A New Method for Maturity Determination in Newborn Infants

Transcription

1 A New Method for Maturity Determination in Newborn Infants by Charles Osayande Eregie Institute of Child Health, University of Benin, Benin City, Nigeria Summary A two-part study was conducted in several centres in Nigeria to develop and evaluate a simple method for maturity determination in newborn infants. The first part involved the development of a sixfeature model which included head circumference, mid-arm circumference, skin texture, ear form, breast size and genitalia. These were features which had highly significant correlation with gestational age in the studied population. The model consisted of a chart showing the regression line of gestational age on total maturity score based on the six selected features. It had comparable accuracy with the Dubowitz method. Different subgroups of term and low-birth weight infants were also reliably identified by the model. In the evaluation of sick newborn infants, the model was more accurate than a previously reported abbreviated method from the same population. The model is suggested as an appropriate clinical tool for rapid and reliable maturity determination in healthy and sick newborn infants. Introduction A simplified method of maturity determination was reported previously from an African population to facilitate rapid and accurate identification of high-risk infants requiring prompt recognition, management and referral as appropriate. 1 Since that report, the clinical usefulness and reliability of the method has been further confirmed. 2 These reports were based on the evaluation of the method in healthy newborn infants. It is, however, consistently observed that the method is less accurate when applied to sick infants especially those with neonatal neurological morbidity. Dubowitz et al., 3 in their clinical manual on maturity determination, also noted that their method was not applied to sick infants. Indeed, in assessing these infants, they omitted head lag and ventral suspension to avoid lifting the infants and merely assigned a score of 1.5 for each of the two criteria. It is suggested that the limited value of these methods in sick infants is probably due to the inclusion of scored neurological criteria. Such infants, however, also require rapid and accurate maturity assessment to facilitate correct diagnosis, management and prognosis. This report Acknowledgements This report was first presented at the 27th Annual General and Scientific Conference of the Paediatric Association of Nigeria held in Maiduguri in January Appreciation is shown to all personnel who helped with some aspects of the study. The support of the Modic Research Foundation is gratefully acknowledged. Correspondence: Dr C. O. Eregie, Institute of Child Health, University of Benin, P.M.B. 1154, Benin City, Nigeria. presents a new method, devoid of neurological features for maturity determination and its usefulness in sick newborn infants. Materials and Methods The study involved two parts: (1) development of the new model; and (2) evaluation of the usefulness of the new model in sick newborn infants. For the first part, data were re-analysed from a previous study 1 conducted at the St. Philomena, Central and University Teaching Hospitals in Benin City from February to November The 21 features of the Dubowitz method 4 were correlated with gestational age by reliable maternal dates using simple regression analysis. Maternal dates were regarded as reliable if there was agreement between last menstrual period (LMP) obtained by interview and that documented in the antenatal notes. 5 Based on the correlation coefficients of the 21 features in the studied African population, 1 the four most highly correlating external features were selected for inclusion in the new model. These were then combined with head circumference and mid-arm circumference measurements using maturity scores assigned to the anthropometric parameters as previously reported. 6 The new six-feature model thus included head circumference, mid-arm circumference, skin texture, ear form, breast size and genitalia. Only the 508 infants that met strict study criteria reported previously 1 were included in further analyses. With the new model, total maturity scores were assigned to the infants and correlated with gestational age by reliable maternal dates. The new model consisted of a 140 Oxford University Press 2000 Journal of Tropical Pediatrics Vol. 46 June 2000

