Accuracy and Clinical Utility of Electrical Cardiometry versus Pulse Contour Analysis for Cardiac Index Monitoring in Major Abdominal Surgery: A prospective observation trial

Accuracy and Clinical Utility of Electrical Cardiometry versus Pulse Contour Analysis for Cardiac Index Monitoring in Major Abdominal Surgery: A prospective observation trial | 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 Accuracy and Clinical Utility of Electrical Cardiometry versus Pulse Contour Analysis for Cardiac Index Monitoring in Major Abdominal Surgery: A prospective observation trial Philipp Kazuo Omuro, Claudia Lenkewitz, Julia Rörig, Annika Mayer, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8389436/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Adequate perioperative hemodynamic management is essential to prevent organ hypoxia. Measuring the cardiac index (CI) provides important information. Electrical cardiometry (EC) has been introduced as a non-invasive alternative for CI monitoring, but existing data of its utility and agreement with pulse contour analysis (PCA) in major abdominal surgery is limited. Methods In this prospective observational study, 54 patients undergoing major abdominal surgery with concurrent advanced hemodynamic monitoring were included. CI was measured using EC and PCA. Time-weighted averages (TWA), time below threshold (TBT), and signal quality index (SQI) were analyzed. Agreement between EC and PCA was assessed by Bland–Altman analysis. Postoperative complications were classified according to Clavien–Dindo. Statistical significance was defined as p 70 in 90% of recorded data). Bland–Altman analysis showed a bias of + 0.24 L/min/m² (95% CI 0.23–0.25 L/min/m 2 ; limits of agreement − 1.39 to + 1.87 L/min/m²), and percentage error of 54%. Hemodynamic risk patterns (hypotension, low CI, vasoplegia) were not significantly associated with postoperative complications. In contrast, male sex (OR 9.0, 95% CI 1.74–46.59), misuse of nicotine (OR 4.81, 95% CI 1.27–18.31), and antihypertensive therapy (OR 5.4; 95% CI 1.27–23.05) were significantly linked to adverse outcomes, including pneumonia, delirium, and organ dysfunction. Conclusion EC and PCA are not interchangeable for absolute CI-measurement. While EC may detect perioperative trends, patient-related factors proved to be stronger predictors of postoperative complications than the hemodynamic markers assessed. Hemodynamic monitoring Electrical Cardiometry Pulse Contour Analysis Outcome Abdominal Surgery Monitoring Risk factors Cardiac Index trending ability Bland–Altman peri-operative hypotension time-weighted average Figures Figure 1 Figure 2 Figure 3 1. Background Diagnostic and treatment of perioperative hemodynamic alterations remain one of the central tasks in anesthesiologic management. The pathophysiology of perioperative hemodynamic changes is complex, and inadequate treatment can lead to hypoxia of the organs and tissues 1 , 2 . As a basic parameter, a mean arterial pressure (MAP) of ≥ 65 mmHg is widely accepted as an adequate target, with organ dysfunction occurring less frequently when pressures above this threshold are maintained 3 , 4 , 5 [4]. Individual factors, however, have a relevant impact on the optimal perioperative MAP, the risk of perioperative hypotension, and subsequent organ dysfunction. Preexisting cardiocirculatory changes and consecutive chronic adaption processes, as well as anesthetic agents, may impair the hemodynamic autoregulation of vital organs 4 , 6 . Furthermore, patient factors such as age, sex, and ASA-status (American society of Anesthesiology) influence the optimal perioperative MAP. Critically, tissue perfusion and resulting organ oxygenation are not solely dependent on MAP, as macro- and micro-perfusion are not necessarily linked and can become decoupled in certain clinical situations 7 , 8 . The complex relationship governing systemic flow involves multiple factors such as preload, contractility, afterload and viscosity 9 , 10 , 11 . Minimal invasive monitoring methods are usually based on pulse contour analysis (PCA). The algorithms which conduct interpretation of pulse wave are externally (e.g. via thermodilution), internally or non-calibrated 9 . The benefits of those devices are the minimal invasiveness, as well as the easy establishing compared to the “old” gold standard of cardiac output determination by pulmonary artery catheter (PAC) 12 . As a completely non-invasive method to monitor advanced hemodynamics, Electrical Cardiometry (EC) has emerged since the 2000s and was evaluated in multiple studies 13 – 15 . These systems derive hemodynamic parameters via ECG-like electrodes. However, data in non-cardiac surgery patients and evaluation of general real-life usability are scarce. The aim of this study was to compare perioperative cardiac index (CI) derived from Electrical Cardiometry (EC; ICON®, Osypka Medical GmbH, Berlin, Germany) with non-calibrated pulse contour analysis (PCA; Hemosphere Advanced Monitor, BD, Franklin Lakes, USA) and to explore associations between hemodynamic exposure and postoperative outcome. Two hypotheses were defined a priori: (1) EC and PCA show acceptable interchangeability for cardiac index measurement (2) Hemodynamic exposure metrics are associated with postoperative complications (EPCO-defined) 16 . 2. Materials and Methods This prospective observational study was conducted at the University Hospital of Cologne, Germany, between June 2024 and April 2025. Ethical approval was obtained from the local ethics committee (No. 23-1310, 14 November 2023; Chairperson: Univ.-Prof. Dr. med. R. Voltz). Written informed consent was obtained from all participants prior to inclusion. A total of 60 patients were screened, of whom 54 were included in the final analysis. Six patients were not enrolled: two were excluded due to relevant pacemaker dependency, and in four cases the planned surgical procedure was cancelled or postponed. 2.1 Inclusion criteria were : Major elective abdominal surgery requiring advanced hemodynamic monitoring according to institutional standards, Age ≥ 18 years, Predominant sinus rhythm (≥ 80% of intraoperative monitoring time, without sustained arrhythmia or pacemaker dependency), and Provision of written informed consent. 2.2 Exclusion criteria were Age < 18 years, Cardiac rhythm other than sinus rhythm, Inability to establish an arterial line, Refusal to provide informed consent, or Cancellation of the planned surgical procedure. 2.3 Anesthetic management: Anesthetic management followed established clinical standards for elective major abdominal surgery and typically included preoperative thoracic epidural analgesia (TEA), endotracheal and placement of arterial and central venous lines. Induction was achieved with 0.2–0.5 µg Sufentanil, 3–5 mg Propofol, and 0.5–0.9 mg/kg Rocuronium. Anesthesia was maintained with Sevoflurane (MAC 0.7–1.0), TEA using Ropivacaine (0.5%, 10–15 ml bolus; 0.15% + Sufentanil 0.75 µg/ml, continuous rate 6 ml/h), and additional Sufentanil (0.1–0.2 µg/kg bolus) or Remifentanil (0.1–0.5 µg/kg/min) as indicated. After extubation, most patients were transferred to the intensive care unit (ICU). Hemodynamic support consisted primarily of continuous Norepinephrine infusion (initiated at 0.03–0.05 µg/kg/min during induction) and balanced crystalloid fluids. Further hemodynamic therapy, including inotropic support, was individualized according to clinical judgment and continuous hemodynamic monitoring. 2.4. Hemodynamic monitoring: Perioperative hemodynamic monitoring used in this study is shown in Fig. 1 (PCA and EC). 2.4.1. Pulse contour analysis: After induction of anesthesia and establishing of an arterial line pulse contour (Hemosphere Advanced Monitor via FloTrac or Acumen IQ Sensor, BD, Franklin Lakes, USA) and continuous central venous pressure (CVP) monitoring was connected. PCA is based on the calculation of stroke volume (SV) using the area under the curve (AUC) of the pressure curve derived from a peripheral arterial line. The Hemosphere monitor uses a statistical model (Arterial Pressure based Cardiac Output, APCO) to correct for differences in compliance and resistance beat by beat (internal calibration) 17 . The recorded data was extracted directly from the device during anesthesia recovery. 2.4.2. Electrical Cardiometry: For electrical cardiometry (EC; ICON®, Osypka Medical GmbH, Berlin, Germany), two left-cervical and two left-thoracic surface electrodes were applied according to the manufacturer’s specifications and secured with adhesive tape. Monitoring was initiated by three trained members of the study group. EC employs the principle of electrical velocimetry, which calculates aortic blood flow from changes in thoracic bioimpedance to derive CI. A low-amplitude, high-frequency alternating current is transmitted via the thoracic electrodes, while the cervical electrodes detect the resulting voltage signal and ECG. During diastole, randomly oriented erythrocytes cause higher impedance; in systole, their alignment with blood flow reduces impedance, allowing continuous assessment of stroke volume and CI 18 . The ICON device calculates a Signal Quality Index (SQI) from the data received. According to the manufacturer a SQI > 70 is considered adequate. Recording was stopped during anesthesia recovery and extracted using the manufacturers software. 2.5. Data collection: Pre- and perioperative clinical data, including patient demographics and comorbidities, were extracted from routine hospital records and manually transferred to Microsoft Excel (Version 16.100, Microsoft, Redmond, USA). Perioperative hemodynamic parameters were recorded by the HemoSphere Advanced Monitor every 20 seconds and by the ICON device every 10 seconds, depending on adequate signal quality, and subsequently compiled in Microsoft Excel. Outcome measures, including the definitions of postoperative complications and Major Adverse Cardiac and Cerebrovascular Events (MACCE), followed the criteria described by Jammer et al. 16 . Detailed lists of postoperative complications and MACCE are provided in the supplement. 2.6. Data management, statistical analysis and sample size calculation: Data management, matching, calculation of TWA, TBT/TAT, quantity of valid measurements (EC vs. PCA), and Signal Quality Index (SQI) analysis were performed in R Studio (Version 2025.05.0, Posit Software, Boston, USA). TWA was defined as the area under the curve below or above a predefined cutoff, and TBT as the percentage of time spent below or above this threshold. Further analyses were conducted in SPSS 29 (IBM, Endicott, USA). Depending on variable type, χ² test, Kruskal–Wallis test, and Spearman correlation were applied. Agreement analysis was performed using a repeated-measures Bland–Altman model with random effects. Limits of agreement were derived from between- and within-subject variance components, and proportional bias was assessed by regressing measurement differences against their means 19 . For graphical visualization, medians were calculated from normalized surgical time (absolute duration / 100) using R Studio. Data are presented as % (n), mean ± SD, median [IQR], or median (95 %CI). Statistical significance was defined as p < .05* and high significance as p < .01**. Correction for multiple tests was not applied considering the exploratory nature of the analysis. Sample size calculation (PASS Software) assumed an adjusted α-level of 1% and a mean difference of 20% for the primary endpoint. Considering an anticipated 20% dropout, 48 subjects per group were required; thus, 60 patients were planned. Illustrations were generated using Microsoft PowerPoint (Version 16.103.1, Microsoft, Redmond, USA). 