Performance of Effective Arterial Elastance as Predictor of Mean Arterial Pressure in Patients with Sepsis or Septic Shock Receiving Fluid Expansion: A Validation Study by Regression Analysis

Performance of Effective Arterial Elastance as Predictor of Mean Arterial Pressure in Patients with Sepsis or Septic Shock Receiving Fluid Expansion: A Validation Study by Regression Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Performance of Effective Arterial Elastance as Predictor of Mean Arterial Pressure in Patients with Sepsis or Septic Shock Receiving Fluid Expansion: A Validation Study by Regression Analysis Yuda Sutherasan, Detajin Junhasavasdikul, Tanachai Petnak, Pongdhep Theerawit This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1673396/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background we conducted this study to compare four arterial load parameters and determine which arterial load parameters directly impacted arterial pressure regarding pressure, flow, and arterial system relationship. Methods We conducted a cross-sectional study in patients with sepsis who underwent volume expansion (VE). Hemodynamic parameters were recorded before and after VE. The relationship between the change of mean arterial pressure (%MAP) and that of the dynamic arterial elastance (Ea Dyn ), effective arterial elastance (Ea eff ), net arterial elastance (Ea Net ), and net arterial resistance (Ra Net ) was analyzed. Results Sixty-two patients were included. The DEa Dyn (%) was not correlated with DMAP(%) (r=0.048, P=0.826). Meanwhile, DEa eff (%), DEa Net (%), and DRa Net (%) were correlated with DMAP(%) (r=0.495, P<0.001; r=0.453, P<0.001; and r=0.485, P<0.001, respectively). A multiple linear regression model was analyzed for identifying predictors of DMAP(%) by including DCO(%) and each %change of arterial parameters. The best-fit model was found by including DCO(%) and DEa eff (%) in the regression equation (R 2 =0.823, adjusted R 2 =0.817). The model was adjusted by age, sex, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, Sequential Organ Failure Assessment (SOFA) score, arterial lactate level, norepinephrine dosage, ventilator setting, method of VE, and fluid responsiveness and found that DCO(%) and DEa eff (%) remained statistically significant predictors of DMAP(%) (P<0.001 and P<0.001, respectively). Conclusion The Ea eff was the best AL parameter that correlated with the changes in MAP. Furthermore, the model that included the ∆Ea eff (%) provided the best predictive performance for ∆MAP(%) induced by VE, independent of the fluid responsiveness and norepinephrine dosage. arterial elastance dynamic arterial elastance sepsis mean arterial pressure fluid responsiveness Figures Figure 1 Figure 2 Introduction Fluid administration is the first treatment of choice in sepsis or septic shock resuscitation. ( 1 ) The main objective of initial fluid therapy is to restore the arterial pressure for adequate tissue perfusion—the arterial pressure results from the interaction between the blood flow and the arterial system. Fluid therapy increases blood flow or cardiac output (CO). Also, it affects the arterial system. ( 2 ) The arterial system parameters were described as the arterial load (AL) according to a two-element Windkessel's model. To date, the arterial parameters researched for an arterial load assessment were vascular resistance ( 3 – 5 ), arterial compliance (elastance) ( 6 ), effective arterial elastance (Ea eff ) ( 7 ), and dynamic arterial elastance (Ea Dyn ) ( 4 ). Garcia et al. validated those parameters predicting mean arterial pressure (MAP) responsiveness to a fluid bolus in patients with circulatory failure. ( 4 ) They concluded that the Ea Dyn was the best predictor for predicting MAP responsiveness. However, the previous study did not clarify which parameters directly influenced to changing of MAP regarding sepsis. In addition, a study in an animal model demonstrated the different changes in arterial elastance, arterial resistance, and Ea dyn after phenylephrine and nitroprusside infusion ( 5 ) The validation of arterial load parameters in sepsis or septic shock patients has never been researched. Therefore, we conducted this study to compare four arterial load parameters and determine which arterial load parameters directly impacted arterial pressure regarding pressure, flow, and arterial system relationship. Methods Patients We conducted a cross-sectional study at a medical intensive care unit in a university hospital in Bangkok, Thailand. Our institutional ethics committee approved the present study. The present study was performed following the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. In addition, we obtained written informed consent from each patient's next-of-kin. This study consecutively enrolled participants aged 18 years or older with newly developed sepsis who had initial MAP less than 65 mmHg at an emergency department combined with all following criteria: 1) required fluid challenge according to physician's discretion at the time of ICU admission; 2) required invasive mechanical ventilation. The exclusion criteria were arrhythmias, pregnancy, pulmonary edema, cardiac failure, brain edema, atrial fibrillation, contraindications for arterial catheter placement, and pulse contour CO monitoring. The criteria made the diagnosis of sepsis of the Surviving Sepsis Campaign Guideline 2016 ( 1 ). Figure 1 illustrates eligible participants as well as all excluded and included participants. Measurement All included participants underwent measurement of hemodynamic parameters at baseline and after volume expansion (VE). The systolic blood pressure, diastolic blood pressure, pulse pressure variation (PPV), stroke volume variation (SVV), CO, and heart rate were recorded simultaneously. We used a pulse contour analysis device (Vigileo monitor, Software Ver. 03.01; Edwards Lifesciences, Irvine, CA, USA) and the FloTrac sensor (Edwards Lifesciences) to measure CO and SVV. The mean values of the three determinations before and after the VE were recorded for further analysis. We measured the PPV by a Philips IntelliVue MP70 monitor (Philips Medical Systems). Before recording, the arterial waveform was verified for signal quality, and standard zeroing and leveling were performed. Acceptable signal quality was determined by the presence of a typical arterial waveform without an over-damped or under-damped signal. The average of three PPV values before and after VE was recorded. The AL was defined as arterial elastance and arterial resistance (Ra Net ). The arterial elastance was classified into 1) effective arterial elastance (Ea eff ); 2) net arterial elastance (Ea Net ); and 3) dynamic arterial elastance (Ea Dyn ). The Ea eff was derived from arterial blood pressure measurement, as described in a study by Kelly et al. ( 7 ). Their research showed a strong correlation between Ea eff and Ea(PV) [a parameter calculated by the left ventricular end-systolic pressure (LVESP) divided by the stroke volume (SV)], which was defined as the gold standard for Ea ( 7 )]. To calculate Ea eff , we first estimated the LVESP from the arterial pressure with 0.9 x systolic arterial pressure. Next, the Ea eff was computed from the estimated LVESP divided by the SV ( 7 ). Another, the Ea Net was calculated by the pulse pressure (PP)/SV ( 5 ). Then, the Ra Net was defined by the PP/CO. Finally, the Ea Dyn was calculated by the PPV/SVV. Study Protocol All eligible participants were temporarily sedated, paralyzed, and placed on fully controlled mechanical ventilation. The ventilation mode was either volume- or pressure-controlled ventilation at the physician's discretion. The tidal volume was 8 mL/kg (predicted body weight). The preset respiratory rate was set at 16 breaths/min. The positive end-expiratory pressure was set at 8 to 10 cm H 2 O. The plateau pressure was kept at ≤ 30 cm H 2 O. All treatments, including the vasopressor dosage, were maintained during the study period. The VE in each subject was prescribed according to the discretion of attending physicians. The VE was performed with 1,000 mL of crystalloid (0.9% normal saline) over 1 hour or 500 mL of 5% human albumin over 30 minutes ( 8 , 9 ). We also collected data about fluid responsiveness. The responders were defined as participants whose CO increased more than 10% from baseline after VE. Statistical analysis A sample size of 58 participants was calculated with a conventional effect size = 0.35, α = 0.05, and 1 − β = 0.98 for a fixed-factor model of multiple regression. All participants were primarily analyzed in