TY  - JOUR
AU  - Reis, Zilma Silveira Nogueira
AU  - Romanelli, Roberta Maia de Castro
AU  - Guimarães, Rodney Nascimento
AU  - Gaspar, Juliano de Souza
AU  - Neves, Gabriela Silveira
AU  - do Vale, Marynea Silva
AU  - Nader, Paulo de Jesus
AU  - de Moura, Martha David Rocha
AU  - Vitral, Gabriela Luíza Nogueira
AU  - dos Reis, Marconi Augusto Aguiar
AU  - Pereira, Marcia Margarida Mendonça
AU  - Marques, Patrícia Franco
AU  - Nader, Silvana Salgado
AU  - Harff, Augusta Luize
AU  - Beleza, Ludmylla de Oliveira
AU  - de Castro, Maria Eduarda Canellas
AU  - Souza, Rayner Guilherme
AU  - Pappa, Gisele Lobo
AU  - de Aguiar, Regina Amélia Pessoa Lopes
PY  - 2022
DA  - 2022/9/7
TI  - Newborn Skin Maturity Medical Device Validation for Gestational Age Prediction: Clinical Trial
JO  - J Med Internet Res
SP  - e38727
VL  - 24
IS  - 9
KW  - gestational age
KW  - prematurity
KW  - childbirth
KW  - skin physiological phenomena
KW  - machine learning
KW  - equipment and supplies
KW  - pregnancy
KW  - reproductive health
KW  - pregnant
KW  - skin
KW  - age
KW  - medical
KW  - device
KW  - newborn
KW  - baby
KW  - trimester
KW  - therapy
KW  - learning model
KW  - ultrasound
AB  - Background: Early access to antenatal care and high-cost technologies for pregnancy dating challenge early neonatal risk assessment at birth in resource-constrained settings. To overcome the absence or inaccuracy of postnatal gestational age (GA), we developed a new medical device to assess GA based on the photobiological properties of newborns’ skin and predictive models. Objective: This study aims to validate a device that uses the photobiological model of skin maturity adjusted to the clinical data to detect GA and establish its accuracy in discriminating preterm newborns. Methods: A multicenter, single-blinded, and single-arm intention-to-diagnosis clinical trial evaluated the accuracy of a novel device for the detection of GA and preterm newborns. The first-trimester ultrasound, a second comparator ultrasound, and data regarding the last menstrual period (LMP) from antenatal reports were used as references for GA at birth. The new test for validation was performed using a portable multiband reflectance photometer device that assessed the skin maturity of newborns and used machine learning models to predict GA, adjusted for birth weight and antenatal corticosteroid therapy exposure. Results: The study group comprised 702 pregnant women who gave birth to 781 newborns, of which 366 (46.9%) were preterm newborns. As the primary outcome, the GA as predicted by the new test was in line with the reference GA that was calculated by using the intraclass correlation coefficient (0.969, 95% CI 0.964-0.973). The paired difference between predicted and reference GAs was −1.34 days, with Bland-Altman limits of −21.2 to 18.4 days. As a secondary outcome, the new test achieved 66.6% (95% CI 62.9%-70.1%) agreement with the reference GA within an error of 1 week. This agreement was similar to that of comparator-LMP-GAs (64.1%, 95% CI 60.7%-67.5%). The discrimination between preterm and term newborns via the device had a similar area under the receiver operating characteristic curve (0.970, 95% CI 0.959-0.981) compared with that for comparator-LMP-GAs (0.957, 95% CI 0.941-0.974). In newborns with absent or unreliable LMPs (n=451), the intent-to-discriminate analysis showed correct preterm versus term classifications with the new test, which achieved an accuracy of 89.6% (95% CI 86.4%-92.2%), while the accuracy for comparator-LMP-GA was 69.6% (95% CI 65.3%-73.7%). Conclusions: The assessment of newborn’s skin maturity (adjusted by learning models) promises accurate pregnancy dating at birth, even without the antenatal ultrasound reference. Thus, the novel device could add value to the set of clinical parameters that direct the delivery of neonatal care in birth scenarios where GA is unknown or unreliable. International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2018-027442 
SN  - 1438-8871
UR  - https://www.jmir.org/2022/9/e38727
UR  - https://doi.org/10.2196/38727
UR  - http://www.ncbi.nlm.nih.gov/pubmed/36069805
DO  - 10.2196/38727
ID  - info:doi/10.2196/38727
ER  -