Prediction of Fluid Responsiveness using a complete and partial Passive Leg Raising maneuver in post-cardiac surgery patients

Prediction of Fluid Responsiveness using a complete and partial Passive Leg Raising maneuver in post-cardiac surgery patients | 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 Short Report Prediction of Fluid Responsiveness using a complete and partial Passive Leg Raising maneuver in post-cardiac surgery patients Loek P.B. Meijs, Alexander J.G.H. Bindels, Arnout N. Roos, Saskia Houterman, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4460439/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 Prediction of fluid responsiveness (FR) in critically ill patients is challenging. Passive leg raising (PLR) has been proven to adequately predict FR. PLR consists of a thoracic (T-PLR) and a limb (L-PLR) movement. Since a complete PLR (C-PLR) is not always feasible, this study focused on investigating the predictive value of partial PLRs on FR. Methods A prospective, observational study was performed in 40 post-cardiac surgery patients. C-PLR was performed, followed by a T-PLR, L-PLR and fluid challenge (FC). Invasive cardiac index (CI) measurements were continuously recorded during all maneuvers. FR was defined as a CI-increase ≥ 15% after FC, thereby identifying responders (R) and non-responders (NR). The predictive value of the PLR-elements was assessed with receiver operating characteristic (ROC) curves. Changes over time were analyzed with generalized linear model (GLM) analyses. Intraclass correlation coefficient (ICC) was used to assess absolute agreement between PLR and FC. Results Forty patients were included (35 R / 5 NR). AUC was similar for all PLRs (C-PLR = 0.84; T-PLR = 0.86, L-PLR = 0.86). ICCs between FC versus the three PLRs were 0.81 (0.63–0.90), 0.78 (0.59–0.88), and 0.71 (0.46–0.85), respectively. Median CI-increase during C-PLR was 27.8% (21–48%) in responders vs. 10.7% (7.5–12.6%) in non-responders (p = 0.012). After FC, median CI-increase was 30.0% (22.2–42.9%) in responders vs. 7.4% (6.3–12.4%) in non-responders (p = 0.002). Conclusion Partial PLRs have similar predictive values compared to a C-PLR. This could improve the prediction of FR in specific patient categories where C-PLR is restricted. passive leg raising fluid challenge venous return cardiac index fluid responsiveness Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Determination of fluid responsiveness (FR) in critically ill patients is challenging. Only patients who increase their cardiac output (CO) following a fluid challenge (FC) will benefit from fluid therapy. Several maneuvers to evaluate FR are clinically available. ( 1 – 4 ) Passive leg raising (PLR) has been proven to adequately predict fluid responsiveness. ( 5 , 6 ) PLR consists of two elements. First, the move from the semi-recumbent position to the supine position (T-PLR). Second, raising the legs to 45° (L-PLR). Recently, studies using only T-PLR or L-PRL have been published. ( 6 , 7 ) The magnitude of these individual elements of the complete PLR (C-PLR) on CO differs. ( 8 ) To date, no studies have compared the predictive value of either the T-PLR or L-PRL to that of the C-PLR, which is labor-intensive and not always possible (e.g. head trauma, pelvic trauma patients). Therefore, if a partial PLR portends significant predictive value, this might improve the use of a PLR to guide fluid therapy. This study systematically investigated the predictive value of the different elements of the PLR. ( 8 – 12 ) Materials and methods This prospective, single-center and observational study (ClinicalTrials.gov ID:NCT02060942; NCT02778620) was approved by the Medical Research Ethics Committee United, Nieuwegein, The Netherlands (MEC-U; NL42274.060.12 / M12-1271; NL42108.060.12 / M12-1270). All patients signed informed consent before study inclusion. Patients Postoperatively sedated and mechanically ventilated cardiac surgery patients (≥18 years; elective aortic valve replacement or elective coronary artery bypass grafting) admitted to the intensive care unit (ICU) for hemodynamic stabilization and weaning after surgery were included. Exclusion criteria were a left ventricular ejection fraction (LVEF) ≤50%, significant valvular disease, severe comorbidity or mechanical circulatory support. Study design and data collection All patients were continuously monitored with a cardiac output catheter (PiCCO®, v6.0) and a central venous catheter. Arterial and venous pressure transducers were referenced and attached to the thorax at the level of the right atrium to maintain the same position during PLR. At study initiation, before and after each maneuver, calibration per thermodilution was performed once stabilization of hemodynamic, respiratory and sedative indices was confirmed. ( 13 , 14 ) CO, cardiac index (CI), right atrial pressure (RAP), mean arterial blood pressure (MAP) and heart rate (HR) were continuously recorded. PLR was continued for at least ≥1 minute to allow a maximum CI increase. ( 15 ) All data were manually checked during offline analysis to identify the optimum hemodynamic effects. A summary of the protocol is shown in Fig. 1 . Patients were placed in the semi-recumbent position with the thorax elevated at 45° and the lower limbs horizontal, followed by baseline measurements. Next, the patient’s thorax was lowered to 0° (supine position) while the lower limbs were successively lifted to 45° using motorized hospital beds (C-PLR). The exact angulation of the trunk and limbs was verified using the bed display and checked each time a maneuver was performed. Patients were then returned to the semi-recumbent position and after a 5-minute interval to allow hemodynamics to return to baseline, the trunk was lowered to the supine position without lifting the lower limbs (T-PLR). Subsequently, after a 5-minute interval, only the lower limbs were lifted to 45°(L-PLR). Finally, after returning the patient to the semi-recumbent posture with a 5-minute lay-over, FC was performed using 500 mL Ringer’s lactate (mean duration of 3.6±0.8 minutes). Hemodynamic recording was maintained for 30 minutes after FC. Statistical analysis A ≥ 15% increase in CI after FC was used to define fluid responsiveness. ( 8 ) Continuous variables are described as mean (standard deviation) or median [interquartile range] according to their distribution. Receiver operating characteristic (ROC) curves were constructed from which sensitivity, specificity, positive and negative predictive values (PPV; NPV), area under the curve (AUC) and the optimal threshold for the prediction of fluid responsiveness were determined. Generalized linear