A reliable antenatal age is a prerequisite for preterm newborn classification in birth care settings and constitutes the first step to delivering the necessary care, considering the risks of prematurity.11 Term newborns, allied with good tone, breathing or crying, are essential elements to determine steps of newborn resuscitation.21 Although that statement in itself seems very simple, the reality is far from it. Without the certainty of the day in the female cycle on which conception occurred, ultrasound measurement of the crown-rump length is a standard consensual reference for pregnancy dating.8 This dependence on early echographic scans has deprived many pregnant women and their babies of reliable gestational age.10 Such a technological gap causes even more disparities than the difference between childbirth scenarios in fully equipped facilities and those ill equipped with scarce technology. Moreover, it can impair the correct classification of infants as premature or growth-restricted.22,23 The main contribution of this clinical trial is to validate a new approach for gestational age estimation independent of fetal ultrasound measures by demonstrating highly accurate outcomes. Based on two pieces of data—birth weight and ACTFM exposition—and the use of a frugal medical device to assess skin maturity and process algorithms, 359 of 366 preterm neonates with less than 37 weeks of gestation were detected, with 96% being correctly classified.
In this combined study covering enhancing and validating prediction models, we believe the application of k-fold cross-validation with the use of machine learning algorithms provided accurate predictions.24 While large data samples are unavailable, the process of training and testing are able to estimate the performance of algorithms until we have finished other ongoing clinical trials for external validation.25 Furthermore, the quantification of uncertainty intervals regarding the predicted gestational age (calculated in days) and comparisons with established references allowed the simulation of real scenarios for application. Furthermore, the confidence intervals accompanying AUROC’s accuracy contributed to revealing the forecast's limits as to discriminating term from preterm newborns at different cutoff points, with clinical relevance. Such strengths are critical to assure the potential value of the new test in facing the challenges of postnatal prematurity identification.26 A new type of data science algorithm has thus emerged with the aim of qualifying pregnancy dating. High-performance reports using learning models based on antenatal ultrasound predictors27 contradistinguished meager outcomes from those using other morphometric postnatal predictors.28 Moreover, valuable algorithms with postnatal combinations on maturity scores of newborns are promising, demanding special skills to apply them.29 Underqualified birth attendants represent a challenge in developing countries, further limiting the use of existing birth care solutions.30 One advantage of our new device is the skin assessment automation that alerts measurement error caused by movement of the newborn or examiner. Previous reports have detailed the human skin's light-skin interaction and optical properties that benefit this technology.19,31
The predictive algorithms used information that health professionals could quickly obtain in childbirth settings—the birth weight and the ACTFM exposition—and which could add value to the physical data of skin maturity. Moreover, the device is capable of providing a gestational age to overcome extreme situations without antenatal records to obtain ACTFM exposure information. However, the algorithm of Model 1 had more comprehensive Bland-Altman 95% limits of 28.6 days compared to the 19.6 days of Model 2 with the full three variables. Working with more flexible forecasts within seven days of error range, 98.7% of newborns had a valuable prediction with Model 2. Considering the simulated scenario with absent or unreliable LMP and lack of ACTFM exposition data—57.7% (n=451) of the sample—the algorithm of Model 1 for gestational age estimation detected 180 of 199 (90.5%) preterm newborns, even with low specificity (48.0%). This result expands the context of use for this medical device since the gestational age based on memory recall of the LMP missed 69 out of 199 preterm newborns, expressing a lower sensitivity (65.8%) when we applied the intent-to-discriminate analysis.