terms of their baseline characteristics. Data were presented as mean ± standard deviation or number, depending on the variable type. The relationship between each AL parameter at baseline and MAP at baseline was analyzed by Pearson's correlation and was presented with the correlation coefficient. The delta value and percentage change from the baseline of all AL parameters were analyzed to determine their relationship with the percentage change of MAP from baseline (%MAP). These results were presented with the correlation coefficient. Multiple linear regression was performed to assess the predictors of %MAP. Physiologically, changes in MAP should be associated with changes in CO and AL parameters. Therefore, we analyzed four different models for predicting %MAP by multiple linear regression and compared the R 2 and adjusted R 2 values to identify the fittest model for predicting %MAP. We also analyzed the fittest model by adjusting age, fluid responsiveness, and vasopressor use by multiple linear regression. The collinearity test was used to identify the relationships among variables in each model. In addition, the tolerance statistic and variance inflation factor were analyzed to determine the degree of collinearity. The participants were also divided into two groups based on an increase or decrease in MAP after VE to analyze the predictive performance of all AL parameters, presented as the area under ROC, sensitivity, specificity, negative predictive value, positive predictive value, and threshold values for prediction. Results In total, 62 participants with sepsis were enrolled in the present study (Fig. 1 ). All participants received fluid resuscitation and norepinephrine infusion before ICU admission. Their baseline characteristics are shown in Table 1 , and their hemodynamic data before and after VE are shown in Table 2 . Table 1 The baseline characteristics Characteristics Values (n = 52) Age, mean (SD), years 60(16) Gender, n(%) female 26(50) male 26(50) Co-morbid, n(%) No co-morbid 1(1.9) Diabetes mellitus 6(11.5) Hypertension 1(1.9) Liver disease 2(3.8) HIV 5(9.6) Cancer 6(11.5) Renal disease 4(7.7) Hematologic disease 21(40.4) Autoimmune disease 3(5.8) Other diseases 3(5.8) APACHE II score, mean(SD) 28(8) SOFA score, mean(SD) 11(4) Receive norepinephrine, n(%) Yes 30(57.7) No 22(42.3) Norepinephrine dosage, median(IQR), mcg/kg/min 0.06(0-0.30) Lactate level, mean(SD), mmol/L 4.4(3.8) Tidal volume, mean(SD), ml/kg of predicted BW 8.6(2.4) PEEP, mean(SD), cm H 2 O 7(2) Heart rate, mean(SD), beat/min 107(28) Systolic blood pressure, mean(SD), mm Hg 97(21) Diastolic blood pressure, mean(SD), mm Hg 53(12) Mean arterial pressure, mean(SD), mm Hg 67.86(13.68) Stroke volume, mean(SD), ml. 50.52(19.62) Cardiac output, mean(SD),L/min 5.8(2.7) Pulse pressure variation, % 14.22(11.39) Stroke volume variation, % 13(10) Mortality rate, % 63.5 Table 2 Comparison between the minimum and maximum parameters between the subgroup of Ea stat before and after volume expansion Ea stat ratio or Ea dyn 1 N = 29 P-value* Before VE After VE Before VE After VE HR, mean (SD), beat/min 99(34) 94(30) .029 114(21) 112(20) c .215 PP, mean (SD), mm Hg 47.64(18.25) 52.52(17.08) .009 41.90(15.27) 50.31(21.93) .007 MAP, mean (SD), mm Hg** 69.65(14.06) 72.65(13.19) .045 65.79 (13.26) 76.08(19.26) .002 SV, mean (SD), ml.*** 56.17(16.37) 62.83(19.82) .023 46.09(21.52) 54.71(29.27) .006 CO, mean (SD), L/min*** 5.4(2.1) 5.8(2.5) .063 5.1(2.3) 6.0(3.0) .006 PPmin, mean (SD), mm Hg 46 (17) 54(20) .299 38 (14) 39(12) c .246 PPmax, mean (SD), mm Hg 49 (18) 58(19) .202 46 (17) 44(12) .196 %changed PP, mean (SD) 8.96 (6.68) 7.55(4.34) .951 21.95 (17.22) a 13.74(8.57) .438 SVmin, mean (SD), mm Hg 52.97 (15.52) 60.19(19.63) .018 43.48 (20.99) 52.40(28.35) .004 SVmax, mean (SD), mm Hg 59.24 (15.88) 65.47(20.07) .030 48.71 (22.18) 57.03(30.22) .008 %changed SV, mean (SD) 12.68 (6.88) 9.36(4.63) .003 15.19 (15.35) 9.63(5.35) .023 Ea stat exp., mean (SD), mm Hg/ml. 0.88 (0.28) 0.90(0.29) .167 1.01 (0.51) 0.93(0.30) .243 Ea stat insp., mean (SD), mm Hg/ml. 0.85 (0.26) 0.88(0.28) .088 1.07 (0.54) 0.97(0.29) .302 %changed Ea stat , mean (SD) -3.26 (2.51) -1.97(1.84) .004 5.88 (5.39) b 5.00(7.05) d .919 Ea stat ratio, mean (SD) 0.97 (0.03) 0.98(0.02) .004 1.06 (0.05) b 1.05(0.07) d .919 Ea (SD), mm Hg/ml. 1.72(0.52) 1.64(0.47) .132 2.16(0.94) 2.28(1.35) c .463 HR, heart rate; PP, pulse pressure; MAP, mean arterial pressure; SV, stroke volume; CO, cardiac output; Ea stat , static arterial elastance; exp, expiration; insp, inspiration; Ea, effective arterial elastance * Analysis with Paired t-test ** The percentage change of MAP between groups shows a statistically significant difference with P = 0.041. *** No significant difference in percentage change of cardiac output and stroke volume between groups. a P < 0.01; b P < 0.001 compared baseline between groups by the Student t-test. c P < 0.05; d P < 0.01 compared after VE between groups by the Student t-test. The VE induced distinct changes in MAP. Sixteen participants developed a reduction of MAP despite receiving VE. Meanwhile, the remaining participants developed an increasing MAP. In the former group, the SV was unchanged after VE, but the MAP was decreased. In the latter group, VE increased both SV and MAP (Table 3). Analysis of correlation between MAP and baseline AL parameters No correlation was found between baseline MAP and AL parameters. Also, the correlation was not found between percentage changes of MAP from baseline (%MAP) and the baseline values of Ea Dyn (r = 0.066, P = 0.641 vs. r = 0.037, P = 0.794), Ea eff (r = 0.123, P = 0.342 vs. r = 0.126, P = 0.328), Ea Net (r = 0.107, P = 0.410 vs. r = 0.135, P = 0.297), or Ra Net (r = 0.011, P = 0.933 vs. r = 0.025, P = 0.846). Analysis of percentage changed values The %MAP was correlated with the percentage change from the baseline of PP (r = 0.717, P < 0.001), SV (r = 0.423, P = 0.001), SVV (r = − 0.309, P = 0.014), CO (r = 0.467, P < 0.001), Ea eff (r = 0.495, P < 0.001), Ea Net (r = 0.453, P < 0.001), and Ra Net (r = 0.485, P < 0.001). No correlation was found between the %MAP and the percentage change from the baseline of Ea Dyn (r = 0.103, P = 0.632). The multiple linear regression was performed to analyze the percentage change from the baseline of all AL parameters. The results showed that only percentage change from baseline of Ea eff (%Ea eff ) correlated with %MAP (P < 0.001). Model analysis Theoretically, changes in MAP were the interaction between CO and AL; therefore, we analyzed the relationships among the changes in MAP, CO, and each AL parameter. We included %MAP, percentage change from baseline of CO (%CO), and Ea Dyn (%Eadyn) in the first model. We then replaced the %Ea Dyn with the % Ea eff , the percentage change from baseline of Ea Net (%Ea Net ) and Ra Net (%Ra Net ) in the remaining models, respectively. Table 4 shows the results of the multiple linear regression analysis of the four models. The highest R 2 was established in the model that included %Ea eff in the equation. Further analysis of adjusted variables was performed to determine the influence of age, sex, APACHE II score, SOFA score, arterial lactate level, the dosage of norepinephrine, fraction of inspired oxygen, minute ventilation, positive end-expiratory pressure, and fluid responsiveness on the predictability of %CO and %Ea eff . The results showed that both %CO and %Ea eff remained predictors of %MAP (Table 5). We performed a collinearity diagnostic test for each model. The analysis showed a weak relationship between %CO and each AL parameter (see supplementary file). Analysis of predictive performance We divided the participants into two groups according to an increase or decrease in MAP after VE. We found that 42 participants (67.7%) showed an increased MAP, and 20 participants (33.3%) showed a decreased MAP after VE. The baseline values of Ea Dyn , Ea eff , Ea Net , and Ra Net had poor predictive performance in predicting an increase or decrease in MAP. The AUC was 0.626 [P = 0.311, 95% CI (confidence interval) = 0.380–0.872], 0.444 (P = 0.655, 95% CI = 0.195–0.694), 0.448 (P = 0.676, 95% CI = 0.200–0.696), and 0.489 (P = 0.929, 95% CI = 0.252–0.725), respectively. Regarding the percentage change in the AL parameters, the AUC of %Ea Dyn , %Ea eff , %Ea Net , and %Ra Net for predicting an increase or decrease in MAP was 0.570 (P = 0.571, 95% CI = 0.314–0.827), 0.859 (P = 0.004, 95% CI = 0.711–1.000), 0.837 (P = 0.007, 95% CI = 0.667–1.000), and 0.822 (P = 0.009, 95% CI = 0.646–0.999), respectively. Discussion The present study demonstrated a