3. Results Table 1 shows baseline characteristics. The study population was predominantly female (53.7% (29)) with an ASA Status of II (53.7% (29)) or III (38.9% (21)) and aged 66 (± 11) years. Common comorbidities included metabolic disorders (57.4% (31)) and cardiovascular diseases (48.1% (26)). Age [years] 66 (±11) Gender [male/female] 46.3%/53.7% (25/29) ASA Status I 7.4% (4). II 53.7% (29). III 38.9% (21) BMI [kg/m 2 ] 25.7 (± 4.9) Cardiovascular diseases 48.1% (26) Nephropathy 1.9% (1) Pneumopathy 11.1% (6) Neuropathy 14.8% (8) Metabolic disorders 57.4% (31) NIDDM 11.1% (6) IDDM 3.8% (2) Thyreopathy 22.2% (12) Hepatopathy 9.3% (5) Misuse of substances 35.2% (19) Misuse of nicotine 27.8% (15) Misuse of alcohol 14.8% (8) Table 1. Baseline patient characteristics. Item [unit]. Mean (±SD). Median [IQR]. % [n]. As seen in Table 2 arterial hypertension (40.7% (22)), preexisting cardiac arrhythmias (14.8% (8)) and coronary artery disease (9.3% (5)) were the most frequent preoperative conditions. Arterial hypertension 40.7% (22) HFpEF 11.1% [6) HFrEF 1.9% (1) Coronary artery disease 9.3% (5) Arrhythmia 14.8% (8) PAD 1.9% (1) LV-EF available 70.4% (33) LV-EF [%] 60.0 [57.5-61.0] COPD 5.6% (3) Asthma bronchiale 7.4% (4) PAH 1.9% (1) Pulmonary function test available 53.7% (29) FEV1 [L] 2.59 [2.26-3.26] FVC [L] 3.66 [2.98-4.20] FEV1/VC [%] 88.07 [76.67-100.00] Table 2. Cardiopulmonary baseline characteristics. Item [unit]. Median [IQR]. % [n]. HFpEF=heart failure preserved ejection fraction. HFrEF=heart failure reduced ejection fraction. PAD=periphery artery disease. LV-EF=left ventricular ejection fraction. COPD=chronic obstructive pulmonary disease. PAH=pulmonary arterial hypertension. FEV=forced expiratory volume. FVC=forced vital capacity. Considering preoperative medication, antihypertensive drugs and diuretics were the most frequent, found in 44.4% (24) and 13.0% (7), respectively (see Table 3). Antihypertensive drugs 44.4% (24) Beta-Blockers 20.4% (11) ACE-Inhibitors 11.1% (6) AT1-Blockers 11.1% (6) Calcium-Antagonists 11.1% (5) Alpha-1-Blockers 9.3% (5) Diuretics 13.0% (7) Loop-Diuretics 5.6% (3) Thiacides 3.7% (2) Aldosterone-Blockers 1.9% (1) Platelet-Inhibitors 11.1% (6) DOAC 16.7% (9) Antidiabetics 11.1% (6) Table 3. Preoperative medication. Item [unit]. % [n]. ACE=Angiotensin-Converting-Enzyme. AT1=Angiotensin II receptor 1. DOAC=direct oral anticoagulants. Table 4 presents perioperative data. Patients in this study underwent a mean duration of surgery of 4.51 [3.0-5.5] h (incision to suture). Most procedures utilized general anesthesia combined with TEA (87.0% (47)). Surgeries were oesophagectomy (37.0% (20)), pancreatic surgery (27.8% (15)), liver resection (18.5% (10)), gastrectomy (11.1% (6)), colon surgery (3.7% (2)) and retroperitoneal compartment resection (1.9% (1)). The mean overall fluid balance (considering blood/urine loss, administered fluids, and transfusions) was +1800 [1280-2190] ml resulting in a normalized fluid balance of +6.22 [4.05-9.88] ml/kg/h. Norepinephrine was used in all patients with a maximal dose of 0.2 [0.12-.0.35] µg/kg/min. Duration of surgery [h] 4.5 [3.0-5.5] TEA 87.0% (47) Intrathoracic surgery 46.3% (25) Blood loss [ml] 250 [125-500] Urine output [ml] 500 [300-725] Crystalloids [ml] 2500 [2000-3000] Transfusion 5.6% (3) Fluid balance [ml] +1800 [1280-2190] Fluid balance [ml/kg/h] +6.22 [4.05-9.88] Noradrenaline 100.0% (54) maximal Noradrenaline dose [µg/kg/min] 0.2 [0.12-0.35] Adrenaline 7.4% (4) maximal Adrenaline dose [µg/kg/min] 0.05 [0.05-0.05] Dobutamine 5.6% (3) Vasopressin 5.6% (3) Table 4. Perioperative items. Item [unit]. Mean [IQR]. % (n). TEA=thoracic epidural analgesia. All perioperative hemodynamic parameters are listed in table 5. TWA MAP < 65 mmHg .17 [.08-.51] mmHg TBT MAP < 65 mmHg 4.85 [.75-9.13] % TWA CI ≤ 2.5 L/min/m 2 .12 [.04-.33] L/min/m 2 TBT CI ≤ 2.5 L/min/m 2 42.6 [11.40-84.55] % TWA CI ≤ 2.2 L/min/m 2 .04 [.01-.11] L/min/m 2 TBT CI ≤ 2.2 L/min/m 2 16.55 [4.15-57.88] % TWA CI ≤ 1.6 L/min/m 2 .01 [.00-.01] L/min/m 2 TBT CI ≤ 1.6 L/min/m 2 1.2 [0.18-4.5] % TWA SVRI < 1970 dynes/cm⁵/m² 47.48 [13.16-176.22] dynes/cm⁵/m² TBT SVRI 2390 dynes/cm⁵/m² 139.79 [38.48-400.20] dynes/cm⁵/m² TAT SVRI > 2390 dynes/cm⁵/m² 30.25 [7.08-55.43] % Table 5. Hemodynamic parameters. Mean [IQR]. TWA=time weighted average. TBT=time below threshold. TAT=time above threshold. TWA=time-weighted average. MAP=Mid Arterial Pressure. CI=Cardiac Index. SVRI=Systemic Vascular Resistance Index. EC=Electrical Cardiometry. SQI=Signal Quality Index. Postoperative outcome is displayed in Table 6. No patient died during hospital stay. Any complication occurred in 48.1% (26) of cases. Major Adverse Cardiac and Cerebrovascular Events (MACCE) were present in 11.1% (6) of cases. If a complication item listed in the Supplement is not reported here, it indicates that no cases of that specific event were observed. Postoperative complications were classified according to Clavien–Dindo as follows: Grade II occurred in 9.3% of patients (5/54), Grade IIIa in 13.0% (7/54), Grade IIIb in 5.6% (3/54), and Grade IV in 22.2% (12/54). Any complication 50,0 % (27) Mortality 0.0% (0) Postoperative organ dysfunction 24.1% (13) MACCE 11.1% (6) New AFIB 9.3% (5) Pulmonary embolism 1.9% (1) AKI 3.7% (2) Delirium 3.7% (2) Pneumonia 22.2% (12) Liver failure 1.9% (1) Other thromboembolism 1.9% (1) Infection 29.6% (16) Wound infection 3.7% (2) postoperative invasive ventilation 9.3% (5) ICU-Admission 94.4% (51) ICU-Readmission 18.5% (10) LOS ICU [days] 2 [1-3] LOS hospital [days] 13 [10-22] Table 6. Postoperative outcome. % (n). MACCE=Major Adverse Cardiac and Cerebrovascular Events. AFIB=atrial fibrillation. ICU=intensive care unit. LOS=length of stay. EC data was available in 64.4 [25.93-78.08] % of perioperative time. Detailed data on SQI is listed in Table 7. There was no correlation between BMI and EC data availability (Spearman’s rho =.10, n.s). A total of 18,588 matched datapoints (corresponding to 201.6 hours of perioperative monitoring) were available for analysis. For the mixed Bland–Altman comparison between EC and PCA, analysis revealed a mean bias of 0.24 L/min/m² (see Figure 2). The limits of agreement (LOA), defined as bias ±1.96 times the standard deviation (SD) of the differences, were –1.39 to +1.87 L/min/m² (Figure 2). Percentage error was 54.3%. The mixed-effects Bland–Altman analysis demonstrated a mean bias of 0.24 L/min/m² (95% CI 0.23-0.25 L/min/m 2 ; 95 % limits of agreement = −1.39 to +1.87 L/min/m²). A significant proportional bias was observed (β = 0.18, p < .01**, R² = 0.025), indicating that EC tended to overestimate CI values at higher ranges compared with PCA. Despite statistical significance, the proportional bias explained only 2.5 % of the overall variance. As shown in figure 3 relevant difference between median CI derived from EC vs. PCA and calculated from n= 18,588 datapoints remains throughout the normalized surgery duration. Neither common hemodynamic risk patterns, including hypotension, hypodynamic cardiac output, vasoplegia, nor vasoconstriction, were found to be associated with a more frequent incidence of postoperative complications (MAP ≤ 65 mmHg: H=.641; n.s.; CI ≤ 2.5 L/min/m 2 : H=3.194; n.s.; CI ≤ 2.2 L/min/m 2 : H=2.870; n.s.; CI ≤ 1.6 L/min/m 2 : H=1.307; n.s.; SVRI < 1970 dynes/cm⁵/m²: H=.987; n.s.; SVRI < 2390 dynes/cm⁵/m²: H=1.161; n.s.). In contrast, several patient-related factors were significantly associated with adverse outcomes. Pneumonia occurred significantly more often in male than in female patients (40.0 % (10/25) vs. 6.9 % (2/29); χ² = 8.51; p < 0.01), corresponding to an odds ratio (OR) of 9.00 (95 % CI 1.74–46.59). A history of misuse of nicotine was significantly associated with postoperative organ dysfunction (OR 4.81 [95 % CI 1.27–18.31]; p < 0.05*), delirium (13.3 % (2/15) vs. 0 % (0/39); χ² = 5.40; p = .02*), and pneumonia (46.7 % (7/15) vs. 12.8 % (5/39); χ² = 7.18; p = .01*).The incidence of nicotine misuse did not differ between male and female patients (32.0 % (8/25) vs. 24.1 % (7/29); χ² = .41.). Similarly, pneumonia was significantly more frequent in patients receiving antihypertensive medication (37.5 % (9/24) vs. 10.0 % (3/30); χ² = 5.83; p = .02*), corresponding to an OR of 5.40 (95 % CI 1.27–23.05). A subgroup analysis indicated a particularly high incidence among those treated with ACE inhibitors (66.7 % (4/6) vs. 16.7 % (8/48); χ² = 7.714; p 2390 dynes/cm⁵/m²; 204.90 [61.44-573.86] vs. 83.56 [15.86-191.38]; H=5.381; p=.02*). However, TWA of vasoplegia was significantly higher in patients without cardiovascular comorbidities (SVRI < 1970 dynes/cm⁵/m²; 105.42 [32.39-273.06] vs. 36.24 [7.23-93.42]; H=5.065; p=.02*). There was no difference in normalized fluid balance (5.26 [4.03-8.02] vs. 6.91 [4.03-11.71] ml/kg/h; H=1.086; n.s.) or maximal Norepinephrine-dose (.2 [.09-.35] vs. .19 [.15-.45] µg/kg/min; H=.598; n.s.) in patients with and without cardiovascular comorbidities. Patients with history of arterial hypertension experienced a significantly higher TWA for CI ≤ 2.5 L/min/m 2 (.22[.08-.48] vs. .08 [.02-.26] L/min/m 2 ; H=4.292; p=.04*), CI ≤ 2.2 L/min/m 2 (.09 [.03-.29] vs. .03 [.01-.07] L/min/m 2 ; H=4.446; p=.4* ) and vasoplegia (36.24 [7.23-93.42] vs. 89.54 [21.75-273.06] dynes/cm⁵/m²; H=4.389; p=.04*). Finally, patients with and without cardiovascular comorbidities (.17 [.12-.59] vs. .20 [.06-.49] mmHg; H=.12; n.s.) or arterial hypertension (.18 [.12-.59] vs. .17 [.05-.49] mmHg; H=.318; n.s.) showed no difference in TWA for hypotension. 4. Discussion This study evaluated the agreement between electrical cardiometry (EC) and pulse contour analysis (PCA) for perioperative CI monitoring. Using a repeated-measures Bland–Altman, a small mean bias but wide LOAs were observed, resulting in a percentage error > 50%. According to current validation criteria, this degree of variability indicates that EC and PCA cannot be used interchangeably for the determination of absolute CI values 20 . Nevertheless, the findings highlight that EC may still provide clinically meaningful information. Although the proportional bias analysis demonstrated a significant relationship between the difference and the mean of both methods, the effect size was small. This suggests that EC tends to slightly overestimate cardiac index at higher values, but the impact of this proportional bias is unlikely to reach clinical relevance. Similar observations have been reported by previous validation studies comparing EC with thermodilution, PCA or echocardiography, which consistently found insufficient interchangeability but acceptable trending ability in humans and even other species 13 – 15 , 21 , 22 . Also, metanalytic data from Suehiro et al. and Sanders et al. report a percentage error for 20–45% in children and 48% in adults. The predominant reference method in both studies was TTE. Considering our data and previous studies, EC shows a high percentage error no matter the reference method. The relatively low proportion of analyzable data likely reflects the specifics of the perioperative setting, particularly in major abdominal surgery. Once the EC electrodes are placed and ideally tightly secured on the neck and thorax, perioperative accessibility is often limited. Surgical personal might cause movement artefacts, extracorporal (e.g., surgical manipulation on the chest wall) or intracorporeal (e.g., tissue preparation) 23 . Furthermore, especially in intrathoracic and surgery of the upper abdomen, electrical interferences by cauterization were frequently observed. Once a disturbance of signal is detected devices can only display new values with a delay. Finally individual factors, such as body mass and composition as well as fluid content of the tissue can alter bioimpedance 24 , 25 . From a clinical perspective, the ability of EC to track directional hemodynamic changes may be valuable during dynamic situations such as volume loading, vasopressor titration, or inotrope administration. Its continuous and operator-independent nature enables real-time assessment without the need for invasive arterial or central venous access, thereby reducing procedural risk and patient discomfort. EC may thus serve as a useful adjunct for hemodynamic optimization in low- to intermediate-risk surgical patients, or in settings where the risks of more invasive monitoring may outweigh the benefit—such as pediatric cases, coagulopathies, or short-duration procedures. In our data, typical perioperative hemodynamic risk patterns (intraoperative hypotension, low cardiac index, vasoplegia, and vasoconstriction) were not associated with an increased incidence of