Swan, Ganz, and Their Catheter: Its Evolution Over the Past Half Century

Thakkar, Anjali B.; Desai, Sukumar P.

doi:10.7326/m17-2145

Cited by 23 publications

(8 citation statements)

References 40 publications

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“…Chronic HF was diagnosed according to the HF guidelines. 12,13 Inclusion criteria for patients in this study: (1) the presence of symptoms and/or signs of HF; (2) N-terminal pro-B-type natriuretic peptide (NT-proBNP) > 125 pg/ml; (3) abnormal findings of echocardiography. Patients who had revascularization or acute coronary syndrome within 7 days prior, had acute pulmonary embolism, had large pericardial effusion or constrictive pericarditis leading to cardiac tamponade, had septic shock, had hypertensive crisis, were receiving mechanical circulatory support or were acutely decompensated were excluded.…”

Section: Study Populationmentioning

confidence: 99%

“…Right heart catheterization (RHC), as an integral aspect of the diagnosis and management of HF, provides clinicians with important hemodynamic profiles. 1 Among various hemodynamic parameters measured by RHC, cardiac output (CO) is the most popular one used to characterize the clinical phenotype of patients with cardiovascular destabilization. However, attempts to predict the prognosis of patients with HF using CO have always been controversial, as many analyses demonstrated that resting CO had less prognostic utility and was not associated with outcomes.…”

Section: Introductionmentioning

confidence: 99%

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Afterload-related cardiac performance is a powerful hemodynamic predictor of mortality in patients with chronic heart failure

Tian

Liang

et al. 2023

Therapeutic Advances in Chronic Disease

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Background: Afterload-related cardiac performance (ACP), a diagnostic parameter for septic cardiomyopathy, integrates both cardiac performance and vascular effects and could predict prognosis in septic shock. Objectives: We hypothesized that ACP would also correlate with clinical outcomes in patients with chronic heart failure (HF). Design: A retrospective study. Methods: We retrospectively studied consecutive patients with chronic HF who underwent right heart catheterization and established an expected cardiac output-systemic vascular resistance (CO-SVR) curve model in chronic HF for the first time. ACP was calculated as COmeasured/COpredicted × 100%. ACP > 80%, 60% < ACP ⩽ 80%, and ACP ⩽ 60% represented less impaired, mildly impaired, and severely impaired cardiovascular function, respectively. The primary outcome was all-cause mortality, and the secondary outcome was event-free survival. Results: A total of 965 individual measurements from 290 eligible patients were used to establish the expected CO-SVR curve model (COpredicted = 53.468 × SVR −0.799). Patients with ACP ⩽ 60% had higher serum NT-proBNP levels ( P < 0.001), lower left ventricular ejection fraction ( P = 0.001), and required dopamine more frequently ( P < 0.001). Complete follow-up data were available in 263 of 290 patients (90.7%). After multivariate adjustment, ACP remained associated with both primary outcome (hazard ratio (HR) 0.956, 95% confidence interval (CI) 0.927–0.987) and secondary outcome (HR 0.977, 95% CI 0.963–0.992). Patients with ACP ⩽ 60% had the worst prognosis (all P < 0.001). ACP was significantly more discriminating (area under the curve of 0.770) than other conventional hemodynamic parameters in predicting mortality (Delong test, all P < 0.05). Conclusion: ACP is a powerful independent hemodynamic predictor of mortality in patients with chronic HF. ACP and the novel CO-SVR two-dimensional graph could be useful in assessing cardiovascular function and making clinical decisions. Clinical trial registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT02664818.

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Section: Study Populationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Afterload-related cardiac performance is a powerful hemodynamic predictor of mortality in patients with chronic heart failure

Tian

Liang

et al. 2023

Therapeutic Advances in Chronic Disease

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“…The most widely accepted standard for monitoring CO is the Swan-Ganz catheter. However, the operation is not only complicated but can also induce severe complications (3). Because of advanced perioperative bedside ultrasound techniques, screening an indicator with good predictive performance, robust results, and easy operation procedure to predict the volume responsiveness and guide fluid therapy has become an urgent requirement for clinicians.…”

Section: Introductionmentioning

confidence: 99%

Value of Corrected Flow Time in Common Carotid Artery in Predicting Volume Responsiveness Under Mechanical Ventilation

