Literature DB >> 33733173

A Proxy for Detecting IUGR Based on Gestational Age Estimation in a Guatemalan Rural Population.

Camilo E Valderrama1, Faezeh Marzbanrad2, Rachel Hall-Clifford3, Peter Rohloff4,5, Gari D Clifford1,6.   

Abstract

In-utero progress of fetal development is normally assessed through manual measurements taken from ultrasound images, requiring relatively expensive equipment and well-trained personnel. Such monitoring is therefore unavailable in low- and middle-income countries (LMICs), where most of the perinatal mortality and morbidity exists. The work presented here attempts to identify a proxy for IUGR, which is a significant contributor to perinatal death in LMICs, by determining gestational age (GA) from data derived from simple-to-use, low-cost one-dimensional Doppler ultrasound (1D-DUS) and blood pressure devices. A total of 114 paired 1D-DUS recordings and maternal blood pressure recordings were selected, based on previously described signal quality measures. The average length of 1D-DUS recording was 10.43 ± 1.41 min. The min/median/max systolic and diastolic maternal blood pressures were 79/102/121 and 50.5/63.5/78.5 mmHg, respectively. GA was estimated using features derived from the 1D-DUS and maternal blood pressure using a support vector regression (SVR) approach and GA based on the last menstrual period as a reference target. A total of 50 trials of 5-fold cross-validation were performed for feature selection. The final SVR model was retrained on the training data and then tested on a held-out set comprising 28 normal weight and 25 low birth weight (LBW) newborns. The mean absolute GA error with respect to the last menstrual period was found to be 0.72 and 1.01 months for the normal and LBW newborns, respectively. The mean error in the GA estimate was shown to be negatively correlated with the birth weight. Thus, if the estimated GA is lower than the (remembered) GA calculated from last menstruation, then this could be interpreted as a potential sign of IUGR associated with LBW, and referral and intervention may be necessary. The assessment system may, therefore, have an immediate impact if coupled with suitable intervention, such as nutritional supplementation. However, a prospective clinical trial is required to show the efficacy of such a metric in the detection of IUGR and the impact of the intervention.
Copyright © 2020 Valderrama, Marzbanrad, Hall-Clifford, Rohloff and Clifford.

Entities:  

Keywords:  fetal heart rate (FHR); gestational age estimation; intra-uterine growth restriction (IUGR); low-and middle-income countries (LMICs); maternal blood pressure; one-dimension Doppler ultrasound (1D-DUS); signal processing; supervised machine learning

Year:  2020        PMID: 33733173      PMCID: PMC7861337          DOI: 10.3389/frai.2020.00056

Source DB:  PubMed          Journal:  Front Artif Intell        ISSN: 2624-8212


  58 in total

1.  Linear and nonlinear parameters for the analysis of fetal heart rate signal from cardiotocographic recordings.

Authors:  Maria G Signorini; Giovanni Magenes; Sergio Cerutti; Domenico Arduini
Journal:  IEEE Trans Biomed Eng       Date:  2003-03       Impact factor: 4.538

2.  Influence of gestational age, heart rate, gender and time of day on fetal heart rate variability.

Authors:  S Lange; P Van Leeuwen; D Geue; W Hatzmann; D Grönemeyer
Journal:  Med Biol Eng Comput       Date:  2005-07       Impact factor: 2.602

3.  Changes in fractal features of fetal heart rate during pregnancy.

Authors:  Akihiko Kikuchi; Nobuya Unno; Tsuguhiro Horikoshi; Toshiyuki Shimizu; Shiro Kozuma; Yuji Taketani
Journal:  Early Hum Dev       Date:  2005-08       Impact factor: 2.079

4.  Antepartum high-frequency fetal heart rate sinusoidal rhythm: computerized detection and fetal anemia.

Authors:  Aparna Reddy; Mary Moulden; Christopher W G Redman
Journal:  Am J Obstet Gynecol       Date:  2008-12-27       Impact factor: 8.661

5.  Last menstrual period provides the best estimate of gestation length for women in rural Guatemala.

Authors:  Lynnette M Neufeld; Jere D Haas; Ruben Grajéda; Reynaldo Martorell
Journal:  Paediatr Perinat Epidemiol       Date:  2006-07       Impact factor: 3.980

6.  New computerized fetal heart rate analysis for surveillance of intrauterine growth restriction.

Authors:  E A Huhn; S Lobmaier; T Fischer; R Schneider; A Bauer; K T Schneider; G Schmidt
Journal:  Prenat Diagn       Date:  2011-02-28       Impact factor: 3.050

7.  Estimating birth weight from observed postnatal weights in a Guatemalan highland community.

Authors:  Camilo E Valderrama; Faezeh Marzbanrad; Michel Juarez; Rachel Hall-Clifford; Peter Rohloff; Gari D Clifford
Journal:  Physiol Meas       Date:  2020-03-06       Impact factor: 2.833

8.  Integrating machine learning techniques and physiology based heart rate features for antepartum fetal monitoring.

Authors:  Maria G Signorini; Nicolò Pini; Alberto Malovini; Riccardo Bellazzi; Giovanni Magenes
Journal:  Comput Methods Programs Biomed       Date:  2019-10-17       Impact factor: 5.428

9.  Comparison of diurnal variations, gestational age and gender related differences in fetal heart rate (FHR) parameters between appropriate-for-gestational-age (AGA) and small-for-gestational-age (SGA) fetuses in the home environment.

Authors:  Habiba Kapaya; Richard Jacques; Dilly Anumba
Journal:  PLoS One       Date:  2018-03-09       Impact factor: 3.240

10.  Template-based Quality Assessment of the Doppler Ultrasound Signal for Fetal Monitoring.

Authors:  Camilo E Valderrama; Faezeh Marzbanrad; Lisa Stroux; Gari D Clifford
Journal:  Front Physiol       Date:  2017-07-18       Impact factor: 4.566

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  1 in total

Review 1.  A review of fetal cardiac monitoring, with a focus on low- and middle-income countries.

Authors:  Camilo E Valderrama; Nasim Ketabi; Faezeh Marzbanrad; Peter Rohloff; Gari D Clifford
Journal:  Physiol Meas       Date:  2020-12-18       Impact factor: 2.688

  1 in total

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