsiveness, with a cut-off value of 2.5%. In addition, at a PVI value of 8 ml/kg of PBW, the percentage change in PVI (%ΔPVI_6 − 8) and ΔPVI after giving a fluid bolus are reliable in predicting fluid responsiveness; however, their sensitivities and specificities are lower than those of ΔPVI_6 − 8.

PVI reliably predicts fluid responsiveness provided that V_T is at least 8 ml.kg^− 1. During low-V_T ventilation, PVI may indicate a non-responsive status, even in responders, as V_T might be insufficient to produce a considerable change in intrathoracic pressure. This finding was also reported by Desebbe et al. [16]. We hypothesized that raising V_T from 6 to 8 ml/kg of PBW increases intrathoracic pressure and the magnitude of the heart-lung interactions and can unmask fluid responsiveness during low-V_T ventilation. We confirmed this hypothesis in our study, which showed that changes in PVI after performing a V_T challenge identified real fluid responders with high sensitivities and specificities, whereas they could not be identified using PVI at low-V_T ventilation. Thus, the V_T challenge helped unmask responders, as the increase in PVI was significant only in responders. Our results are consistent with a study that showed a considerable rise in PVI at V_T 10 ml/kg as compared with 6 ml/kg [16].

In addition, we found that PVI at 8 ml/kg V_T has high sensitivity (95%) and specificity (54%) and a cut-off value of 10%. These findings are supported by studies carried out by Desebbe et al. (sensitivity 83%, specificity 80%, and a cut-off value of 12%) [16] and Desgranges et al. (sensitivity 74%, specificity 67%, and a cut-off value of 12%) [17]. Our study showed that the PVI value at low-V_T ventilation (6 ml.kg^− 1) used in a protective lung strategy is not significant, which matches that observed by Desebbe et al., who found that PVI could predict fluid responsiveness only when V_T was 8 ml/kg [16].

In our study, we found that HR as a static index of fluid responsiveness was useless (AUC < 0.5; p > 0.05) in predicting fluid responsiveness. These results are consistent with data obtained by Cannesson et al., who have found a non-significant effect (p-value = 0.06) before and after volume expansion [18].

We found that MAP was a non-specific and non-sensitive parameter for predicting changes in fluid responsiveness (AUC:0.5). In accordance with the present results, Cannesson et al. found a non-significant response for MAP either in responders or non-responders, with a p-value = 0.76 [18]. Also, our results agree with the findings of Le Manach et al., which showed that the changes in standard arterial pressure parameters cannot be used for conducting cardiac output optimization in the perioperative period [19].

Regarding CVP, we found no relation between fluid responsiveness either in low- (6 ml.kg^− 1) or high- (8 ml.kg^− 1) V_T ventilation, which is supported by several studies and meta-analyses [20–22]. Marik and Cavallazzi conducted a systematic review that demonstrated a very poor relationship between CVP and blood volume as well as the inability of CVP/ΔCVP to predict the hemodynamic response to fluid challenge, recommending that CVP should not be used to make clinical decisions about fluid management [23]. On the other hand, surveys regularly report that clinicians continue to use CVP for predicting fluid responsiveness [23].

Our study demonstrated that SVI showed a highly significant difference between responders and non-responders, with a significant increase after the fluid bolus. These results are consistent with those of Vergnaud et al., who concluded that SVI, non-invasively measured by bioreactance, can predict fluid responsiveness in sedated and mechanically ventilated pediatric patients after surgery [24]. Also, our results match those observed by Cannesson et al., who found a significant difference in SVI before and after volume expansion (p < 0.001) [18].

We found the non-significance of PI in the estimation of fluid responsiveness. Our results are supported by Cannesson et al. (AUC = 0.43; p = 0.61) [18], although there was a statistically significant change after giving fluid therapy either to responders or non-responders, which proved that giving fluids improves patients’ PI.

Our study did not find a significant relationship between SVV and fluid responsiveness either with 6 ml/kg or 8 ml/kg V_T ventilation. This outcome is contrary to that of Myatra et al., who found that SVV is a good predictor of fluid responsiveness when V_T is 8 ml/kg [11]. This result may be due to the different equipment used or types of patients examined in the two studies. We used electrical cardiometry and oesophageal Doppler, while Myatra et al. used pulse-induced continuous cardiac output. Furthermore, Myatra et al. studied ICU patients with acute respiratory failure, while we investigated perioperative patients who had undergone tumor resection.

A novel parameter in our study for predicting fluid responsiveness with high sensitivity and specificity is electrical cardiometry ICON, which is supposed to be independent of load conditions, either in low-V_T ventilation (6 ml.kg^− 1) with 80% sensitivity, 65% specificity, and with a 45.5% cut-off value or in high-V_T ventilation (8 ml.kg^− 1) with 85% sensitivity, 58% specificity and a 40.7% cut-off value. Also, ICON showed statistical significance between responders and non-responders at baseline V_T ventilation (6 ml.kg^− 1), after the V_T challenge, and after giving the fluid bolus, which may prove that it is a promising parameter for monitoring and guiding fluid therapy in patients. However, studies on ICON are rare but have concluded that electrical cardiometry can be a useful and easy-to-manage (four skin patches) tool for detecting left ventricular function and failure and can provide help to patients [25].