postoperative complications. This finding is somewhat unexpected, as prior evidence has suggested links between hypotension or impaired cardiac output and adverse outcomes. Several explanations may account for this discrepancy. First, complications after major abdominal surgery are multifactorial and may not be adequately captured by single hemodynamic surrogates. Second, our time-weighted average (TWA) approach integrates cumulative exposure rather than capturing short, critical episodes, which may dilute the clinical impact of brief but physiologically relevant hypotensive events. However, TWA for hypotension was rather low compared to available literature (.17 [.08-.51] mmHg). Frassanito et al. found a TWA of .14 [.04-.66] mmHg in patients under Hypotension Prediction Index-guided (HPI) perioperative therapy compared to .77 [.36 − 1.30] mmHg in control during major gynaecooncologic surgery 26 . A comparable analysis by Wijnberg et al. revealed a TWA of .44 [.23-.72] Hg without HPI- and .10 [.01-.43] mmHg under HPI-guided therapy in non-cardiac surgery 27 . In a high-risk cohort undergoing noncardiac surgery Maheshwari et al. reported a TWA for MAP < 65 mmHg of .05 [.00-.22] and .11 [.00-.54] respectively gathered by non-invasive continuous blood pressure monitoring 28 . Third, comprehensive and quick reaction to perioperative hemodynamic changes may have mitigated the severity and potential consequences of transient hemodynamic instability. In contrast, patient-related factors demonstrated stronger associations with postoperative outcomes. Male sex, nicotine misuse, and preoperative antihypertensive medication were associated with pneumonia, organ dysfunction, and delirium. These findings are consistent with prior literature identifying smoking and comorbidities as important determinants of surgical morbidity 29 , 30 , 31 , 32 . Moreover, patients with cardiovascular comorbidities and arterial hypertension displayed distinct hemodynamic profiles, characterized by prolonged exposure to low cardiac index and vasoconstriction, whereas vasoplegia was more pronounced in patients without cardiovascular disease There are several notable strengths of this study. The prospective design in a cohort undergoing complex major abdominal surgery reflects real-world clinical application of EC. High-frequency data acquisition yielded more than 18,000 paired datapoints, enabling a robust and granular comparison with PCA. The use of TWA, TBT/TAT metrics allowed quantification of cumulative hemodynamic exposure beyond isolated single readings. Moreover, this study is among the first to systematically evaluate EC performance against non-calibrated PCA in a high-risk non-cardiac surgical population. Finally, by integrating perioperative hemodynamic profiles with postoperative outcomes, we demonstrated that patient-related factors (male sex, misuse of nicotine, antihypertensive therapy) outweighed isolated hemodynamic surrogates—provided that mean arterial pressure was adequately maintained—as predictors of adverse events. This study has several limitations. First, it was conducted as a single-center observational trial in a relatively small cohort of patients undergoing major abdominal surgery, which may limit external validity. Second, hemodynamic exposure was primarily quantified using TWA. Although this captures cumulative burden, brief but clinically relevant fluctuations may have been underestimated. Third, PCA was used as the comparator method; while widely implemented, it does not represent the thermodilution gold standard. Finally, the sample size was designed to assess the accuracy and reliability of EC and PCA rather than postoperative complication rates. Therefore, conclusions regarding hemodynamic target parameters and postoperative organ dysfunction should be interpreted with caution. Our results give first hints for the limited predictive value of isolated perioperative hemodynamic markers for postoperative complications, while emphasizing the importance of patient-related risk factors. This finding is consistent with a recent randomized clinical trial by Saugel et al, which investigated individualized perioperative blood pressure management 4 . The authors reported no significant difference in the composite endpoint—including acute kidney injury, myocardial injury, nonfatal cardiac arrest, or death within the first 7 postoperative days—between patients assigned to individualized mean arterial pressure targets. This suggests that perioperative outcome prediction should integrate both hemodynamic monitoring data and baseline patient characteristics, rather than relying on hemodynamic thresholds alone. Furthermore, low CI situations happen frequently in major abdominal surgery, interestingly in a population without a relevant amount of preoperative cardiac impairment. Even CI < 1.6 L/min/m 2 was not associated with adverse outcome. Further research is necessary to define the optimal clinical setting and patient populations for the use of EC. While current evidence supports its utility for trend monitoring and non-invasive assessment of cardiac function, the precise patient cohorts that benefit most from EC-guided management remain to be established. Future studies should aim to identify risk constellations in which EC-derived parameters—such as cardiac index, stroke volume variation, or thoracic fluid content—provide the highest predictive or therapeutic value. Large-scale, prospective, and possibly multicenter studies integrating EC into structured perioperative hemodynamic care protocols are therefore needed to clarify its role within modern parameter-directed therapy algorithms and to investigate its impact on outcomes and cost-effectiveness. 5. Conclusion Electrical cardiometry did not provide clinically acceptable agreement with Pulse Contour Analysis for absolute cardiac index measurement in major abdominal surgery. Its potential value may lie in monitoring relative changes and hemodynamic trends rather than replacing established reference methods. Patient-related factors outweighed hemodynamic markers as predictors of postoperative complications. Declarations Ethics approval and consent to participate: This prospective observational study was conducted at the University Hospital of Cologne, Germany, between June 2024 and April 2025. Ethical approval was obtained from the local ethics committee (No. 23-1310, 14 November 2023; Chairperson: Univ.-Prof. Dr. med. R. Voltz). Written informed consent was obtained from all participants prior to inclusion. Author Contribution PKO: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Validation, Visualization, Writing- Original DraftCL: Methodology, Data CurationJR: Data CurationAM: Data CurationDS: Data Curation, Writing-Review & EditingTK: Conceptualization, Writing- Review & Editing, Supervision Acknowledgements: The authors thank the perioperative nursing staff and anesthesia technicians of the Department of Anesthesiology and Intensive Care Medicine, University Hospital of Cologne, for their support during data acquisition. Further, we especially thank our patients for their trust in our care and participation in our study. Financial support: None. Conflicts of interest: The authors declare that they have no conflicts of interest related to this study. Presentation: Preliminary data from this study will be presented as a research poster at the DIVI Congress 2025 on December 5th 2025 in Hamburg, Germany. Research data availability statement: The datasets generated and analysed during the current study are not publicly available due to patient privacy and ethical restrictions but are available from the corresponding author upon reasonable request. Data sharing requires prior approval by the Ethics Committee of the University of Cologne (reference 23-1310) and will be provided in a de-identified format. Consent for publication: Consent for publication was not required as no identifiable individual data are included in this manuscript. Data Availability The datasets generated and analysed during the current study are not publicly available due to patient privacy and ethical restrictions but are available from the corresponding author upon reasonable request. Data sharing requires prior approval by the Ethics Committee of the University of Cologne (reference 23-1310) and will be provided in a de-identified format. References Rex S, De Waal EEC, Buhre W. Perioperatives hämodynamisches monitoring. Anasthesiologie und Intensivmedizin. 2010;51:160–77. Anesthesiologists AS. of (2020) Standards for Basic Anesthetic Monitoring, Committee of Origin: Standards and Practice Parameters. American Society of Anesthesiologists 1–3. Kaufmann T, Van Der Horst ICC, Scheeren TWL. This is your toolkit in hemodynamic monitoring. Curr Opin Crit Care. 2020;26:303–12. Saugel B, Meidert AS, Brunkhorst FM et al. (2025) Individualized Perioperative Blood Pressure Management in Patients Undergoing Major Abdominal Surgery The IMPROVE-multi Randomized Clinical Trial. 1–12. Schnetz MP, Danks DJ, Mahajan A. Preoperative Identification of Patient-Dependent Blood Pressure Targets Associated With Low Risk of Intraoperative Hypotension During Noncardiac Surgery. Cardiovasc Pathophysiology Outcomes. 2023;136:194–203. Ackland GL, Brudney CS, Cecconi M, et al. Perioperative Quality Initiative consensus statement on the physiology of arterial blood pressure control in perioperative medicine. Br J Anaesth. 2019;122:542–51. Bakker J, Ince C. Monitoring coherence between the macro and microcirculation in septic shock. Curr Opin Crit Care. 2020;26:267–72. Boerma EC, Ince C. The role of vasoactive agents in the resuscitation of microvascular perfusion and tissue oxygenation in critically ill patients. Intensive Care Med. 2010;36:2004–18. Saugel B, Hoppe P, Nicklas JY, Kouz K, Körner A, Hempel JC, Vos JJ, Schön G, Scheeren TWL. Continuous noninvasive pulse wave analysis using finger cuff technologies for arterial blood pressure and cardiac output monitoring in perioperative and intensive care medicine: a systematic review and meta-analysis. Br J Anaesth. 2020;125:25–37. Kanaris AG, Anastasiou AD, Paras SV. Modeling the effect of blood viscosity on hemodynamic factors in a small bifurcated artery. Chem Eng Sci. 2012;71:202–11. Naumann DN, Hazeldine J, Bishop J, Midwinter MJ, Harrison P, Nash G, Hutchings SD. Impact of plasma viscosity on microcirculatory flow after traumatic haemorrhagic shock: A prospective observational study. Clin Hemorheol Microcirc. 2019;71:71–82. Scheeren TWL, Ramsay MAE. New Developments in Hemodynamic Monitoring. J Cardiothorac Vasc Anesth. 2019;33:S67–72. Cox PBW, Den Ouden AM, Theunissen M, Montenij LJ, Kessels AGH, Lancé MD, Buhre WFFA, Marcus MAE. Accuracy, precision, and trending ability of electrical cardiometry cardiac index versus continuous pulmonary artery thermodilution method: A prospective, observational study. Biomed Res Int. 2017. https://doi.org/10.1155/2017/2635151 . Zoremba N, Bickenbach J, Krauss B, Rossaint R, Kuhlen R, Schälte G. Comparison of electrical velocimetry and thermodilution techniques for the measurement of cardiac output. Acta Anaesthesiol Scand. 2007;51:1314–9. Schmidt C, Theilmeier G, Van Aken H, Korsmeier P, Wirtz SP, Berendes E, Hoffmeier A, Meissner A. Comparison of electrical velocimetry and transoesophageal Doppler echocardiography for measuring stroke volume and cardiac output. Br J Anaesth. 2005;95:603–10. Jammer I, Wickboldt N, Sander M, et al. Standards for definitions and use of outcome measures for clinical effectiveness research in perioperative medicine: European Perioperative Clinical Outcome (EPCO) definitions: A statement from the ESA-ESICM joint taskforce on perioperative outcome measur. Eur J Anaesthesiol. 2015;32:88–105. Saugel B, Kouz K, Scheeren TWL, Greiwe G, Hoppe P, Romagnoli S, de Backer D. Cardiac output estimation using pulse wave analysis—physiology, algorithms, and technologies: a narrative review. Br J Anaesth. 2021;126:67–76. Paranjape VV, Henao-Guerrero N, Menciotti G, Saksena S, Agostinho M. (2023) Agreement between Electrical Cardiometry and Pulmonary Artery Thermodilution for Measuring Cardiac Output in Isoflurane-Anesthetized Dogs. Animals. https://doi.org/10.3390/ani13081420 Parker RA, Scott C, Inácio V, Stevens NT. Using multiple agreement methods for continuous repeated measures data: A tutorial for practitioners. BMC Med Res Methodol. 