model (GLM) analyses were performed to test changes in parameters over time (before and after a maneuver) between responders (R) and non-responders (NR). Differences between groups were assessed with the Mann-Whitney U test. Intraclass correlation coefficient (ICC) was calculated to test absolute agreement between increases in CI during PLR and FC. A p-value < 0.05 was considered to indicate statistical significance. Analyses were performed using SPSS (version 27.0) and GraphPad Prism (version 8.2.1). Results Forty patients were included, 35 of whom were identified as responders vs. 5 non-responders. Baseline characteristics are presented in Table 1. Prediction of fluid responsiveness for the different PLR elements is presented in Figure 2. For C-PLR, the AUC was 0.84 with an optimal threshold of 18.8% (sensitivity 91%; specificity 40%; PPV 91% and NPV 40%). T-PLR showed an AUC of 0.86 with an optimal threshold of 17.9% (sensitivity 91%; specificity 40%; PPV 92%; NPV 39%). With L-PLR, the AUC was 0.86 with an optimal threshold of 18.6% (sensitivity 97%; specificity 40%; PPV 92%; NPV 67%). Changes in hemodynamic parameters during the maneuvers are presented in Table 2. Median baseline CI did not significantly differ between R and NR (p = 0.317). The median increase in CI in responders after C-PLR compared to FC was not statistically different (27.8% vs. 30.0%; p = 0.241) (Figure 3). Only 2 out of 35 responders to a C-PLR showed no fluid responsiveness after a FC. T-PLR and L-PLR each represented half of the increase in CI in responders, when compared to C-PLR and FC (Figure 4 / Table 2). Changes in CI after T-PLR and L-PLR in responders were not significantly different compared to FR after FC (p = 0.143, p = 0.349 respectively). RAP increased significantly during C-PLR, T-PLR, L-PLR and FC in both responders as non-responders (p < 0.001). Responders had a significantly lower baseline RAP compared to NR (Table 2). MAP and HR did not significantly change in either group during any of maneuvers (Table 2). The ICCs between the changes in CI after C-PLR, T-PLR and L-PLR vs. FC were 0.81 [0.63 – 0.90], 0.78 [0.59 – 0.88], and 0.71 [0.46 – 0.85] respectively (p=0.01). Discussion This study shows that both complete and partial passive leg raising maneuvers are good predictors of fluid responsiveness. Our study is the first to prove the predictive value of these different PLR elements. Although partial PLRs have been investigated previously, their predictive ability has not been investigated. ( 7 ) Our findings justify the use of partial PLRs in patients with contraindications for a complete PLR. It may also lower the threshold for performing a PLR in general, since a partial PLR is less laborious than a complete PLR. The predictive value of the complete PLR in our study is in line with results from two recent meta-analyses on PLR, which corroborates the translation of the conclusions from our study to other populations. ( 8 , 9 ) In contrast to other studies, we found lower AUCs for PLR. ( 8 , 16 , 17 ) A possible explanation could be the difference in patient populations as most studies were performed in patients with sepsis or septic shock. Overall, studies involving cardiac surgery patients reported lower AUCs. ( 10 – 12 ) Patients defined as non-responders still showed some, though non-significant, increase in CI during PLR and FC. The fact that non-responders reveal this behavior, raises questions about what true fluid responsiveness is about. The current cutoff values for the definition of fluid responsiveness are historically defined based on the margins of error of the measuring devices used, thereby lacking a physiological basis for this distinction. ( 14 ) Thus, defining the success rate of volume infusion based upon this adjustable threshold is arbitrary and could lead to a self-fulfilling prophecy in terms of fluid responsiveness. Indeed, when changing the cutoff value for fluid responsiveness (an increase in CI ≥ 10% or CI ≥ 20% respectively), the AUCs for the different PLR-elements (C-PLR, T-PLR and L-PLR) differ markedly (Additional file 1). Caution should therefore be used when defining thresholds of volume responsiveness. It also provides an opportunity to further investigate why non-responders show some (non-significant) increase in cardiac output during volume loading conditions. The effects of a PLR maneuver on cardiac output vary widely and to date it is unclear whether this can be attributed to the auto-volume challenge itself or to the cardiac contractility reserve. ( 5 ) It is essential to state that volume status and fluid responsiveness are two different entities. If PLR increases cardiac preload, cardiac output should increase depending on the degree of biventricular reserve. Only in preload dependent ventricles, PLR may result in increased stroke volume. In our study these effects can be observed both in responders and non-responders, although non-responders show a (according to current FR standards) non-significant behavior to a volume shift. Along with the analysis of a positive or negative test, comes the binary definition of success. The reason for performing a PLR maneuver is to estimate the chance that additional volume infusion will increase cardiac output. This is highly dependent on where the patient is situated on the CO (Starling)-curve and whether it intersects with the venous return curve (Guytonian-curve). ( 18 ) This position is scalar and continuous, rather than dichotomous. Recently, Aneman and Sondergaard proposed methods to evaluate fluid responsiveness without using cutoff-values which could represent a promising approach. ( 19 , 20 ) Conclusion Partial PLRs are reliable tests for determining fluid responsiveness in patients for whom complete PLRs are not feasible. As partial PLR techniques are easier to perform, these could serve as good alternatives for predicting volume responsiveness. Abbreviations FR Fluid responsiveness CO Cardiac output FC Fluid challenge PLR Passive leg raising T-PLR Thoracic element of passive leg raising L-PLR Limb element of passive leg raising C-PLR Complete passive leg raising MEC-U Medical research ethics committee united ICU Intensive care unit LVEF Left ventricular ejection fraction PiCCO® Pulse contour cardiac output CI Cardiac index RAP Right atrial pressure MAP Mean arterial blood pressure HR Heart rate ROC Receiver operating characteristic curves PPV Positive predictive value NPV Negative predictive value AUC Area under the curve R Responders NR Non-responders GLM Generalized linear model analyses ICC Intraclass correlation coefficient Declarations Ethics approval and consent to participate Ethical