relationship between %MAP changes with the %Ea eff , %Ea Net , and %Ra Net , but not with %Ea Dyn . We also found that the %Ea eff provided the highest predictability of %MAP in the multiple linear regression model compared with the others. Furthermore, the %Ea eff had the highest performance for discriminating participants who developed an increase or decrease in MAP after VE. As a result, our study suggests that the %Ea eff has a strong relationship with %MAP and could be the best surrogate arterial load parameter. The Ea eff has been shown to strongly correlate with the arterial elastance derived from the pressure-volume curve of the heart [Ea(PV)], which is known as a gold standard. ( 7 ) Ea eff changes in response to arterial tone and vasoactive infusion. ( 5 ) Also, it reduces after fluid administration ( 4 ), which is consistent with the physiologic response of arterial tone to fluid therapy. Interestingly, our results showed that the baseline Ea Dyn was a poor predictor of increasing or decreasing MAP after VE. In the previous study, the Ea Dyn has been defined as an arterial load parameter ( 4 ) because it is calculated from pressure and volume variation changed along with the cyclic alteration of lung volume. Therefore, the Ea Dyn depends not only on the arterial system but also on the lung volume change. Theoretically, the actual arterial elastance given arterial property is believed to be constant during the cardiac cycle. ( 10 ) Also, it should be assumed to be stable during the breathing cycle. Therefore, we are concerned about being an arterial load parameter of the Ea Dyn , which may be affected by the lung volume, and might not represent the actual arterial elastance. Even though the Ea Dyn was proved, by Garcia et al., to be an excellent predictor of MAP responsiveness compared to the arterial elastance and SVR in patients with circulatory shock receiving the fluid challenge. ( 3 , 4 ) However, the study by Khwannimit et al. ( 11 ) demonstrated no significantly different Ea Dyn between MAP responders and non-responders. In addition, the Ea dyn differently responded to vasodilator and vasoconstrictor from the arterial elastance and resistance variables. ( 5 ) It was also found to have an inverse correlation with MAP in the study, as mentioned earlier. Regarding the Ea Net and Ra Net , both variables share similar variables for calculation, namely the PP. However, the PP is not constant along with the mechanical ventilation. The cyclic mechanical ventilation affects the PP, making the Ea Net and Ra Net vary, despite unchanged arterial properties. This reason might contribute to the results of poorer predictors in both parameters. However, the study by Chemla et al. ( 6 ) validated net compliance (reciprocal of Ea Net ) to estimate total arterial compliance by area method, used as a gold standard. They showed a strong correlation (r = 0.98, P < 0.001) between both parameters. In this regard, Chemla and the team enrolled different populations from our study; therefore, the interpretation should not be made similarly. Our study's limitation was that we did not measure the Ea(PV) at the bedside, which is considered the gold standard, as performed in the study by Monge García et al. ( 2 ). However, the Ea eff has been validated as a surrogate parameter of Ea(PV) ( 7 ) and is feasible at the bedside. A second limitation was that we did not use the thermodilution method for CO measurement. Pulse contour analysis by the Vigileo system is not the gold standard of CO measurement. Finally, we could not control some potential confounding factors, such as the artery's vasomotor tone in participants with sepsis during the VE period, which may have affected the arterial load. Future human studies are required to understand better the physiologic effect of medications on the change in the Ea eff . Conclusions The %Ea eff was the best AL parameter correlated well with %MAP after VE. In addition, the model that included the % Ea eff provided the best predictive performance for the %MAP induced by VE, independent of the fluid responsiveness and dosage of norepinephrine. Meanwhile, % Ea Dyn did not correlate and was not a predictor for the %MAP induced by VE. Abbreviations MAP, mean arterial pressure; CO, cardiac output; AL, arterial load; Ea, arterial elastance; Ea(PV), arterial elastance derived from a pressure-volume relationship; Ea eff , effective arterial elastance; Ea Dyn , dynamic arterial elastance; VE, volume challenge; PPV, pulse pressure variation; SVV, stroke volume variation; PP, pulse pressure; SV, stroke volume; APACHE II, Acute Physiology and Chronic Health Evaluation II; SOFA, Sequential Organ Failure Assessment; Ea Net , net arterial elastance; Ra Net , net arterial resistance; LVESP, left ventricular end-systolic pressure; AUC, area under the receiver operating characteristic curve; CI, confidence interval Declarations Ethics approval and consent to participate: The study protocol was approved by the Institutional Ethics Committee and was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Consent for publication: Not applicable. Availability of data and materials: Not applicable. Competing interests: The authors declare that they have no competing interests. Funding: No funding was received. Authors' contributions: Pongdhep Theerawit formulated the research idea, designed the research methodology, verified the data, designed the data analysis, analyzed the data, drafted the manuscript, approved the manuscript, submitted the manuscript, and served as the corresponding author. Tanawat Tengsirikomol performed the research, collected the data, verified the data, and approved the manuscript. Yuda Sutherasan designed the research methodology, verified the data, analyzed the data, and drafted and approved the manuscript. Acknowledgment: We would like to express our gratitude to all pulmonary and critical care fellows and medical intensive care nurses. We also thank Angela Morben, DVM, ELS, from Edanz Group (https://en-author-services.edanzgroup.com/), for editing a draft of this manuscript. References Rhodes A, Evans LE, Alhazzani W, Levy MM, Antonelli M, Ferrer R, et al. Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016. Critical care medicine. 2017;45(3):486–552. Monge Garcia MI, Guijo Gonzalez P, Gracia Romero M, Gil Cano A, Oscier C, Rhodes A, et al. Effects of fluid administration on arterial load in septic shock patients. Intensive care medicine. 2015;41(7):1247–55. Monge Garcia MI, Gil Cano A, Gracia Romero M. Dynamic arterial elastance to predict arterial pressure response to volume loading in preload-dependent patients. Critical care (London, England). 2011;15(1):R15. Garcia MI, Romero MG, Cano AG, Aya HD, Rhodes A, Grounds RM, et al. Dynamic arterial elastance as a predictor of arterial pressure response to fluid administration: a validation study. Critical care (London, England). 2014;18(6):626. Monge Garcia MI, Guijo Gonzalez P, Gracia Romero M, Gil Cano A, Rhodes A, Grounds RM, et al. Effects of arterial load variations on dynamic arterial elastance: an experimental study. British journal of anaesthesia. 2017;118(6):938–46. Chemla D, Hebert JL, Coirault C, Zamani K, Suard I, Colin P, et al. Total arterial compliance estimated by stroke volume-to-aortic pulse pressure ratio in humans. The American journal of physiology. 1998;274(2):H500-5. Kelly RP, Ting CT, Yang TM, Liu CP, Maughan WL, Chang MS, et al. Effective arterial elastance as index of arterial vascular load in humans. Circulation. 1992;86(2):513–21. Vincent JL, Weil MH. Fluid challenge revisited. Critical care medicine. 2006;34(5):1333–7. Theerawit P, Morasert T, Sutherasan Y. Inferior vena cava diameter variation compared with pulse pressure variation as predictors of fluid responsiveness in patients with sepsis. J Crit Care. 2016;36:246–51. Quick CM, Mohiuddin MW, Laine GA, Noordergraaf A. The arterial system pressure-volume loop. Physiol Meas. 2005;26(6):N29-35. Khwannimit B, Bhurayanontachai R. Prediction of fluid responsiveness in septic shock patients: comparing stroke volume variation by FloTrac/Vigileo and automated pulse pressure variation. Eur J Anaesthesiol. 2012;29(2):64–9. Additional Declarations No competing interests reported. Supplementary Files 6Supplementaryfiles.