2 Journal of Tropical Pediatrics Vol. 46 June Table 1 Criteria, definitions and scores a for estimating gestational age Criteria Head circumference (cm) b < :4 and < :8 and < :6 and < :4 and < :7 2 Mid-arm circumference (cm) b < and < and < and < and < Skin texture c Very thin, Thin and smooth Smooth, medium Slight thickening, Thick and gelatinous thickness, rash or superficial cracking parchment-like, superficial peeling and peeling especially superficial or deep hands and feet cracking 4 Ear form c Pinna flat and Incurving of part Partial incurving Well-defined incurving shapeless, little or no of edge of pinna whole of upper whole of upper pinna incurving of edge pinna 5 Breast size c No breast tissue Breast tissue on one Breast tissue both Breast tissue both sides; palpable or both sides < 0.5 cm sides; one or both one or both > 1 cm diameter cm 6 Genitalia c Male Neither testis in At least one testis At least one testis scrotum high in scrotum right down Female Labia majora widely Labia majora almost Labia majora separated, labia cover labia minora completely cover minora protruding labia minora a If the score for an individual criterion differs on the two sides of the baby, take the mean. For female genitalia scoring, keep the hips half abducted. b Features 1 and 2 as in previous report. 6 c Features 3 6 as in previous report. 4 Correlation coefficients of the six features, adapted from previous reports, 1,6 range from to (p < 0:001Þ: C. O. EREGIE

3 chart showing the regression line of gestational age on total maturity score based on the six selected features. A prospective sample of 262 infants of various gestational age and birthweight categories was reinvestigated to determine the usefulness of the new method using the Dubowitz method 4 as the gold standard. The characteristics of this new sample were also presented previously. 1 The author, without prior knowledge of the maternal LMP and maturity previously assigned by the Dubowitz method, determined the maturity of the infants using the new model. Maturity assigned by the new model and the Dubowitz method were then compared with gestational age by dates. Maturity determination was accurate if it was within 2 weeks of gestational age by dates. 5 This prospective sample was further analysed, using the new model, in subgroups of low-birth weight (LBW) and term infants. The LBW group included preterm appropriate-forgestational age (AGA) and term small-for-gestational age (SGA) infants while the term group included AGA and SGA infants. The second part of the study involved the investigation of another prospective sample of sick infants in the Specialist Hospital, Yola from January 1992 to December Informed parental consent was obtained prior to the inclusion of each infant in the study. Only 89 consecutive sick infants whose mothers had reliable LMP were analysed and they included 11 preterm, 20 low-birth weight (LBW) and 21 SGA infants among others. They all had signs of neonatal neurological morbidity (e.g. marked hypotonia, seizures, poor/absent suck, lethargy, persistent cortical fisting, head lag etc.). Infants with congenital and chromosomal anomalies were excluded. The previously reported method 1 and the new model were then compared for their usefulness in maturity determination in this sample of sick newborn infants. The author, without prior knowledge of the maternal LMP, determined their maturity using the abbreviated model reported previously 1 while a Senior Medical Officer in Paediatrics used the new model independently and without prior knowledge of the maternal LMP. Maturity determination by the Senior Medical Officer was validated previously. Accuracy of maturity assigned by the two methods were then compared. Sample means were analysed using Student s t-test with self-paired non-directional test for correlated samples. Simple regression analysis was used for correlation studies. An IBM System 3 computer was used for analyses of data. Results A total of 508 AGA infants were re-analysed in the first sample for the development of the new model. It included 276 females and 232 males. There were 146 preterm and 124 LBW infants in the sample. Their gestational ages ranged from 27 to 43 weeks. Other characteristics of this sample were as reported previously. 1 Fig. 1. Regression line of gestational age on total maturity score using the new model. The correlation coefficients of the 21 Dubowitz features with gestational age in this African population were presented previously. 1 The four most highly correlating external features selected for the new model had coefficients ranging from to (p < 0:001). The coefficients for head circumference (0.867, p < 0:001) and mid-arm circumference (0.856, p < 0:001), were presented previously in a separate report. 