2020;20:1–14. Critchley LAH, Critchley JAJH. A meta-analysis of studies using bias and precision statistics to compare cardiac output measurement techniques. J Clin Monit Comput. 1999;15:85–91. Song W, Guo J, Cao D, Jiang J, Yang T, Ma X, Yuan H, Wu J, Guan X, Si X. Comparison of noninvasive electrical cardiometry and transpulmonary thermodilution for cardiac output measurement in critically ill patients: a prospective observational study. BMC Anesthesiol. 2025;25:1–9. Paranjape VV, Garcia-Pereira FL, Menciotti G, Saksena S, Henao-Guerrero N, Ricco-Pereira CH. (2023) Evaluation of Electrical Cardiometry for Measuring Cardiac Output and Derived Hemodynamic Variables in Comparison with Lithium Dilution in Anesthetized Dogs. Animals. https://doi.org/10.3390/ani13142362 Teboul JL, Saugel B, Cecconi M, et al. Less invasive hemodynamic monitoring in critically ill patients. Intensive Care Med. 2016;42:1350–9. Critchley LA, Lee A, Ho AMH. A critical review of the ability of continuous cardiac output monitors to measure trends in cardiac output. Anesth Analg. 2010;111:1180–92. Peng ZY, Critchley LAH, Fok BSP. An investigation to show the effect of lung fluid on impedance cardiac output in the anaesthetized dog. Br J Anaesth. 2005;95:458–64. Frassanito L, Giuri PP, Vassalli F, Piersanti A, Garcia MIM, Sonnino C, Zanfini BA, Catarci S, Antonelli M, Draisci G. Hypotension Prediction Index guided Goal Directed therapy and the amount of Hypotension during Major Gynaecologic Oncologic Surgery: a Randomized Controlled clinical Trial. J Clin Monit Comput. 2023;37:1081–93. Wijnberge M, Geerts BF, Hol L, et al. Effect of a Machine Learning–Derived Early Warning System for Intraoperative Hypotension vs Standard Care on Depth and Duration of Intraoperative Hypotension During Elective Noncardiac Surgery: The HYPE Randomized Clinical Trial. JAMA. 2020;323:1052–60. Maheshwari K, Khanna S, Bajracharya GR, Makarova N, Riter Q, Raza S, Cywinski JB, Argalious M, Kurz A, Sessler DI. A randomized trial of continuous noninvasive blood pressure monitoring during noncardiac surgery. Anesth Analg. 2018;127:424–31. Luo M, Wang D, Shi Y, et al. Risk factors of postoperative delirium following spine surgery: A meta-analysis of 50 cohort studies with 1.1 million participants. Heliyon. 2024;10:e24967. Musallam KM, Rosendaal FR, Zaatari G, et al. Smoking and the risk of mortality and vascular and respiratory events in patients undergoing major surgery. JAMA Surg. 2013;148:755–62. Vu JV, Lussiez A. Smoking Cessation for Preoperative Optimization. Clin Colon Rectal Surg. 2023;36:175–83. van Kooten RT, Bahadoer RR, Peeters KCMJ, Hoeksema JHL, Steyerberg EW, Hartgrink HH, van de Velde CJH, Wouters MWJM, Tollenaar RAEM. Preoperative risk factors for major postoperative complications after complex gastrointestinal cancer surgery: A systematic review. Eur J Surg Oncol. 2021;47:3049–58. Additional Declarations No competing interests reported. Supplementary Files Supplement.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 13 Feb, 2026 Editor assigned by journal 19 Dec, 2025 Submission checks completed at journal 19 Dec, 2025 First submitted to journal 17 Dec, 2025 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. 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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-8389436","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591733018,"identity":"2441e817-6d93-4323-b2fd-2ed19095c8b5","order_by":0,"name":"Philipp Kazuo Omuro","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYDACCcbGAwlwXgWDAQMPkH7AwMDYgFtLA5KWM1AtCXi1MDAcgHMY24jQYi7d3HDg4Y47DPxiZ8w+fJx32JiB5/DBB4k7GGT7cWixnHOw4UDimWcMkrNzjGfO3HbYjIG3Ldkg8QyD8Uwc1hjcSARqaTvMYHA7x5iZd9thG/vzPGYSiW0MiRsOEKVlzmEbBn4e8x8gLfuJ09IAcliPGQPYFlx+mZEI9guP5Oy0YsYZx9KB3j+WDHSYhPEMHLaYS6Q/fPhzxx05funkzQwfaqwNG3iSD3742GYj24/L+yACGJs86BISOJyF0IJTwSgYBaNgFIwCBgAxnGKbKIl0jAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Cologne","correspondingAuthor":true,"prefix":"","firstName":"Philipp","middleName":"Kazuo","lastName":"Omuro","suffix":""},{"id":591733020,"identity":"1689558f-2c4c-41da-bf4c-caeac3922a86","order_by":1,"name":"Claudia Lenkewitz","email":"","orcid":"","institution":"University of Cologne","correspondingAuthor":false,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Lenkewitz","suffix":""},{"id":591733021,"identity":"642ab3fd-36ce-4dee-8685-59b682fe173f","order_by":2,"name":"Julia Rörig","email":"","orcid":"","institution":"University of Cologne","correspondingAuthor":false,"prefix":"","firstName":"Julia","middleName":"","lastName":"Rörig","suffix":""},{"id":591733024,"identity":"960e06cd-e2c1-41e8-a8c9-493ec2a1afe9","order_by":3,"name":"Annika Mayer","email":"","orcid":"","institution":"University of Cologne","correspondingAuthor":false,"prefix":"","firstName":"Annika","middleName":"","lastName":"Mayer","suffix":""},{"id":591733025,"identity":"1474ce14-47a9-4518-9830-9625ce5443f9","order_by":4,"name":"David Sander","email":"","orcid":"","institution":"University of Cologne","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Sander","suffix":""},{"id":591733026,"identity":"339ad7cd-29d7-41be-ac23-3d2d39f4ea67","order_by":5,"name":"Tobias Kammerer","email":"","orcid":"","institution":"University of Cologne","correspondingAuthor":false,"prefix":"","firstName":"Tobias","middleName":"","lastName":"Kammerer","suffix":""}],"badges":[],"createdAt":"2025-12-17 22:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8389436/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8389436/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102908823,"identity":"7a65391f-ae58-447a-888b-f5ceddf1b87f","added_by":"auto","created_at":"2026-02-18 09:52:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86081,"visible":true,"origin":"","legend":"\u003cp\u003ePerioperative haemodynamic monitoring via Electrical Cardiometry (ICON®, Osypka medical GmbH, Berlin, Germany) with 2 cervical and 2 thoracic electrodes and Pulse Contour Analysis with arterial line (Hemosphere Advanced Monitor with FloTrac or Acumen IQ Sensor, BD, Franklin Lakes, USA).\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8389436/v1/d5c7e65f816c8f24acc4adf6.png"},{"id":102908821,"identity":"a41adbe2-3944-4928-b8b5-f231394ff5fb","added_by":"auto","created_at":"2026-02-18 09:52:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":122830,"visible":true,"origin":"","legend":"\u003cp\u003eMixed Bland-Altman-Plot for agreement of Electrical Cardiometry (EC) vs. Pulse Contour Analysis (PCA) of Cardiac Index. N= 18588. LOA=Limit of Agreement.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8389436/v1/28a376e9a02289a3e5d25b97.png"},{"id":102908820,"identity":"3ff3c08d-a625-4598-8bc0-38c2c0d15ac1","added_by":"auto","created_at":"2026-02-18 09:52:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":74602,"visible":true,"origin":"","legend":"\u003cp\u003eCardiac Index derived from Electrical Cardiometry (EC) vs. Pulse Contour Analysis (PCA). Cardiac Index (L/min/m\u003csup\u003e2\u003c/sup\u003e) as median [IQR]. Surgery duration\u003c/p\u003e\n\u003cp\u003enormalized as [%]. n=18588.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8389436/v1/27aaf9550e8900a170dfe7d3.png"},{"id":102963717,"identity":"64eb23a8-1755-49db-b8ac-333f286b9bd3","added_by":"auto","created_at":"2026-02-19 04:20:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1079994,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8389436/v1/7618e867-3d0d-49ee-9836-7d213e3a46be.pdf"},{"id":102908822,"identity":"7285417d-9e79-4b9c-b7fa-572dcc3ae3b0","added_by":"auto","created_at":"2026-02-18 09:52:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15323,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-8389436/v1/12a59fb1cf9c0f0a9ef1b701.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Accuracy and Clinical Utility of Electrical Cardiometry versus Pulse Contour Analysis for Cardiac Index Monitoring in Major Abdominal Surgery: A prospective observation trial","fulltext":[{"header":"1. Background","content":"\u003cp\u003eDiagnostic and treatment of perioperative hemodynamic alterations remain one of the central tasks in anesthesiologic management. The pathophysiology of perioperative hemodynamic changes is complex, and inadequate treatment can lead to hypoxia of the organs and tissues \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e2\u003c/sup\u003e. As a basic parameter, a mean arterial pressure (MAP) of \u0026ge;\u0026thinsp;65 mmHg is widely accepted as an adequate target, with organ dysfunction occurring less frequently when pressures above this threshold are maintained \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e4\u003c/sup\u003e, \u003csup\u003e5\u003c/sup\u003e[4]. Individual factors, however, have a relevant impact on the optimal perioperative MAP, the risk of perioperative hypotension, and subsequent organ dysfunction. Preexisting cardiocirculatory changes and consecutive chronic adaption processes, as well as anesthetic agents, may impair the hemodynamic autoregulation of vital organs \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e6\u003c/sup\u003e. Furthermore, patient factors such as age, sex, and ASA-status (American society of Anesthesiology) influence the optimal perioperative MAP. Critically, tissue perfusion and resulting organ oxygenation are not solely dependent on MAP, as macro- and micro-perfusion are not necessarily linked and can become decoupled in certain clinical situations \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e8\u003c/sup\u003e. The complex relationship governing systemic flow involves multiple factors such as preload, contractility, afterload and viscosity \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e10\u003c/sup\u003e,\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMinimal invasive monitoring methods are usually based on pulse contour analysis (PCA). The algorithms which conduct interpretation of pulse wave are externally (e.g. via thermodilution), internally or non-calibrated \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The benefits of those devices are the minimal invasiveness, as well as the easy establishing compared to the \u0026ldquo;old\u0026rdquo; gold standard of cardiac output determination by pulmonary artery catheter (PAC) \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs a completely non-invasive method to monitor advanced hemodynamics, Electrical Cardiometry (EC) has emerged since the 2000s and was evaluated in multiple studies \u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. These systems derive hemodynamic parameters via ECG-like electrodes. However, data in non-cardiac surgery patients and evaluation of general real-life usability are scarce.\u003c/p\u003e \u003cp\u003eThe aim of this study was to compare perioperative cardiac index (CI) derived from Electrical Cardiometry (EC; ICON\u0026reg;, Osypka Medical GmbH, Berlin, Germany) with non-calibrated pulse contour analysis (PCA; Hemosphere Advanced Monitor, BD, Franklin Lakes, USA) and to explore associations between hemodynamic exposure and postoperative outcome.\u003c/p\u003e \u003cp\u003eTwo hypotheses were defined a priori:\u003c/p\u003e \u003cp\u003e(1) EC and PCA show acceptable interchangeability for cardiac index measurement\u003c/p\u003e \u003cp\u003e(2) Hemodynamic exposure metrics are associated with postoperative complications (EPCO-defined) \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eThis prospective observational study was conducted at the University Hospital of Cologne, Germany, between June 2024 and April 2025. Ethical approval was obtained from the local ethics committee (No. 23-1310, 14 November 2023; Chairperson: Univ.-Prof. Dr. med. R. Voltz). Written informed consent was obtained from all participants prior to inclusion.\u003c/p\u003e \u003cp\u003eA total of 60 patients were screened, of whom 54 were included in the final analysis. Six patients were not enrolled: two were excluded due to relevant pacemaker dependency, and in four cases the planned surgical procedure was cancelled or postponed.