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.

Gestational age assessed by optical skin reflection in low-birth-weight newborns: Applications in classification at birth.

Introduction: A new medical device was previously developed to estimate gestational age (GA) at birth by processing a machine learning algorithm on the light scatter signal acquired on the newborn's skin. The study aims to validate GA calculated by the new device (test), comparing the result with the best available GA in newborns with low birth weight (LBW). Methods: We conducted a multicenter, non-randomized, and single-blinded clinical trial in three urban referral centers for perinatal care in Brazil and Mozambique. LBW newborns with a GA over 24 weeks and weighing between 500 and 2,500 g were recruited in the first 24 h of life. All pregnancies had a GA calculated by obstetric ultrasound before 24 weeks or by reliable last menstrual period (LMP). The primary endpoint was the agreement between the GA calculated by the new device (test) and the best available clinical GA, with 95% confidence limits. In addition, we assessed the accuracy of using the test in the classification of preterm and SGA. Prematurity was childbirth before 37 gestational weeks. The growth standard curve was Intergrowth-21st, with the 10th percentile being the limit for classifying SGA. Results: Among 305 evaluated newborns, 234 (76.7%) were premature, and 139 (45.6%) were SGA. The intraclass correlation coefficient between GA by the test and reference GA was 0.829 (95% CI: 0.785-0.863). However, the new device (test) underestimated the reference GA by an average of 2.8 days (95% limits of agreement: -40.6 to 31.2 days). Its use in classifying preterm or term newborns revealed an accuracy of 78.4% (95% CI: 73.3-81.6), with high sensitivity (96.2%; 95% CI: 92.8-98.2). The accuracy of classifying SGA newborns using GA calculated by the test was 62.3% (95% CI: 56.6-67.8). Discussion: The new device (test) was able to assess GA at birth in LBW newborns, with a high agreement with the best available GA as a reference. The GA estimated by the device (test), when used to classify newborns on the first day of life, was useful in identifying premature infants but not when applied to identify SGA infants, considering current algohrithm. Nonetheless, the new device (test) has the potential to provide important information in places where the GA is unknown or inaccurate.

Prematurity detection evaluating interaction between the skin of the newborn and light: protocol for the preemie-test multicentre clinical trial in Brazilian hospitals to validate a new medical device.

INTRODUCTION: Recognising prematurity is critical in order to attend to immediate needs in childbirth settings, guiding the extent of medical care provided for newborns. A new medical device has been developed to carry out the preemie-test, an innovative approach to estimate gestational age (GA), based on the photobiological properties of the newborn's skin. First, this study will validate the preemie-test for GA estimation at birth and its accuracy to detect prematurity. Second, the study intends to associate the infant's skin reflectance with lung maturity, as well as evaluate safety, precision and usability of a new medical device to offer a suitable product for health professionals during childbirth and in neonatal care settings. METHODS AND ANALYSIS: Research protocol for diagnosis, single-group, single-blinding and single-arm multicenter clinical trial with a reference standard. Alive newborns, with 24 weeks or more of pregnancy age, will be enrolled during the first 24 hours of life. Sample size is 787 subjects. The primary outcome is the difference between the GA calculated by the photobiological neonatal skin assessment methodology and the GA calculated by the comparator antenatal ultrasound or reliable last menstrual period (LMP). Immediate complications caused by pulmonary immaturity during the first 72 hours of life will be associated with skin reflectance in a nested case-control study. ETHICS AND DISSEMINATION: Each local independent ethics review board approved the trial protocol. The authors intend to share the minimal anonymised dataset necessary to replicate study findings. TRIAL REGISTRATION NUMBER: RBR-3f5bm5.

Skin thickness as a potential marker of gestational age at birth despite different fetal growth profiles: A feasibility study.

BACKGROUND: New methodologies to estimate gestational age (GA) at birth are demanded to face the limited access to obstetric ultrasonography and imprecision of postnatal scores. The study analyzed the correlation between neonatal skin thickness and pregnancy duration. Secondarily, it investigated the influence of fetal growth profiles on tissue layer dimensions. METHODS AND FINDINGS: In a feasibility study, 222 infants selected at a term-to-preterm ratio of 1:1 were assessed. Reliable information on GA was based on the early ultrasonography-based reference. The thicknesses of the epidermal and dermal skin layers were examined using high-frequency ultrasonography. We scanned the skin over the forearm and foot plantar surface of the newborns. A multivariate regression model was adjusted to determine the correlation of GA with skin layer dimensions. The best model to correlate skin thickness with GA was fitted using the epidermal layer on the forearm site, adjusted to cofactors, as follows: Gestational age (weeks) = -28.0 + 12.8 Ln (Thickness) - 4.4 Incubator staying; R2 = 0.604 (P<0.001). In this model, the constant value for the standard of fetal growth was statistically null. The dermal layer thickness on the forearm and plantar surfaces had a negative moderate linear correlation with GA (R = -0.370, P<0.001 and R = -0.421, P<0.001, respectively). The univariate statistical analyses revealed the influence of underweight and overweight profiles on neonatal skin thickness at birth. Of the 222 infants, 53 (23.9%) had inappropriate fetal growths expected for their GA. Epidermal thickness was not fetal growth standard dependent as follows: 172.2 (19.8) µm for adequate for GA, 171.4 (20.6) µm for SGA, and 177.7 (15.2) µm for LGA (P = 0.525, mean [SD] on the forearm). CONCLUSIONS: The analysis highlights a new opportunity to relate GA at birth to neonatal skin layer thickness. As this parameter was not influenced by the standard of fetal growth, skin maturity can contribute to clinical applications.

