Central Venous Pressure Waveform Research Articles

Predicting stroke volume variation using central venous pressure waveform: a deep learning approach

Objective. This study evaluated the predictive performance of a deep learning approach to predict stroke volume variation (SVV) from central venous pressure (CVP) waveforms.Approach. Long short-term memory (LSTM) and the feed-forward neural network were sequenced to predict SVV using CVP waveforms obtained from the VitalDB database, an open-source registry. The input for the LSTM consisted of 10 s CVP waveforms sampled at 2 s intervals throughout the anesthesia duration. Inputs of the feed-forward network were the outputs of LSTM and demographic data such as age, sex, weight, and height. The final output of the feed-forward network was the SVV. The performance of SVV predicted by the deep learning model was compared to SVV estimated derived from arterial pulse waveform analysis using a commercialized model, EV1000.Main results. The model hyperparameters consisted of 12 memory cells in the LSTM layer and 32 nodes in the hidden layer of the feed-forward network. A total of 224 cases comprising 1717 978 CVP waveforms and EV1000/SVV data were used to construct and test the deep learning models. The concordance correlation coefficient between estimated SVV from the deep learning model were 0.993 (95% confidence interval, 0.992-0.993) for SVV measured by EV1000.Significance. Using a deep learning approach, CVP waveforms can accurately approximate SVV values close to those estimated using commercial arterial pulse waveform analysis.

Central venous pressure waveform analysis during sleep/rest: a novel approach to enhance intensive care unit post-extubation monitoring of extubation failure.

This pilot study aimed to investigate the relation between cardio-respiratory parameters derived from Central Venous Pressure (CVP) waveform and Extubation Failure (EF) in mechanically ventilated ICU patients during post-extubation period. This study also proposes a new methodology for analysing these parameters during rest/sleep periods to try to improve the identification of EF. We conducted a prospective observational study, computing CVP-derived parameters including breathing effort, spectral analyses, and entropy in twenty critically ill patients post-extubation. The Dynamic Warping Index (DWi) was calculated from the respiratory component extracted from the CVP signal to identify rest/sleep states. The obtained parameters from EF patients and patients without EF were compared both during arbitrary periods and during reduced DWi (rest/sleep). We have analysed data from twenty patients of which nine experienced EF. Our findings may suggest significantly increased respiratory effort in EF patients compared to those successfully extubated. Our study also suggests the occurrence of significant change in the frequency dispersion of the cardiac signal component. We also identified a possible improvement in the differentiation between the two groups of patients when assessed during rest/sleep states. Although with caveats regarding the sample size, the results of this pilot study may suggest that CVP-derived cardio-respiratory parameters are valuable for monitoring respiratory failure during post-extubation, which could aid in managing non-invasive interventions and possibly reduce the incidence of EF. Our findings also indicate the possible importance of considering sleep/rest state when assessing cardio-respiratory parameters, which could enhance respiratory failure detection/monitoring.

A multimodal deep learning tool for detection of junctional ectopic tachycardia in children with congenital heart disease

Junctional ectopic tachycardia (JET) is a prevalent life-threatening arrhythmia in children with congenital heart disease. It has a marked resemblance to normal sinus rhythm, often leading to delay in diagnosis and management. The study sought to develop a novel multimodal automated arrhythmia detection tool that outperforms existing JET detection tools. This is a cohort study performed on 40 patients with congenital heart disease at Texas Children's Hospital. Electrocardiogram and central venous pressure waveform data produced by bedside monitors are captured by the Sickbay platform. Convolutional neural networks (CNNs) were trained to classify each heartbeat as either normal sinus rhythm or JET based only on raw electrocardiogram signals. Our best model improved the area under the curve from 0.948 to 0.952 and the true positive rate at 5% false positive rate from 71.8% to 80.6%. Using a 3-model ensemble further improved the area under the curve to 0.953 and the true positive rate at 5% false positive rate to 85.2%. Results on a subset of data show that adding central venous pressure can significantly improve area under the receiver-operating characteristic curve from 0.646 to 0.825. This study validates the efficacy of deep neural networks to notably improve JET detection accuracy. We have built a performant and reliable model that can be used to create a bedside alarm that diagnoses JET, allowing for precise diagnosis of this life-threatening postoperative arrhythmia and prompt intervention. Future validation of the model in a larger cohort is needed.

