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

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.

A quantitative cross-sectional analysis of the melanin index in the skin of preterm newborns and its association with gestational age at birth.

BACKGROUND: Estimation of gestational age (GA) is important to make timely decisions and provide appropriate neonatal care. Clinical maturity scales to estimate GA have used skin texture and color to assess maturity at birth facing situations of the uncertainty of pregnancy dating. The size and darkness of the areola around the nipple to grade skin characteristics are based on visual appearance. The melanin index (M-Index) is an optical skin parameter related to the melanin content in the tissue. This study is aimed to associate the M-Index of the skin with the GA. METHODS: A cross-sectional study evaluated 80 newborns at birth. A photometer device quantified the skin pigmentation on the areolae, forearms, and soles. Paired average differences of M-Index were compared among the three body sites. The skin M-Indexes were compared between subgroups of newborns until 34 weeks or with 34 and more. RESULTS: The skin over the areola had the highest values of M-Index compared with the forearm or sole areas (P < .001 for both). Infants with a GA between 34 and <37 weeks had higher M-Index values over the areola than the group with a GA with 24 to <34 weeks: 41.7 (8.9) and 38.3 (10.5) median (IQR), P = .005. CONCLUSIONS: The measurable M-Index values have the potential to improve physical evaluation in assessing GA at birth.

A Mobile Serious Game About the Pandemic (COVID-19 - Did You Know?): Design and Evaluation Study.

BACKGROUND: No treatment for COVID-19 is yet available; therefore, providing access to information about SARS-CoV-2, the transmission route of the virus, and ways to prevent the spread of infection is a critical sanitary measure worldwide. Serious games have advantages in the dissemination of reliable information during the pandemic; they can provide qualified content while offering interactivity to the user, and they have great reach over the internet. OBJECTIVE: This study aimed to develop a serious game with the purpose of providing science-based information on the prevention of COVID-19 and personal care during the pandemic while assessing players' knowledge about COVID-19-related topics. METHODS: The study was conducted with the interdisciplinary collaboration of specialists in health sciences, computing, and design at the Federal University of Minas Gerais, Brazil. The health recommendations were grouped into six thematic blocks, presented in a quiz format. The software languages were based on the progressive web app development methodology with the Ionic framework, JavaScript, HTML5, cascading style sheets, and TypeScript (Angular). Open data reports of how users interact with the serious game were obtained using the Google Analytics application programming interface. The visual identity, logo, infographics, and icons were carefully developed by considering a selection of colors, typography, sounds, and images that are suitable for young audiences. Cards with cartoon characters were introduced at the end of each thematic topic to interact with the player, reinforcing their correct answers or alerting them to the need to learn more about the disease. The players' performance was assessed by the rate of incorrect and correct answers and analyzed by linear correlation coefficient over 7 weeks. The agile SCRUM development methodology enabled quick and daily interactions of developers through a webchat and sequential team meetings. RESULTS: The game "COVID-19-Did You Know?" was made available for free on a public university website on April 1, 2020. The game had been accessed 17,571 times as of September 2020. Dissemination actions such as reports on social media and television showed a temporal correspondence with the access number. The players' error rate in the topic "Mask" showed a negative trend (r=-.83; P=.01) over the weeks of follow-up. The other topics showed no significant trend over the weeks. CONCLUSIONS: The gamification strategy for health education content on the theme of COVID-19 reached a young audience, which is considered to be a priority in the strategy of orientation toward social distancing. Specific educational reinforcement measures were proposed and implemented based on the players' performance. The improvement in the users' performance on the topic about the use of masks may reflect an increase in information about or adherence to mask use over time.