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2.1 Inclusion criteria were\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMajor elective abdominal surgery requiring advanced hemodynamic monitoring according to institutional standards,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;18 years,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePredominant sinus rhythm (\u0026ge;\u0026thinsp;80% of intraoperative monitoring time, without sustained arrhythmia or pacemaker dependency), and\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eProvision of written informed consent.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2.2 Exclusion criteria were\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAge\u0026thinsp;\u0026lt;\u0026thinsp;18 years,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCardiac rhythm other than sinus rhythm,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInability to establish an arterial line,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRefusal to provide informed consent, or\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCancellation of the planned surgical procedure.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Anesthetic management:\u003c/h2\u003e \u003cp\u003eAnesthetic management followed established clinical standards for elective major abdominal surgery and typically included preoperative thoracic epidural analgesia (TEA), endotracheal and placement of arterial and central venous lines. Induction was achieved with 0.2\u0026ndash;0.5 \u0026micro;g Sufentanil, 3\u0026ndash;5 mg Propofol, and 0.5\u0026ndash;0.9 mg/kg Rocuronium. Anesthesia was maintained with Sevoflurane (MAC 0.7\u0026ndash;1.0), TEA using Ropivacaine (0.5%, 10\u0026ndash;15 ml bolus; 0.15% + Sufentanil 0.75 \u0026micro;g/ml, continuous rate 6 ml/h), and additional Sufentanil (0.1\u0026ndash;0.2 \u0026micro;g/kg bolus) or Remifentanil (0.1\u0026ndash;0.5 \u0026micro;g/kg/min) as indicated. After extubation, most patients were transferred to the intensive care unit (ICU). Hemodynamic support consisted primarily of continuous Norepinephrine infusion (initiated at 0.03\u0026ndash;0.05 \u0026micro;g/kg/min during induction) and balanced crystalloid fluids. Further hemodynamic therapy, including inotropic support, was individualized according to clinical judgment and continuous hemodynamic monitoring.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Hemodynamic monitoring:\u003c/h2\u003e \u003cp\u003ePerioperative hemodynamic monitoring used in this study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (PCA and EC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Pulse contour analysis:\u003c/h2\u003e \u003cp\u003eAfter induction of anesthesia and establishing of an arterial line pulse contour (Hemosphere Advanced Monitor via FloTrac or Acumen IQ Sensor, BD, Franklin Lakes, USA) and continuous central venous pressure (CVP) monitoring was connected. PCA is based on the calculation of stroke volume (SV) using the area under the curve (AUC) of the pressure curve derived from a peripheral arterial line. The Hemosphere monitor uses a statistical model (Arterial Pressure based Cardiac Output, APCO) to correct for differences in compliance and resistance beat by beat (internal calibration) \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The recorded data was extracted directly from the device during anesthesia recovery.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Electrical Cardiometry:\u003c/h2\u003e \u003cp\u003eFor electrical cardiometry (EC; ICON\u0026reg;, Osypka Medical GmbH, Berlin, Germany), two left-cervical and two left-thoracic surface electrodes were applied according to the manufacturer\u0026rsquo;s specifications and secured with adhesive tape. Monitoring was initiated by three trained members of the study group. EC employs the principle of electrical velocimetry, which calculates aortic blood flow from changes in thoracic bioimpedance to derive CI. A low-amplitude, high-frequency alternating current is transmitted via the thoracic electrodes, while the cervical electrodes detect the resulting voltage signal and ECG. During diastole, randomly oriented erythrocytes cause higher impedance; in systole, their alignment with blood flow reduces impedance, allowing continuous assessment of stroke volume and CI \u003csup\u003e18\u003c/sup\u003e. The ICON device calculates a Signal Quality Index (SQI) from the data received. According to the manufacturer a SQI\u0026thinsp;\u0026gt;\u0026thinsp;70 is considered adequate. Recording was stopped during anesthesia recovery and extracted using the manufacturers software.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Data collection:\u003c/h2\u003e \u003cp\u003ePre- and perioperative clinical data, including patient demographics and comorbidities, were extracted from routine hospital records and manually transferred to Microsoft Excel (Version 16.100, Microsoft, Redmond, USA). Perioperative hemodynamic parameters were recorded by the HemoSphere Advanced Monitor every 20 seconds and by the ICON device every 10 seconds, depending on adequate signal quality, and subsequently compiled in Microsoft Excel. Outcome measures, including the definitions of postoperative complications and Major Adverse Cardiac and Cerebrovascular Events (MACCE), followed the criteria described by Jammer et al. \u003csup\u003e16\u003c/sup\u003e. Detailed lists of postoperative complications and MACCE are provided in the supplement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Data management, statistical analysis and sample size calculation:\u003c/h2\u003e \u003cp\u003eData management, matching, calculation of TWA, TBT/TAT, quantity of valid measurements (EC vs. PCA), and Signal Quality Index (SQI) analysis were performed in R Studio (Version 2025.05.0, Posit Software, Boston, USA). TWA was defined as the area under the curve below or above a predefined cutoff, and TBT as the percentage of time spent below or above this threshold. Further analyses were conducted in SPSS 29 (IBM, Endicott, USA). Depending on variable type, χ\u0026sup2; test, Kruskal\u0026ndash;Wallis test, and Spearman correlation were applied. Agreement analysis was performed using a repeated-measures Bland\u0026ndash;Altman model with random effects. Limits of agreement were derived from between- and within-subject variance components, and proportional bias was assessed by regressing measurement differences against their means \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. For graphical visualization, medians were calculated from normalized surgical time (absolute duration / 100) using R Studio. Data are presented as % (n), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, median [IQR], or median (95 %CI). Statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;.05* and high significance as p\u0026thinsp;\u0026lt;\u0026thinsp;.01**. Correction for multiple tests was not applied considering the exploratory nature of the analysis.\u003c/p\u003e \u003cp\u003eSample size calculation (PASS Software) assumed an adjusted α-level of 1% and a mean difference of 20% for the primary endpoint. Considering an anticipated 20% dropout, 48 subjects per group were required; thus, 60 patients were planned.\u003c/p\u003e \u003cp\u003eIllustrations were generated using Microsoft PowerPoint (Version 16.103.1, Microsoft, Redmond, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eTable 1 shows baseline characteristics. The study population was predominantly female (53.7% (29)) with an ASA Status of II (53.7% (29)) or III (38.9% (21)) and aged 66 (\u0026plusmn; 11) years. Common comorbidities included metabolic disorders (57.4% (31)) and cardiovascular diseases (48.1% (26)).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eAge [years]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e66 (\u0026plusmn;11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eGender [male/female]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e46.3%/53.7% (25/29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eASA Status\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003eI 7.4% (4). II 53.7% (29). III 38.9% (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eBMI [kg/m\u003csup\u003e2\u003c/sup\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e25.7 (\u0026plusmn;\u0026nbsp;4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eCardiovascular diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e48.1% (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eNephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e1.9% (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003ePneumopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e11.1% (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eNeuropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e14.8% (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eMetabolic disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e57.4% (31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eNIDDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e11.1% (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eIDDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e3.8% (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eThyreopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e22.2% (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eHepatopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e9.3% (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eMisuse of substances\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e35.2% (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eMisuse of nicotine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e27.8% (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eMisuse of alcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e14.8% (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 1. Baseline patient characteristics. Item [unit]. Mean (\u0026plusmn;SD). Median [IQR]. % [n].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs seen in Table 2 arterial hypertension (40.7% (22)), preexisting cardiac arrhythmias (14.8% (8)) and coronary artery disease (9.3% (5)) were the most frequent preoperative conditions.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eArterial hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e40.7% (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eHFpEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e11.1% [6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eHFrEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e1.9% (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eCoronary artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e9.3% (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eArrhythmia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e14.8% (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003ePAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e1.9% (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eLV-EF available\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e70.4% (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eLV-EF [%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e60.0 [57.5-61.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e5.6% (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eAsthma bronchiale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e7.4% (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003ePAH\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e1.9% (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003ePulmonary function test available\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e53.7% (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eFEV1 [L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e2.59 [2.26-3.26]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eFVC [L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e3.66 [2.98-4.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eFEV1/VC [%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e88.07 [76.67-100.