Newborn skin reflection: Proof of concept for a new approach for predicting gestational age at birth. A cross-sectional study.

BACKGROUND: Current methods to assess the gestational age during prenatal care or at birth are a global challenge. Disadvantages, such as low accessibility, high costs, and imprecision of clinical tests and ultrasonography measurements, may compromise health decisions at birth, based on the gestational age. Newborns' organs and tissues can indirectly indicate their physical maturity, and we hypothesized that evolutionary changes in their skin, detected using an optoelectronic device meter, may aid in estimating the gestational age. This study analyzed the feasibility of using newborn skin reflectance to estimate the gestational age at birth noninvasively. METHODS AND FINDINGS: A cross-sectional study evaluated the skin reflectance of selected infants, preferably premature, at birth. The first-trimester ultrasound was the reference for gestational age. A prototype of a new noninvasive optoelectronic device measured the backscattering of light from the skin, using a light emitting diode at wavelengths of 470 nm, 575 nm, and 630 nm. Univariate and multivariate regression analysis models were employed to predict gestational age, combining skin reflectance with clinical variables for gestational age estimation. The gestational age at birth of 115 newborns from 24.1 to 41.8 weeks of gestation correlated with the light at 630 nm wavelength reflectance 3.3 mm/6.5 mm ratio distant of the sensor, at the forearm and sole (Pearson's correlation = 0.505, P < 0.001 and 0.710, P < 0.001, respectively). The best-combined variables to predict the gold standard gestational age at birth was the skin reflectance at wavelengths of 630 nm and 470 nm in combination with birth weight, phototherapy, and adjusted to include incubator stay, and sex (R2 = 0.828, P < 0.001). The main limitation of the study is that it was very specific to the premature population we studied and needs to be studied in a broader spectrum of newborns. CONCLUSIONS: A novel automated skin reflectometer device, in combination with clinical variables, was able to predict the gestational age and could be useful when the information is in doubt or is unknown. Multivariable predictive models associated the skin reflectance with easy to obtain clinical parameters, at the birth scenario. External validation needs to be proven in an actual population with the real incidence of premature infants.

Sistematização do atendimento clínico especializado baseado em paradigmas da computação ubíqua / Systematization of clinical care based paradigms of ubiquitous computing / Sistematización de laasistencia clínica basadaen paradigmas de computaciónubicua

Objetivo: Disponibilizar uma ferramenta de apoio à sistematização do atendimento clínico em todas as especialidades médicas utilizando tecnologia da informação móvel, pervasiva e ubíqua, para auxiliar no cadastro/pesquisa diretamente no leito do paciente. Métodos: Nesse contexto foram utilizados paradigmas da computação ubíqua para o gerenciamento de um banco de dados relacional a partir de softwares com código fonte aberto. Resultados: Estruturação e informatização dos dados clínicos que compõem o histórico do paciente, promovendo, respectivamente, a sistematização e o acesso multiusuário entre os profissionais de saúde, conduzindo à redução do tempo de atendimento no cadastro das informações do paciente. Conclusão: A adoção desta solução tecnológica pode contribuir para: sistematizar/uniformizar os dados coletados nas fichas clínicas, auxiliar no raciocínio médico sobre hipóteses diagnósticas, facilitar o tratamento de emergência e monitoração dos pacientes e a otimização da aquisição, armazenamento, busca e uso das informações do paciente.

Objective: Provide a tool to support the systematization of clinical care in all medical specialties using Information technology, mobile, pervasive and ubiquitous computing technologies, to assist in the registration/research directly into the patient?s bedside. Methods: In this context we used the ubiquitous computing paradigms for managing a relational database from software with open source. Results: Allow interbreeding information promoting research, composition of the patient?s history, allows multiuser access between healthcare professionals and reduce the service time in the registration of patient information. Conclusion: The adoption of this technology solution can help: systematize/standardize the data collected in the clinical records, assist in medical reasoning on diagnostic hypotheses, facilitate emergency treatment and monitoring of patients and the optimization of the acquisition, storage, search and use of patient information.

Objetivo: Proporcionar una herramienta de apoyo a la sistematización de la atención médica en todas las especialidades de computación que utilizan la Tecnologia de la información, la movilidad, las interfaces móviles (iPads), para ayudar em el registro/investigación diretamente em la cabecera del paciente. Métodos: En este contexto, se han utilizado los paradigmas de computación ubicua para la gestión de una base de datos relacional de software de código abierto. Resultados: Estructuración y la informatización de los datos clínicos que conforman la historia del paciente, respectivamente promover la sistematización y acceso multiusuario entre los profesionales de la salud, lo que lleva a una reducción en el tiempo de servicio en el registro de la información del paciente. Conclusión: La adopción de esta solución tecnológica puede ayudar a: sistematizar/estandarizar los datos recogidos en las historias clínicas, ayudar en el razonamiento médico sobre hipótesis diagnósticas, facilitar el tratamento de urgencia y seguimiento de los pacientes y la optimización de la adquisición, el almacenamiento, la búsqueda y uso de la información del paciente.