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1673396","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":108305979,"identity":"a6c62df2-9de3-4777-814f-e1aba58f4f65","order_by":0,"name":"Yuda Sutherasan","email":"","orcid":"","institution":"Mahidol University","correspondingAuthor":false,"prefix":"","firstName":"Yuda","middleName":"","lastName":"Sutherasan","suffix":""},{"id":108305981,"identity":"6c9f3c4b-a5e6-4700-88dd-6d1dc523064a","order_by":1,"name":"Detajin Junhasavasdikul","email":"","orcid":"","institution":"Mahidol University","correspondingAuthor":false,"prefix":"","firstName":"Detajin","middleName":"","lastName":"Junhasavasdikul","suffix":""},{"id":108305987,"identity":"4c40e76a-54c4-4911-8da1-d6721377e467","order_by":2,"name":"Tanachai Petnak","email":"","orcid":"","institution":"Mahidol University","correspondingAuthor":false,"prefix":"","firstName":"Tanachai","middleName":"","lastName":"Petnak","suffix":""},{"id":108305989,"identity":"b619c06e-da9d-4493-b5b7-81cdf5be9670","order_by":3,"name":"Pongdhep Theerawit","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIie3Sv0rDQBzA8d9xcFkuda1U9BVaAgkBja9yx0GnNEtHBTOdS+LcpfgWh+OVgC7BuaNQEJdCJkEE9ZI4dEhaR4f7DhnCfbi/ADbbf81N6y8FqAAYAYZ083t4iBAKaNESaAj9C8FmIgaHSODkxdv24TyBs3y1uYijZOAIreE6gstR2knC7HkaLsvpHMhAeDMl5oS+Mg2PAuiJ7iTjdex7rix4Sqg/minM5TAeayDaLIztI981CT5DdfNLvvYSb+NK3cyCkSpagmQ/CbPSR0tptkBi7zhXT1zWe+F3gtJ1NwmczKu2MkqOcDmpPtQVv78Vq5fqPTp1Fj0LMxdSn7+5wd1Y8xi6MwRX7RibzWaz9fQD0Htc68r6rhIAAAAASUVORK5CYII=","orcid":"","institution":"Mahidol University","correspondingAuthor":true,"prefix":"","firstName":"Pongdhep","middleName":"","lastName":"Theerawit","suffix":""}],"badges":[],"createdAt":"2022-05-19 13:29:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1673396/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1673396/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":21924897,"identity":"f16040b6-5e2b-4303-b03c-ee1261b0c72a","added_by":"auto","created_at":"2022-05-26 15:50:20","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":273232,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not available with this version.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"4Figure1Eastatmaxminregression.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1673396/v1/43678cc1585028bad0133a5a.jpg"},{"id":21924899,"identity":"bb8ea6b9-3499-4e81-aa96-be68d1ddeb7e","added_by":"auto","created_at":"2022-05-26 15:50:20","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":260067,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not available with this version.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"4Figure2EastatvsEadynregression.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1673396/v1/242568a056020de4a7e1733b.jpg"},{"id":21924900,"identity":"42d002df-f686-4e3f-aa88-d908c5bc192b","added_by":"auto","created_at":"2022-05-26 15:50:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":444112,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1673396/v1/3f8fb7d0-9612-4a60-830d-3e8db35c099e.pdf"},{"id":21924901,"identity":"469c073e-f71a-4157-8c2e-9912009bde82","added_by":"auto","created_at":"2022-05-26 15:50:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":444112,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1673396/v1/a0f49b85-5b92-4b40-821a-7632ab91a791.pdf"},{"id":21924898,"identity":"1595732c-ecba-4bba-aa0e-d61d81c7a94a","added_by":"auto","created_at":"2022-05-26 15:50:20","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":109631,"visible":true,"origin":"","legend":"","description":"","filename":"6Supplementaryfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-1673396/v1/fd45325783f83a16815e00b5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePerformance of Effective Arterial Elastance as Predictor of Mean Arterial Pressure in Patients with Sepsis or Septic Shock Receiving Fluid Expansion: A Validation Study by Regression Analysis\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFluid administration is the first treatment of choice in sepsis or septic shock resuscitation. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) The main objective of initial fluid therapy is to restore the arterial pressure for adequate tissue perfusion\u0026mdash;the arterial pressure results from the interaction between the blood flow and the arterial system. Fluid therapy increases blood flow or cardiac output (CO). Also, it affects the arterial system. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe arterial system parameters were described as the arterial load (AL) according to a two-element Windkessel's model. To date, the arterial parameters researched for an arterial load assessment were vascular resistance (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), arterial compliance (elastance) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), effective arterial elastance (Ea\u003csub\u003eeff\u003c/sub\u003e) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), and dynamic arterial elastance (Ea\u003csub\u003eDyn\u003c/sub\u003e) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Garcia et al. validated those parameters predicting mean arterial pressure (MAP) responsiveness to a fluid bolus in patients with circulatory failure. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) They concluded that the Ea\u003csub\u003eDyn\u003c/sub\u003e was the best predictor for predicting MAP responsiveness. However, the previous study did not clarify which parameters directly influenced to changing of MAP regarding sepsis. In addition, a study in an animal model demonstrated the different changes in arterial elastance, arterial resistance, and Ea\u003csub\u003edyn\u003c/sub\u003e after phenylephrine and nitroprusside infusion (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe validation of arterial load parameters in sepsis or septic shock patients has never been researched. Therefore, we conducted this study to compare four arterial load parameters and determine which arterial load parameters directly impacted arterial pressure regarding pressure, flow, and arterial system relationship.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003e We conducted a cross-sectional study at a medical intensive care unit in a university hospital in Bangkok, Thailand. Our institutional ethics committee approved the present study. The present study was performed following the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. In addition, we obtained written informed consent from each patient's next-of-kin.\u003c/p\u003e \u003cp\u003eThis study consecutively enrolled participants aged 18 years or older with newly developed sepsis who had initial MAP less than 65 mmHg at an emergency department combined with all following criteria: 1) required fluid challenge according to physician's discretion at the time of ICU admission; 2) required invasive mechanical ventilation. The exclusion criteria were arrhythmias, pregnancy, pulmonary edema, cardiac failure, brain edema, atrial fibrillation, contraindications for arterial catheter placement, and pulse contour CO monitoring. The criteria made the diagnosis of sepsis of the Surviving Sepsis Campaign Guideline 2016 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates eligible participants as well as all excluded and included participants.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement\u003c/h2\u003e \u003cp\u003eAll included participants underwent measurement of hemodynamic parameters at baseline and after volume expansion (VE). The systolic blood pressure, diastolic blood pressure, pulse pressure variation (PPV), stroke volume variation (SVV), CO, and heart rate were recorded simultaneously. We used a pulse contour analysis device (Vigileo monitor, Software Ver. 03.01; Edwards Lifesciences, Irvine, CA, USA) and the FloTrac sensor (Edwards Lifesciences) to measure CO and SVV. The mean values of the three determinations before and after the VE were recorded for further analysis.\u003c/p\u003e \u003cp\u003eWe measured the PPV by a Philips IntelliVue MP70 monitor (Philips Medical Systems). Before recording, the arterial waveform was verified for signal quality, and standard zeroing and leveling were performed. Acceptable signal quality was determined by the presence of a typical arterial waveform without an over-damped or under-damped signal. The average of three PPV values before and after VE was recorded.