6 Thus, the six-feature model included head circumference, mid-arm circumference, ear form, skin texture, breast size and genitalia with coefficients ranging from to (p < 0:001) (Table 1). The correlation of total maturity scores, using the model, with gestational age was highly significant (r ¼ 0:921; p < 0:001). Figure 1 shows the new model as a chart of the regression line of gestational age on total maturity score as obtained by using the six selected features. The intercept of the regression line was weeks. Maturity determination with the model was reliable when compared with the Dubowitz system using the reanalysed prospective sample of 262 infants (Table 2). Indeed, the model had comparable accuracy of maturity determination (94.3 per cent) with the Dubowitz system (93.1 per cent). Table 2 Maturity determination in 262 infants using this study model and the Dubowitz method Gestational age No. of (weeks) a Accuracy b Method infants Mean (SD) No. (%) Dubowitz (2.4) 244 (93.1) This study (2.5) 247 (94.3) a t ¼ 1:42; p > 0:05 (difference between means). b Estimated gestational age accurate if within 2 weeks of maturity by reliable dates. 142 Journal of Tropical Pediatrics Vol. 46 June 2000

4 Table 3 Analysis of 53 low-birthweight infants by mean birthweight and mean gestational age using the model Mean birthweight Mean gestational No. of (kg) b age (weeks) c Group a infants (SD) (SD) Pre-term AGA (0.38) 32.9 (2.1) Term SGA (0.16) 38.5 (1.5) a Age-weight classification was by Olowe Chart. 12 b t ¼ 1:20; p > 0:10 (difference between means). c t ¼ 10:24; p < 0:001 (difference between means). The mean gestational ages of term and preterm LBW infants determined by the model were significantly different (p < 0:001; Table 3). There was, however, no significant difference between their mean birthweight (p > 0:10). Table 4 shows no significant difference between the mean gestational ages of term AGA and SGA infants determined by the new model (p > 0:10). Their mean birthweights were, however, significantly different (p < 0:001). The second prospective sample of 89 sick newborn infants was evaluated using the simplified method reported previously 1 and the new model with significant difference between the mean gestational ages determined by the two methods (p < 0:001) (Table 5). Also, the accuracy of maturity determination by the new model in sick infants was 91.0 per cent which was higher than 69.7 per cent using the previously reported simplified method. Discussion The new model represents a combination of external features and anthropometric parameters which reflect, respectively, maturational skin changes and intrauterine growth of the brain and fat/muscle mass. Combination of features which reflect different spheres of foetal growth and development results in improved reliability of maturity determination. 1,4,7,8 The model had comparable accuracy of maturity determination when compared with the Dubowitz Table 4 Analysis of 158 term infants by mean birthweight and mean gestational age using the model Mean birthweight Mean gestational No. of (kg) b age (weeks) c Group a infants (SD) (SD) Term SGA (0.16) 38.5 (1.5) Term AGA (0.41) 39.0 (1.3) a Age-weight classification was by Olowe Chart. 12 b t ¼ 12:28; p < 0:001 (difference between means). c t ¼ 1:54; p < 0:01 (difference between means) Table 5 Analysis of 89 sick infants by gestational age and accuracy of maturity determination using two abbreviated methods Mean gestational No. of age (weeks) a Accuracy b Method infants (SD) No. (%) Eregie and Muogbo c (1.7) 62 (69.66) This study (1.4) 81 (91.01) a t-value ¼ 16.82; p < 0:001 (difference between means). b Estimated gestational age accurate if within 2 weeks of maturity by reliable dates. c Abbreviated method reported previously. 1 Features included: posture, scarf sign, skin texture, ear form, breast size and genitalia. Journal of Tropical Pediatrics Vol. 46 June

5 system. This may be due to the combination of four external features of the Dubowitz system, which showed the highest correlation with gestational age in the African population, with two anthropometric parameters which, in fact, had better correlation with gestational age in the same population. Parkin et al. 9 had reported poor intercorrelation among the four external features included in the new model. The exclusion of neurological features from the new model may have also improved the accuracy of maturity determination since previous reports have documented better correlation of the external features with gestational age than the neurological features. 1,4 Malan et al. 10 and Dubowitz et al. 3 have also shown that whites and non-whites score differently with neurological features possibly due to the effect of race or socioeconomic status. The samples investigated in this study are non-white populations with the potential for over-estimation of maturity with neurological features. 