Noninvasive Cardiac Output Monitoring Using Electrical Cardiometry and Outcomes in Critically Ill Children.
Lydia SumbelMuthiah R AnnamalaiAanchal WatsMohammed SalamehArpit AgarwalUtpal S BhalalaPublished in: Journal of pediatric intensive care (2020)
Cardiac output (CO) measurement is an important element of hemodynamic assessment in critically ill children and existing methods are difficult and/or inaccurate. There is insufficient literature regarding CO as measured by noninvasive electrical cardiometry (EC) as a predictor of outcomes in critically ill children. We conducted a retrospective chart review in children <21 years, admitted to our pediatric intensive care unit (PICU) between July 2018 and November 2018 with acute respiratory failure and/or shock and who were monitored with EC (ICON monitor). We collected demographic information, data on CO measurements with EC and with transthoracic echocardiography (TTE), and data on ventilator days, PICU and hospital days, inotrope score, and mortality. We analyzed the data using Chi-square and multiple linear regression analysis. Among 327 recordings of CO as measured by EC in 61 critically ill children, the initial, nadir, and median CO (L/min; median [interquartile range (IQR)]) were 3.4 (1.15, 5.6), 2.39 (0.63, 4.4), and 2.74 (1.03, 5.2), respectively. Low CO as measured with EC did not correlate well with TTE ( p = 0.9). Both nadir and mean CO predicted ventilator days ( p = 0.05 and 0.01, respectively), and nadir CO was correlated with peak inotrope score (correlation coefficient of -0.3). In our cohort of critically ill children with respiratory failure and/or shock, CO measured with EC did not correlate with TTE. Both nadir and median CO measured with EC predicted outcomes in critically ill children.
Keyphrases
- respiratory failure
- young adults
- intensive care unit
- mechanical ventilation
- systematic review
- extracorporeal membrane oxygenation
- left ventricular
- acute respiratory distress syndrome
- emergency department
- electronic health record
- cardiovascular disease
- type diabetes
- magnetic resonance
- skeletal muscle
- metabolic syndrome
- machine learning
- coronary artery disease
- data analysis
- social media
- glycemic control
- deep learning