\u003c/p\u003e \u003cp\u003eThe AL was defined as arterial elastance and arterial resistance (Ra\u003csub\u003eNet\u003c/sub\u003e). The arterial elastance was classified into 1) effective arterial elastance (Ea\u003csub\u003eeff\u003c/sub\u003e); 2) net arterial elastance (Ea\u003csub\u003eNet\u003c/sub\u003e); and 3) dynamic arterial elastance (Ea\u003csub\u003eDyn\u003c/sub\u003e). The Ea\u003csub\u003eeff\u003c/sub\u003e was derived from arterial blood pressure measurement, as described in a study by Kelly et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Their research showed a strong correlation between Ea\u003csub\u003eeff\u003c/sub\u003e and Ea(PV) [a parameter calculated by the left ventricular end-systolic pressure (LVESP) divided by the stroke volume (SV)], which was defined as the gold standard for Ea (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)]. To calculate Ea\u003csub\u003eeff\u003c/sub\u003e, we first estimated the LVESP from the arterial pressure with 0.9 x systolic arterial pressure. Next, the Ea\u003csub\u003eeff\u003c/sub\u003e was computed from the estimated LVESP divided by the SV (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Another, the Ea\u003csub\u003eNet\u003c/sub\u003e was calculated by the pulse pressure (PP)/SV (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Then, the Ra\u003csub\u003eNet\u003c/sub\u003e was defined by the PP/CO. Finally, the Ea\u003csub\u003eDyn\u003c/sub\u003e was calculated by the PPV/SVV.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStudy Protocol\u003c/h2\u003e \u003cp\u003eAll eligible participants were temporarily sedated, paralyzed, and placed on fully controlled mechanical ventilation. The ventilation mode was either volume- or pressure-controlled ventilation at the physician's discretion. The tidal volume was 8 mL/kg (predicted body weight). The preset respiratory rate was set at 16 breaths/min. The positive end-expiratory pressure was set at 8 to 10 cm H\u003csub\u003e2\u003c/sub\u003eO. The plateau pressure was kept at \u0026le;\u0026thinsp;30 cm H\u003csub\u003e2\u003c/sub\u003eO. All treatments, including the vasopressor dosage, were maintained during the study period.\u003c/p\u003e \u003cp\u003eThe VE in each subject was prescribed according to the discretion of attending physicians. The VE was performed with 1,000 mL of crystalloid (0.9% normal saline) over 1 hour or 500 mL of 5% human albumin over 30 minutes (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe also collected data about fluid responsiveness. The responders were defined as participants whose CO increased more than 10% from baseline after VE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eA sample size of 58 participants was calculated with a conventional effect size\u0026thinsp;=\u0026thinsp;0.35, α\u0026thinsp;=\u0026thinsp;0.05, and 1\u0026thinsp;\u0026minus;\u0026thinsp;β\u0026thinsp;=\u0026thinsp;0.98 for a fixed-factor model of multiple regression. All participants were primarily analyzed in terms of their baseline characteristics. Data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or number, depending on the variable type. The relationship between each AL parameter at baseline and MAP at baseline was analyzed by Pearson's correlation and was presented with the correlation coefficient. The delta value and percentage change from the baseline of all AL parameters were analyzed to determine their relationship with the percentage change of MAP from baseline (%MAP). These results were presented with the correlation coefficient. Multiple linear regression was performed to assess the predictors of %MAP.\u003c/p\u003e \u003cp\u003ePhysiologically, changes in MAP should be associated with changes in CO and AL parameters. Therefore, we analyzed four different models for predicting %MAP by multiple linear regression and compared the R\u003csup\u003e2\u003c/sup\u003e and adjusted R\u003csup\u003e2\u003c/sup\u003e values to identify the fittest model for predicting %MAP. We also analyzed the fittest model by adjusting age, fluid responsiveness, and vasopressor use by multiple linear regression. The collinearity test was used to identify the relationships among variables in each model. In addition, the tolerance statistic and variance inflation factor were analyzed to determine the degree of collinearity.\u003c/p\u003e \u003cp\u003eThe participants were also divided into two groups based on an increase or decrease in MAP after VE to analyze the predictive performance of all AL parameters, presented as the area under ROC, sensitivity, specificity, negative predictive value, positive predictive value, and threshold values for prediction.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eIn total, 62 participants with sepsis were enrolled in the present study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All participants received fluid resuscitation and norepinephrine infusion before ICU admission. Their baseline characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and their hemodynamic data before and after VE are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe baseline characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValues\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean (SD), years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60(16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo-morbid, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo co-morbid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(11.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(3.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(9.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(11.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(7.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematologic disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21(40.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutoimmune disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(5.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(5.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE II score, mean(SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28(8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA score, mean(SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceive norepinephrine, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(57.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(42.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorepinephrine dosage, median(IQR), mcg/kg/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06(0-0.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate level, mean(SD), mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.4(3.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTidal volume, mean(SD), ml/kg of predicted BW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.6(2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEEP, mean(SD), cm H\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate, mean(SD), beat/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107(28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure, mean(SD), mm Hg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97(21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure, mean(SD), mm Hg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53(12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean arterial pressure, mean(SD), mm Hg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.86(13.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke volume, mean(SD), ml.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.52(19.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac output, mean(SD),L/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.8(2.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse pressure variation, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.22(11.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke volume variation, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortality rate, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison between the minimum and maximum parameters between the subgroup of Ea\u003csub\u003estat\u003c/sub\u003e before and after volume expansion\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEa\u003csub\u003estat\u003c/sub\u003e ratio or Ea\u003csub\u003edyn\u003c/sub\u003e \u0026lt; 1\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;21\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eEa\u003csub\u003estat\u003c/sub\u003e ratio or Ea\u003csub\u003edyn\u003c/sub\u003e \u0026gt; 1\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;29\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBefore VE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAfter VE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBefore VE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAfter VE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR, mean (SD), beat/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99(34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94(30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114(21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e112(20) \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP, mean (SD), mm Hg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.64(18.