Estimation of the transpulmonary pressure from the central venous pressure in mechanically ventilated patients

Transpulmonary pressure (PL) calculation requires esophageal pressure (PES) as a surrogate of pleural pressure (Ppl), but its calibration is a cumbersome technique. Central venous pressure (CVP) swings may reflect tidal variations in Ppl and could be used instead of PES, but the interpretation of CVP waveforms could be difficult due to superposition of heartbeat-induced pressure changes. Thus, we developed a digital filter able to remove the cardiac noise to obtain a filtered CVP (f-CVP). The aim of the study was to evaluate the accuracy of CVP and filtered CVP swings (ΔCVP and Δf-CVP, respectively) in estimating esophageal respiratory swings (ΔPES) and compare PL calculated with CVP, f-CVP and PES; then we tested the diagnostic accuracy of the f-CVP method to identify unsafe high PL levels, defined as PL>10 cmH2O. Twenty patients with acute respiratory failure (defined as PaO2/FiO2 ratio below 200 mmHg) treated with invasive mechanical ventilation and monitored with an esophageal balloon and central venous catheter were enrolled prospectively. For each patient a recording session at baseline was performed, repeated if a modification in ventilatory settings occurred. PES, CVP and airway pressure during an end-inspiratory and -expiratory pause were simultaneously recorded; CVP, f-CVP and PES waveforms were analyzed off-line and used to calculate transpulmonary pressure (PLCVP, PLf-CVP, PLPES, respectively). Δf-CVP correlated better than ΔCVP with ΔPES (r = 0.8, p = 0.001 vs. r = 0.08, p = 0.73), with a lower bias in Bland Altman analysis in favor of PLf-CVP (mean bias − 0.16, Limits of Agreement (LoA) -1.31, 0.98 cmH2O vs. mean bias − 0.79, LoA − 3.14, 1.55 cmH2O). Both PLf-CVP and PLCVP correlated well with PLPES (r = 0.98, p < 0.001 vs. r = 0.94, p < 0.001), again with a lower bias in Bland Altman analysis in favor of PLf-CVP (0.15, LoA − 0.95, 1.26 cmH2O vs. 0.80, LoA − 1.51, 3.12, cmH2O). PLf-CVP discriminated high PL value with an area under the receiver operating characteristic curve 0.99 (standard deviation, SD, 0.02) (AUC difference = 0.01 [-0.024; 0.05], p = 0.48). In mechanically ventilated patients with acute respiratory failure, the digital filtered CVP estimated ΔPES and PL obtained from digital filtered CVP represented a reliable value of standard PL measured with the esophageal method and could identify patients with non-protective ventilation settings.

Using central venous pressure waveform to confirm the placement of an internal jugular central venous catheter in the intensive care unit

BackgroundA mobile chest X-ray is traditionally performed to confirm the position of an internal jugular central venous catheter (CVC) after placement in the intensive care unit (ICU). Using chest radiography to confirm CVC position often results in delays in authorising the use of the CVC, requires the deployment of additional human resources, and is costly. ObjectiveThis study aimed to determine the feasibility and accuracy of using the central venous pressure (CVP) waveform to confirm the placement of internal jugular CVCs. MethodsThis retrospective study was conducted in a single quaternary ICU over a 6-month period. We included adult patients who had internal jugular CVC inserted and CVP transduced as part of their routine care in the ICU. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of CVP waveform analysis in confirming the position of internal jugular CVC relative to chest radiography were calculated. ResultsA total of 241 internal jugular CVCs were inserted (in 219 patients, 35.6% female), and the CVP waveform was assessed. In 231 cases, this suggested adequate placement in a central vein, which corresponded with a correct position on subsequent chest X-ray. On six occasions, the CVP waveforms were interpreted as suboptimal; however, on chest X-rays, the CVCs were noted to be in a suitable position (sensitivity: 97.5%). Four suboptimal CVP waveforms were obtained, and they correctly identified CVC malposition on subsequent chest X-ray (specificity: 100%). The average time from CVC insertion to radiological completion was 118 minutes. ConclusionCVP waveform analysis provides a feasible and reliable method for confirming adequate internal jugular CVC position. The use of chest radiography can be limited to cases where suboptimal CVP waveforms are obtained.