approval was granted by the Medical Research Ethics Committee United, Nieuwegein, The Netherlands (MEC-U; NL42274.060.12 / M12-1271; NL42108.060.12 / M12-1270) and the study was registered at ClinicalTrials.gov (NCT02060942; NCT02778620). All patients signed written informed consent before study inclusion. Consent for publication Not applicable. Availability of data and material The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding Funding for this study was provided by the Catharina Hospital Intensive Care Research Fund. The funding body had no involvement in study design, data collection, analysis, interpretation or manuscript writing. Authors’ contributions LPBM, AJGHB, ANR and JB designed the study. LPBM collected data in the study. LPBM drafted the manuscript. LPBM and SHS performed the statistical analyses. SHN provided statistical advice, approval and correction for the manuscript. JH provided advice and review of the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. Author details 1 Department of Intensive Care, Jeroen Bosch Hospital, ‘s-Hertogenbosch, The Netherlands; 2 Department of Intensive Care, Catharina Hospital, Eindhoven, The Netherlands; 3 Department of Research and Education, Catharina Hospital, Eindhoven, The Netherlands; 4 Department of Cardiothoracic Surgery, Maastricht University Medical Center+, Maastricht, The Netherlands; 5 Department of Anesthesiology, Catharina Hospital, Eindhoven, The Netherlands; 6 Department of Intensive Care, Erasmus MC University Medical Centre, Rotterdam, The Netherlands; 7 Division of Pulmonary, Allergy, and Critical Care Medicine, Columbia University Medical Center, New York, NY, USA; 8 Department of Intensive Care, Pontificia Universidad Católica de Chile, Santiago, Chile. References Mair S, Tschirdewahn J, Gotz S, Frank J, Phillip V, Henschel B, et al. Applicability of stroke volume variation in patients of a general intensive care unit: a longitudinal observational study. J Clin Monit Comput. 2017;31(6):1177-87. Lansdorp B, Lemson J, van Putten MJ, de Keijzer A, van der Hoeven JG, Pickkers P. Dynamic indices do not predict volume responsiveness in routine clinical practice. Br J Anaesth. 2012;108(3):395-401. Cecconi M, Aya HD. Central venous pressure cannot predict fluid-responsiveness. Evid Based Med. 2014;19(2):63. Delannoy B, Wallet F, Maucort-Boulch D, Page M, Kaaki M, Schoeffler M, et al. Applicability of Pulse Pressure Variation during Unstable Hemodynamic Events in the Intensive Care Unit: A Five-Day Prospective Multicenter Study. Crit Care Res Pract. 2016;2016:7162190. Mesquida J, Gruartmoner G, Ferrer R. Passive leg raising for assessment of volume responsiveness: a review. Curr Opin Crit Care. 2017;23(3):237-43. Monnet X, Teboul JL. Passive leg raising: five rules, not a drop of fluid! Crit Care. 2015;19:18. Jabot J, Teboul JL, Richard C, Monnet X. Passive leg raising for predicting fluid responsiveness: importance of the postural change. Intensive Care Med. 2009;35(1):85-90. Monnet X, Marik P, Teboul JL. Passive leg raising for predicting fluid responsiveness: a systematic review and meta-analysis. Intensive Care Med. 2016;42(12):1935-47. Cherpanath TG, Hirsch A, Geerts BF, Lagrand WK, Leeflang MM, Schultz MJ, et al. Predicting Fluid Responsiveness by Passive Leg Raising: A Systematic Review and Meta-Analysis of 23 Clinical Trials. Crit Care Med. 2016;44(5):981-91. Benomar B, Ouattara A, Estagnasie P, Brusset A, Squara P. Fluid responsiveness predicted by noninvasive bioreactance-based passive leg raise test. Intensive Care Med. 2010;36(11):1875-81. Fellahi JL, Fischer MO, Dalbera A, Massetti M, Gerard JL, Hanouz JL. Can endotracheal bioimpedance cardiography assess hemodynamic response to passive leg raising following cardiac surgery? Ann Intensive Care. 2012;2(1):26. Fischer MO, Rebet O, Guinot PG, Lemetayer C, Saplacan V, Gerard JL, et al. Assessment of changes in cardiac index with calibrated pulse contour analysis in cardiac surgery: A prospective observational study. Anaesth Crit Care Pain Med. 2016;35(4):261-7. Godje O, Hoke K, Goetz AE, Felbinger TW, Reuter DA, Reichart B, et al. Reliability of a new algorithm for continuous cardiac output determination by pulse-contour analysis during hemodynamic instability. Crit Care Med. 2002;30(1):52-8. Critchley LA, Critchley JA. A meta-analysis of studies using bias and precision statistics to compare cardiac output measurement techniques. J Clin Monit Comput. 1999;15(2):85-91. Monnet X, Marik PE, Teboul JL. Prediction of fluid responsiveness: an update. Ann Intensive Care. 2016;6(1):111. Monnet X, Bataille A, Magalhaes E, Barrois J, Le Corre M, Gosset C, et al. End-tidal carbon dioxide is better than arterial pressure for predicting volume responsiveness by the passive leg raising test. Intensive Care Med. 2013;39(1):93-100. Galarza L, Mercado P, Teboul JL, Girotto V, Beurton A, Richard C, et al. Estimating the rapid haemodynamic effects of passive leg raising in critically ill patients using bioreactance. Br J Anaesth. 2018;121(3):567-73. Meijs LPB, Bindels AJGH, Bakker J, Pinsky MR. Cardiac Function (Cardiac Output and Its Determinants). In: Lima AAP, Silva E, editors. Monitoring Tissue Perfusion in Shock: From Phsyiology to the Bedside. Springer; 2018. p. 51-76. Gupta K, Sondergaard S, Parkin G, Leaning M, Aneman A. Applying mean systemic filling pressure to assess the response to fluid boluses in cardiac post-surgical patients. Intensive Care Med. 2015;41(2):265-72. Aneman A, Sondergaard S. Understanding the passive leg raising test. Intensive Care Med. 2016;42(9):1493-5. Tables Table 1. Baseline characteristics Patient characteristics Data ( n =40) Age (y) 67 [60-74] Gender (M/F) 30/10 Height (cm) 175 [169-180] Body weight (kg) 80 [70-89] Body surface area (m 2 ) 1.96 [1.81-2.06] Procedure • CABG • AVR Duration ECC (min) Duration aortic clamping (min) 20 (50%) 20 (50%) 83 [62-102] 58 [41-75] Vasoactive and sedative drugs Norepinephrine ( n patients) N/A Dobutamine ( n patients) Milrinon ( n patients) Propofol ( n patients; dosage mg/kg/min) Ventilator settings Tidal volume (mL/kg predicted body weight) N/A N/A 40; 2.1±0.88 6.3±1.1 Respiratory rate (breath/min) 12.5±1.6 Total PEEP (cmH 2 O) FiO2 (%) 5±0 0.4±0 Values are expressed as mean ± standard deviation, median value [interquartile range] or absolute numbers (%), as appropriate. M , male; F , female; CABG , coronary artery bypass grafting; AVR , aortic valve replacement; ECC , extra-corporeal circulation; N/A , not applicable; PEEP , positive end-expiratory pressure; FiO2 , inspired oxygen fraction. Table 2. Hemodynamic parameters during passive leg raising and fluid challenge in responders and non-responders ( n =40) Quantitative data are given as median value [interquartile range] over different time points. CI, cardiac index; RAP, right atrial pressure; MAP, mean arterial pressure; HR, heart rate. * Significant difference after PLR / FC (p <0.001) † Significant difference between responders vs. non-responders (p <0.001) Additional Declarations No competing interests reported. Supplementary Files Datasupplement1ROCFRCIthresholds1020deltaCI.