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.52(17.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.90(15.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.31(21.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP, mean (SD), mm Hg**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.65(14.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.65(13.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.79 (13.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.08(19.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSV, mean (SD), ml.***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.17(16.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.83(19.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.09(21.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.71(29.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO, mean (SD), L/min***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.4(2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8(2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.1(2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.0(3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPmin, mean (SD), mm Hg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54(20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39(12) \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPmax, mean (SD), mm Hg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58(19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44(12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%changed PP, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.96 (6.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.55(4.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.95 (17.22) \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.74(8.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVmin, mean (SD), mm Hg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.97 (15.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.19(19.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.48 (20.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.40(28.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVmax, mean (SD), mm Hg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.24 (15.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.47(20.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.71 (22.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.03(30.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%changed SV, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.68 (6.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.36(4.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.19 (15.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.63(5.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEa\u003csub\u003estat\u003c/sub\u003e exp., mean (SD), mm Hg/ml.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88 (0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90(0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01 (0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.93(0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEa\u003csub\u003estat\u003c/sub\u003e insp., mean (SD), mm Hg/ml.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88(0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07 (0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97(0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%changed Ea\u003csub\u003estat\u003c/sub\u003e, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.26 (2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.97(1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.88 (5.39) \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.00(7.05) \u003csup\u003e\u003cb\u003ed\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEa\u003csub\u003estat\u003c/sub\u003e ratio, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06 (0.05) \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05(0.07) \u003csup\u003e\u003cb\u003ed\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEa (SD), mm Hg/ml.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.72(0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.64(0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.16(0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.28(1.35) \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eHR, heart rate; PP, pulse pressure; MAP, mean arterial pressure; SV, stroke volume; CO, cardiac output; Ea\u003csub\u003estat\u003c/sub\u003e, static arterial elastance; exp, expiration; insp, inspiration; Ea, effective arterial elastance\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e* Analysis with Paired t-test\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e** The percentage change of MAP between groups shows a statistically significant difference with\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eP\u0026thinsp;=\u0026thinsp;0.041.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*** No significant difference in percentage change of cardiac output and stroke volume between groups.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003e P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u003csup\u003eb\u003c/sup\u003e P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 compared baseline between groups by the Student t-test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ec\u003c/sup\u003e P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; \u003csup\u003ed\u003c/sup\u003e P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 compared after VE between groups by the Student t-test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe VE induced distinct changes in MAP. Sixteen participants developed a reduction of MAP despite receiving VE. Meanwhile, the remaining participants developed an increasing MAP. In the former group, the SV was unchanged after VE, but the MAP was decreased. In the latter group, VE increased both SV and MAP (Table\u0026nbsp;3).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of correlation between MAP and baseline AL parameters\u003c/h2\u003e \u003cp\u003eNo correlation was found between baseline MAP and AL parameters. Also, the correlation was not found between percentage changes of MAP from baseline (%MAP) and the baseline values of Ea\u003csub\u003eDyn\u003c/sub\u003e (r\u0026thinsp;=\u0026thinsp;0.066, P\u0026thinsp;=\u0026thinsp;0.641 vs. r\u0026thinsp;=\u0026thinsp;0.037, P\u0026thinsp;=\u0026thinsp;0.794), Ea\u003csub\u003eeff\u003c/sub\u003e (r\u0026thinsp;=\u0026thinsp;0.123, P\u0026thinsp;=\u0026thinsp;0.342 vs. r\u0026thinsp;=\u0026thinsp;0.126, P\u0026thinsp;=\u0026thinsp;0.328), Ea\u003csub\u003eNet\u003c/sub\u003e (r\u0026thinsp;=\u0026thinsp;0.107, P\u0026thinsp;=\u0026thinsp;0.410 vs. r\u0026thinsp;=\u0026thinsp;0.135, P\u0026thinsp;=\u0026thinsp;0.297), or Ra\u003csub\u003eNet\u003c/sub\u003e (r\u0026thinsp;=\u0026thinsp;0.011, P\u0026thinsp;=\u0026thinsp;0.933 vs. r\u0026thinsp;=\u0026thinsp;0.025, P\u0026thinsp;=\u0026thinsp;0.846).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of percentage changed values\u003c/h2\u003e \u003cp\u003eThe %MAP was correlated with the percentage change from the baseline of PP (r\u0026thinsp;=\u0026thinsp;0.717, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SV (r\u0026thinsp;=\u0026thinsp;0.423, P\u0026thinsp;=\u0026thinsp;0.001), SVV (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.309, P\u0026thinsp;=\u0026thinsp;0.014), CO (r\u0026thinsp;=\u0026thinsp;0.467, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Ea\u003csub\u003eeff\u003c/sub\u003e (r\u0026thinsp;=\u0026thinsp;0.495, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Ea\u003csub\u003eNet\u003c/sub\u003e (r\u0026thinsp;=\u0026thinsp;0.453, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Ra\u003csub\u003eNet\u003c/sub\u003e (r\u0026thinsp;=\u0026thinsp;0.485, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No correlation was found between the %MAP and the percentage change from the baseline of Ea\u003csub\u003eDyn\u003c/sub\u003e (r\u0026thinsp;=\u0026thinsp;0.103, P\u0026thinsp;=\u0026thinsp;0.632). The multiple linear regression was performed to analyze the percentage change from the baseline of all AL parameters. The results showed that only percentage change from baseline of Ea\u003csub\u003eeff\u003c/sub\u003e (%Ea\u003csub\u003eeff\u003c/sub\u003e) correlated with %MAP (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eModel analysis\u003c/h2\u003e \u003cp\u003eTheoretically, changes in MAP were the interaction between CO and AL; therefore, we analyzed the relationships among the changes in MAP, CO, and each AL parameter. We included %MAP, percentage change from baseline of CO (%CO), and Ea\u003csub\u003eDyn\u003c/sub\u003e (%Eadyn) in the first model. We then replaced the %Ea\u003csub\u003eDyn\u003c/sub\u003e with the % Ea\u003csub\u003eeff\u003c/sub\u003e, the percentage change from baseline of Ea\u003csub\u003eNet\u003c/sub\u003e (%Ea\u003csub\u003eNet\u003c/sub\u003e) and Ra\u003csub\u003eNet\u003c/sub\u003e (%Ra\u003csub\u003eNet\u003c/sub\u003e) in the remaining models, respectively. Table\u0026nbsp;4 shows the results of the multiple linear regression analysis of the four models. The highest R\u003csup\u003e2\u003c/sup\u003e was established in the model that included %Ea\u003csub\u003eeff\u003c/sub\u003e in the equation.