ABSTRACT
RESUMEN Introducción: la preeclampsia (PE) es la principal causa de morbimortalidad materno-fetal en nuestro país. Alteraciones hemodinámicas precoces durante el embarazo podrían predecir la evolución a PE. El machine learning (ML) permite el hallazgo de patrones ocultos que podrían detectar precozmente el desarrollo de PE. Objetivos: desarrollar un árbol de clasificación con variables de hemodinamia no invasiva para predecir precozmente desarrollo de PE. Material y métodos: estudio observacional prospectivo con embarazadas de alto riesgo (n=1155) derivadas del servicio de Obstetricia desde enero 2016 a octubre 2022 para el muestreo de entrenamiento por ML con árbol de clasificación j48. Se seleccionaron 112 embarazadas entre semanas 10 a 16, sin tratamiento farmacológico y que completaron el seguimiento con el término de su embarazo con evento final combinado (PE): preeclampsia, eclampsia y síndrome HELLP. Se evaluaron simultáneamente con cardiografía de impedancia y velocidad de onda del pulso y con monitoreo ambulatorio de presión arterial de 24 hs (MAPA). Resultados: presentaron PE 17 pacientes (15,18%). Se generó un árbol de clasificación predictivo con las siguientes variables: índice de complacencia arterial (ICA), índice cardíaco (IC), índice de trabajo sistólico (ITS), cociente de tiempos eyectivos (CTE), índice de Heather (IH). Se clasificaron correctamente el 93,75%; coeficiente Kappa 0,70, valor predictivo positivo (VPP) 0,94 y negativo (VPN) 0,35. Precisión 0,94, área bajo la curva ROC 0,93. Conclusión: las variables ICA, IC, ITS, CTE e IH predijeron en nuestra muestra el desarrollo de PE con excelente discriminación y precisión, de forma precoz, no invasiva, segura y con bajo costo.
ABSTRACT Background: Preeclampsia (PE) is the main cause of maternal-fetal morbidity and mortality in our country. Early hemodynamic changes during pregnancy could predict progression to PE. Machine learning (ML) enables the discovery of hidden patterns that could early detect PE development. Objectives: The aim of this study was to build a classification tree with non-invasive hemodynamic variables for the early prediction of PE occurrence. Results: Seventeen patients (15.18%) presented PE. A predictive classification tree was generated with arterial compliance index (ACI), cardiac index (CI), cardiac work index (CWI), ejective time ratio (ETR), and Heather index (HI). A total of 93.75% patients were correctly classified (Kappa 0.70, positive predictive value 0.94 and negative predictive value 0.35; accuracy 0.94, and area under the ROC curve 0.93). Conclusion: ACI, CI, CWI, ETR and HI variables predicted the early development of PE in our sample with excellent discrimination and accuracy, non-invasively, safely and at low cost.
ABSTRACT
BACKGROUND: The interplay between cardiac function and the arterial system is currently defined as ventricular-arterial coupling (VAC) and it is an expression of global cardiovascular efficiency. VAC involves a variety of complex interactions between the heart and the vasculature. A basic index of VAC is the ratio of effective arterial elastance (Ea)/ end-systolic elastance (Ees). While this is often done with echocardiography, obtaining Ea/Ees using impedance cardiography is feasible, although this possibility has not been explored so far. OBJECTIVE: The aim of this study was to compare the Ea/Ees values obtained using echocardiography and impedance cardiography. APPROACH: Two independent operators estimated Ea/Ees in 91 (41 ± 14 years old, women 51%) untreated apparently healthy individuals using (1) Doppler echocardiography with the single-beat method developed by Chen et al (2001 J. Am. Coll. Cardiol. 38 2028-34); and (2) data provided by impedance cardiography. The differences between Ea/Ees values were compared and correlation between both methods was estimated. MAIN RESULTS: Although Ea and Ees values calculated by impedance cardiography were lower than those estimated by echocardiography (-0.201 ± 0.457 mmHg ml-1 and -0.193 ± 0.413 mmHg ml-1), Ea/Ees ratio values were similar. Thus, there was no significant difference between the mean values of Ea/Ees estimated by impedance cardiography or echocardiography (Ea/Ees impedance cardiography - Ea/Ees echocardiography = -0.015 ± 0.096, pâ = 0.150). Ea/Ees values calculated by both methods were highly correlated (r = 0.85, pâ < 0.001), as well as the pre-ejection and left ventricular ejection time (r = 0.83 and r = 0.91, respectively). SIGNIFICANCE: In healthy individuals, estimation of Ea/Ees by impedance cardiography yielded similar values to those obtained using echocardiography.