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-4460439","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":310260363,"identity":"45cace01-ec82-4d77-a175-eb0b9b12bb8e","order_by":0,"name":"Loek P.B. 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For C-PLR, an area under the curve (AUC) of 0.84 with an optimal threshold of 18.8% was found (sensitivity 91%; specificity 40%; PPV 91% and NPV 40%). For T-PLR the AUC was 0.86 with an optimal threshold of 17.9% (sensitivity 91%; specificity 40%; PPV 92%; NPV 100%). The optimal threshold for L-PLR was 18.6% with an AUC of 0.86 (sensitivity 97%; specificity 40%; PPV 92%; NPV 67%).\u003c/p\u003e","description":"","filename":"Figure2ROCPLRFC.png","url":"https://assets-eu.researchsquare.com/files/rs-4460439/v1/e0901772303c0ce44cfbe3cb.png"},{"id":58077140,"identity":"5d71130e-e43a-4c0a-9049-438dd8f1ad94","added_by":"auto","created_at":"2024-06-10 22:27:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":70308,"visible":true,"origin":"","legend":"\u003cp\u003ePercentual change of cardiac index (CI) (y-axis) during passive leg raising (PLR) and fluid challenge (FC) between responders (R) and non-responders (NR). Data are presented as median values with interquartile range. Within responders the median increase in CI after C-PLR was 27.8% (21-48%) vs. 10.7% (7.5-22%) in non-responders (p = 0.012). After FC the median increase in CI was 30.0% (22.2-52.9%) in responders vs. 7.4% (6.3-12.4%) in non-responders (p = 0.002).\u003c/p\u003e","description":"","filename":"Figure3PercentualchangeCIPLRFCRvsNR.png","url":"https://assets-eu.researchsquare.com/files/rs-4460439/v1/b4217f94c8ce4fa515d73e0f.png"},{"id":58076315,"identity":"5ee7e29e-95f3-49b1-b7da-a4491a5574b8","added_by":"auto","created_at":"2024-06-10 22:19:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":145034,"visible":true,"origin":"","legend":"\u003cp\u003eAbsolute changes in cardiac index (CI) during passive leg raising (PLR) subsets and fluid challenge (FC) in responders (R) and non-responders (NR). Quantitative data are presented as median value with interquartile range over different time points. In responders, T-PLR and L-PLR each represented half of the increase in CI in comparison to C-PLR and FC.\u003c/p\u003e","description":"","filename":"Figure4AbsolutechangeCIPLRFCRvsNR.png","url":"https://assets-eu.researchsquare.com/files/rs-4460439/v1/a59951c14c2d9a5c0fa62f79.png"},{"id":58423536,"identity":"67c8f4d8-4f97-4cf5-8b51-377b8c432beb","added_by":"auto","created_at":"2024-06-15 17:04:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":840662,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4460439/v1/6f5b010c-276c-42da-b16e-9717eb9cf76f.pdf"},{"id":58076312,"identity":"65d65b24-5e68-48cd-8e34-7c4cdde61b2e","added_by":"auto","created_at":"2024-06-10 22:19:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24863,"visible":true,"origin":"","legend":"","description":"","filename":"Datasupplement1ROCFRCIthresholds1020deltaCI.docx","url":"https://assets-eu.researchsquare.com/files/rs-4460439/v1/6d9ca755f667766cf4e9b067.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of Fluid Responsiveness using a complete and partial Passive Leg Raising maneuver in post-cardiac surgery patients","fulltext":[{"header":"Background","content":"\u003cp\u003eDetermination of fluid responsiveness (FR) in critically ill patients is challenging. Only patients who increase their cardiac output (CO) following a fluid challenge (FC) will benefit from fluid therapy. Several maneuvers to evaluate FR are clinically available.\u003csup\u003e(\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/sup\u003e Passive leg raising (PLR) has been proven to adequately predict fluid responsiveness.\u003csup\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePLR consists of two elements. First, the move from the semi-recumbent position to the supine position (T-PLR). Second, raising the legs to 45\u0026deg; (L-PLR). Recently, studies using only T-PLR or L-PRL have been published.\u003csup\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/sup\u003e The magnitude of these individual elements of the complete PLR (C-PLR) on CO differs.\u003csup\u003e(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/sup\u003e To date, no studies have compared the predictive value of either the T-PLR or L-PRL to that of the C-PLR, which is labor-intensive and not always possible (e.g. head trauma, pelvic trauma patients). Therefore, if a partial PLR portends significant predictive value, this might improve the use of a PLR to guide fluid therapy. This study systematically investigated the predictive value of the different elements of the PLR.\u003csup\u003e(\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e This prospective, single-center and observational study (ClinicalTrials.gov ID:NCT02060942; NCT02778620) was approved by the Medical Research Ethics Committee United, Nieuwegein, The Netherlands (MEC-U; NL42274.060.12 / M12-1271; NL42108.060.12 / M12-1270). All patients signed informed consent before study inclusion.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003ePostoperatively sedated and mechanically ventilated cardiac surgery patients (\u0026ge;18 years; elective aortic valve replacement or elective coronary artery bypass grafting) admitted to the intensive care unit (ICU) for hemodynamic stabilization and weaning after surgery were included. Exclusion criteria were a left ventricular ejection fraction (LVEF) \u0026le;50%, significant valvular disease, severe comorbidity or mechanical circulatory support.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and data collection\u003c/h2\u003e \u003cp\u003eAll patients were continuously monitored with a cardiac output catheter (PiCCO\u0026reg;, v6.0) and a central venous catheter. Arterial and venous pressure transducers were referenced and attached to the thorax at the level of the right atrium to maintain the same position during PLR. At study initiation, before and after each maneuver, calibration per thermodilution was performed once stabilization of hemodynamic, respiratory and sedative indices was confirmed.