Respiratory Variations of Central Venous Pressure as Indices of Pleural Pressure Swings: A Narrative Review

The measurement of pleural (or intrathoracic) pressure is a key element for a proper setting of mechanical ventilator assistance as both under- and over-assistance may cause detrimental effects on both the lungs and the diaphragm. Esophageal pressure (Pes) is the gold standard tool for such measurements; however, it is invasive and seldom used in daily practice, and easier, bedside-available tools that allow for rapid and continuous monitoring are greatly needed. The tidal swing of central venous pressure (CVP) has long been proposed as a surrogate for pleural pressure (Ppl); however, despite the wide availability of central venous catheters, this variable is very often overlooked in critically ill patients. In the present narrative review, the physiological basis for the use of CVP waveforms to estimate Ppl is presented; the findings of previous and recent papers that addressed this topic are systematically reviewed, and the studies are divided into those reporting positive findings (i.e., CVP was found to be a reliable estimate of Pes or Ppl) and those reporting negative findings. Both the strength and pitfalls of this approach are highlighted, and the current knowledge gaps and direction for future research are delineated.

Central venous pressure estimation with force-coupled ultrasound of the internal jugular vein

We estimate central venous pressure (CVP) with force-coupled ultrasound imaging of the internal jugular vein (IJV). We acquire ultrasound images while measuring force applied over the IJV by the ultrasound probe imaging surface. We record collapse force, the force required to completely occlude the vein, in 27 healthy subjects. We find supine collapse force and jugular venous pulsation height (JVP), the clinical noninvasive standard, have a linear correlation coefficient of r2 = 0.89 and an average absolute difference of 0.23 mmHg when estimating CVP. We perturb our estimate negatively by tilting 16 degrees above supine and observe decreases in collapse force for every subject which are predictable from our CVP estimates. We perturb venous pressure positively to values experienced in decompensated heart failure by having subjects perform the Valsalva maneuver while the IJV is being collapsed and observe an increase in collapse force for every subject. Finally, we derive a CVP waveform with an inverse three-dimensional finite element optimization that uses supine collapse force and segmented force-coupled ultrasound data at approximately constant force.

Central Venous Waveform Analysis and Cardiac Output in a Porcine Model of Endotoxemic Hypotension and Resuscitation.

Cardiac output (CO) is a valuable proxy for perfusion, and governs volume responsiveness during resuscitation from distributive shock. The underappreciated venous system has nuanced physiology that confers valuable hemodynamic information. In this investigation, deconvolution of the central venous waveform by the fast Fourier transformation (FFT) algorithm is performed to assess its ability to constitute a CO surrogate in a porcine model of endotoxemia-induced distributive hypotension and resuscitation. Ten pigs were anesthetized, catheterized, and intubated. A lipopolysaccharides infusion protocol was used to precipitate low systemic vascular resistance hypotension. Four crystalloid boluses (10 cc/kg) were then given in succession, after which heart rate, mean arterial pressure, thermodilution-derived CO, central venous pressure (CVP), and the central venous waveform were collected, the last undergoing fast Fourier transformation analysis. The amplitude of the fundamental frequency of the central venous waveform's cardiac wave (f0-CVP) was obtained. Heart rate, mean arterial pressure, CVP, f0-CVP, and CO were plotted over the course of the boluses to determine whether f0-CVP tracked with CO better than the vital signs, or than CVP itself. Distributive hypotension to a 25% mean arterial pressure decrement was achieved, with decreased systemic vascular resistance (mean 918 ± 227 [SD] dyne/s/cm-5 vs 685 ± 180 dyne/s/cm-5; p = 0.038). Full hemodynamic parameters characterizing this model were reported. Slopes of linear regression lines of heart rate, mean arterial pressure, CVP, f0-CVP, and CO were -2.8, 1.7, 1.8, 0.40, and 0.35, respectively, demonstrating that f0-CVP values closely track with CO over the 4-bolus range. Fast Fourier transformation analysis of the central venous waveform may allow real-time assessment of CO during resuscitation from distributive hypotension, possibly offering a venous-based approach to clinical estimation of volume responsiveness.