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2. Cardiopulmonary baseline characteristics. Item [unit]. Median [IQR]. % [n]. HFpEF=heart failure preserved ejection fraction. HFrEF=heart failure reduced ejection fraction. PAD=periphery artery disease. LV-EF=left ventricular ejection fraction. COPD=chronic obstructive pulmonary disease. PAH=pulmonary arterial hypertension. FEV=forced expiratory volume. FVC=forced vital capacity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsidering preoperative medication, antihypertensive drugs and diuretics were the most frequent, found in 44.4% (24) and 13.0% (7), respectively (see Table 3).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eAntihypertensive drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e44.4% (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eBeta-Blockers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e20.4% (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eACE-Inhibitors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e11.1% (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eAT1-Blockers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e11.1% (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eCalcium-Antagonists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e11.1% (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eAlpha-1-Blockers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e9.3% (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eDiuretics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e13.0% (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eLoop-Diuretics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e5.6% (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eThiacides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e3.7% (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eAldosterone-Blockers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e1.9% (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003ePlatelet-Inhibitors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e11.1% (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eDOAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e16.7% (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2781%;\"\u003e\n \u003cp\u003eAntidiabetics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.7219%;\"\u003e\n \u003cp\u003e11.1% (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3. Preoperative medication. Item [unit]. % [n]. ACE=Angiotensin-Converting-Enzyme. AT1=Angiotensin II receptor 1. DOAC=direct oral anticoagulants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4 presents perioperative data. Patients in this study underwent a mean duration of surgery of 4.51 [3.0-5.5] h (incision to suture). Most procedures utilized general anesthesia combined with TEA (87.0% (47)). Surgeries were oesophagectomy (37.0% (20)), pancreatic surgery (27.8% (15)), liver resection (18.5% (10)), gastrectomy (11.1% (6)), colon surgery (3.7% (2)) and retroperitoneal compartment resection (1.9% (1)). The mean overall fluid balance (considering blood/urine loss, administered fluids, and transfusions) was +1800 [1280-2190] ml resulting in a normalized fluid balance of +6.22 [4.05-9.88] ml/kg/h. Norepinephrine was used in all patients with a maximal dose of 0.2 [0.12-.0.35] \u0026micro;g/kg/min.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5702%;\"\u003e\n \u003cp\u003eDuration of surgery [h]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48.4298%;\"\u003e\n \u003cp\u003e4.5 [3.0-5.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5702%;\"\u003e\n \u003cp\u003eTEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48.4298%;\"\u003e\n \u003cp\u003e87.0% (47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5702%;\"\u003e\n \u003cp\u003eIntrathoracic surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48.4298%;\"\u003e\n \u003cp\u003e46.3% (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5702%;\"\u003e\n \u003cp\u003eBlood loss [ml]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48.4298%;\"\u003e\n \u003cp\u003e250 [125-500]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5702%;\"\u003e\n \u003cp\u003eUrine output [ml]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48.4298%;\"\u003e\n \u003cp\u003e500 [300-725]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5702%;\"\u003e\n \u003cp\u003eCrystalloids [ml]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48.4298%;\"\u003e\n \u003cp\u003e2500 [2000-3000]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5702%;\"\u003e\n \u003cp\u003eTransfusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48.4298%;\"\u003e\n \u003cp\u003e5.6% (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5702%;\"\u003e\n \u003cp\u003eFluid balance [ml]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48.4298%;\"\u003e\n \u003cp\u003e+1800 [1280-2190]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5702%;\"\u003e\n \u003cp\u003eFluid balance [ml/kg/h]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48.4298%;\"\u003e\n \u003cp\u003e+6.22 [4.05-9.88]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5702%;\"\u003e\n \u003cp\u003eNoradrenaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48.4298%;\"\u003e\n \u003cp\u003e100.0% (54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5702%;\"\u003e\n \u003cp\u003emaximal Noradrenaline dose [\u0026micro;g/kg/min]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48.4298%;\"\u003e\n \u003cp\u003e0.2 [0.12-0.35]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5702%;\"\u003e\n \u003cp\u003eAdrenaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48.4298%;\"\u003e\n \u003cp\u003e7.4% (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5702%;\"\u003e\n \u003cp\u003emaximal Adrenaline dose [\u0026micro;g/kg/min]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48.4298%;\"\u003e\n \u003cp\u003e0.05 [0.05-0.05]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5702%;\"\u003e\n \u003cp\u003eDobutamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48.4298%;\"\u003e\n \u003cp\u003e5.6% (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51.5702%;\"\u003e\n \u003cp\u003eVasopressin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48.4298%;\"\u003e\n \u003cp\u003e5.6% (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Perioperative items. Item [unit]. Mean [IQR]. % (n). TEA=thoracic epidural\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eanalgesia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll perioperative hemodynamic parameters are listed in table 5.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.5544%;\"\u003e\n \u003cp\u003eTWA MAP \u0026lt; 65 mmHg\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50.4456%;\"\u003e\n \u003cp\u003e.17 [.08-.51] mmHg\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.5544%;\"\u003e\n \u003cp\u003eTBT MAP \u0026lt; 65 mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50.4456%;\"\u003e\n \u003cp\u003e4.85 [.75-9.13] %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.5544%;\"\u003e\n \u003cp\u003eTWA CI \u0026le; 2.5 L/min/m\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50.4456%;\"\u003e\n \u003cp\u003e.12 [.04-.33] L/min/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.5544%;\"\u003e\n \u003cp\u003eTBT CI \u0026le; 2.5 L/min/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50.4456%;\"\u003e\n \u003cp\u003e42.6 [11.40-84.55] %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.5544%;\"\u003e\n \u003cp\u003eTWA CI \u0026le; 2.2 L/min/m\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50.4456%;\"\u003e\n \u003cp\u003e.04 [.01-.11] L/min/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.5544%;\"\u003e\n \u003cp\u003eTBT CI \u0026le; 2.2 L/min/m\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50.4456%;\"\u003e\n \u003cp\u003e16.55 [4.15-57.88] %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.5544%;\"\u003e\n \u003cp\u003eTWA CI \u0026le; 1.6 L/min/m\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50.4456%;\"\u003e\n \u003cp\u003e.01 [.00-.01] L/min/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.5544%;\"\u003e\n \u003cp\u003eTBT CI \u0026le; 1.6 L/min/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50.4456%;\"\u003e\n \u003cp\u003e1.2 [0.18-4.5] %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.5544%;\"\u003e\n \u003cp\u003eTWA SVRI \u0026lt; 1970 dynes/cm⁵/m\u0026sup2;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50.4456%;\"\u003e\n \u003cp\u003e47.48 [13.16-176.22] dynes/cm⁵/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.5544%;\"\u003e\n \u003cp\u003eTBT SVRI \u0026lt; 1970 dynes/cm⁵/m\u0026sup2;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50.4456%;\"\u003e\n \u003cp\u003e27.4 [6.30-57.88] %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.5544%;\"\u003e\n \u003cp\u003eTWA SVRI \u0026gt; 2390 dynes/cm⁵/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50.4456%;\"\u003e\n \u003cp\u003e139.79 [38.48-400.20] dynes/cm⁵/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.5544%;\"\u003e\n \u003cp\u003eTAT SVRI \u0026gt; 2390 dynes/cm⁵/m\u0026sup2;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50.4456%;\"\u003e\n \u003cp\u003e30.25 [7.08-55.43] %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 5. Hemodynamic parameters. Mean [IQR]. TWA=time weighted average. TBT=time below threshold. TAT=time above threshold. TWA=time-weighted average. MAP=Mid Arterial Pressure. CI=Cardiac Index. SVRI=Systemic Vascular Resistance Index. EC=Electrical Cardiometry. SQI=Signal Quality Index.\u003c/p\u003e\n\u003cp\u003ePostoperative outcome is displayed in Table 6. No patient died during hospital stay. Any complication occurred in 48.1% (26) of cases. Major Adverse Cardiac and Cerebrovascular Events (MACCE) were present in 11.1% (6) of cases. If a complication item listed in the Supplement is not reported here, it indicates that no cases of that specific event were observed. Postoperative complications were classified according to Clavien\u0026ndash;Dindo as follows: Grade II occurred in 9.3% of patients (5/54), Grade IIIa in 13.0% (7/54), Grade IIIb in 5.6% (3/54), and Grade IV in 22.2% (12/54).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.7086%;\"\u003e\n \u003cp\u003eAny complication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.2914%;\"\u003e\n \u003cp\u003e50,0 % (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.7086%;\"\u003e\n \u003cp\u003eMortality\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.2914%;\"\u003e\n \u003cp\u003e0.0% (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.7086%;\"\u003e\n \u003cp\u003ePostoperative organ dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.2914%;\"\u003e\n \u003cp\u003e24.1% (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.7086%;\"\u003e\n \u003cp\u003eMACCE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.2914%;\"\u003e\n \u003cp\u003e11.1% (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.7086%;\"\u003e\n \u003cp\u003eNew AFIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.2914%;\"\u003e\n \u003cp\u003e9.3% (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.7086%;\"\u003e\n \u003cp\u003ePulmonary embolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.2914%;\"\u003e\n \u003cp\u003e1.9% (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.7086%;\"\u003e\n \u003cp\u003eAKI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.2914%;\"\u003e\n \u003cp\u003e3.7% (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.7086%;\"\u003e\n \u003cp\u003eDelirium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.2914%;\"\u003e\n \u003cp\u003e3.7% (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.7086%;\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.2914%;\"\u003e\n \u003cp\u003e22.2% (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.7086%;\"\u003e\n \u003cp\u003eLiver failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.2914%;\"\u003e\n \u003cp\u003e1.9% (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.7086%;\"\u003e\n \u003cp\u003eOther thromboembolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.2914%;\"\u003e\n \u003cp\u003e1.9% (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.7086%;\"\u003e\n \u003cp\u003eInfection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.2914%;\"\u003e\n \u003cp\u003e29.6% (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.7086%;\"\u003e\n \u003cp\u003eWound infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.2914%;\"\u003e\n \u003cp\u003e3.7% (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.7086%;\"\u003e\n \u003cp\u003epostoperative invasive ventilation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.2914%;\"\u003e\n \u003cp\u003e9.3% (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.7086%;\"\u003e\n \u003cp\u003eICU-Admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.2914%;\"\u003e\n \u003cp\u003e94.4% (51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.7086%;\"\u003e\n \u003cp\u003eICU-Readmission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.2914%;\"\u003e\n \u003cp\u003e18.5% (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.7086%;\"\u003e\n \u003cp\u003eLOS ICU [days]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.2914%;\"\u003e\n \u003cp\u003e2 [1-3]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.7086%;\"\u003e\n \u003cp\u003eLOS hospital [days]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.2914%;\"\u003e\n \u003cp\u003e13 [10-22]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 6. Postoperative outcome. % (n). MACCE=Major Adverse Cardiac and Cerebrovascular Events. AFIB=atrial fibrillation. ICU=intensive care unit. LOS=length of stay.