Simplified models to assess newborn gestational age in low-middle income countries: findings from a multicountry, prospective cohort study

被引：13

作者：

Aftab, Fahad ^{[1
]}

Ahmed, Parvez ^{[2
]}

Ahmed, Salahuddin ^{[3
]}

Ali, Said Mohammed ^{[1
]}

Bahl, Rajiv ^{[4
]}

Banda, Bowen ^{[5
]}

Baqui, Abdullah H. ^{[6
]}

Akonkwa, Corneille Bashagaluke ^{[7
]}

Begum, Nazma ^{[3
]}

Deb, Saikat ^{[8
]}

Dhingra, Pratibha ^{[1
]}

Dhingra, Usha ^{[1
]}

Dutta, Arup ^{[1
]}

Edmond, Karen ^{[9
]}

Grogan, Caroline ^{[10
]}

Hamer, Davidson H. ^{[11
,12
]}

Herlihy, Julie ^{[13
]}

Hurt, Lisa ^{[14
]}

Hussain, Atiya ^{[15
]}

Ilyas, Muhammad ^{[15
]}

Jehan, Fyezah ^{[16
]}

Kapasa, Monica Lulu ^{[17
]}

Karim, Muhammad ^{[15
]}

Kausar, Farzana ^{[15
]}

Kirkwood, Betty R. ^{[18
]}

Lee, Anne C. C. ^{[19
,20
]}

Manu, Alexander ^{[21
]}

Mehmood, Usma ^{[15
]}

Mitra, Dipak ^{[22
]}

Mohammed, Mohammed ^{[1
]}

Mweene, Fern ^{[23
]}

Nadeem, Naila ^{[15
]}

Nisar, Muhammad Imran ^{[15
]}

Paul, Rina ^{[24
]}

Rahman, Mahmoodur ^{[25
]}

Rahman, Sayedur ^{[3
]}

Sajid, Muhammad ^{[15
]}

Sazawal, Sunil ^{[1
]}

Semrau, Katherine E. ^{[10
,26
]}

Shannon, Caitlin ^{[27
]}

Straszak--Suri, Marina ^{[28
]}

Suleiman, Atifa ^{[8
]}

Uddin, Mohammad J. ^{[29
]}

Wilbur, Jayson ^{[30
]}

Wylie, Blair ^{[20
,31
]}

Yoshida, Sachiyo ^{[4
]}

机构：

[1] Ctr Publ Hlth Kinet, New Delhi, India

[2] Inst Epidemiol Dis Control & Res, Dhaka, Bangladesh

[3] Projahnmo Res Fdn, Dhaka, Bangladesh

[4] WHO, Dept Maternal Newborn Child & Adolescent Hlth & A, Geneva, Switzerland

[5] North West Univ, Res Unit Environm Sci & Management, Potchefstroom, South Africa

[6] Johns Hopkins Univ, Dept Int Hlth, Bloomberg Sch Publ Hlth, Baltimore, MD USA

[7] McGill Univ, Hlth Ctr, Montreal, PQ, Canada

[8] Publ Hlth Lab Ivo Carneri, Pemba, Tanzania

[9] Kings Coll London, Maternal & Child Hlth, London, England

[10] Brigham & Womens Hosp, Harvard TH Chan Sch Publ Hlth, Ariadne Labs, 75 Francis St, Boston, MA 02115 USA

[11] Boston Univ, Sch Publ Hlth, Boston, MA USA

[12] Boston Univ, Sch Med, Infect Dis Sect, Boston, MA 02118 USA

[13] Boston Univ, Sch Med, Pediat, Boston, MA 02118 USA

[14] Cardiff Univ, Sch Med, Div Populat Med, Cardiff, Wales

[15] Aga Khan Univ, Karachi, Sindh, Pakistan

[16] Aga Khan Univ, Dept Paediat & Child Hlth, Karachi, Pakistan

[17] Univ Zambia, Pediat, Lusaka, Zambia

[18] London Sch Hyg & Trop Med, Epidemiol & Populat Hlth, London, England

[19] Brigham & Womens Hosp, Global Adv Infant & Maternal Hlth Lab, 75 Francis St, Boston, MA 02115 USA

[20] Harvard Med Sch, Boston, MA 02115 USA

[21] Univ Ghana, Sch Publ Hlth, Accra, Greater Accra, Ghana

[22] North South Univ, Dept Publ Hlth, Dhaka, Bangladesh

[23] Kazungula Dist Hosp, Kazungula, Zambia

[24] BRAC Univ, James P Grant Sch Publ Hlth, Ctr Noncommunicable Dis & Nutr, Dhaka, Bangladesh

[25] Int Ctr Diarrhoeal Dis Res, Maternal & Child Hlth Div, Dhaka, Bangladesh

[26] Harvard Med Sch, Dept Med, Boston, MA 02115 USA

[27] CARE USA, New York, NY USA

[28] Univ Ottawa, Obstet & Gynecol, Ottawa, ON, Canada

[29] Save Children Bangladesh, Dhaka, Bangladesh

[30] Metrum Res Grp, Tariffville, CT USA

[31] Beth Israel Deaconess Med Ctr, Div Maternal Fetal Med, Boston, MA 02215 USA

来源：

BMJ GLOBAL HEALTH | 2021年 / 6卷 / 09期

关键词：

obstetrics; child health; LAST MENSTRUAL PERIOD; INTERNATIONAL STANDARDS; PRETERM; ULTRASOUND; MORTALITY; SCORE;

D O I：

10.1136/bmjgh-2021-005688

中图分类号：

R1 [预防医学、卫生学];

学科分类号：

1004 ; 120402 ;

摘要：

Introduction Preterm birth is the leading cause of child mortality. This study aimed to develop and validate programmatically feasible and accurate approaches to estimate newborn gestational age (GA) in low resource settings. Methods The WHO Alliance for Maternal and Newborn Health Improvement (AMANHI) study recruited pregnant women from population-based cohorts in five countries (Bangladesh, Ghana, Pakistan, Tanzania and Zambia). Women <20 weeks gestation by ultrasound-based dating were enrolled. Research staff assessed newborns for: (1) anthropometry, (2) neuromuscular/physical signs and (3) feeding maturity. Machine-learning techniques were used to construct ensemble models. Diagnostic accuracy was assessed by areas under the receiver operating curve (AUC) and Bland-Altman analysis. Results 7428 liveborn infants were included (n=536 preterm, <37 weeks). The Ballard examination was biased compared with ultrasound dating (mean difference: +9 days) with 95% limits of agreement (LOA) -15.3 to 33.6 days (precision +/- 24.5 days). A model including 10 newborn characteristics (birth weight, head circumference, chest circumference, foot length, breast bud diameter, breast development, plantar creases, skin texture, ankle dorsiflexion and infant sex) estimated GA with no bias, 95% LOA +/- 17.3 days and an AUC=0.88 for classifying the preterm infant. A model that included last menstrual period (LMP) with the 10 characteristics had 95% LOA +/- 15.7 days and high diagnostic accuracy (AUC 0.91). An alternative simpler model including birth weight and LMP had 95% LOA of +/- 16.7 and an AUC of 0.88. Conclusion The best machine-learning model (10 neonatal characteristics and LMP) estimated GA within +/- 15.7 days of early ultrasound dating. Simpler models performed reasonably well with marginal increases in prediction error. These models hold promise for newborn GA estimation when ultrasound dating is unavailable.

引用

页数：11

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