\u003csup\u003e(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/sup\u003e CO, cardiac index (CI), right atrial pressure (RAP), mean arterial blood pressure (MAP) and heart rate (HR) were continuously recorded. PLR was continued for at least \u0026ge;1 minute to allow a maximum CI increase.\u003csup\u003e(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/sup\u003e All data were manually checked during offline analysis to identify the optimum hemodynamic effects.\u003c/p\u003e \u003cp\u003eA summary of the protocol is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients were placed in the semi-recumbent position with the thorax elevated at 45\u0026deg; and the lower limbs horizontal, followed by baseline measurements. Next, the patient\u0026rsquo;s thorax was lowered to 0\u0026deg; (supine position) while the lower limbs were successively lifted to 45\u0026deg; using motorized hospital beds (C-PLR). The exact angulation of the trunk and limbs was verified using the bed display and checked each time a maneuver was performed. Patients were then returned to the semi-recumbent position and after a 5-minute interval to allow hemodynamics to return to baseline, the trunk was lowered to the supine position without lifting the lower limbs (T-PLR). Subsequently, after a 5-minute interval, only the lower limbs were lifted to 45\u0026deg;(L-PLR). Finally, after returning the patient to the semi-recumbent posture with a 5-minute lay-over, FC was performed using 500 mL Ringer\u0026rsquo;s lactate (mean duration of 3.6\u0026plusmn;0.8 minutes). Hemodynamic recording was maintained for 30 minutes after FC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eA\u0026thinsp;\u0026ge;\u0026thinsp;15% increase in CI after FC was used to define fluid responsiveness.\u003csup\u003e(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/sup\u003e Continuous variables are described as mean (standard deviation) or median [interquartile range] according to their distribution. Receiver operating characteristic (ROC) curves were constructed from which sensitivity, specificity, positive and negative predictive values (PPV; NPV), area under the curve (AUC) and the optimal threshold for the prediction of fluid responsiveness were determined. Generalized linear model (GLM) analyses were performed to test changes in parameters over time (before and after a maneuver) between responders (R) and non-responders (NR). Differences between groups were assessed with the Mann-Whitney U test. Intraclass correlation coefficient (ICC) was calculated to test absolute agreement between increases in CI during PLR and FC. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statistical significance. Analyses were performed using SPSS (version 27.0) and GraphPad Prism (version 8.2.1).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eForty patients were included, 35 of whom were identified as responders vs. 5 non-responders. Baseline characteristics are presented in Table 1. Prediction of fluid responsiveness for the different PLR elements is presented in Figure 2. For C-PLR, the AUC was 0.84 with an optimal threshold of 18.8% (sensitivity 91%; specificity 40%; PPV 91% and NPV 40%). T-PLR showed an AUC of 0.86 with an optimal threshold of 17.9% (sensitivity 91%; specificity 40%; PPV 92%; NPV 39%). With L-PLR, the AUC was 0.86 with an optimal threshold of 18.6% (sensitivity 97%; specificity 40%; PPV 92%; NPV 67%).\u003c/p\u003e\n\u003cp\u003eChanges in hemodynamic parameters during the maneuvers are presented in Table 2. Median baseline CI did not significantly differ between R and NR (p = 0.317). The median increase in CI in responders after C-PLR compared to FC was not statistically different (27.8% vs. 30.0%; p = 0.241) (Figure 3). Only 2 out of 35 responders to a C-PLR showed no fluid responsiveness after a FC. T-PLR and L-PLR each represented half of the increase in CI in responders, when compared to C-PLR and FC (Figure 4 / Table 2). Changes in CI after T-PLR and L-PLR in responders were not significantly different compared to FR after FC (p = 0.143, p = 0.349 respectively).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRAP increased significantly during C-PLR, T-PLR, L-PLR and FC in both responders as non-responders (p \u0026lt; 0.001). Responders had a significantly lower baseline RAP compared to NR (Table 2). MAP and HR did not significantly change in either group during any of maneuvers (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe ICCs between the changes in CI after C-PLR, T-PLR and L-PLR vs. FC were 0.81 [0.63 \u0026ndash; 0.90], 0.78 [0.59 \u0026ndash; 0.88], and 0.71 [0.46 \u0026ndash; 0.85] respectively (p=0.01).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study shows that both complete and partial passive leg raising maneuvers are good predictors of fluid responsiveness. Our study is the first to prove the predictive value of these different PLR elements. Although partial PLRs have been investigated previously, their predictive ability has not been investigated.\u003csup\u003e(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/sup\u003e Our findings justify the use of partial PLRs in patients with contraindications for a complete PLR. It may also lower the threshold for performing a PLR in general, since a partial PLR is less laborious than a complete PLR.\u003c/p\u003e \u003cp\u003eThe predictive value of the complete PLR in our study is in line with results from two recent meta-analyses on PLR, which corroborates the translation of the conclusions from our study to other populations.\u003csup\u003e(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn contrast to other studies, we found lower AUCs for PLR.\u003csup\u003e(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/sup\u003e A possible explanation could be the difference in patient populations as most studies were performed in patients with sepsis or septic shock. Overall, studies involving cardiac surgery patients reported lower AUCs.\u003csup\u003e(\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePatients defined as non-responders still showed some, though non-significant, increase in CI during PLR and FC. The fact that non-responders reveal this behavior, raises questions about what true fluid responsiveness is about. The current cutoff values for the definition of fluid responsiveness are historically defined based on the margins of error of the measuring devices used, thereby lacking a physiological basis for this distinction.