Central Venous Pressure Waveform Analysis Predicts Perfusion in a Porcine Model of Endotoxemic Hypotension and Resuscitation

Introduction: Cardiac output (CO), a proxy for perfusion, is a critical measure during sepsis and resuscitation. The understudied venous system has nuanced physiology that confers valuable information. In this investigation, deconvolution of the central venous pressure (CVP) waveform by the fast Fourier transformation (FFT) algorithm is performed to derive a CO surrogate in a “sepsis-like” porcine model of endotoxemic hypotension and resuscitation. Methods: Nine pigs were anesthetized, catheterized and intubated. A lipopolysaccharides infusion protocol was employed to precipitate low systemic vascular resistance (SVR) hypotension. Four subsequent crystalloid boluses (10 cc/kg) were given, after which thermodilution-CO, CVP and the CVP waveform were collected, the latter undergoing FFT analysis. The amplitude of the fundamental frequency of CVP waveform’s cardiac wave (“f0-CVP”) was obtained. Mean CVP, f0-CVP and CO were plotted over the course of the boluses to determine whether f0-CVP tracked with CO better than CVP itself. Results: Distributive hypotension to a 25% mean arterial pressure decrement was achieved, with decreased SVR (918±227 [standard deviation] dynes/seconds/cm-5 vs 685±180 dynes/seconds/cm-5; P = 0.038). Over four boluses, slopes of linear regression lines of CVP (r2=0.27; P < 0.001), f0-CVP (r2=0.15; P = 0.01) and CO (r2=0.08; P = 0.06) were 0.447, 0.148 and 0.178, respectively, demonstrating that f0-CVP constitutes a more accurate CO surrogate than CVP. Conclusion: FFT analysis of CVP waveforms offers a novel paradigm to interpret readily obtainable hemodynamic information. It offers potential for real-time utility in assessing volume responsiveness in sepsis and resuscitation, while further offering potential translation to the less-invasive peripheral venous pressure waveform.

Using generalized additive models to decompose time series and waveforms, and dissect heart–lung interaction physiology

Common physiological time series and waveforms are composed of repeating cardiac and respiratory cycles. Often, the cardiac effect is the primary interest, but for, e.g., fluid responsiveness prediction, the respiratory effect on arterial blood pressure also convey important information. In either case, it is relevant to disentangle the two effects. Generalized additive models (GAMs) allow estimating the effect of predictors as nonlinear, smooth functions. These smooth functions can represent the cardiac and respiratory cycles’ effects on a physiological signal. We demonstrate how GAMs allow a decomposition of physiological signals from mechanically ventilated subjects into separate effects of the cardiac and respiratory cycles. Two examples are presented. The first is a model of the respiratory variation in pulse pressure. The second demonstrates how a central venous pressure waveform can be decomposed into a cardiac effect, a respiratory effect and the interaction between the two cycles. Generalized additive models provide an intuitive and flexible approach to modelling the repeating, smooth, patterns common in medical monitoring data.

The use of point-of-care assessments and advanced hemodynamic monitoring in a patient with Eisenmenger syndrome for cesarean section: A case report.