FTc can predict fluid responsiveness only when patient V_T is at least 8 ml.kg^− 1, having a cut-off of 332 ms, 70% sensitivity, and 75% specificity. Our finding is consistent with the cut-off value of FTc from a previous study, which lies between 330 and 350 ms [26]. Furthermore, Hamimy et al. also found the same best cut-off value, concluding that FTc better predicted fluid responsiveness than CVP in septic patients [22].

What is surprising in our study is that all men who underwent pancreatic resection had a history of smoking. Our finding is supported by Koyanagi et al., who stated that there is a well-known association between smoking and pancreatic cancer and that quitting smoking would be beneficial for pancreatic cancer prevention, especially in men [27].

Our study has some limitations. The V_T challenge cannot overcome other limitations of PVI during low-V_T ventilation, such as the presence of spontaneous breathing, cardiac arrhythmias, open chest, raised intra-abdominal pressure, and right ventricular dysfunction. Also, the increase in the end-tidal CO₂, which may be due to the use of 6 ml/kg V_T, can be overcome partially by increasing the respiratory rate.

In conclusion, changes in PVI value obtained by transiently increasing V_T are superior to PVI value measured alone for predicting fluid responsiveness during low-V_T ventilation. Also, ICON and FTc can be used as good and reliable predictors of fluid responsiveness to discriminate between fluid responders and non-responders.

Funding

The authors declared that no funds, grants, or other support were received during the preparation of this manuscript.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

All authors contributed to the study's conception and design. Material preparation, data collection, and analysis were performed by Yasser Salem Mostafa Mahmoud. The first draft of the manuscript was written by Hany Mahmoud Yassin and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript

Ethics approval

The study was approved by the Ethical and Scientific Committee of Fayoum University Hospital and the National Liver Institute according to

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Acknowledgments

The authors thank the anesthesia staff and technicians of the National Liver Institute for their cooperation during this study.

