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Reis ZSN, Romanelli RMDC, Guimarães RN, Gaspar JDS, Neves GS, do Vale MS, Nader PDJ, de Moura MDR, Vitral GLN, Dos Reis MAA, Pereira MMM, Marques PF, Nader SS, Harff AL, Beleza LDO, de Castro MEC, Souza RG, Pappa GL, de Aguiar RAPL. Newborn Skin Maturity Medical Device Validation for Gestational Age Prediction: Clinical Trial. J Med Internet Res 2022; 24:e38727. [PMID: 36069805 PMCID: PMC9494223 DOI: 10.2196/38727] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 07/04/2022] [Accepted: 07/25/2022] [Indexed: 11/26/2022] Open
Abstract
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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Affiliation(s)
- Zilma Silveira Nogueira Reis
- Health Informatics Center, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.,Department of Gynecology and Obstetrics, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.,Center for Artificial Intelligence, Innovation and Health, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | | | - Juliano de Souza Gaspar
- Department of Gynecology and Obstetrics, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Marynea Silva do Vale
- Maternal and Child Unit, University Hospital, Universidade Federal do Maranhão, São Luis, Brazil
| | | | | | | | | | | | - Patrícia Franco Marques
- Maternal and Child Unit, University Hospital, Universidade Federal do Maranhão, São Luis, Brazil
| | | | - Augusta Luize Harff
- University Hospital of Canoas, Universidade Luterana do Brasil, Canoas, Brazil
| | | | | | - Rayner Guilherme Souza
- Department of Gynecology and Obstetrics, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Gisele Lobo Pappa
- Center for Artificial Intelligence, Innovation and Health, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.,Computer Science Department, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
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