\u003c/p\u003e \u003cp\u003eFurther analysis of adjusted variables was performed to determine the influence of age, sex, APACHE II score, SOFA score, arterial lactate level, the dosage of norepinephrine, fraction of inspired oxygen, minute ventilation, positive end-expiratory pressure, and fluid responsiveness on the predictability of %CO and %Ea\u003csub\u003eeff\u003c/sub\u003e. The results showed that both %CO and %Ea\u003csub\u003eeff\u003c/sub\u003e remained predictors of %MAP (Table\u0026nbsp;5).\u003c/p\u003e \u003cp\u003eWe performed a collinearity diagnostic test for each model. The analysis showed a weak relationship between %CO and each AL parameter (see supplementary file).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of predictive performance\u003c/h2\u003e \u003cp\u003eWe divided the participants into two groups according to an increase or decrease in MAP after VE. We found that 42 participants (67.7%) showed an increased MAP, and 20 participants (33.3%) showed a decreased MAP after VE. The baseline values of Ea\u003csub\u003eDyn\u003c/sub\u003e, Ea\u003csub\u003eeff\u003c/sub\u003e, Ea\u003csub\u003eNet\u003c/sub\u003e, and Ra\u003csub\u003eNet\u003c/sub\u003e had poor predictive performance in predicting an increase or decrease in MAP. The AUC was 0.626 [P\u0026thinsp;=\u0026thinsp;0.311, 95% CI (confidence interval)\u0026thinsp;=\u0026thinsp;0.380\u0026ndash;0.872], 0.444 (P\u0026thinsp;=\u0026thinsp;0.655, 95% CI\u0026thinsp;=\u0026thinsp;0.195\u0026ndash;0.694), 0.448 (P\u0026thinsp;=\u0026thinsp;0.676, 95% CI\u0026thinsp;=\u0026thinsp;0.200\u0026ndash;0.696), and 0.489 (P\u0026thinsp;=\u0026thinsp;0.929, 95% CI\u0026thinsp;=\u0026thinsp;0.252\u0026ndash;0.725), respectively. Regarding the percentage change in the AL parameters, the AUC of %Ea\u003csub\u003eDyn\u003c/sub\u003e, %Ea\u003csub\u003eeff\u003c/sub\u003e, %Ea\u003csub\u003eNet\u003c/sub\u003e, and %Ra\u003csub\u003eNet\u003c/sub\u003e for predicting an increase or decrease in MAP was 0.570 (P\u0026thinsp;=\u0026thinsp;0.571, 95% CI\u0026thinsp;=\u0026thinsp;0.314\u0026ndash;0.827), 0.859 (P\u0026thinsp;=\u0026thinsp;0.004, 95% CI\u0026thinsp;=\u0026thinsp;0.711\u0026ndash;1.000), 0.837 (P\u0026thinsp;=\u0026thinsp;0.007, 95% CI\u0026thinsp;=\u0026thinsp;0.667\u0026ndash;1.000), and 0.822 (P\u0026thinsp;=\u0026thinsp;0.009, 95% CI\u0026thinsp;=\u0026thinsp;0.646\u0026ndash;0.999), respectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study demonstrated a relationship between %MAP changes with the %Ea\u003csub\u003eeff\u003c/sub\u003e, %Ea\u003csub\u003eNet\u003c/sub\u003e, and %Ra\u003csub\u003eNet\u003c/sub\u003e, but not with %Ea\u003csub\u003eDyn\u003c/sub\u003e. We also found that the %Ea\u003csub\u003eeff\u003c/sub\u003e provided the highest predictability of %MAP in the multiple linear regression model compared with the others. Furthermore, the %Ea\u003csub\u003eeff\u003c/sub\u003e had the highest performance for discriminating participants who developed an increase or decrease in MAP after VE. As a result, our study suggests that the %Ea\u003csub\u003eeff\u003c/sub\u003e has a strong relationship with %MAP and could be the best surrogate arterial load parameter.\u003c/p\u003e \u003cp\u003eThe Ea\u003csub\u003eeff\u003c/sub\u003e has been shown to strongly correlate with the arterial elastance derived from the pressure-volume curve of the heart [Ea(PV)], which is known as a gold standard. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) Ea\u003csub\u003eeff\u003c/sub\u003e changes in response to arterial tone and vasoactive infusion. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Also, it reduces after fluid administration (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), which is consistent with the physiologic response of arterial tone to fluid therapy.\u003c/p\u003e \u003cp\u003eInterestingly, our results showed that the baseline Ea\u003csub\u003eDyn\u003c/sub\u003e was a poor predictor of increasing or decreasing MAP after VE. In the previous study, the Ea\u003csub\u003eDyn\u003c/sub\u003e has been defined as an arterial load parameter (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) because it is calculated from pressure and volume variation changed along with the cyclic alteration of lung volume. Therefore, the Ea\u003csub\u003eDyn\u003c/sub\u003e depends not only on the arterial system but also on the lung volume change. Theoretically, the actual arterial elastance given arterial property is believed to be constant during the cardiac cycle. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) Also, it should be assumed to be stable during the breathing cycle. Therefore, we are concerned about being an arterial load parameter of the Ea\u003csub\u003eDyn\u003c/sub\u003e, which may be affected by the lung volume, and might not represent the actual arterial elastance.\u003c/p\u003e \u003cp\u003eEven though the Ea\u003csub\u003eDyn\u003c/sub\u003e was proved, by Garcia et al., to be an excellent predictor of MAP responsiveness compared to the arterial elastance and SVR in patients with circulatory shock receiving the fluid challenge. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) However, the study by Khwannimit et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) demonstrated no significantly different Ea\u003csub\u003eDyn\u003c/sub\u003e between MAP responders and non-responders. In addition, the Ea\u003csub\u003edyn\u003c/sub\u003e differently responded to vasodilator and vasoconstrictor from the arterial elastance and resistance variables. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) It was also found to have an inverse correlation with MAP in the study, as mentioned earlier.\u003c/p\u003e \u003cp\u003eRegarding the Ea\u003csub\u003eNet\u003c/sub\u003e and Ra\u003csub\u003eNet\u003c/sub\u003e, both variables share similar variables for calculation, namely the PP. However, the PP is not constant along with the mechanical ventilation. The cyclic mechanical ventilation affects the PP, making the Ea\u003csub\u003eNet\u003c/sub\u003e and Ra\u003csub\u003eNet\u003c/sub\u003e vary, despite unchanged arterial properties. This reason might contribute to the results of poorer predictors in both parameters. However, the study by Chemla et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) validated net compliance (reciprocal of Ea\u003csub\u003eNet\u003c/sub\u003e) to estimate total arterial compliance by area method, used as a gold standard. They showed a strong correlation (r\u0026thinsp;=\u0026thinsp;0.98, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between both parameters. In this regard, Chemla and the team enrolled different populations from our study; therefore, the interpretation should not be made similarly.\u003c/p\u003e \u003cp\u003eOur study's limitation was that we did not measure the Ea(PV) at the bedside, which is considered the gold standard, as performed in the study by Monge Garc\u0026iacute;a et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). However, the Ea\u003csub\u003eeff\u003c/sub\u003e has been validated as a surrogate parameter of Ea(PV) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) and is feasible at the bedside. A second limitation was that we did not use the thermodilution method for CO measurement. Pulse contour analysis by the Vigileo system is not the gold standard of CO measurement. Finally, we could not control some potential confounding factors, such as the artery's vasomotor tone in participants with sepsis during the VE period, which may have affected the arterial load. Future human studies are required to understand better the physiologic effect of medications on the change in the Ea\u003csub\u003eeff\u003c/sub\u003e.