Simon Tilma Vistisen

Ultrasound of the inferior vena cava does not predict hemodynamic response to early hemorrhage.

BACKGROUND Ultrasonographic evaluation of the inferior vena cava (IVC) provides information on central hemodynamics and predicts fluid responsiveness during positive pressure ventilation. In spontaneously breathing patients, the correlations between IVC dynamics and the hemodynamic response to volume shifts remain to be described. OBJECTIVES We aimed to describe the correlation between IVC dynamics and the changes in cardiac output (CO) caused by controlled hemorrhage. METHODS Healthy donors from the blood bank were eligible for inclusion. Measurements of the IVC and CO were performed before and immediately after blood donation using ultrasound methods. A control group served to evaluate the effect of resting. RESULTS Thirty-seven participants completed the study. IVC collapsibility index (IVC-CI) and IVC end expiratory diameter (IVCe) both changed significantly after blood donation (p < 0.001). The baseline IVC-CI and IVCe did not correlate with the change in CO (p-values ≥ 0.40). The alterations in IVC-CI and IVCe induced by blood donation also did not correlate with the change in CO (p ≥ 0.71). The sensitivities of IVC-CI or IVCe, defined as an increase in IVC-CI and a decrease in IVCe, for picking up any decrease in CO were 81.3% and 84.4%, respectively. In the control group, no effect was seen between measurements. CONCLUSION IVC-CI and IVCe did not correlate with the magnitude of hemodynamic response to early hemorrhage. The sensitivity of serial IVC measurements was approximately 80% for detecting early blood loss.

Using an expiratory resistor, arterial pulse pressure variations predict fluid responsiveness during spontaneous breathing: an experimental porcine study

IntroductionFluid responsiveness prediction is difficult in spontaneously breathing patients. Because the swings in intrathoracic pressure are minor during spontaneous breathing, dynamic parameters like pulse pressure variation (PPV) and systolic pressure variation (SPV) are usually small. We hypothesized that during spontaneous breathing, inspiratory and/or expiratory resistors could induce high arterial pressure variations at hypovolemia and low variations at normovolemia and hypervolemia. Furthermore, we hypothesized that SPV and PPV could predict fluid responsiveness under these conditions.MethodsEight prone, anesthetized and spontaneously breathing pigs (20 to 25 kg) were subjected to a sequence of 30% hypovolemia, normovolemia, and 20% and 40% hypervolemia. At each volemic level, the pigs breathed in a randomized order either through an inspiratory and/or an expiratory threshold resistor (7.5 cmH2O) or only through the tracheal tube without any resistor. Hemodynamic and respiratory variables were measured during the breathing modes. Fluid responsiveness was defined as a 15% increase in stroke volume (ΔSV) following fluid loading.ResultsStroke volume was significantly lower at hypovolemia compared with normovolemia, but no differences were found between normovolemia and 20% or 40% hypervolemia. Compared with breathing through no resistor, SPV was magnified by all resistors at hypovolemia whereas there were no changes at normovolemia and hypervolemia. PPV was magnified by the inspiratory resistor and the combined inspiratory and expiratory resistor. Regression analysis of SPV or PPV versus ΔSV showed the highest R2 (0.83 for SPV and 0.52 for PPV) when the expiratory resistor was applied. The corresponding sensitivity and specificity for prediction of fluid responsiveness were 100% and 100%, respectively, for SPV and 100% and 81%, respectively, for PPV.ConclusionsInspiratory and/or expiratory threshold resistors magnified SPV and PPV in spontaneously breathing pigs during hypovolemia. Using the expiratory resistor SPV and PPV predicted fluid responsiveness with good sensitivity and specificity.

Should dynamic parameters for prediction of fluid responsiveness be indexed to the tidal volume

Background: The respiratory variation in the pre‐ejection period (ΔPEP) has been used to predict fluid responsiveness in mechanically ventilated patients. Recently, we modified this parameter (PEPV) and showed that it was a reliable predictor for post‐cardiac surgery, mainly paced, patients when moderately low tidal volumes were used. One of the modifications involved tidal volume indexation, which had not been proposed before for dynamic parameters. The aim of the present animal study was to investigate whether indexation to tidal volume should be part of a new definition of dynamic parameters such as the case for our newly proposed PEPV.

Automated pre-ejection period variation indexed to tidal volume predicts fluid responsiveness after cardiac surgery

Background: Reliable continuous monitoring of fluid responsiveness is an unsolved issue in patients ventilated with low tidal volume. We hypothesised that variations in the pre‐ejection period (PEP) defined as the time interval between electrocardiogram (ECG) R‐wave and onset of systolic upstroke in arterial blood pressure could reliably predict fluid responsiveness in patients ventilated with moderately low tidal volume. Furthermore, we hypothesised that indexing dynamic parameters to tidal volume would improve their prediction. The aim was to refine and automate a previously suggested algorithm for PEP variation (ΔPEP) and to test this new parameter indexed to tidal volume (PEPV), as a marker of fluid responsiveness along with central venous pressure (CVP), pulse pressure variation (PPV) and ΔPEP. Additionally, the aim was to evaluate the concept of indexing dynamic parameters to tidal volume.