The choice and analysis of two algorithms for gestational age prediction (Model 1 and Model 2) depended on uncertainties regarding the effect of corticosteroids on forecasts. Antenatal corticosteroids to improve newborn outcomes are a practical, evidence-based intervention recommended for women at risk of preterm birth.32 However, even with the acceleration of lung maturity, the effect of the drug occurs in other organs. The early fetal presence of receptors of corticosteroid hormone receptors in skin epithelial cells indicates that glucocorticoids may play an important role in the differentiation and development of human skin.33 However, clinical evidence of the effect of ACTFM exposure on skin maturity is weak, and the topic remains unsubstantiated.34 Thus, until proven otherwise, we interpreted that the importance of ACTFM exposition data to better adjust the gestational age modeling is related to an effect on skin maturity. Even so, we cannot deny that antenatal exposure to corticotherapy is more common in premature infants—72.3% of preterm newborns in this sample. In this respect, this predictor variable could imply a bias favoring preterm newborn detection. The aforementioned ongoing study for external validation of the algorithms could further elucidate this issue because the enrollment process of newborns introduced the Mozambican birth scenario, where unfortunately ACTFM is not guaranteed for every woman at risk of preterm birth.25
Birth weight is a known estimator of risks to newborns. As part of primary routines in childbirth settings, this information has practical applicability, even in facilities with scarce high-cost technologies.1 Meanwhile, predicting preterm birth based on birth weight when lacking a gold standard is an imperfect solution.9 Additionally, the LMP reference and the postnatal scores of newborn maturity have demonstrated low accuracy in determining gestational age and identifying prematurity.35 Later prenatal care and unqualified date recollection justify efforts to enhance the reliability of pregnancy dating through more accurate and accessible technologies, seeking to improve pregnancy outcomes and neonatal survival.10 In our study, qualifying the LMP at birth with questions about memory of date, menstrual cycles, and checking antenatal clinical documents at birth provided a gestational age able to identify 160/167 (95.8%) preterm newborns. Current approaches to calculating gestational age are sensitive to data quality, resulting in misplaced classification of prematurity.9 The present study was committed to representing a real scenario in terms of data quality, as stated in the research protocol, with data collection and curation to assure the best reference and comparators for the analysis. Before opening the blinding of the trial, a consistency process confronted data entries with digital images of clinical documents taken during the enrollment. Furthermore, dedicated software was developed exclusively for the clinical trial, considering the quality of the variables and their constraints. Part of the enrollment occurred during the COVID-19 pandemic, causing a minimal amount of missing data, such as maternal diseases (7/781 newborns) and ACMF information (4/781 newborns).
Regarding the generalizability of outcomes, this multicenter trial gathered perinatal centers from the northern, central, southwestern, and southern regions of Brazil. This collaborative evaluation contributed to sampling a mixed population of newborns with high miscegenation and involved 15 examiners who attended good clinical practice training. The intraobserver error and interobserver error of measures were slow, corroborating previous results.19 The number of preterm newborns was enough to analyze subcategories of prematurity as extreme preterm (n=42); however, the overall rate of preterm newborns was 46.9%, values observed in referral facilities and not in the general population of Brazilian newborns.36 The number of neonatal deaths during 72 h of follow-up was 14, with 12 deaths occurring in newborns with gestational age below 28 weeks due to extreme prematurity complications. We expect to target worse childbirth scenarios for this technology implementation.30 In addition, the safety of this device is similar to other optical technologies already used in neonatal care, such as transcutaneous bilirubinometer and pulse oximetry.21,37
With regard to limitations, the adoption of the new test deserves caution. Impaired fetal growth and large-for-gestational age newborn influence were not included in the analysis. Nevertheless, photometer-based technology for skin maturity assessment has a basis on skin transparency in part associated with epidermal thickness.19 In a previous analysis of high-frequency ultrasound of newborn skin, we reported that the epidermal layer had no significant influence on the fetal growth pattern when associated with gestational age.38 Furthermore, the epidermal thickness of newborns was itself a predictor of gestational age when analyzing postmortem histological skin slices from the sole.39 For the future, comparisons are expected based on postnatal approaches for gestational age estimation, such as scores of maturity and foot length or image combinations.28
Identifying preterm newborns is the first step to attending to their needs. The global rate of neonatal mortality corresponds to 6,700 neonatal daily deaths, mostly from preventable or treatable conditions in scenarios of healthcare scarcity.40 We hope that strengthening the data sources of healthcare facilities with a reliable gestational age can help in identifying vulnerable newborns in situations with the absence or lack of such information.