IntroductionEisenmenger syndrome should be diagnosed before pregnancy and surgically corrected if possible. Cesarean section in a patient with Eisenmenger syndrome is high risk as morbidity and mortality are very high. Delivery in hospitals with intensive care units should be recommended. Use of point-of-care assessments and advanced monitors allow accurate management.Case presentationA primigravid with congestive heart failure from a patent ductus arteriosus in Eisenmenger syndrome, presented with threatened preterm labor and uncontrolled hypertension prompting cesarean delivery under epidural anesthesia. Pre-induction focused echocardiography revealed normal right ventricular function and severe pulmonary hypertension. Intraoperatively, hemodynamics became unstable. The decision to use fluids, vasopressor and inotrope was guided by analyses of arterial pulse contour, central venous pressure waveform and blood exams. Hemodynamics improved and a live baby was delivered. Postoperative course was unremarkable.DiscussionThe cause of hemodynamic instability must be accurately determined as inappropriate use of fluid or medication may be detrimental to a patient with Eisenmenger syndrome. In this case, advanced hemodynamic monitoring showed changes in central venous pressure, cardiac output and systemic vascular resistance which differentiated the causes of hypotension and desaturation. Point-of-care blood analysis showed acidosis and hypoxia which may have worsened the right-to-left shunt, contributing to the desaturation. Fluid and drug infusions to address identified problems were then guided by advanced monitors.ConclusionThe use of point-of-care assessments and advanced hemodynamic monitoring allowed accurate diagnoses and goal-directed therapies leading to improved patient safety and outcomes. The need for prolonged intensive care in this case was prevented.

Detection of Junctional Ectopic Tachycardia by Central Venous Pressure.

Central venous pressure (CVP) is the blood pressure in the venae cavae, near the right atrium of the heart. This signal waveform is commonly collected in clinical settings, and yet there has been limited discussion of using this data for detecting arrhythmia and other cardiac events. In this paper, we develop a signal processing and feature engineering pipeline for CVP waveform analysis. Through a case study on pediatric junctional ectopic tachycardia (JET), we show that our extracted CVP features reliably detect JET with comparable results to the more commonly used electrocardiogram (ECG) features. This machine learning pipeline can thus improve the clinical diagnosis and ICU monitoring of arrhythmia. It also corroborates and complements the ECG-based diagnosis, especially when the ECG measurements are unavailable or corrupted.

Assessment of a Non-Invasive Brain Oximeter in a Sheep Model of Acute Brain Injury.

IntroductionEvidence suggests treatments guided by brain oxygen levels improve patient outcomes following severe traumatic brain injury; however, brain oxygen levels are not routinely monitored as an effective non-invasive method has not been established. We undertook a study, in a sheep model of acute brain injury, to assess a new non-invasive brain oximeter. The monitor uses the principles of pulse oximetry to record a pulse and oxygen levels.MethodsWe studied 8 sheep. An acute increase in intracranial pressure was induced with an injection of blood into the cranial vault. The temporal changes in the brain oximeter, intracranial pressure and cerebral perfusion pressure were recorded. Simultaneous conventional skin pulse oximetry was also recorded to assess the possible influence of skin blood flow on the brain oximeter signal.ResultsAt baseline, a pulsatile waveform consistent with the brain circulation was obtained in 7 animals. The baseline brain pulse was quite distinct from the simultaneous conventional skin pulse and similar in shape to a central venous pressure waveform. Injection of blood into the cranial vault triggered an immediate increase in intracranial pressure and fall in cerebral perfusion pressure, by 60-s cerebral perfusion pressure recovered. The brain oximeter oxygen levels demonstrated similar changes with an immediate fall and recovery by 60 s. Periods of high intracranial pressure were also associated with high-frequency oscillations in the brain pulse waveform; there was, however, no change in the conventional skin pulse oximeter pulse waveform.ConclusionThe brain oximeter detected acute changes in both oxygen levels and the brain pulse waveform following an increase in intracranial pressure levels. The brain oximeter could assist clinicians in the management of acute brain injury.