Automated pre-ejection period variation predicts fluid responsiveness in low tidal volume ventilated pigs.

Introduction: The respiratory variation in the pre‐ejection period (ΔPEP) has been used to predict fluid responsiveness in mechanically ventilated patients. Recently, we automated this parameter and indexed it to tidal volume (PEPV) and showed that it was a reliable predictor for post‐cardiac surgery, mainly paced, patients ventilated with low tidal volumes. The aims of the present animal study were to investigate PEPVs ability to predict fluid responsiveness under different fluid loading conditions and natural heart rates during low tidal volume ventilation (6 ml/kg) and to compare the performance of PEPV with other markers of fluid responsiveness.

Heart rate variability in neurorehabilitation patients with severe acquired brain injury.

Abstract Introduction: Acquired brain injury (ABI) cause neural deficits. In addition to motor and cognitive deficits, the autonomic nervous system may be affected. This has been shown for neurorehabilitation patients with traumatic brain injury (TBI) by means of reduced heart rate variability (HRV). It was hypothesized that patient groups with other ABI aetiology (mainly stroke, subarachnoid haemorrhage and anoxia) would also present reduced HRV. Methods: Patients consecutively admitted and severely ABI injured were considered for HRV measurements. HRV was extracted as a mean of four 5-minute ECG recordings at 6 pm, 10 pm, 2 am and 6 am the following day (scheduled resting periods). One 5-minute HRV recording from a sex- and age-matched group of healthy volunteers constituted control data. Standard deviation of normal-to-normal intervals (SDNN) and low frequency (LF) were primary HRV variables. Results: Of 71 admitted patients, HRV was extracted from 49 patients. Patient SDNN and LF were reduced compared to controls (SDNN: 13 ms (CI = [10.8; 15.3]) vs 40.3 ms (CI = [36.6; 44.2]), p < 0.0001; LF: 29.4 ms2 (CI = [19.8; 43.7]) vs 518 ms2 (CI = [419; 639]), p < 0.0001). HRV appeared identical across ABI aetiology. Conclusion: It was found that HRV was considerably reduced in an heterogenic ABI patient group admitted for neurorehabilitation.

Association between the sensory-motor nervous system and the autonomic nervous system in neurorehabilitation patients with severe acquired brain injury.

Abstract Introduction: The relation between motor and cognitive function and autonomic nervous system (ANS) function during neurorehabilitation following acquired brain injury (ABI) has only been investigated sporadically. In the present study, it was hypothesized that clinical measures in severely injured patients would relate to heart rate variability (HRV), a measure of autonomic function. Methods: HRV measurements were initially performed on 49 patients (enrolled in a previous study) and follow-up (> 28 days) HRV measurements were performed. Standard deviation of normal-to-normal intervals (SDNN) and low frequency (LF) were extracted and these HRV variables were related to the clinical measures, Early Functional Ability (EFA) and Functional Independence Measure (FIM). Associations between HRV and clinical measures were analysed on admission data (only EFA), at follow-up and for the longitudinal change in measures. Results: Follow-up HRV was extracted from 19 patients. SDNN and LF were significantly correlated (p < 0.05) to the EFA and FIM at follow-up, but not at admission. SDNN and LF changes were significantly correlated to EFA changes, but not FIM changes. Admission SDNN and LF were unable to provide prognostic information for the EFA and FIM at follow-up. Conclusion: HRV and its change during neurorehabilitation were associated to EFA and EFA changes over time. Further studies are required to clarify a number of limitations arising from this observational study.

