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

Assessment of Skin Maturity by LED Light at Birth and Its Association With Lung Maturity: Clinical Trial Secondary Outcomes

BACKGROUND

Clinicians face barriers when assessing lung maturity at birth due to global inequalities. Still, strategies for testing based solely on gestational age to predict the likelihood of respiratory distress syndrome (RDS) do not offer a comprehensive approach to addressing the challenge of uncertain outcomes. We hypothesize that a noninvasive assessment of skin maturity may indicate lung maturity.

OBJECTIVE

This study aimed to assess the association between a newborn's skin maturity and RDS occurrence.

METHODS

We conducted a case-control nested in a prospective cohort study, a secondary endpoint of a multicenter clinical trial. The study was carried out in 5 Brazilian urban reference centers for highly complex perinatal care. Of 781 newborns from the cohort study, 640 were selected for the case-control analysis. Newborns with RDS formed the case group and newborns without RDS were the controls. All newborns with other diseases exhibiting respiratory manifestations were excluded. Skin maturity was assessed from the newborn's skin over the sole by an optical device that acquired a reflection signal through an LED sensor. The device, previously validated, measured and recorded skin reflectance. Clinical data related to respiratory outcomes were gathered from medical records during the 72-hour follow-up of the newborn, or until discharge or death, whichever occurred first. The main outcome measure was the association between skin reflectance and RDS using univariate and multivariate binary logistic regression. Additionally, we assessed the connection between skin reflectance and factors such as neonatal intensive care unit (NICU) admission and the need for ventilatory support.

RESULTS

Out of 604 newborns, 470 (73.4%) were from the RDS group and 170 (26.6%) were from the control group. According to comparisons between the groups, newborns with RDS had a younger gestational age (31.6 vs 39.1 weeks, P<.001) and birth weight (1491 vs 3121 grams, P<.001) than controls. Skin reflectance was associated with RDS (odds ratio [OR] 0.982, 95% CI 0.979-0.985, R2=0.632, P<.001). This relationship remained significant when adjusted by the cofactors antenatal corticosteroid and birth weight (OR 0.994, 95% CI 0.990-0.998, R2=0.843, P<.001). Secondary outcomes also showed differences in skin reflectance. The mean difference was 0.219 (95% CI 0.200-0.238) between newborns that required ventilatory support versus those that did not and 0.223 (95% CI 0.205-0.241) between newborns that required NICU admission versus those that did not. Skin reflectance was associated with ventilatory support (OR 0.996, 95% CI 0.992-0.999, R2=0.814, P=.01) and with NICU admission (OR 0.994, 95% CI 0.990-0.998, R2=0.867, P=.004).

CONCLUSIONS

Our findings present a potential marker of lung immaturity at birth using the indirect method of skin assessment. Using the RDS clinical condition and a medical device, this study demonstrated the synchrony between lung and skin maturity.

TRIAL REGISTRATION

Registro Brasileiro de Ensaios Clínicos (ReBEC) RBR-3f5bm5; https://tinyurl.com/9fb7zrdb.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1136/bmjopen-2018-027442.

Respiratory distress syndrome prediction at birth by optical skin maturity assessment and machine learning models for limited-resource settings: a development and validation study

Background

A handheld optical device was developed to evaluate a newborn's skin maturity by assessing the photobiological properties of the tissue and processing it with other variables to predict early neonatal prognosis related to prematurity. This study assessed the device's ability to predict respiratory distress syndrome (RDS).

Methods

To assess the device's utility we enrolled newborns at childbirth in six urban perinatal centers from two multicenter single-blinded clinical trials. All newborns had inpatient follow-up until 72 h of life. We trained supervised machine learning models with data from 780 newborns in a Brazilian trial and provided external validation with data from 305 low-birth-weight newborns from another trial that assessed Brazilian and Mozambican newborns. The index test measured skin optical reflection with an optical sensor and adjusted acquired values with clinical variables such as birth weight and prenatal corticoid exposition for lung maturity, maternal diabetes, and hypertensive disturbances. The performance of the models was evaluated using intrasample k-parts cross-validation and external validation in an independent sample.

Results

Models adjusting three predictors (skin reflection, birth weight, and antenatal corticoid exposure) or five predictors had a similar performance, including or not maternal diabetes and hypertensive diseases. The best global accuracy was 89.7 (95% CI: 87.4 to 91.8, with a high sensitivity of 85.6% (80.2 to 90.0) and specificity of 91.3% (95% CI: 88.7 to 93.5). The test correctly discriminated RDS newborns in external validation, with 82.3% (95% CI: 77.5 to 86.4) accuracy. Our findings demonstrate a new way to assess a newborn's lung maturity, providing potential opportunities for earlier and more effective care.

Trial registration

RBR-3f5bm5 (online access: http://www.ensaiosclinicos.gov.br/rg/RBR-3f5bm5/), and RBR-33mjf (online access: https://ensaiosclinicos.gov.br/rg/RBR-33rnjf/).

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.

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

Real world external validation of metabolic gestational age assessment in Kenya

Using data from Ontario Canada, we previously developed machine learning-based algorithms incorporating newborn screening metabolites to estimate gestational age (GA). The objective of this study was to evaluate the use of these algorithms in a population of infants born in Siaya county, Kenya. Cord and heel prick samples were collected from newborns in Kenya and metabolic analysis was carried out by Newborn Screening Ontario in Ottawa, Canada. Postnatal GA estimation models were developed with data from Ontario with multivariable linear regression using ELASTIC NET regularization. Model performance was evaluated by applying the models to the data collected from Kenya and comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound. Heel prick samples were collected from 1,039 newborns from Kenya. Of these, 8.9% were born preterm and 8.5% were small for GA. Cord blood samples were also collected from 1,012 newborns. In data from heel prick samples, our best-performing model estimated GA within 9.5 days overall of reference GA [mean absolute error (MAE) 1.35 (95% CI 1.27, 1.43)]. In preterm infants and those small for GA, MAE was 2.62 (2.28, 2.99) and 1.81 (1.57, 2.07) weeks, respectively. In data from cord blood, model accuracy slightly decreased overall (MAE 1.44 (95% CI 1.36, 1.53)). Accuracy was not impacted by maternal HIV status and improved when the dating ultrasound occurred between 9 and 13 weeks of gestation, in both heel prick and cord blood data (overall MAE 1.04 (95% CI 0.87, 1.22) and 1.08 (95% CI 0.90, 1.27), respectively). The accuracy of metabolic model based GA estimates in the Kenya cohort was lower compared to our previously published validation studies, however inconsistency in the timing of reference dating ultrasounds appears to have been a contributing factor to diminished model performance.