Blood pressure waveform contour analysis for assessing peripheral resistance changes in sepsis

BackgroundThis paper proposes a methodology for helping bridge the gap between the complex waveform information frequently available in an intensive care unit and the simple, lumped values favoured for rapid clinical diagnosis and management. This methodology employs a simple waveform contour analysis approach to compare aortic, femoral and central venous pressure waveforms on a beat-by-beat basis and extract lumped metrics pertaining to the pressure drop and pressure-pulse amplitude attenuation as blood passes through the various sections of systemic circulation.ResultsValidation encompasses a comparison between novel metrics and well-known, analogous clinical metrics such as mean arterial and venous pressures, across an animal model of induced sepsis. The novel metric Ofe → vc, the direct pressure offset between the femoral artery and vena cava, and the clinical metric, ΔMP, the difference between mean arterial and venous pressure, performed well. However, Ofe → vc reduced the optimal average time to sepsis detection after endotoxin infusion from 46.2 min for ΔMP to 11.6 min, for a slight increase in false positive rate from 1.8 to 6.2%. Thus, the novel Ofe → vc provided the best combination of specificity and sensitivity, assuming an equal weighting to both, of the metrics assessed.ConclusionsOverall, the potential of these novel metrics in the detection of diagnostic shifts in physiological behaviour, here driven by sepsis, is demonstrated.

Venous waveform morphological changes associated with treatment of symptomatic venous sinus stenosis

IntroductionVenous outflow obstruction is recognized as a contributing factor in a subset of patients with idiopathic intracranial hypertension (IIH). Little is known about venous sinus waveform morphology or how it...

Correlation of IVC Diameter and Collapsibility Index With Central Venous Pressure in the Assessment of Intravascular Volume in Critically Ill Patients.

ObjectiveThe objective of our study is to assess the correlation between inferior vena cava (IVC) diameters, central venous pressure (CVP) and the IVC collapsibility index for estimating the volume status in critically ill patients.MethodsThis cross-sectional study used the convenient sampling of 100 adult medical intensive care unit (ICU) patients for a period of three months. Patients ≥ 18 years of age with an intrathoracic central venous catheter terminating in the distal superior vena cava connected to the transducer to produce a CVP waveform were included in the study. A Mindray diagnostic ultrasound system model Z6 ultrasound machine (Mindray, NJ, USA) was used for all examinations. An Ultrasonic Transducer model 3C5P (Mindray, NJ, USA) for IVC imaging was utilized. A paired sampled t-test was used to compute the p-values.ResultsA total of 32/100 (32%) females and 68/100 (68%) males were included in the study with a mean age of 50.4 ± 19.3 years. The mean central venous pressure maintained was 10.38 ± 4.14 cmH2O with an inferior vena cava collapsibility index of 30.68 ± 10.93. There was a statistically significant relation among the mean CVP pressure, the IVC collapsibility index, the mean maximum and minimum IVC between groups as determined by one-way analysis of variance (ANOVA) (p < 0.001). There was a strong negative correlation between CVP and IVC collapsibility index (%), which was statistically significant (r = -0.827, n = 100, p < 0.0005). A strong positive correlation between CVP and maximum IVC diameter (r = 0.371, n = 100, p < 0.0005) and minimum IVC diameter (r = 0.572, n = 100, p < 0.0005) was found.ConclusionThere is a positive relationship of CVP with minimum and maximum IVC diameters but an inverse relationship with the IVC collapsibility index.

Randomised controlled trial of central venous catheterisation through external jugular vein: A comparison of success with or without body manoeuvres.

Background and Aims:The external jugular vein (EJV), often used for resuscitation, has been underutilised for central venous catheterisation (CVC) in view of an unpredictable success rate. There is an encouraging literature on the improved success rate of CVC through EJV with the inclusion of certain body manoeuvres. This prospective randomised controlled study was conducted with the aim of evaluating the efficacy of body manoeuvres in improving the success rate of CVC through EJV.Methods:One hundred patients aged 18–50 years, scheduled for elective surgery requiring CVC, were randomly assigned to either undergo CVC using Seldinger technique with body manoeuvres or a control group undergoing CVC without body manoeuvres. The primary outcome was the success rate of CVC, as observed in the post-procedure chest radiograph. Secondary outcomes included quality of central venous pressure waveform, catheterisation attempts, total time for CVC, complications.Results:CVC was achieved in 98% (49/50) of patients in study group and 80% (40/50) of patients in control group (P = 0.008). Mean catheterisation time was significantly lower in the study group (151.06 ± 40.50 s) compared to control group (173.50 ± 50.66 s) (P = 0.023). The incidence of catheter misplacement and failure to cannulate were lower in the study group (0%, 2% vs. 20%, 12.5%, respectively). Groups did not differ in a number of catheterisation attempts and incidence of haematoma.Conclusion:Inclusion of various body manoeuvres to Seldinger technique significantly improves the success rate of CVC through EJV.