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEC data was available in 64.4 [25.93-78.08] % of perioperative time. Detailed data on SQI is listed in Table 7. There was no correlation between BMI and EC data availability (Spearman\u0026rsquo;s rho =.10, n.s).\u003c/p\u003e\n\u003cp\u003eA total of 18,588 matched datapoints (corresponding to 201.6 hours of perioperative monitoring) were available for analysis. For the mixed Bland\u0026ndash;Altman comparison between EC and PCA, analysis revealed a mean bias of 0.24 L/min/m\u0026sup2; (see Figure 2).\u003c/p\u003e\n\u003cp\u003eThe limits of agreement (LOA), defined as bias \u0026plusmn;1.96 times the standard deviation (SD) of the differences, were \u0026ndash;1.39 to +1.87 L/min/m\u0026sup2; (Figure 2). Percentage error was 54.3%. The mixed-effects Bland\u0026ndash;Altman analysis demonstrated a mean bias of 0.24 L/min/m\u0026sup2; (95% CI 0.23-0.25 L/min/m\u003csup\u003e2\u003c/sup\u003e; 95 % limits of agreement = \u0026minus;1.39 to +1.87 L/min/m\u0026sup2;). A significant proportional bias was observed (\u0026beta; = 0.18, p \u0026lt; .01**, R\u0026sup2; = 0.025), indicating that EC tended to overestimate CI values at higher ranges compared with PCA. Despite statistical significance, the proportional bias explained only 2.5 % of the overall variance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs shown in figure 3 relevant difference between median CI derived from EC vs. PCA and calculated from n= 18,588 datapoints remains throughout the normalized surgery duration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNeither common hemodynamic risk patterns, including hypotension, hypodynamic cardiac output, vasoplegia, nor vasoconstriction, were found to be associated with a more frequent incidence of postoperative complications (MAP \u0026le; 65 mmHg: H=.641; n.s.; CI \u0026le; 2.5 L/min/m\u003csup\u003e2\u003c/sup\u003e: H=3.194; n.s.; CI \u0026le; 2.2 L/min/m\u003csup\u003e2\u003c/sup\u003e: H=2.870; n.s.; CI \u0026le; 1.6 L/min/m\u003csup\u003e2\u003c/sup\u003e: H=1.307; n.s.; SVRI \u0026lt; 1970 dynes/cm⁵/m\u0026sup2;: H=.987; n.s.; SVRI \u0026lt; 2390 dynes/cm⁵/m\u0026sup2;: H=1.161; n.s.). In contrast, several patient-related factors were significantly associated with adverse outcomes. Pneumonia occurred significantly more often in male than in female patients (40.0 % (10/25) vs. 6.9 % (2/29); \u0026chi;\u0026sup2; = 8.51;\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), corresponding to an odds ratio (OR) of 9.00 (95 % CI 1.74\u0026ndash;46.59).\u0026nbsp;\u003cbr\u003eA history of misuse of nicotine was significantly associated with postoperative organ dysfunction (OR 4.81 [95 % CI 1.27\u0026ndash;18.31]; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05*), delirium (13.3 % (2/15) vs. 0 % (0/39); \u0026chi;\u0026sup2; = 5.40; \u003cem\u003ep\u003c/em\u003e = .02*), and pneumonia (46.7 % (7/15) vs. 12.8 % (5/39); \u0026chi;\u0026sup2; = 7.18; \u003cem\u003ep\u003c/em\u003e = .01*).The incidence of nicotine misuse did not differ between male and female patients (32.0 % (8/25) vs. 24.1 % (7/29); \u0026chi;\u0026sup2; = .41.). Similarly, pneumonia was significantly more frequent in patients receiving antihypertensive medication (37.5 % (9/24) vs. 10.0 % (3/30); \u0026chi;\u0026sup2; = 5.83; \u003cem\u003ep\u003c/em\u003e = .02*), corresponding to an OR of 5.40 (95 % CI 1.27\u0026ndash;23.05). A subgroup analysis indicated a particularly high incidence among those treated with ACE inhibitors (66.7 % (4/6) vs. 16.7 % (8/48); \u0026chi;\u0026sup2; = 7.714; \u003cem\u003ep\u003c/em\u003e \u0026lt;.01**). Patients with cardiovascular comorbidities experienced a significantly higher TWA of\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCI \u0026le; 2.5 L/min/m\u003csup\u003e2\u003c/sup\u003e (.22 [.078-.37] vs. .08 [.02-.26] L/min/m\u003csup\u003e2\u003c/sup\u003e; H=4.115) and vasoconstriction (SVRI \u0026gt; 2390 dynes/cm⁵/m\u0026sup2;; 204.90 [61.44-573.86] vs. 83.56 [15.86-191.38]; H=5.381; p=.02*). However, TWA of vasoplegia was significantly higher in patients without cardiovascular comorbidities (SVRI \u0026lt; 1970 dynes/cm⁵/m\u0026sup2;; 105.42 [32.39-273.06] vs. 36.24 [7.23-93.42]; H=5.065; p=.02*). There was no difference in normalized fluid balance (5.26 [4.03-8.02] vs. 6.91 [4.03-11.71] ml/kg/h; H=1.086; n.s.) or maximal Norepinephrine-dose (.2 [.09-.35] vs. .19 [.15-.45] \u0026micro;g/kg/min; H=.598; n.s.) in patients with and without cardiovascular comorbidities. Patients with history of arterial hypertension experienced a significantly higher TWA for CI \u0026le; 2.5 L/min/m\u003csup\u003e2\u003c/sup\u003e (.22[.08-.48] vs. .08 [.02-.26] L/min/m\u003csup\u003e2\u003c/sup\u003e; H=4.292; p=.04*), CI \u0026le; 2.2 L/min/m\u003csup\u003e2\u003c/sup\u003e (.09 [.03-.29] vs. .03 [.01-.07] L/min/m\u003csup\u003e2\u003c/sup\u003e; H=4.446; p=.4* ) and vasoplegia (36.24 [7.23-93.42] vs. 89.54 [21.75-273.06] dynes/cm⁵/m\u0026sup2;; H=4.389; p=.04*). Finally, patients with and without cardiovascular comorbidities (.17 [.12-.59] vs. .20 [.06-.49] mmHg; H=.12; n.s.) or arterial hypertension (.18 [.12-.59] vs. .17 [.05-.49] mmHg; H=.318; n.s.) showed no difference in TWA for hypotension.\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study evaluated the agreement between electrical cardiometry (EC) and pulse contour analysis (PCA) for perioperative CI monitoring. Using a repeated-measures Bland\u0026ndash;Altman, a small mean bias but wide LOAs were observed, resulting in a percentage error\u0026thinsp;\u0026gt;\u0026thinsp;50%. According to current validation criteria, this degree of variability indicates that EC and PCA cannot be used interchangeably for the determination of absolute CI values \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNevertheless, the findings highlight that EC may still provide clinically meaningful information. Although the proportional bias analysis demonstrated a significant relationship between the difference and the mean of both methods, the effect size was small. This suggests that EC tends to slightly overestimate cardiac index at higher values, but the impact of this proportional bias is unlikely to reach clinical relevance. Similar observations have been reported by previous validation studies comparing EC with thermodilution, PCA or echocardiography, which consistently found insufficient interchangeability but acceptable trending ability in humans and even other species \u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Also, metanalytic data from Suehiro et al. and Sanders et al. report a percentage error for 20\u0026ndash;45% in children and 48% in adults. The predominant reference method in both studies was TTE. Considering our data and previous studies, EC shows a high percentage error no matter the reference method.\u003c/p\u003e \u003cp\u003eThe relatively low proportion of analyzable data likely reflects the specifics of the perioperative setting, particularly in major abdominal surgery. Once the EC electrodes are placed and ideally tightly secured on the neck and thorax, perioperative accessibility is often limited. Surgical personal might cause movement artefacts, extracorporal (e.g., surgical manipulation on the chest wall) or intracorporeal (e.g., tissue preparation) \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, especially in intrathoracic and surgery of the upper abdomen, electrical interferences by cauterization were frequently observed. Once a disturbance of signal is detected devices can only display new values with a delay. Finally individual factors, such as body mass and composition as well as fluid content of the tissue can alter bioimpedance \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, the ability of EC to track directional hemodynamic changes may be valuable during dynamic situations such as volume loading, vasopressor titration, or inotrope administration. Its continuous and operator-independent nature enables real-time assessment without the need for invasive arterial or central venous access, thereby reducing procedural risk and patient discomfort. EC may thus serve as a useful adjunct for hemodynamic optimization in low- to intermediate-risk surgical patients, or in settings where the risks of more invasive monitoring may outweigh the benefit\u0026mdash;such as pediatric cases, coagulopathies, or short-duration procedures.\u003c/p\u003e \u003cp\u003eIn our data, typical perioperative hemodynamic risk patterns (intraoperative hypotension, low cardiac index, vasoplegia, and vasoconstriction) were not associated with an increased incidence of postoperative complications. This finding is somewhat unexpected, as prior evidence has suggested links between hypotension or impaired cardiac output and adverse outcomes. Several explanations may account for this discrepancy. First, complications after major abdominal surgery are multifactorial and may not be adequately captured by single hemodynamic surrogates. Second, our time-weighted average (TWA) approach integrates cumulative exposure rather than capturing short, critical episodes, which may dilute the clinical impact of brief but physiologically relevant hypotensive events. However, TWA for hypotension was rather low compared to available literature (.17 [.08-.51] mmHg). Frassanito et al. found a TWA of .14 [.04-.66] mmHg in patients under Hypotension Prediction Index-guided (HPI) perioperative therapy compared to .77 [.36\u0026thinsp;\u0026minus;\u0026thinsp;1.30] mmHg in control during major gynaecooncologic surgery \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. A comparable analysis by Wijnberg et al. revealed a TWA of .44 [.23-.72] Hg without HPI- and .10 [.01-.43] mmHg under HPI-guided therapy in non-cardiac surgery \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In a high-risk cohort undergoing noncardiac surgery Maheshwari et al. reported a TWA for MAP\u0026thinsp;\u0026lt;\u0026thinsp;65 mmHg of .05 [.00-.22] and .11 [.00-.54] respectively gathered by non-invasive continuous blood pressure monitoring \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Third, comprehensive and quick reaction to perioperative hemodynamic changes may have mitigated the severity and potential consequences of transient hemodynamic instability. In contrast, patient-related factors demonstrated stronger associations with postoperative outcomes. Male sex, nicotine misuse, and preoperative antihypertensive medication were associated with pneumonia, organ dysfunction, and delirium. These findings are consistent with prior literature identifying smoking and comorbidities as important determinants of surgical morbidity \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, \u003csup\u003e30\u003c/sup\u003e, \u003csup\u003e31\u003c/sup\u003e, \u003csup\u003e32\u003c/sup\u003e. Moreover, patients with cardiovascular comorbidities and arterial hypertension displayed distinct hemodynamic profiles, characterized by prolonged exposure to low cardiac index and vasoconstriction, whereas vasoplegia was more pronounced in patients without cardiovascular disease\u003c/p\u003e \u003cp\u003eThere are several notable strengths of this study. The prospective design in a cohort undergoing complex major abdominal surgery reflects real-world clinical application of EC. High-frequency data acquisition yielded more than 18,000 paired datapoints, enabling a robust and granular comparison with PCA. The use of TWA, TBT/TAT metrics allowed quantification of cumulative hemodynamic exposure beyond isolated single readings. Moreover, this study is among the first to systematically evaluate EC performance against non-calibrated PCA in a high-risk non-cardiac surgical population. Finally, by integrating perioperative hemodynamic profiles with postoperative outcomes, we demonstrated that patient-related factors (male sex, misuse of nicotine, antihypertensive therapy) outweighed isolated hemodynamic surrogates\u0026mdash;provided that mean arterial pressure was adequately maintained\u0026mdash;as predictors of adverse events.