Wang

Chen

Cheng

et al. 2022

Shock

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The present study aimed to investigate whether corrected flow time (FTc) in common carotid artery could predict volume responsiveness under mechanical ventilation and to further explore whether the sensitivity and specificity would be influenced by positive end-expiratory pressure (PEEP). Methods: The first stage of this study included 80 patients from the general surgery department undergoing laparotomy. After induction of general anesthesia, FTc in the common carotid artery was measured when hemodynamic indicators, such as blood pressure, heart rate, and cardiac output (CO), were stabilized. Then, 7 mg/kg (ideal body weight) of hydroxyethyl starch 130/0.4 sodium chloride was rapidly infused from the peripheral venous system. The infusion was completed within 15 minutes, and hemodynamic indicators were measured again immediately to evaluate volume responsiveness. The patients with change rate of CO (ΔCO ≥15%) were categorized into the responsive (R) group, whereas those with ΔCO <15% were categorized into the nonresponsive group (NR) group. In the second stage, 29 patients undergoing laparotomy were included. After induction of general anesthesia, PEEP of 0, 5, and 10 cmH 2 O was applied sequentially. Corrected flow time and hemodynamic indicators were recorded. Then, 7 mg/g of hydroxyethyl starch 130/0.4 sodium chloride was rapidly infused for 15 minutes, after which PEEP of 0, 5, and 10 cmH 2 O was applied sequentially, and the indicators were measured again. The patients with FTc equal to or less than the threshold in the first stage were categorized into the R group, otherwise into the NR group. Results: In the first stage of the study, CO and FTc differed significantly between the 2 groups, before and after volume load ( P < 0.05). Mean arterial pressure in the R group was significantly different, whereas heart rate did not differ before and after fluid infusion. Also, heart rate and mean arterial pressure were not significantly different before and after fluid infusion in the NR group. The area under the receiver operating characteristic curve was 0.786 ± 0.056 (95% confidence interval, 0.676-0.896; P = 0.00) for FTc before infusing volume load for predicting volume responsiveness. In the second stage of the study, PEEP did not have significant effects on FTc (F 2, 56 = 1.930, P = 0.155), whereas volume load had statistically significant effects on FTc (F 1, 28 ) = 9.381, P < 0.05). Moreover, FTc differed significantly different before and after fluid infusion (P < 0.05). The area under the receiver operating characteristic curve for FTc in predicting volume responsiveness was 0.921, 0.805, and 0.719 when PEEP was 0, 5, and 10 cmH 2 O (P < 0.05), respectively, and the cutoff value of FTc for diagnosing volume responsiveness was 323.42 milliseconds, 326.69 milliseconds, and 312.03 milliseconds, respectively. Conclusion: Corrected flow time in the common carotid artery can predict volume responsiveness under mechanical ventilation, and the predictive performance is not influenced by PEEP. Clinical Trial Regi...

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“…For more than half a century, physicians caring for critically ill patients have sought to tailor intravenous (IV) fluid resuscitation to patients' physiologic states 1 . Routine use of invasive hemodynamic monitoring by pulmonary artery catheterization fell from favor in the early 2000s when high‐quality trial data demonstrated increased patient risk without improved outcomes 2 .…”

mentioning

confidence: 99%

Development of a Deep Learning Network to Classify Inferior Vena Cava Collapse to Predict Fluid Responsiveness

Blaivas

Philips

et al. 2020

J of Ultrasound Medicine

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Objectives-To create a deep learning algorithm capable of video classification, using a long short-term memory (LSTM) network, to analyze collapsibility of the inferior vena cava (IVC) to predict fluid responsiveness in critically ill patients.Methods-We used a data set of IVC ultrasound (US) videos to train the LSTM network. The data set was created from IVC US videos of spontaneously breathing critically ill patients undergoing intravenous fluid resuscitation as part of 2 prior prospective studies. We randomly selected 90% of the IVC videos to train the LSTM network and 10% of the videos to test the LSTM network's ability to predict fluid responsiveness. Fluid responsiveness was defined as a greater than 10% increase in the cardiac index after a 500-mL fluid bolus, as measured by bioreactance.Results-We analyzed 211 videos from 175 critically ill patients: 191 to train the LSTM network and 20 to test it. Using standard data augmentation techniques, we increased our sample size from 191 to 3820 videos. Of the 175 patients, 91 (52%) were fluid responders. The LSTM network was able to predict fluid responsiveness moderately well, with an area under the receiver operating characteristic curve of 0.70 (95% confidence interval [CI], 0.43-1.00), a positive likelihood ratio of infinity, and a negative likelihood ratio of 0.3 (95% CI, 0.12-0.77). In comparison, point-of-care US experts using video review offline and manual diameter measurement via software caliper tools achieved an area under the receiver operating characteristic curve of 0.94 (95% CI, 0.83-0.99).Conclusions-We demonstrated that an LSTM network can be trained by using videos of IVC US to classify IVC collapse to predict fluid responsiveness. Our LSTM network performed moderately well given the small training cohort but worse than point-of-care US experts. Further training and testing of the LSTM network with a larger data sets is warranted.

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Swan, Ganz, and Their Catheter: Its Evolution Over the Past Half Century

Cited by 23 publications

References 40 publications

Afterload-related cardiac performance is a powerful hemodynamic predictor of mortality in patients with chronic heart failure

Afterload-related cardiac performance is a powerful hemodynamic predictor of mortality in patients with chronic heart failure

Value of Corrected Flow Time in Common Carotid Artery in Predicting Volume Responsiveness Under Mechanical Ventilation

Development of a Deep Learning Network to Classify Inferior Vena Cava Collapse to Predict Fluid Responsiveness

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