3,10 Interscorer agreement with neurological features is also poorer compared with external features. 5 Therefore, excluding neurological features possibly improved the accuracy of the model in maturity determination in this report. The gestational age range of the first re-investigated sample of 508 AGA infants and the intercept of the regression line of gestational age on total maturity scores using the new model (i.e., weeks) suggest that the model may be reliable for maturity determination down to 27 weeks. The model was able to differentiate among the subgroups of term and LBW infants (Tables 3 and 4) which is an essential requirement for any useful clinical method of maturity determination since the identified subgroups have different problems and approaches to their management. Although the model includes anthropometric parameters, its usefulness among SGA infants may still be guaranteed since there is relative sparing of brain growth, and hence head circumference measurements, in intrauterine growth retardation. 11 Indeed, the prospective samples of 262 infants reanalysed and 89 sick infants included SGA infants and yet the model still showed reasonable accuracy of maturity determination. Among the sick infants, the model appeared to be more accurate than the simplified method 1 reported previously. This may be due to the exclusion of neurological features in the new model and their replacement by anthropometric features which had better correlation than the four most highly correlating external features. Neonatal neurological morbidity results in the assignment of falsely low scores for the neurological features, with the resultant underestimation of maturity in such sick infants. This has implications for diagnosis, management and prognosis. The features included in the new model are unlikely to be affected by such neurological morbidity (excluding, of course, gross congenital and chromosomal anomalies) and hence provide better accuracy of maturity determination in this study. It is, however, pertinent to emphasize that this new model was developed from, and evaluated in, an African population. It is hoped that this observation will be validated or replicated in other populations because of the recognized influence of race on such clinical methods. 1,3,10,12,13 This report presents a new model for simple, reliable and rapid determination of maturity in newborn infants. Its usefulness in sick infants, especially with neonatal neurological morbidity, is particularly highlighted. These infants require accurate maturity determination to facilitate correct diagnosis, management and prognosis. The model is simple and represents an appropriate clinical tool for prompt and reliable identification of high-risk infants, particularly in developing countries. References 1. Eregie CO, Muogbo DC. A simplified method of estimation of gestational age in an African Population Dev Med Child Neurol 1991; 33: Eregie CO. Clinical determination of maturity of newborn infants: Comparison of some simplified methods. W Afr J Med 1994; 13: Dubowitz LMS, Dubowitz V, Goldberg C. Clinical manual: gestational age of the newborn. London, Addison-Wesley, Dubowitz LMS, Dubowitz V, Goldberg C. Clinical assessment of gestational age in the newborn infant. J Pediatr 1979; 77: Ballard JL, Novak KK, Driver M. A simplified score for assessment of fetal maturation of newly born infants. J Pediatr 1979; 95: Eregie CO. Assessment of gestational age: the value of a maturity scoring system for head circumference and midarm circumference. J Trop Pediatr 1991; 37: Caesar P, Akiyama Y. The estimation of post-menstrual age: a comprehensive review. Dev Med Chld Neurol 1979; 12: Finnstrom O. Studies on maturity in newborn infants VI: Comparison between different methods for maturity estimation. Acta Paediatr Scand 1972; 61: Parkin JM, Hey EN, Clowes JS. Rapid assessment of gestational age at birth. Arch Dis Chldh 1976; 51: Malan AF, Evans A, De V Smith NB, De V Hease H. Intrauterine growth: A study of the birthweight of live-born infants. S Afr Med J 1967; 41: Freedman LS, Samuel S, Fish I. Sparing of the brain in neonatal undernutrition: amino acid transport incorporation into brain and muscle. Science 1980; 207: Olowe SA. Standards of intrauterine growth for an African population at sea-level. J Pediatr 1981; 99: Damoulaki-Sfakianaki E, Robertson A, Cordero L. Skin creases on the sole of the foot as a physical index of maturity: comparison between caucasian and negro infants. Paediatrics 1972; 50: Journal of Tropical Pediatrics Vol. 46 June 2000