\u003csup\u003e(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/sup\u003e Thus, defining the success rate of volume infusion based upon this adjustable threshold is arbitrary and could lead to a self-fulfilling prophecy in terms of fluid responsiveness. Indeed, when changing the cutoff value for fluid responsiveness (an increase in CI \u0026ge; 10% or CI \u0026ge; 20% respectively), the AUCs for the different PLR-elements (C-PLR, T-PLR and L-PLR) differ markedly (Additional file 1). Caution should therefore be used when defining thresholds of volume responsiveness. It also provides an opportunity to further investigate why non-responders show some (non-significant) increase in cardiac output during volume loading conditions.\u003c/p\u003e \u003cp\u003eThe effects of a PLR maneuver on cardiac output vary widely and to date it is unclear whether this can be attributed to the auto-volume challenge itself or to the cardiac contractility reserve.\u003csup\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/sup\u003e It is essential to state that volume status and fluid responsiveness are two different entities. If PLR increases cardiac preload, cardiac output should increase depending on the degree of biventricular reserve. Only in preload dependent ventricles, PLR may result in increased stroke volume. In our study these effects can be observed both in responders and non-responders, although non-responders show a (according to current FR standards) non-significant behavior to a volume shift. Along with the analysis of a positive or negative test, comes the binary definition of success. The reason for performing a PLR maneuver is to estimate the chance that additional volume infusion will increase cardiac output. This is highly dependent on where the patient is situated on the CO (Starling)-curve and whether it intersects with the venous return curve (Guytonian-curve).\u003csup\u003e(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/sup\u003e This position is scalar and continuous, rather than dichotomous. Recently, Aneman and Sondergaard proposed methods to evaluate fluid responsiveness without using cutoff-values which could represent a promising approach.\u003csup\u003e(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePartial PLRs are reliable tests for determining fluid responsiveness in patients for whom complete PLRs are not feasible. As partial PLR techniques are easier to perform, these could serve as good alternatives for predicting volume responsiveness.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eFR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Fluid responsiveness\u003c/p\u003e\n\u003cp\u003eCO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Cardiac output\u003c/p\u003e\n\u003cp\u003eFC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Fluid challenge\u003c/p\u003e\n\u003cp\u003ePLR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Passive leg raising\u003c/p\u003e\n\u003cp\u003eT-PLR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Thoracic element of passive leg raising\u003c/p\u003e\n\u003cp\u003eL-PLR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Limb element of passive leg raising\u003c/p\u003e\n\u003cp\u003eC-PLR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Complete passive leg raising\u003c/p\u003e\n\u003cp\u003eMEC-U\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Medical research ethics committee united\u003c/p\u003e\n\u003cp\u003eICU\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Intensive care unit\u003c/p\u003e\n\u003cp\u003eLVEF\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Left ventricular ejection fraction\u003c/p\u003e\n\u003cp\u003ePiCCO\u0026reg;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Pulse contour cardiac output\u003c/p\u003e\n\u003cp\u003eCI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Cardiac index\u003c/p\u003e\n\u003cp\u003eRAP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Right atrial pressure\u003c/p\u003e\n\u003cp\u003eMAP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Mean arterial blood pressure\u003c/p\u003e\n\u003cp\u003eHR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Heart rate\u003c/p\u003e\n\u003cp\u003eROC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Receiver operating characteristic curves\u003c/p\u003e\n\u003cp\u003ePPV\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Positive predictive value\u003c/p\u003e\n\u003cp\u003eNPV\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Negative predictive value\u003c/p\u003e\n\u003cp\u003eAUC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Area under the curve\u003c/p\u003e\n\u003cp\u003eR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Responders\u003c/p\u003e\n\u003cp\u003eNR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Non-responders\u003c/p\u003e\n\u003cp\u003eGLM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Generalized linear model analyses\u003c/p\u003e\n\u003cp\u003eICC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Intraclass correlation coefficient\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003e\u003cu\u003eEthics approval and consent to participate\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was granted by the Medical Research Ethics Committee United, Nieuwegein, The Netherlands (MEC-U; NL42274.060.12 / M12-1271; NL42108.060.12 / M12-1270) and the study was registered at ClinicalTrials.gov (NCT02060942; NCT02778620). All patients signed written informed consent before study inclusion.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eConsent for publication\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eAvailability of data and material\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eCompeting interests\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFunding for this study was provided by the Catharina Hospital Intensive Care Research Fund. The funding body had no involvement in study design, data collection, analysis, interpretation or manuscript writing.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eAuthors’ contributions\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLPBM, AJGHB, ANR and JB designed the study. LPBM collected data in the study. LPBM drafted the manuscript. LPBM and SHS performed the statistical analyses. SHN provided statistical advice, approval and correction for the manuscript. JH provided advice and review of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eAcknowledgements\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eAuthor details\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Intensive Care, Jeroen Bosch Hospital, ‘s-Hertogenbosch, The Netherlands; \u003csup\u003e2\u003c/sup\u003eDepartment of Intensive Care, Catharina Hospital, Eindhoven, The Netherlands; \u003csup\u003e\u0026nbsp;3\u003c/sup\u003eDepartment of Research and Education, Catharina Hospital, Eindhoven, The Netherlands; \u003csup\u003e4\u003c/sup\u003eDepartment of Cardiothoracic Surgery, Maastricht University Medical Center+, Maastricht, The Netherlands; \u003csup\u003e5\u003c/sup\u003eDepartment of Anesthesiology, Catharina Hospital, Eindhoven, The Netherlands; \u003csup\u003e6\u003c/sup\u003eDepartment of Intensive Care, Erasmus MC University Medical Centre, Rotterdam, The Netherlands; \u003csup\u003e7\u003c/sup\u003eDivision of Pulmonary, Allergy, and Critical Care Medicine, Columbia University Medical Center, New York, NY, USA; \u003csup\u003e8\u003c/sup\u003eDepartment of Intensive Care, Pontificia Universidad Católica de Chile, Santiago, Chile.