Journal of Clinical Monitoring and Computing最新文献

This study retrospectively examined the hemodynamic effects of passive leg raising (PLR) in mechanically ventilated patients during fluid removal before spontaneous breathing trials. In previous studies, we noticed varying cardiac responses after PLR completion, particularly in positive tests. Using a bioreactance monitor, we recorded and analyzed hemodynamic parameters, including stroke volume and cardiac index (CI), before and after PLR in post-acute ICU patients. We included 27 patients who underwent 60 PLR procedures. In preload-unresponsive patients, no significant CI changes were observed (CI_t-6 = 3.7 [2.6; 4.7] mL/min/m² vs. CI_t9 = 3.3 [2.5; 3.4] mL/min/m²; p = 0.306), while in preload-responsive patients, two distinct CI response types to PLR were identified: a transient peak with immediate return to baseline (CI_t-6 = 2.7 [2.5; 3.1] mL/min/m² vs. 3.3 [2.6; 3.8] L/min/m²; p = 0.119) and a sustained CI elevation lasting beyond the PLR maneuver (CI_t-6 = 2.8 [2.3; 2.9] L/min/m² vs. 3.3 [2.8; 3.9] ml/min/m²; p = 0.034). The latter was particularly noted when ΔCI during PLR exceeded 25%. Our findings suggest that in certain preload-responsive patients, PLR can induce a more sustained increase in CI, indicating a possible persistent hemodynamic effect. This effect could be due to a combination of autotransfusion and sympathetic activation affecting venous return and vascular tone. Further research in larger cohorts and more comprehensive hemodynamic assessments are warranted to validate these observations and elucidate the possible underlying mechanisms.The Fluid unLoading On Weaning (FLOW) study was prospectively registered under the ID NCT04496583 on 2020-07-29 at ClinicalTrials.gov.

Aneurysmal subarachnoid haemorrhage (aSAH) can lead to complications such as acute hydrocephalic congestion. Treatment of this acute condition often includes establishing an external ventricular drainage (EVD). However, chronic hydrocephalus develops in some patients, who then require placement of a permanent ventriculoperitoneal (VP) shunt. The aim of this study was to employ recurrent neural network (RNN)-based machine learning techniques to identify patients who require VP shunt placement at an early stage. This retrospective single-centre study included all patients who were diagnosed with aSAH and treated in the intensive care unit (ICU) between November 2010 and May 2020 (n = 602). More than 120 parameters were analysed, including routine neurocritical care data, vital signs and blood gas analyses. Various machine learning techniques, including RNNs and gradient boosting machines, were evaluated for their ability to predict VP shunt dependency. VP-shunt dependency could be predicted using an RNN after just one day of ICU stay, with an AUC-ROC of 0.77 (CI: 0.75-0.79). The accuracy of the prediction improved after four days of observation (Day 4: AUC-ROC 0.81, CI: 0.79-0.84). At that point, the accuracy of the prediction was 76% (CI: 75.98-83.09%), with a sensitivity of 85% (CI: 83-88%) and a specificity of 74% (CI: 71-78%). RNN-based machine learning has the potential to predict VP shunt dependency on Day 4 after ictus in aSAH patients using routine data collected in the ICU. The use of machine learning may allow early identification of patients with specific therapeutic needs and accelerate the execution of required procedures.

Blood velocities measured by Transcranial Doppler (TCD) are dependent on the angle between the incident ultrasound beam and the direction of blood flow (known as the Doppler angle). However, when TCD examinations are performed without imaging the Doppler angle for each vessel segment is not known. We have measured Doppler angles in the basal cerebral arteries examined with TCD using three-dimensional (3D) vessel models generated from computed tomography angiography (CTA) scans. This approach produces angle statistics that are not accessible during non-imaging TCD studies. We created 3D models of the basal cerebral arteries for 24 vasospasm patients. Standard acoustic windows were mapped to the specific anatomy of each patient. Virtual ultrasound transmit beams were generated that originated from the acoustic window and intersected the centerline of each arterial segment. Doppler angle measurements were calculated and compiled for each vessel segment. Doppler angles were smallest for the middle cerebral artery M1 segment (median 24.6°) and ophthalmic artery (median 25.0°), and largest for the anterior cerebral artery A2 segment (median 76.4°) and posterior cerebral artery P2 segment (median 75.8°). The ophthalmic artery had the highest proportion of Doppler angles that were less than 60° (99%) while the anterior cerebral artery A2 segment had the lowest proportion of Doppler angles that were less than 60° (10%). These angle measurements indicate the expected deviation between measured and true velocities in the cerebral arteries, highlighting specific segments that may be prone to underestimation of velocity.

Haemodynamic monitoring and management are cornerstones of perioperative care. The goal of haemodynamic management is to maintain organ function by ensuring adequate perfusion pressure, blood flow, and oxygen delivery. We here present guidelines on "Intraoperative haemodynamic monitoring and management of adults having non-cardiac surgery" that were prepared by 18 experts on behalf of the German Society of Anaesthesiology and Intensive Care Medicine (Deutsche Gesellschaft für Anästhesiologie und lntensivmedizin; DGAI).