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe %Ea\u003csub\u003eeff\u003c/sub\u003e was the best AL parameter correlated well with %MAP after VE. In addition, the model that included the % Ea\u003csub\u003eeff\u003c/sub\u003e provided the best predictive performance for the %MAP induced by VE, independent of the fluid responsiveness and dosage of norepinephrine. Meanwhile, % Ea\u003csub\u003eDyn\u003c/sub\u003e did not correlate and was not a predictor for the %MAP induced by VE.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMAP, mean arterial pressure; CO, cardiac output; AL, arterial load; Ea, arterial elastance; Ea(PV), arterial elastance derived\u0026nbsp;from a pressure-volume relationship;\u0026nbsp;Ea\u003csub\u003eeff\u003c/sub\u003e, effective\u0026nbsp;arterial elastance; Ea\u003csub\u003eDyn\u003c/sub\u003e, dynamic arterial elastance; VE, volume challenge; PPV, pulse pressure variation; SVV, stroke volume variation; PP, pulse pressure; SV, stroke volume; APACHE II,\u0026nbsp;Acute Physiology and Chronic Health Evaluation II; SOFA, Sequential Organ Failure Assessment; Ea\u003csub\u003eNet\u003c/sub\u003e, net arterial elastance; Ra\u003csub\u003eNet\u003c/sub\u003e, net arterial resistance; LVESP, left ventricular end-systolic pressure; AUC, area under the receiver operating characteristic curve; CI, confidence interval\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe study protocol was approved by the Institutional Ethics Committee and was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNo funding was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePongdhep Theerawit\u0026nbsp;formulated the research idea, designed the research methodology, verified the data, designed the data analysis, analyzed the data, drafted the manuscript, approved the manuscript, submitted the manuscript, and served as the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTanawat Tengsirikomol performed the research, collected the data, verified the data, and approved the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYuda Sutherasan designed the research methodology, verified the data, analyzed the data, and drafted and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u0026nbsp;\u003c/strong\u003eWe would like to express our gratitude to all pulmonary and critical care fellows and medical intensive care nurses. We also thank Angela Morben, DVM, ELS, from Edanz Group (https://en-author-services.edanzgroup.com/), for editing a draft of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRhodes A, Evans LE, Alhazzani W, Levy MM, Antonelli M, Ferrer R, et al. Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016. Critical care medicine. 2017;45(3):486\u0026ndash;552.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonge Garcia MI, Guijo Gonzalez P, Gracia Romero M, Gil Cano A, Oscier C, Rhodes A, et al. Effects of fluid administration on arterial load in septic shock patients. Intensive care medicine. 2015;41(7):1247\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonge Garcia MI, Gil Cano A, Gracia Romero M. Dynamic arterial elastance to predict arterial pressure response to volume loading in preload-dependent patients. Critical care (London, England). 2011;15(1):R15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarcia MI, Romero MG, Cano AG, Aya HD, Rhodes A, Grounds RM, et al. Dynamic arterial elastance as a predictor of arterial pressure response to fluid administration: a validation study. Critical care (London, England). 2014;18(6):626.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonge Garcia MI, Guijo Gonzalez P, Gracia Romero M, Gil Cano A, Rhodes A, Grounds RM, et al. Effects of arterial load variations on dynamic arterial elastance: an experimental study. British journal of anaesthesia. 2017;118(6):938\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChemla D, Hebert JL, Coirault C, Zamani K, Suard I, Colin P, et al. Total arterial compliance estimated by stroke volume-to-aortic pulse pressure ratio in humans. The American journal of physiology. 1998;274(2):H500-5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelly RP, Ting CT, Yang TM, Liu CP, Maughan WL, Chang MS, et al. Effective arterial elastance as index of arterial vascular load in humans. Circulation. 1992;86(2):513\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVincent JL, Weil MH. Fluid challenge revisited. Critical care medicine. 2006;34(5):1333\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTheerawit P, Morasert T, Sutherasan Y. Inferior vena cava diameter variation compared with pulse pressure variation as predictors of fluid responsiveness in patients with sepsis. J Crit Care. 2016;36:246\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuick CM, Mohiuddin MW, Laine GA, Noordergraaf A. The arterial system pressure-volume loop. Physiol Meas. 2005;26(6):N29-35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhwannimit B, Bhurayanontachai R. Prediction of fluid responsiveness in septic shock patients: comparing stroke volume variation by FloTrac/Vigileo and automated pulse pressure variation. Eur J Anaesthesiol. 2012;29(2):64\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"arterial elastance, dynamic arterial elastance, sepsis, mean arterial pressure, fluid responsiveness","lastPublishedDoi":"10.21203/rs.3.rs-1673396/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1673396/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\u003cp\u003ewe conducted this study to compare four arterial load parameters and determine which arterial load parameters directly impacted arterial pressure regarding pressure, flow, and arterial system relationship.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eWe conducted a cross-sectional study in patients with sepsis who underwent volume expansion (VE). Hemodynamic parameters were recorded before and after VE. The relationship between the change of mean arterial pressure (%MAP) and that of the dynamic arterial elastance (Ea\u003csub\u003eDyn\u003c/sub\u003e), effective arterial elastance (Ea\u003csub\u003eeff\u003c/sub\u003e), net arterial elastance (Ea\u003csub\u003eNet\u003c/sub\u003e), and net arterial resistance (Ra\u003csub\u003eNet\u003c/sub\u003e) was analyzed.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eSixty-two patients were included. The DEa\u003csub\u003eDyn\u003c/sub\u003e(%) was not correlated with DMAP(%) (r=0.048, P=0.826). Meanwhile, DEa\u003csub\u003eeff\u003c/sub\u003e(%), DEa\u003csub\u003eNet\u003c/sub\u003e(%), and DRa\u003csub\u003eNet\u003c/sub\u003e(%) were correlated with DMAP(%) (r=0.495, P\u0026lt;0.001; r=0.453, P\u0026lt;0.001; and r=0.485, P\u0026lt;0.001, respectively). A multiple linear regression model was analyzed for identifying predictors of DMAP(%) by including DCO(%) and each %change of arterial parameters. The best-fit model was found by including DCO(%) and DEa\u003csub\u003eeff\u003c/sub\u003e(%) in the regression equation (R\u003csup\u003e2\u003c/sup\u003e=0.823, adjusted R\u003csup\u003e2\u003c/sup\u003e=0.817). The model was adjusted by age, sex, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, Sequential Organ Failure Assessment (SOFA) score, arterial lactate level, norepinephrine dosage, ventilator setting, method of VE, and fluid responsiveness and found that DCO(%) and DEa\u003csub\u003eeff\u003c/sub\u003e(%) remained statistically significant predictors of DMAP(%) (P\u0026lt;0.001 and P\u0026lt;0.001, respectively).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe Ea\u003csub\u003eeff\u003c/sub\u003e was the best AL parameter that correlated with the changes in MAP. Furthermore, the model that included the ∆Ea\u003csub\u003eeff\u003c/sub\u003e(%) provided the best predictive performance for ∆MAP(%) induced by VE, independent of the fluid responsiveness and norepinephrine dosage.\u003c/p\u003e","manuscriptTitle":"Performance of Effective Arterial Elastance as Predictor of Mean Arterial Pressure in Patients with Sepsis or Septic Shock Receiving Fluid Expansion: A Validation Study by Regression Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-05-26 15:50:18","doi":"10.21203/rs.3.rs-1673396/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"05fa7e19-eb0e-4aad-b9f3-8e06b4432241","owner":[],"postedDate":"May 26th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-05-26T15:50:19+00:00","versionOfRecord":[],"versionCreatedAt":"2022-05-26 15:50:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1673396","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1673396","identity":"rs-1673396","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}