Dataset Information

A color-coded tape for uterine height measurement: a tool to identify preterm pregnancies in low resource settings.

ABSTRACT:

Introduction

Neonatal mortality associated with preterm birth can be reduced with antenatal corticosteroids (ACS), yet <10% of eligible pregnant women in low-middle income countries. The inability to accurately determine gestational age (GA) leads to under-identification of high-risk women who could receive ACS or other interventions. To facilitate better identification in low-resource settings, we developed a color-coded tape for uterine height (UH) measurement and estimated its accuracy identifying preterm pregnancies.

Methods

We designed a series of colored-coded tapes with segments corresponding to UH measurements for 20-23.6 weeks, 24.0-35.6 weeks, and >36.0 weeks GA. In phase 1, UH measurements were collected prospectively in the Democratic Republic of Congo, India and Pakistan, using distinct tapes to address variation across regions and ethnicities. In phase 2, we tested accuracy in 250 pregnant women with known GA from early ultrasound enrolled at prenatal clinics in Argentina, India, Pakistan and Zambia. Providers masked to the ultrasound GA measured UH. Receiver operating characteristics (ROC) analysis was conducted.

Results

1,029 pregnant women were enrolled. In all countries the tapes were most effective identifying pregnancies between 20.0-35.6 weeks, compared to the other GAs. The ROC areas under the curves and 95% confidence intervals were: Argentina 0.69 (0.63, 0.74); Zambia 0.72 (0.66, 0.78), India 0.84 (0.80, 0.89), and Pakistan 0.83 (0.78, 0.87). The sensitivity and specificity (and 95% confidence intervals) for identifying pregnancies between 20.0-35.6 weeks, respectively, were: Argentina 87% (82%-92%) and 51% (42%-61%); Zambia 91% (86%-95%) and 50% (40%-60%); India 78% (71%-85%) and 89% (83%-94%); Pakistan 63% (55%-70%) and 94% (89%-99%).

Conclusions

We observed moderate-good accuracy identifying pregnancies ? 35.6 weeks gestation, with potential usefulness at the community level in low-middle income countries to facilitate the preterm identification and interventions to reduce preterm neonatal mortality. Further research is needed to validate these findings on a population basis.

SUBMITTER: Althabe F

PROVIDER: S-EPMC4379082 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

A color-coded tape for uterine height measurement: a tool to identify preterm pregnancies in low resource settings.

Althabe Fernando F Berrueta Mabel M Hemingway-Foday Jennifer J Mazzoni Agustina A Bonorino Carolina Astoul CA Gowdak Andrea A Gibbons Luz L Bellad M B MB Metgud M C MC Goudar Shivaprasad S Kodkany Bhalchandra S BS Derman Richard J RJ Saleem Sarah S Iqbal Samina S Ala Syed Hasan SH Goldenberg Robert L RL Chomba Elwyn E Manasyan Albert A Chiwila Melody M Imenda Edna E Mbewe Florence F Tshefu Antoinette A Lokomba Victor V Bose Carl L CL Moore Janet J Meleth Sreelatha S McClure Elizabeth M EM Koso-Thomas Marion M Buekens Pierre P Belizán José M JM

PloS one 20150330 3

<h4>Introduction</h4>Neonatal mortality associated with preterm birth can be reduced with antenatal corticosteroids (ACS), yet <10% of eligible pregnant women in low-middle income countries. The inability to accurately determine gestational age (GA) leads to under-identification of high-risk women who could receive ACS or other interventions. To facilitate better identification in low-resource settings, we developed a color-coded tape for uterine height (UH) measurement and estimated its accurac ...[more]