Nitric oxide (NO), a selective pulmonary vasodilator, can be delivered via conventional ICU and anesthesia machine ventilators. Anesthesia machines are designed for rebreathing of circulating gases, reducing volatile anesthetic agent quantity used. Current cylinder- and ionizing-based NO delivery technologies use breathing circuit flow to determine NO delivery and do not account for recirculated gases; therefore, they cannot accurately dose NO at FGF below patient minute ventilation (MV). A novel, cassette-based NO delivery system (GENOSYL^® DS, Vero Biotech Inc.) uses measured NO concentration in the breathing circuit as an input to an advanced feedback control algorithm, providing accurate NO delivery regardless of FGF and recirculation of gases. This study evaluated GENOSYL^® DS accuracy with different anesthesia machines, ventilation parameters, FGFs, and volatile anesthetics. GENOSYL^® DS was tested with GE Aisys and Dräger Fabius anesthesia machines to determine NO dose accuracy with FGF < patient MV, and with a Getinge Flow-i anesthesia machine to determine NO dose accuracy when delivering various volatile anesthetic agents. Neonatal and adult mechanical ventilation parameters and circuits were used. GENOSYL® DS maintained accurate NO delivery with all three anesthesia machines, at low FGF with recirculation of gases, and with all volatile anesthetic agents at different concentrations. Measured NO₂ levels remained acceptable at ≤ 1 ppm with set NO dose ≤ 40 ppm. GENOSYL^® DS, with its advanced feedback control algorithm, is the only NO delivery system capable of accurately dosing NO with anesthesia machines with rebreathing ventilation parameters (FGF < MV) regardless of anesthetic agent.

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.

General Anaesthesia (GA) is accompanied by a marked decrease in sympathetic outflow and thus loss of vasomotor control of cardiac preload. The use of vasoconstriction during GA has mainly focused on maintaining blood pressure. Phenylephrine (PE) is a pure α1-agonist without inotropic effects widely used to correct intraoperative hypotension. The potential of PE for augmenting cardiac stroke volume (SV) and -output (CO) by venous recruitment is controversial and no human studies have explored the effects of PE in preload dependent circulation using indicator dilution technique. We hypothesized that PE-infusion in patients with cardiac stroke volume limited by reduced preload would restore preload and thus augment SV and CO. 20 patients undergoing GA for gastrointestinal surgery were monitored with arterial catheter and LiDCO unity monitor. Upon stable haemodynamics after induction patients were placed in head-up tilt (HUT). All patients became preload responsive as verified by a stroke volume variation (SVV) of > 12%. PE-infusion was then started at 15-20mikrg/min and adjusted until preload was restored (SVV < 12%). Li-dilution cardiac output (CO) was initially measured after induction (baseline), again with HUT in the preload responsive phase, and finally when preload was restored with infusion of PE.At baseline SVV was 10 ± 3% (mean ± st.dev.), CI was 2,6 ± 0,4 L/min*m², and SVI 43 ± 7mL/m². With HUT SVV was 19 ± 4%, CI was 2,2 ± 0,4 L/min*m², SVI 35 ± 7mL/m². During PE-infusion SVV was reduced to 6 ± 3%, CI increased to 2,6 ± 0,5 L/min*m², and SVI increased to 49 ± 11mL/m². All differences p < 0,001. In conclusion: Infusion of phenylephrine during preload dependency increased venous return abolishing preload dependency as evaluated by SVV and increased cardiac stroke volume and -output as measured by indicator-dilution technique. (ClinicalTrials.gov NCT05193097).

Purpose: This study introduces a method to non-invasively and automatically quantify respiratory muscle effort (P_mus) during mechanical ventilation (MV). The methodology hinges on numerically solving the respiratory system's equation of motion, utilizing measurements of airway pressure (P_aw) and airflow (F_aw). To evaluate the technique's effectiveness, P_mus was correlated with expected physiological responses. In volume-control (VC) mode, where tidal volume (V_T) is pre-determined, P_mus is expected to be linked to P_aw fluctuations. In contrast, during pressure-control (PC) mode, where P_aw is held constant, P_mus should correlate with V_T variations.

Methods: The study utilized data from 250 patients on invasive MV. The data included detailed recordings of P_aw and F_aw, sampled at 31.25 Hz and saved in 131.1-second epochs, each covering 34 to 41 breaths. The algorithm identified 51,268 epochs containing breaths on either VC or PC mode exclusively. In these epochs, P_mus and its pressure-time product (P_musPTP) were computed and correlated with P_aw's pressure-time product (P_awPTP) and V_T, respectively.

Results: There was a strong correlation of P_musPTP with P_awPTP in VC mode (R² = 0.91 [0.76, 0.96]; n = 17,648 epochs) and with V_T in PC mode (R² = 0.88 [0.74, 0.94]; n = 33,620 epochs), confirming the hypothesis. As expected, negligible correlations were observed between P_musPTP and V_T in VC mode (R² = 0.03) and between P_musPTP and P_awPTP in PC mode (R² = 0.06).

Conclusion: The study supports the feasibility of assessing respiratory effort during MV non-invasively through airway signal analysis. Further research is warranted to validate this method and investigate its clinical applications.