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMair S, Tschirdewahn J, Gotz S, Frank J, Phillip V, Henschel B, et al. Applicability of stroke volume variation in patients of a general intensive care unit: a longitudinal observational study. J Clin Monit Comput. 2017;31(6):1177-87.\u003c/li\u003e\n\u003cli\u003eLansdorp B, Lemson J, van Putten MJ, de Keijzer A, van der Hoeven JG, Pickkers P. Dynamic indices do not predict volume responsiveness in routine clinical practice. Br J Anaesth. 2012;108(3):395-401.\u003c/li\u003e\n\u003cli\u003eCecconi M, Aya HD. Central venous pressure cannot predict fluid-responsiveness. Evid Based Med. 2014;19(2):63.\u003c/li\u003e\n\u003cli\u003eDelannoy B, Wallet F, Maucort-Boulch D, Page M, Kaaki M, Schoeffler M, et al. Applicability of Pulse Pressure Variation during Unstable Hemodynamic Events in the Intensive Care Unit: A Five-Day Prospective Multicenter Study. Crit Care Res Pract. 2016;2016:7162190.\u003c/li\u003e\n\u003cli\u003eMesquida J, Gruartmoner G, Ferrer R. Passive leg raising for assessment of volume responsiveness: a review. Curr Opin Crit Care. 2017;23(3):237-43.\u003c/li\u003e\n\u003cli\u003eMonnet X, Teboul JL. Passive leg raising: five rules, not a drop of fluid! Crit Care. 2015;19:18.\u003c/li\u003e\n\u003cli\u003eJabot J, Teboul JL, Richard C, Monnet X. Passive leg raising for predicting fluid responsiveness: importance of the postural change. Intensive Care Med. 2009;35(1):85-90.\u003c/li\u003e\n\u003cli\u003eMonnet X, Marik P, Teboul JL. Passive leg raising for predicting fluid responsiveness: a systematic review and meta-analysis. Intensive Care Med. 2016;42(12):1935-47.\u003c/li\u003e\n\u003cli\u003eCherpanath TG, Hirsch A, Geerts BF, Lagrand WK, Leeflang MM, Schultz MJ, et al. Predicting Fluid Responsiveness by Passive Leg Raising: A Systematic Review and Meta-Analysis of 23 Clinical Trials. Crit Care Med. 2016;44(5):981-91.\u003c/li\u003e\n\u003cli\u003eBenomar B, Ouattara A, Estagnasie P, Brusset A, Squara P. Fluid responsiveness predicted by noninvasive bioreactance-based passive leg raise test. Intensive Care Med. 2010;36(11):1875-81.\u003c/li\u003e\n\u003cli\u003eFellahi JL, Fischer MO, Dalbera A, Massetti M, Gerard JL, Hanouz JL. Can endotracheal bioimpedance cardiography assess hemodynamic response to passive leg raising following cardiac surgery? Ann Intensive Care. 2012;2(1):26.\u003c/li\u003e\n\u003cli\u003eFischer MO, Rebet O, Guinot PG, Lemetayer C, Saplacan V, Gerard JL, et al. Assessment of changes in cardiac index with calibrated pulse contour analysis in cardiac surgery: A prospective observational study. Anaesth Crit Care Pain Med. 2016;35(4):261-7.\u003c/li\u003e\n\u003cli\u003eGodje O, Hoke K, Goetz AE, Felbinger TW, Reuter DA, Reichart B, et al. Reliability of a new algorithm for continuous cardiac output determination by pulse-contour analysis during hemodynamic instability. Crit Care Med. 2002;30(1):52-8.\u003c/li\u003e\n\u003cli\u003eCritchley LA, Critchley JA. A meta-analysis of studies using bias and precision statistics to compare cardiac output measurement techniques. J Clin Monit Comput. 1999;15(2):85-91.\u003c/li\u003e\n\u003cli\u003eMonnet X, Marik PE, Teboul JL. Prediction of fluid responsiveness: an update. Ann Intensive Care. 2016;6(1):111.\u003c/li\u003e\n\u003cli\u003eMonnet X, Bataille A, Magalhaes E, Barrois J, Le Corre M, Gosset C, et al. End-tidal carbon dioxide is better than arterial pressure for predicting volume responsiveness by the passive leg raising test. Intensive Care Med. 2013;39(1):93-100.\u003c/li\u003e\n\u003cli\u003eGalarza L, Mercado P, Teboul JL, Girotto V, Beurton A, Richard C, et al. Estimating the rapid haemodynamic effects of passive leg raising in critically ill patients using bioreactance. Br J Anaesth. 2018;121(3):567-73.\u003c/li\u003e\n\u003cli\u003eMeijs LPB, Bindels AJGH, Bakker J, Pinsky MR. Cardiac Function (Cardiac Output and Its Determinants). In: Lima AAP, Silva E, editors. Monitoring Tissue Perfusion in Shock: From Phsyiology to the Bedside. Springer; 2018. p. 51-76.\u003c/li\u003e\n\u003cli\u003eGupta K, Sondergaard S, Parkin G, Leaning M, Aneman A. Applying mean systemic filling pressure to assess the response to fluid boluses in cardiac post-surgical patients. Intensive Care Med. 2015;41(2):265-72.\u003c/li\u003e\n\u003cli\u003eAneman A, Sondergaard S. Understanding the passive leg raising test. Intensive Care Med. 2016;42(9):1493-5.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eTable 1.\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003cu\u003eBaseline characteristics\u003c/u\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"655\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"60%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eData (\u003cem\u003en\u003c/em\u003e=40)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"60%\" valign=\"top\"\u003e\n \u003cp\u003eAge (y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40%\" valign=\"top\"\u003e\n \u003cp\u003e67 [60-74]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"60%\" valign=\"top\"\u003e\n \u003cp\u003eGender (M/F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40%\" valign=\"top\"\u003e\n \u003cp\u003e30/10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"60%\" valign=\"top\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40%\" valign=\"top\"\u003e\n \u003cp\u003e175 [169-180]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"60%\" valign=\"top\"\u003e\n \u003cp\u003eBody weight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40%\" valign=\"top\"\u003e\n \u003cp\u003e80 [70-89]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"60%\" valign=\"top\"\u003e\n \u003cp\u003eBody surface area (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40%\" valign=\"top\"\u003e\n \u003cp\u003e1.96 [1.81-2.06]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"60%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"60%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProcedure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"60%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026bull;\u0026nbsp; CABG\u003c/p\u003e\n \u003cp\u003e\u0026bull;\u0026nbsp; AVR\u003c/p\u003e\n \u003cp\u003eDuration ECC (min)\u003c/p\u003e\n \u003cp\u003eDuration aortic clamping (min)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40%\" valign=\"top\"\u003e\n \u003cp\u003e20 (50%)\u003c/p\u003e\n \u003cp\u003e20 (50%)\u003c/p\u003e\n \u003cp\u003e83 [62-102]\u003c/p\u003e\n \u003cp\u003e58 [41-75]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"60%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVasoactive and sedative drugs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"60%\" valign=\"top\"\u003e\n \u003cp\u003eNorepinephrine (\u003cem\u003en\u003c/em\u003e patients)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40%\" valign=\"top\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"60%\" valign=\"top\"\u003e\n \u003cp\u003eDobutamine (\u003cem\u003en\u003c/em\u003e patients) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Milrinon (\u003cem\u003en\u003c/em\u003e patients)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Propofol (\u003cem\u003en\u003c/em\u003e patients; dosage mg/kg/min)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVentilator settings\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Tidal volume (mL/kg predicted body weight)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40%\" valign=\"top\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003cp\u003e40; 2.1\u0026plusmn;0.88\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6.3\u0026plusmn;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"60%\" valign=\"top\"\u003e\n \u003cp\u003eRespiratory rate (breath/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40%\" valign=\"top\"\u003e\n \u003cp\u003e12.5\u0026plusmn;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"60%\" valign=\"top\"\u003e\n \u003cp\u003eTotal PEEP (cmH\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e\n \u003cp\u003eFiO2 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40%\" valign=\"top\"\u003e\n \u003cp\u003e5\u0026plusmn;0\u003c/p\u003e\n \u003cp\u003e0.4\u0026plusmn;0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are expressed as mean \u0026plusmn; standard deviation, median value [interquartile range] or absolute numbers (%), as appropriate. \u003cem\u003eM\u003c/em\u003e, male;\u003cem\u003e\u0026nbsp;F\u003c/em\u003e, female; \u003cem\u003eCABG\u003c/em\u003e, coronary artery bypass grafting; \u003cem\u003eAVR\u003c/em\u003e, aortic valve replacement; \u003cem\u003eECC\u003c/em\u003e, extra-corporeal circulation; \u003cem\u003eN/A\u003c/em\u003e, not applicable; \u003cem\u003ePEEP\u003c/em\u003e, positive end-expiratory pressure; \u003cem\u003eFiO2\u003c/em\u003e, inspired oxygen fraction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eTable 2.\u003c/u\u003e\u003c/strong\u003e\u003cu\u003e\u0026nbsp;Hemodynamic parameters during passive leg raising and fluid challenge in responders and non-responders (\u003cem\u003en\u003c/em\u003e=40)\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"835\" height=\"249\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eQuantitative data are given as median value [interquartile range] over different time points. CI, cardiac index; RAP, right atrial pressure; MAP, mean arterial pressure; HR, heart rate.\u003c/p\u003e\n\u003cp\u003e* Significant difference after PLR / FC \u003cem\u003e(p \u0026lt;0.001)\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026dagger; Significant difference between responders vs. non-responders \u003cem\u003e(p \u0026lt;0.001)\u003c/em\u003e\u003c/p\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":"passive leg raising, fluid challenge, venous return, cardiac index, fluid responsiveness","lastPublishedDoi":"10.21203/rs.3.rs-4460439/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4460439/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePrediction of fluid responsiveness (FR) in critically ill patients is challenging. Passive leg raising (PLR) has been proven to adequately predict FR. PLR consists of a thoracic (T-PLR) and a limb (L-PLR) movement. Since a complete PLR (C-PLR) is not always feasible, this study focused on investigating the predictive value of partial PLRs on FR.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA prospective, observational study was performed in 40 post-cardiac surgery patients. C-PLR was performed, followed by a T-PLR, L-PLR and fluid challenge (FC). Invasive cardiac index (CI) measurements were continuously recorded during all maneuvers. FR was defined as a CI-increase\u0026thinsp;\u0026ge;\u0026thinsp;15% after FC, thereby identifying responders (R) and non-responders (NR). The predictive value of the PLR-elements was assessed with receiver operating characteristic (ROC) curves. Changes over time were analyzed with generalized linear model (GLM) analyses. Intraclass correlation coefficient (ICC) was used to assess absolute agreement between PLR and FC.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eForty patients were included (35 R / 5 NR). AUC was similar for all PLRs (C-PLR\u0026thinsp;=\u0026thinsp;0.84; T-PLR\u0026thinsp;=\u0026thinsp;0.86, L-PLR\u0026thinsp;=\u0026thinsp;0.86). ICCs between FC versus the three PLRs were 0.81 (0.63\u0026ndash;0.90), 0.78 (0.59\u0026ndash;0.88), and 0.71 (0.46\u0026ndash;0.85), respectively. Median CI-increase during C-PLR was 27.8% (21\u0026ndash;48%) in responders vs. 10.7% (7.5\u0026ndash;12.6%) in non-responders (p\u0026thinsp;=\u0026thinsp;0.012). After FC, median CI-increase was 30.0% (22.2\u0026ndash;42.9%) in responders vs. 7.4% (6.3\u0026ndash;12.4%) in non-responders (p\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePartial PLRs have similar predictive values compared to a C-PLR. This could improve the prediction of FR in specific patient categories where C-PLR is restricted.\u003c/p\u003e","manuscriptTitle":"Prediction of Fluid Responsiveness using a complete and partial Passive Leg Raising maneuver in post-cardiac surgery patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-10 22:19:51","doi":"10.21203/rs.3.rs-4460439/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":"e03bfea4-7c3f-4dcf-bf00-4230c6f15a08","owner":[],"postedDate":"June 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-15T16:55:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-10 22:19:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4460439","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4460439","identity":"rs-4460439","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}