Quantitative Transthoracic Echocardiography of the Response to Dobutamine in Cardiac Surgery Patients With Low Cardiac Output Syndrome

Zarragoikoetxea, Iratxe; Vicente, R.; Pajares, A.; Carmona, Paula; López, Marta; Moreno, Ignacio Crespo; Argente, P.; Hornero, Fernando; Valera, Francisco; Agüero, J.

doi:10.1053/j.jvca.2019.08.019

Cited by 9 publications

(5 citation statements)

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“…SHAP summary plot revealed that LVEF, Lac, LVDd and MABP < 65 time were the most significant predictors of LCOS, with lower LVEF, higher Lac, larger LVDd and longer MABP < 65 time indicating increased possibility of the prospective onset of LCOS. LVEF is the most widely used estimate of left ventricular systolic function and a decreased LVEF is an independent risk factor for LCOS ( 27 , 28 ). Serum lac is a well-recognized biomarker of tissue perfusion, and elevated lac can serve as a sensitive indicator of LCOS.…”

Section: Discussionmentioning

confidence: 99%

Prediction of low cardiac output syndrome in patients following cardiac surgery using machine learning

Hong

et al. 2022

Front. Med.

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BackgroundThis study aimed to develop machine learning models to predict Low Cardiac Output Syndrome (LCOS) in patients following cardiac surgery using machine learning algorithms.MethodsThe clinical data of cardiac surgery patients in Nanjing First Hospital between June 2019 and November 2020 were retrospectively extracted from the electronic medical records. Six conventional machine learning algorithms, including logistic regression, support vector machine, decision tree, random forest, extreme gradient boosting and light gradient boosting machine, were employed to construct the LCOS predictive models with all predictive features (full models) and selected predictive features (reduced models). The discrimination of these models was evaluated by the area under the receiver operating characteristic curve (AUC) and the calibration of the models was assessed by the calibration curve. Shapley Additive explanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were used to interpret the predictive models.ResultsData from 1,585 patients [982 (62.0%) were male, aged 18 to 88, 212 (13.4%) with LCOS] were employed to train and validate the LCOS models. Among the full models, the RF model (AUC: 0.909, 95% CI: 0.875–0.943; Sensitivity: 0.849, 95% CI: 0.724–0.933; Specificity: 0.835, 95% CI: 0.796–0.869) and the XGB model (AUC: 0.897, 95% CI: 0.859–0.935; Sensitivity: 0.830, 95% CI: 0.702–0.919; Specificity: 0.809, 95% CI: 0.768–0.845) exhibited well predictive power for LCOS. Eleven predictive features including left ventricular ejection fraction (LVEF), first post-operative blood lactate (Lac), left ventricular diastolic diameter (LVDd), cumulative time of mean artery blood pressure (MABP) lower than 65 mmHg (MABP < 65 time), hypertension history, platelets level (PLT), age, blood creatinine (Cr), total area under curve above threshold central venous pressure (CVP) 12 mmHg and 16 mmHg, and blood loss during operation were used to build the reduced models. Among the reduced models, RF model (AUC: 0.895, 95% CI: 0.857–0.933; Sensitivity: 0.830, 95% CI: 0.702–0.919; Specificity: 0.806, 95% CI: 0.765–0.843) revealed the best performance. SHAP and LIME plot showed that LVEF, Lac, LVDd and MABP < 65 time significantly contributed to the prediction model.ConclusionIn this study, we successfully developed several machine learning models to predict LCOS after surgery, which may avail to risk stratification, early detection and management of LCOS after cardiac surgery.

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Section: Discussionmentioning

confidence: 99%

Prediction of low cardiac output syndrome in patients following cardiac surgery using machine learning

Hong

et al. 2022

Front. Med.

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“…2 In this issue of the Journal of Cardiothoracic and Vascular Anesthesia, Zarragoikoetxea et al report on their use of early quantitative TTE to identify patients with low cardiac output syndrome (LCOS) and furthermore to assess these patients' response to inotropic therapy after elective cardiac surgery. 3 This response was determined through TTE-obtained parameters, hemodynamic data, and biologic markers. The authors imaged 55 patients in total, resulting in 38 patients with LCOS and 12 control patients without LCOS; 5 patients were excluded because hypovolemia caused their hemodynamic instability.…”

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confidence: 99%

“…The authors found that the left ventricular outflow tract velocity time integral (LVOT VTI) was the best indicator of LCOS, followed by tissue doppler imaging (TDI) of the right ventricular lateral s' velocity, and the left ventricular E/e' ratio. 3 They recommended a cutoff of 16.6 cm when using LVOT VTI for the diagnosis of LCOS because it provided 100% sensitivity and specificity with a LCOS group mean of 11.75 § 1.97 and control group mean of 19.08 § 2.15. 3 This proposed cutoff should be interpreted with caution because, in isolation, it can be a low-normal variant.…”

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confidence: 99%

“…3 They recommended a cutoff of 16.6 cm when using LVOT VTI for the diagnosis of LCOS because it provided 100% sensitivity and specificity with a LCOS group mean of 11.75 § 1.97 and control group mean of 19.08 § 2.15. 3 This proposed cutoff should be interpreted with caution because, in isolation, it can be a low-normal variant. However, with clinical evidence of end-organ hypoperfusion (hypotension, hypoxia, lactate elevation, or low urine output), it can raise early concern at the bedside as a finding consistent with LCOS.…”

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confidence: 99%

“…Zarragoikoetxea et al noted the following limitations of their study: its observational nature; small patient cohort; heterogenous surgical procedures (isolated coronary artery bypass grafting, isolated valve surgery, and combined coronary artery bypass grafting-valve); and exclusion of nonelective surgery. 3 However, another limitation, which was mentioned briefly, was the lack of data available from a pulmonary artery catheter (PAC). A comparison of TTE parameters with PAC-derived measures of cardiac output, cardiac index, and pulmonary hemodynamics would have added further validation to the use of point-of-care TTE for the diagnosis of LCOS.…”

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confidence: 99%

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