Premature or Small for Gestational Age Discrimination: International Multicenter Trial Protocol for Classification of the Low-Birth-Weight Newborn Through the Optical Properties of the Skin

Reis, Zilma Silveira Nogueira; Vitral, Gabriela Luiza Nogueira; Guimarães, R.; Gaspar, Juliano de Souza; Colosimo, Enrico A.; Taunde, Sergio; Mussagy, Nilza; Santos, Rita Rosado; Ayres-de-Campos, Diogo; Romanelli, Roberta Maia de Castro

doi:10.2196/16477

Cited by 9 publications

(16 citation statements)

References 30 publications

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“…In this combined study covering the enhancement of the prediction model for postnatal GA and validation of the device, we believe that the application of k-fold cross-validation with the use of machine learning algorithms provided accurate predictions [ 33 ]. While large data samples are unavailable, the process of training and testing was able to estimate the performance of algorithms until we have finished other ongoing clinical trials for external validation [ 34 ]. Furthermore, the quantification of uncertainty intervals regarding the predicted GA (calculated in days) and comparisons with established references allowed the simulation of realistic scenarios for application.…”

Section: Discussionmentioning

confidence: 99%

“…In this respect, this regressor variable could imply a bias toward preterm newborn detection. The aforementioned ongoing study for external validation of the algorithms could further elucidate this issue because the enrollment process of newborns introduced the Mozambican birth scenario, where, unfortunately, ACTFM is not guaranteed for every woman at risk of preterm birth [ 34 ]. Furthermore, the accuracy is unknown for newborns with diseases that alter skin structure, which is an exclusion criterion in this study.…”

Section: Discussionmentioning

confidence: 99%

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Newborn Skin Maturity Medical Device Validation for Gestational Age Prediction: Clinical Trial

et al. 2022

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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

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Newborn Skin Maturity Medical Device Validation for Gestational Age Prediction: Clinical Trial

et al. 2022

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“…24 While large data samples are unavailable, the process of training and testing are able to estimate the performance of algorithms until we have nished other ongoing clinical trials for external validation. 25 Furthermore, the quanti cation of uncertainty intervals regarding the predicted gestational age (calculated in days) and comparisons with established references allowed the simulation of real scenarios for application. Furthermore, the con dence intervals accompanying AUROC's accuracy contributed to revealing the forecast's limits as to discriminating term from preterm newborns at different cutoff points, with clinical relevance.…”

Section: Discussionmentioning

confidence: 99%

“…The aforementioned ongoing study for external validation of the algorithms could further elucidate this issue because the enrollment process of newborns introduced the Mozambican birth scenario, where unfortunately ACTFM is not guaranteed for every woman at risk of preterm birth. 25 Birth weight is a known estimator of risks to newborns. As part of primary routines in childbirth settings, this information has practical applicability, even in facilities with scarce high-cost technologies.…”

Section: Discussionmentioning

confidence: 99%

Newborn skin maturity models for gestational age prediction

Reis

Guimarães

Romanelli

et al. 2021

Preprint

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A multicenter clinical trial evaluated the accuracy of a novel device to detect preterm newborns. A portable multiband reflectance photometric device assessed 781 newborns’ skin maturity and used machine learning models to predict reference gestational age, adjusting it to birth weight and antenatal corticosteroid therapy exposition. The day difference between the reference and the test had a median of -1.4 (IQR: -2.1). Using established methods such as comparator ultrasound and last menstrual period (LMP), the medians were 0 (IQR: 4) and 0.01 (IQR: 4), respectively. For prematurity discrimination, the area under the receiver operating characteristic curve (AUROC) was 0.986 (95% CI: 0.977 to 0.994). In newborns with absent or unreliable LMP, the intent-to-discriminate analysis showed that the test generated correct classifications 95.8% of the time. The assessment of the newborn's skin maturity adjusted by learning models promises accurate pregnancy dating at birth without the use of antenatal ultrasound reference.

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“…Considering 7 days of error range, this device correctly detected 98.7% of gestational ages, preprint report of the author [10.21203/rs.3.rs-1,216,628/v2]. A new ongoing study to evaluate the device's ability to detect prematurity or small for gestational age, or both conditions simultaneously and predict short-term pulmonary complications in a cohort of low-birthweight newborns has been conducted [24].…”

Section: Noninvasive Assessment Of Skinmentioning

confidence: 98%

New Technologies to Dating Pregnancy at Birth

Reis¹,

Neves²,

Romanelli³

2023

Current Challenges in Childbirth

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The chapter provides a vision of new methods of pregnancy dating at birth to overcome the high costs of existing approaches or lack of access to the existing technologies. The authors have presented a literature review on recent scientific reports exploring novel technologies, such as those based on the newborn’s skin maturity assessment as machine learning models based on clinical data. The effortlessness of new approaches based on simplified clinical systems contrasting with molecular genetics and newborns screening analytes is discussed, even in scientific validation. Without the intention of an exhaustive or systematic review, we searched databases for reports concerning postnatal gestational age, prediction or estimate, novel approach, low and medium-income countries since 2015. Therefore, the authors did not compromise to offer a comprehensive picture of all postnatal gestational age methods. On the other hand, prematurity identification at birth remains a challenge in many birth settings, mainly in a scenario with scarce resources. Although postnatal pregnancy dating methods have strengths and disadvantages, this information is critical to recognize the risk of the newborn during the first hours of life, justifying technological investments.

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Premature or Small for Gestational Age Discrimination: International Multicenter Trial Protocol for Classification of the Low-Birth-Weight Newborn Through the Optical Properties of the Skin

Cited by 9 publications

References 30 publications

Newborn Skin Maturity Medical Device Validation for Gestational Age Prediction: Clinical Trial

Newborn Skin Maturity Medical Device Validation for Gestational Age Prediction: Clinical Trial

Newborn skin maturity models for gestational age prediction

New Technologies to Dating Pregnancy at Birth

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