Tympa A, Theodoraki K, Tsaroucha A, Arkadopoulos N, Vassiliou I, Smyrniotis V. Anesthetic considerations in hepatectomies under hepatic vascular control. HPB Surg. 2012;2012:1–12. https://doi.org/10.1155/2012/720754.
Ramsay M. Hepatic physiology & anesthesia. In: Butterworth JF, Mackey DC, Wasnick JD, editors. Morgan Mikhail’s Clin. Anesthesiol. 6th ed. New York: McGraw-Hill Education; 2018. pp. 1164–92.
Grocott MPW, Mythen MG, Gan TJ. Perioperative fluid management and clinical outcomes in adults. Anesth Analg. 2005;100:1093–106. https://doi.org/10.1213/01.ANE.0000148691.33690.AC.
Joshi GP. Intraoperative fluid restriction improves outcome after major elective gastrointestinal surgery. Anesth Analg. 2005;101:601–5. https://doi.org/10.1213/01.ANE.0000159171.26521.31.
Smyrniotis V, Kostopanagiotou G, Theodoraki K, Tsantoulas D, Contis JC. The role of central venous pressure and type of vascular control in blood loss during major liver resections. Am J Surg. 2004;187:398–402. https://doi.org/10.1016/j.amjsurg.2003.12.001.
Konur H, Erdogan Kayhan G, Toprak HI, Bucak N, Aydogan MS, Yologlu S, et al. Evaluation of pleth variability index as a predictor of fluid responsiveness during orthotopic liver transplantation. Kaohsiung J Med Sci. 2016;32:373–80. https://doi.org/10.1016/j.kjms.2016.05.014.
Lee H-C, Tsai Y-F, Tsai H-I, Chung PC-H, Yu H-P, Lee W-C, et al. Pulse oximeter–derived pleth variability index is a reliable indicator of cardiac preload in patients undergoing liver transplantation. Transpl Proc. 2016;48:1055–8. https://doi.org/10.1016/j.transproceed.2015.12.106.
Desgranges F-P, Evain J-N, Pereira de Souza Neto E, Raphael D, Desebbe O, Chassard D. Does the plethysmographic variability index predict fluid responsiveness in mechanically ventilated children? A meta-analysis. Br J Anaesth. 2016;117:409–10. https://doi.org/10.1093/bja/aew245.
Antonsen LP, Kirkebøen KA. Evaluation of fluid responsiveness: is photoplethysmography a noninvasive alternative? Anesthesiol Res Pract. 2012;2012:1–10. https://doi.org/10.1155/2012/617380.
Futier E, Constantin JM, Paugam-Burtz C, Pascal J, Eurin M, Neuschwander A, et al. A trial of intraoperative low-tidal-volume ventilation in abdominal surgery. N Engl J Med. 2013;369:428–37. https://doi.org/10.1056/NEJMoa1301082.
Myatra SN, Prabu NR, Divatia JV, Monnet X, Kulkarni AP, Teboul J-L. The changes in pulse pressure variation or stroke volume variation after a “tidal volume challenge” reliably predict fluid responsiveness during low tidal volume ventilation. Crit Care Med. 2017;45:415–21. https://doi.org/10.1097/CCM.0000000000002183.
Min JJ, Gil N-S, Lee J-H, Ryu DK, Kim CS, Lee SM. Predictor of fluid responsiveness in the ‘grey zone’: augmented pulse pressure variation through a temporary increase in tidal volume. Br J Anaesth. 2017;119:50–6. https://doi.org/10.1093/bja/aex074.
Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29–36. https://doi.org/10.1148/radiology.143.1.7063747.
Ríos JE, Tenorio JH-M, Ricce ML, Encinas CG, Matteucci SR, Vila Gutierrez S, et al. Prospective validation of the Rockall Scoring System in patients with upper gastrointestinal bleeding in Cayetano Heredia Hospital Lima- Peru. Rev Gastroenterol Peru. 2009;29:111–7.
Ray P, Manach Y, Le, Riou B, Houle TT. Statistical evaluation of a biomarker. Anesthesiology. 2010;112:1023–40. https://doi.org/10.1097/ALN.0b013e3181d47604.
Desebbe O, Boucau C, Farhat F, Bastien O, Lehot JJ, Cannesson M. The ability of pleth variability index to predict the hemodynamic effects of positive end-expiratory pressure in mechanically ventilated patients under general anesthesia. Anesth Analg. 2010;110:792–8. https://doi.org/10.1213/ANE.0b013e3181cd6d06.
Desgranges FP, Desebbe O, Ghazouani A, Gilbert K, Keller G, Chiari P, et al. Influence of the site of measurement on the ability of plethysmographic variability index to predict fluid responsiveness. Br J Anaesth. 2011;107:329–35. https://doi.org/10.1093/bja/aer165.
Cannesson M, Desebbe O, Rosamel P, Delannoy B, Robin J, Bastien O, et al. Pleth variability index to monitor the respiratory variations in the pulse oximeter plethysmographic waveform amplitude and predict fluid responsiveness in the operating theatre. Br J Anaesth. 2008;101:200–6. https://doi.org/10.1093/bja/aen133.
Le Manach Y, Hofer CK, Lehot JJ, Vallet B, Goarin JP, Tavernier B, et al. Can changes in arterial pressure be used to detect changes in cardiac output during volume expansion in the perioperative period? Anesthesiology. 2012;117:1165–74. https://doi.org/10.1097/ALN.0b013e318275561d.
Marik PE, Cavallazzi R. Does the central venous pressure predict fluid responsiveness? An updated meta-analysis and a plea for some common sense. Crit Care Med. 2013;41:1774–81. https://doi.org/10.1097/CCM.0b013e31828a25fd.
Bentzer P, Griesdale DE, Boyd J, MacLean K, Sirounis D, Ayas NT. Will this hemodynamically unstable patient respond to a bolus of intravenous fluids? JAMA -. J Am Med Assoc. 2016;316:1298–309. https://doi.org/10.1001/jama.2016.12310.
Hamimy W, Mukhtar A, Zaghloul A, Salem M. Comparing transesophageal Doppler corrected systolic flow time versus central venous pressure as a guide for fluid resuscitation in septic shock. Egypt J Anaesth. 2016;32:181–7. https://doi.org/10.1016/j.egja.2015.12.004.
Cannesson M, Pestel G, Ricks C, Hoeft A, Perel A. Hemodynamic monitoring and management in patients undergoing high-risk surgery: A survey among North American and European anesthesiologists. Crit Care. 2011;15:R197. https://doi.org/10.1186/cc10364.
Vergnaud E, Vidal C, Verchère J, Miatello J, Meyer P, Carli P, et al. Stroke volume variation and indexed stroke volume measured using bioreactance predict fluid responsiveness in postoperative children †. Br J Anaesth. 2015;114:103–9. https://doi.org/10.1093/bja/aeu361.
Boet A, Guihaire J, Le Bret E, Jourdain G, Hascoet S, Lambert V, et al. Electrical cardiometry: ICON (contractility index) and detection of left ventricular failure. Arch Cardiovasc Dis Suppl. 2020;12:171–2. https://doi.org/https://doi.org/10.1016/j.acvdsp.2019.09.354.
Monnet X, Rienzo M, Osman D, Anguel N, Richard C, Pinsky MR, et al. Esophageal Doppler monitoring predicts fluid responsiveness in critically ill ventilated patients. Intensive Care Med. 2005;31:1195–201. https://doi.org/10.1007/s00134-005-2731-0.
Koyanagi YN, Ito H, Matsuo K, Sugawara Y, Hidaka A, Sawada N, et al. Smoking and pancreatic cancer incidence: A pooled analysis of 10 population-based cohort studies in Japan. Cancer Epidemiol Biomarkers Prev. 2019;28:1370–8. https://doi.org/10.1158/1055-9965.EPI-18-1327.

No competing interests reported.

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Effects of tidal volume challenge on the reliability of plethysmography variability index in hepatobiliary and pancreatic surgeries: a prospective interventional study

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