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, it was conducted as a single-center observational trial in a relatively small cohort of patients undergoing major abdominal surgery, which may limit external validity. Second, hemodynamic exposure was primarily quantified using TWA. Although this captures cumulative burden, brief but clinically relevant fluctuations may have been underestimated. Third, PCA was used as the comparator method; while widely implemented, it does not represent the thermodilution gold standard. Finally, the sample size was designed to assess the accuracy and reliability of EC and PCA rather than postoperative complication rates. Therefore, conclusions regarding hemodynamic target parameters and postoperative organ dysfunction should be interpreted with caution.\u003c/p\u003e \u003cp\u003eOur results give first hints for the limited predictive value of isolated perioperative hemodynamic markers for postoperative complications, while emphasizing the importance of patient-related risk factors. This finding is consistent with a recent randomized clinical trial by Saugel et al, which investigated individualized perioperative blood pressure management \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The authors reported no significant difference in the composite endpoint\u0026mdash;including acute kidney injury, myocardial injury, nonfatal cardiac arrest, or death within the first 7 postoperative days\u0026mdash;between patients assigned to individualized mean arterial pressure targets. This suggests that perioperative outcome prediction should integrate both hemodynamic monitoring data and baseline patient characteristics, rather than relying on hemodynamic thresholds alone. Furthermore, low CI situations happen frequently in major abdominal surgery, interestingly in a population without a relevant amount of preoperative cardiac impairment. Even CI\u0026thinsp;\u0026lt;\u0026thinsp;1.6 L/min/m\u003csup\u003e2\u003c/sup\u003e was not associated with adverse outcome.\u003c/p\u003e \u003cp\u003eFurther research is necessary to define the optimal clinical setting and patient populations for the use of EC. While current evidence supports its utility for trend monitoring and non-invasive assessment of cardiac function, the precise patient cohorts that benefit most from EC-guided management remain to be established. Future studies should aim to identify risk constellations in which EC-derived parameters\u0026mdash;such as cardiac index, stroke volume variation, or thoracic fluid content\u0026mdash;provide the highest predictive or therapeutic value.\u003c/p\u003e \u003cp\u003eLarge-scale, prospective, and possibly multicenter studies integrating EC into structured perioperative hemodynamic care protocols are therefore needed to clarify its role within modern parameter-directed therapy algorithms and to investigate its impact on outcomes and cost-effectiveness.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eElectrical cardiometry did not provide clinically acceptable agreement with Pulse Contour Analysis for absolute cardiac index measurement in major abdominal surgery. Its potential value may lie in monitoring relative changes and hemodynamic trends rather than replacing established reference methods. Patient-related factors outweighed hemodynamic markers as predictors of postoperative complications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e \u003cp\u003eThis prospective observational study was conducted at the University Hospital of Cologne, Germany, between June 2024 and April 2025. Ethical approval was obtained from the local ethics committee (No. 23-1310, 14 November 2023; Chairperson: Univ.-Prof. Dr. med. R. Voltz). Written informed consent was obtained from all participants prior to inclusion.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePKO: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Validation, Visualization, Writing- Original DraftCL: Methodology, Data CurationJR: Data CurationAM: Data CurationDS: Data Curation, Writing-Review \u0026amp;amp; EditingTK: Conceptualization, Writing- Review \u0026amp;amp; Editing, Supervision\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003e The authors thank the perioperative nursing staff and anesthesia technicians of the Department of Anesthesiology and Intensive Care Medicine, University Hospital of Cologne, for their support during data acquisition. Further, we especially thank our patients for their trust in our care and participation in our study.\u003c/p\u003e \u003cp\u003eFinancial support:\u003c/p\u003e \u003cp\u003eNone.\u003c/p\u003e \u003cp\u003eConflicts of interest:\u003c/p\u003e \u003cp\u003eThe authors declare that they have no conflicts of interest related to this study.\u003c/p\u003e \u003cp\u003ePresentation:\u003c/p\u003e \u003cp\u003ePreliminary data from this study will be presented as a research poster at the DIVI Congress 2025 on December 5th 2025 in Hamburg, Germany.\u003c/p\u003e \u003cp\u003eResearch data availability statement:\u003c/p\u003e \u003cp\u003eThe datasets generated and analysed during the current study are not publicly available due to patient privacy and ethical restrictions but are available from the corresponding author upon reasonable request. Data sharing requires prior approval by the Ethics Committee of the University of Cologne (reference 23-1310) and will be provided in a de-identified format.\u003c/p\u003e \u003cp\u003e Consent for publication:\u003c/p\u003e \u003cp\u003e Consent for publication was not required as no identifiable individual data are included in this manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analysed during the current study are not publicly available due to patient privacy and ethical restrictions but are available from the corresponding author upon reasonable request. Data sharing requires prior approval by the Ethics Committee of the University of Cologne (reference 23-1310) and will be provided in a de-identified format.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eRex S, De Waal EEC, Buhre W. Perioperatives h\u0026auml;modynamisches monitoring. 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Clin Colon Rectal Surg. 2023;36:175\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003evan Kooten RT, Bahadoer RR, Peeters KCMJ, Hoeksema JHL, Steyerberg EW, Hartgrink HH, van de Velde CJH, Wouters MWJM, Tollenaar RAEM. Preoperative risk factors for major postoperative complications after complex gastrointestinal cancer surgery: A systematic review. Eur J Surg Oncol. 2021;47:3049\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"perioperative-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"peri","sideBox":"Learn more about [Perioperative Medicine](http://perioperativemedicinejournal.biomedcentral.com)","snPcode":"13741","submissionUrl":"https://submission.nature.com/new-submission/13741/3","title":"Perioperative Medicine","twitterHandle":"@EMSurgeryBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hemodynamic monitoring, Electrical Cardiometry, Pulse Contour Analysis, Outcome, Abdominal Surgery, Monitoring, Risk factors, Cardiac Index, trending ability, Bland–Altman, peri-operative hypotension, time-weighted average","lastPublishedDoi":"10.21203/rs.3.rs-8389436/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8389436/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAdequate perioperative hemodynamic management is essential to prevent organ hypoxia. Measuring the cardiac index (CI) provides important information. Electrical cardiometry (EC) has been introduced as a non-invasive alternative for CI monitoring, but existing data of its utility and agreement with pulse contour analysis (PCA) in major abdominal surgery is limited.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this prospective observational study, 54 patients undergoing major abdominal surgery with concurrent advanced hemodynamic monitoring were included. CI was measured using EC and PCA. Time-weighted averages (TWA), time below threshold (TBT), and signal quality index (SQI) were analyzed. Agreement between EC and PCA was assessed by Bland\u0026ndash;Altman analysis. Postoperative complications were classified according to Clavien\u0026ndash;Dindo. Statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eEC data was available in 64% of monitoring time (with SQI\u0026thinsp;\u0026gt;\u0026thinsp;70 in 90% of recorded data). Bland\u0026ndash;Altman analysis showed a bias of +\u0026thinsp;0.24 L/min/m\u0026sup2; (95% CI 0.23\u0026ndash;0.25 L/min/m\u003csup\u003e2\u003c/sup\u003e; limits of agreement \u0026minus;\u0026thinsp;1.39 to +\u0026thinsp;1.87 L/min/m\u0026sup2;), and percentage error of 54%. Hemodynamic risk patterns (hypotension, low CI, vasoplegia) were not significantly associated with postoperative complications. In contrast, male sex (OR 9.0, 95% CI 1.74\u0026ndash;46.59), misuse of nicotine (OR 4.81, 95% CI 1.27\u0026ndash;18.31), and antihypertensive therapy (OR 5.4; 95% CI 1.27\u0026ndash;23.05) were significantly linked to adverse outcomes, including pneumonia, delirium, and organ dysfunction.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eEC and PCA are not interchangeable for absolute CI-measurement. While EC may detect perioperative trends, patient-related factors proved to be stronger predictors of postoperative complications than the hemodynamic markers assessed.\u003c/p\u003e","manuscriptTitle":"Accuracy and Clinical Utility of Electrical Cardiometry versus Pulse Contour Analysis for Cardiac Index Monitoring in Major Abdominal Surgery: A prospective observation trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 09:52:44","doi":"10.21203/rs.3.rs-8389436/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-02-13T11:30:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-19T13:29:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-19T13:24:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Perioperative Medicine","date":"2025-12-17T22:26:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"perioperative-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"peri","sideBox":"Learn more about [Perioperative Medicine](http://perioperativemedicinejournal.biomedcentral.com)","snPcode":"13741","submissionUrl":"https://submission.nature.com/new-submission/13741/3","title":"Perioperative Medicine","twitterHandle":"@EMSurgeryBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9e7878c6-9bd7-465e-9c37-6f558a2047b2","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-18T09:52:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 09:52:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8389436","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8389436","identity":"rs-8389436","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}