Predicting Central Venous Pressure by Measuring Femoral Venous Diameter Using Ultrasonography

ObjectivesThe objective of this exploratory study was to find out the correlation of femoral vein diameter (FVD) to central venous pressure (CVP) measurements and to derive a prediction equation to help ascertain the fluid volume status in a critical patient.Patients and methodsThis was a single-centered prospective cohort study designed and conducted by the critical care department of Shifa International hospital in Islamabad, Pakistan. Patients were enrolled from the medical and surgical intensive care units. The inclusion criteria consisted of patients > 18 years of age, and an intrathoracic central venous catheterization (CVC) in place for producing CVP waveform through the transducer. Patients having contraindications to CVP placement and those unable to lie supine were excluded from the study. Critical Care fellows with sufficient training in performing venous ultrasonography measured the FVD. They were blinded to the CVP values of the same patients.ResultsThe study included 108 patients. Among these 70/108 (64.8%) were males. Mean age was 53.85 (SD=16.74). The CVP and femoral vein diameter were measured in all patients. Mean CVP was 9.89 cmH2O (SD=3.46) and mean femoral vein diameter was 0.92 cm (SD=0.27). Multiple regression was used to generate a prediction model. FVD, age and sex of the patient were used as predictor variables to predict CVP diameter. The model was statistically significant with a p-value of < 0.000 and an F-value of 104.806. R-squared value for this model came out to be 0.744, thus the model was able to explain about 74.4% of the variance in the values observed for CVP. When controlled for age and sex, FVD was found highly correlated with CVP diameter with a p-value of < 0.000. A regression equation was derived that can be used to generate predicted values of CVP in millimeters of mercury with an R-square of 0.745 if FVD in centimeters is provided; CVP (cmH2O) = -0.039 + 10.718* FVD.ConclusionsFVD was found highly correlated to CVP measurements and it suggests an alternate non-invasive method of ascertaining the volume status in the critically ill.

36th International Symposium on Intensive Care and Emergency Medicine : Brussels, Belgium. 15-18 March 2016.

36th International Symposium on Intensive Care and Emergency Medicine : Brussels, Belgium. 15-18 March 2016.

Real-time in-plane ultrasound-guided supraclavicular approach to subclavian vein cannulation in cardiac surgery: An underused approach

BackgroundAlthough the subclavian vein has several anatomical advantages; it has been underused in cardiac surgery. In this feasibility study, the author aimed to test real-time in-plane ultrasound-guided supraclavicular subclavian vein cannulation during elective cardiac surgical procedures as an acceptable alternative for the routinely used internal jugular vein.MethodsThis prospective feasibility study included forty adult patients undergoing elective on-pump cardiac surgical procedures performed during the period from June 2012 to January 2013. The aim of this study is to test real-time in-plane ultrasound-guided supraclavicular subclavian vein cannulation in terms of time of placement, number of attempts to puncture the vein, inadvertent arterial puncture, usability before and after sternal retractor expansion, and catheter tip position.ResultsSuccessful cannulation of the subclavian vein was accomplished in an average time of 43.8 (14.9) s. The median number of skin punctures was 1 (range 1–3). All lumens of the catheters were usable both before and after using the sternal retractor in all cases except one. The central venous pressure waveform has been recorded in all cases except one. Using transesophageal echocardiography all catheter tips were found to lie within 1.6 cm from the crista terminalis.ConclusionReal-time in-plane ultrasound-guided supraclavicular subclavian vein cannulation is an easy and safe approach to be used in adult patients undergoing cardiac surgical procedures.