Procedural Aspects and Physiologic Mechanisms of the Deep Inspiratory Maneuver

Sir, we have read the paper by S. Preau et al. “Hemodynamic changes during a deep inspiration maneuver predict fluid responsiveness in spontaneously breathing patients” with great interest. Based on the fundamental ideas of dynamic hemodynamic monitoring, inducing cardiac preload variations and monitoring corresponding pulse pressure variations [1], the paper presents a novel technique of varying cardiac preload in spontaneously breathing (SB) patients that does not necessitate specialised equipment apart from routine clinical monitoring. The paper is indeed relevant because it is, to our knowledge, the first to present the feasibility of a deep breathing method (deep inspiratory maneuver, DIM) for inducing preload changes. For the purpose of better understanding the presented technique, we wish to pose the following questions for the authors. Concerning compliance and applicability in terms of intention-to-treat: it is not clear which proportion of SB patients with acute circulatory failure was able to comply with the DIM. Can compliance be expected to be as high as 87% (26 of 30) for this generally defined patient group? Concerning the calculations: DIM-induced changes are defined by the authors as.   DIM-induced changes = (maximal value during DIM − minimal value during quiet SB prior to DIM)/((maximal value during DIM − minimal value during quiet SB prior to DIM)/2). How large was the time window when estimating the minimal value during quiet SB prior to DIM? Did the time window vary or was it fixed? The maximal value during DIM was found in either phase 2 or phase 4 of the DIM. In which phase was the maximal value typically found? Were the maximal values during the two phases generally of the same magnitude? Instructing the patients to perform the DIM: this is important should the technique be validated by other study groups and in turn gain widespread use. Did the authors in any way evaluate if the slow continuous inspiration strain was associated with a more or less constant inspiratory flow or was most air generally inspired in the first phase of inspiration, perhaps amplifying maximal value during DIM phase 2? How were instructions given on inspiratory flow rate? Did you make any instructions to the patients (directly or indirectly) what tidal volume should be during DIM? Did they inspire to, for example, maximal or convenient lung volume? Concerning the physiological mechanisms: did you experience any patients with significant heart rate variability (HRV)? The respiratory part of HRV (with its subsequent varying passive filling time of the ventricles) could be another mechanism inducing preload variations and as such it could confound results obtained with the DIM technique. On the other hand, HRV is known to be significantly reduced during sepsis [2]. We also have a few comments and suggestions to the proposed physiological mechanisms. The decrease in left ventricular stroke volume during DIM phase 1 is suggested to be induced by increasing ventricular afterload. However, we think that another and perhaps more important mechanism is the reduced intrathoracic pressure reducing left ventricular preload during the initial phase of inspiration—until the increasing right ventricular stroke volume with the delay of pulmonary transit time again increases left ventricular preload substantially. In line with this suggestion, but as a “reversed mechanism,” we also believe that the increasing intrathoracic pressure during passive expiration (DIM phase 4) contributes somewhat to the increasing left ventricular (preload and) stroke volume in DIM phase 4 in addition to the suggested overall increase in intrathoracic blood volume. As a last comment, we would like to suggest that the DIM phases 2 and 4 could potentially disclose right ventricular failure: a large maximal value during DIM phase 2 relies on biventricular preload responsiveness whereas a large value in DIM phase 4 relies mostly, if not fully, on left ventricular preload responsiveness. Even though the authors did not encounter any cases of preload unresponsiveness associated with high ΔPPdim (specificity was 100%) low phase 2 values associated with high phase 4 values could theoretically disclose right ventricular failure—even when taking interventricular dependence into account.

Gastrointestinal transit time and heart rate variability in patients with mild acquired brain injury

Background Constipation is suspected to occur frequently after acquired brain injury (ABI). In patients with ABI, heart rate variability (HRV) is reduced suggesting autonomic dysfunction. Autonomic dysfunction may be associated with prolonged gastrointestinal transit time (GITT). The primary aim of this study was to investigate if GITT is prolonged in patients with ABI. Secondarily, HRV and its correlation with GITT was investigated. Methods We included 25 patients with ABI (18 men, median age: 61.3 years, range [30.7–74.5]). GITT was assessed using radio-opaque markers and HRV was calculated from 24-hour electrocardiograms. Medical records were reviewed for important covariates, including primary diagnosis, time since injury, functional independence measure, and use of medication. The GITT assessed in patients was compared to a control group of 25 healthy subjects (18 men, median age: 61.5 years, range [34.0–70.9]). Results In ABI patients, the mean GITT was significantly longer than in healthy controls (2.68 days, 95% CI [2.16–3.19] versus (1.92 days, 95% CI [1.62–2.22], p = 0.011)). No correlation was found between HRV and GITT. Conclusion Patients with mild to moderate ABI have prolonged GITT unrelated to the HRV.

Prevalence and Temporal Distribution of Extrasystoles in Septic ICU Patients: The Feasibility of Predicting Fluid Responsiveness Using Extrasystoles

Background Extrasystoles may be useful for predicting the response to fluid therapy in hemodynamically unstable patients but their prevalence is unknown. The aim of this study was to estimate the availability of extrasystoles in intensive care unit patients diagnosed with sepsis. The study aim was not to validate the fluid responsiveness prediction ability of extrasystoles. Methods Twenty-four-hour ECG recordings from a convenience sample of 50 patients diagnosed with sepsis were extracted from the MIMIC-II waveform database, and ECGs were visually examined for correct QRS complex detection. Custom-made algorithms identified potential extrasystoles based on RR intervals. Two raters visually confirmed or rejected the potential extrasystoles and then classified them as ventricular, supraventricular, or unknown origin. Extrasystole availability was calculated as extrasystolic coverage for each 24 h ECG recording, that is, the percentage of the 24 h recording where an extrasystole had occurred in the preceding 30 minutes. Results Mean extrasystolic coverage was 53.3% (confidence interval: [42.8; 63.6]%) and ventricular extrasystolic coverage was 21.4 [13.5; 29.8]%. Interrater reliability was strong for confirming/rejecting extrasystoles. Conclusions Extrasystoles are available for fluid responsiveness prediction in septic patients in about half of the time. With this extrasystolic availability, we believe the method to be considered for clinical use, provided that future studies validate the methods fluid responsiveness prediction ability.