Newborn Skin Maturity Medical Device Validation for Gestational Age Prediction: Clinical Trial
BackgroundEarly 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 dev...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2022-09-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2022/9/e38727 |
| _version_ | 1797734822383190016 |
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| author | Zilma Silveira Nogueira Reis Roberta Maia de Castro Romanelli Rodney Nascimento Guimarães Juliano de Souza Gaspar Gabriela Silveira Neves Marynea Silva do Vale Paulo de Jesus Nader Martha David Rocha de Moura Gabriela Luíza Nogueira Vitral Marconi Augusto Aguiar dos Reis Marcia Margarida Mendonça Pereira Patrícia Franco Marques Silvana Salgado Nader Augusta Luize Harff Ludmylla de Oliveira Beleza Maria Eduarda Canellas de Castro Rayner Guilherme Souza Gisele Lobo Pappa Regina Amélia Pessoa Lopes de Aguiar |
| author_facet | Zilma Silveira Nogueira Reis Roberta Maia de Castro Romanelli Rodney Nascimento Guimarães Juliano de Souza Gaspar Gabriela Silveira Neves Marynea Silva do Vale Paulo de Jesus Nader Martha David Rocha de Moura Gabriela Luíza Nogueira Vitral Marconi Augusto Aguiar dos Reis Marcia Margarida Mendonça Pereira Patrícia Franco Marques Silvana Salgado Nader Augusta Luize Harff Ludmylla de Oliveira Beleza Maria Eduarda Canellas de Castro Rayner Guilherme Souza Gisele Lobo Pappa Regina Amélia Pessoa Lopes de Aguiar |
| author_sort | Zilma Silveira Nogueira Reis |
| collection | DOAJ |
| description |
BackgroundEarly 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.
ObjectiveThis 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.
MethodsA 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.
ResultsThe 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%).
ConclusionsThe 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 |
| first_indexed | 2024-03-12T12:49:01Z |
| format | Article |
| id | doaj.art-eb42a2c0b0074b589c1f233c89df109a |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-03-12T12:49:01Z |
| publishDate | 2022-09-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-eb42a2c0b0074b589c1f233c89df109a2023-08-28T23:02:04ZengJMIR PublicationsJournal of Medical Internet Research1438-88712022-09-01249e3872710.2196/38727Newborn Skin Maturity Medical Device Validation for Gestational Age Prediction: Clinical TrialZilma Silveira Nogueira Reishttps://orcid.org/0000-0001-6374-9295Roberta Maia de Castro Romanellihttps://orcid.org/0000-0002-1660-0751Rodney Nascimento Guimarãeshttps://orcid.org/0000-0003-4522-0878Juliano de Souza Gasparhttps://orcid.org/0000-0003-0670-9021Gabriela Silveira Neveshttps://orcid.org/0000-0001-6765-2968Marynea Silva do Valehttps://orcid.org/0000-0002-1544-5470Paulo de Jesus Naderhttps://orcid.org/0000-0002-6670-3724Martha David Rocha de Mourahttps://orcid.org/0000-0002-7851-375XGabriela Luíza Nogueira Vitralhttps://orcid.org/0000-0001-7306-8776Marconi Augusto Aguiar dos Reishttps://orcid.org/0000-0001-5294-1361Marcia Margarida Mendonça Pereirahttps://orcid.org/0000-0002-2699-2963Patrícia Franco Marqueshttps://orcid.org/0000-0001-6812-6687Silvana Salgado Naderhttps://orcid.org/0000-0001-9360-6201Augusta Luize Harffhttps://orcid.org/0000-0003-3220-1643Ludmylla de Oliveira Belezahttps://orcid.org/0000-0001-9975-562XMaria Eduarda Canellas de Castrohttps://orcid.org/0000-0001-8727-8328Rayner Guilherme Souzahttps://orcid.org/0000-0003-2440-2242Gisele Lobo Pappahttps://orcid.org/0000-0002-0349-4494Regina Amélia Pessoa Lopes de Aguiarhttps://orcid.org/0000-0003-2470-3539 BackgroundEarly 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. ObjectiveThis 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. MethodsA 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. ResultsThe 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%). ConclusionsThe 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-2018-027442https://www.jmir.org/2022/9/e38727 |
| spellingShingle | Zilma Silveira Nogueira Reis Roberta Maia de Castro Romanelli Rodney Nascimento Guimarães Juliano de Souza Gaspar Gabriela Silveira Neves Marynea Silva do Vale Paulo de Jesus Nader Martha David Rocha de Moura Gabriela Luíza Nogueira Vitral Marconi Augusto Aguiar dos Reis Marcia Margarida Mendonça Pereira Patrícia Franco Marques Silvana Salgado Nader Augusta Luize Harff Ludmylla de Oliveira Beleza Maria Eduarda Canellas de Castro Rayner Guilherme Souza Gisele Lobo Pappa Regina Amélia Pessoa Lopes de Aguiar Newborn Skin Maturity Medical Device Validation for Gestational Age Prediction: Clinical Trial Journal of Medical Internet Research |
| title | Newborn Skin Maturity Medical Device Validation for Gestational Age Prediction: Clinical Trial |
| title_full | Newborn Skin Maturity Medical Device Validation for Gestational Age Prediction: Clinical Trial |
| title_fullStr | Newborn Skin Maturity Medical Device Validation for Gestational Age Prediction: Clinical Trial |
| title_full_unstemmed | Newborn Skin Maturity Medical Device Validation for Gestational Age Prediction: Clinical Trial |
| title_short | Newborn Skin Maturity Medical Device Validation for Gestational Age Prediction: Clinical Trial |
| title_sort | newborn skin maturity medical device validation for gestational age prediction clinical trial |
| url | https://www.jmir.org/2022/9/e38727 |
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