PMID: 25822529

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Project description:BackgroundMaternal and neonatal mortality is still very high at a global level, even though its reduction is a goal established among the Sustainable Development Goals by the United Nations. In order to improve prenatal care to address this challenge, this article proposes a strategy to detect and refer high risk pregnancies in rural setting through a portable ultrasound system combined with blood and urine strip tests.MethodsThe Healthy Pregnancy project was conceived as a single, explanatory and positivist case study, with a sample of ten thousand pregnant women attended by itinerant nurses of the Departments of Alta Verapaz and San Marcos. These nurses were trained and equipped with 31 portable ultrasound, and blood and urine tests to detect common obstetric pathology. Moreover, two obstetricians were responsible for remotely supervising the quality of prenatal care. Target communities were selected by the Health Directorates of the public health system from those that had the highest maternal mortality in previous years.ResultsThe project attended to 10,108 women in 2?years and 3?months. 55 twin gestations (0.54%) were diagnosed. Non-cephalic presentation was found in 14.87% of the pregnant women attended from week 32 onwards. 20 patients were referred for non-evolutive gestation. An 11.08% prevalence of anemia was detected. Urine infections were diagnosed in 16.43% of the cases. Proteinuria was detected in 2.6% of patients, but only 17 of them presented high blood pressure and were therefore referred with a suspected pre-eclampsia.DiscussionThe results obtained indicate that an intervention of these characteristics makes it possible to improve the quality of care of rural pregnant women in low and middle-income countries.ConclusionThe results show that with suitable equipment, training, and supervision, the nursing staff in charge of care in rural areas can identify and refer most of the obstetric risks in time, which may contribute to the reduction of maternal mortality.Trial registrationThis research was not registered because it is a case study in which the assignment of the medical intervention was not at the discretion of the investigators.

Project description:BACKGROUND:Globally, preterm birth is the leading cause of neonatal death with estimated prevalence and associated mortality highest in low- and middle-income countries (LMICs). Accurate identification of preterm infants is important at the individual level for appropriate clinical intervention as well as at the population level for informed policy decisions and resource allocation. As early prenatal ultrasound is commonly not available in these settings, gestational age (GA) is often estimated using newborn assessment at birth. This approach assumes last menstrual period to be unreliable and birthweight to be unable to distinguish preterm infants from those that are small for gestational age (SGA). We sought to leverage machine learning algorithms incorporating maternal factors associated with SGA to improve accuracy of preterm newborn identification in LMIC settings. METHODS AND FINDINGS:This study uses data from an ongoing obstetrical cohort in Lusaka, Zambia that uses early pregnancy ultrasound to estimate GA. Our intent was to identify the best set of parameters commonly available at delivery to correctly categorize births as either preterm (<37 weeks) or term, compared to GA assigned by early ultrasound as the gold standard. Trained midwives conducted a newborn assessment (<72 hours) and collected maternal and neonatal data at the time of delivery or shortly thereafter. New Ballard Score (NBS), last menstrual period (LMP), and birth weight were used individually to assign GA at delivery and categorize each birth as either preterm or term. Additionally, machine learning techniques incorporated combinations of these measures with several maternal and newborn characteristics associated with prematurity and SGA to develop GA at delivery and preterm birth prediction models. The distribution and accuracy of all models were compared to early ultrasound dating. Within our live-born cohort to date (n = 862), the median GA at delivery by early ultrasound was 39.4 weeks (IQR: 38.3-40.3). Among assessed newborns with complete data included in this analysis (n = 468), the median GA by ultrasound was 39.6 weeks (IQR: 38.4-40.3). Using machine learning, we identified a combination of six accessible parameters (LMP, birth weight, twin delivery, maternal height, hypertension in labor, and HIV serostatus) that can be used by machine learning to outperform current GA prediction methods. For preterm birth prediction, this combination of covariates correctly classified >94% of newborns and achieved an area under the curve (AUC) of 0.9796. CONCLUSIONS:We identified a parsimonious list of variables that can be used by machine learning approaches to improve accuracy of preterm newborn identification. Our best-performing model included LMP, birth weight, twin delivery, HIV serostatus, and maternal factors associated with SGA. These variables are all easily collected at delivery, reducing the skill and time required by the frontline health worker to assess GA. TRIAL REGISTRATION:ClinicalTrials.gov Identifier: NCT02738892.

Project description:BACKGROUND:Inadequate blood pressure (BP) control is a frequent challenge in general practice. The objective of this study was to determine whether a color-coded BP booklet using a traffic light scheme (red, >180 mmHg systolic BP and/or >110 mmHg diastolic BP; yellow, >140-180 mmHg systolic BP or >90-110 mmHg diastolic BP; green, ≤140 mmHg systolic BP and ≤90 mmHg diastolic BP) improves BP control and adherence with home BP measurement. METHODS:In this two-group, randomized controlled trial, general practitioners recruited adult patients with a BP >140 mmHg systolic and/or >90 mmHg diastolic. Patients in the control group received a standard BP booklet and the intervention group used a color-coded booklet for daily home BP measurement. The main outcomes were changes in BP, BP control (treatment goal <140/90 mmHg), and adherence with home BP measurement after 6 months. RESULTS:One hundred and twenty-one of 137 included patients qualified for analysis. After 6 months, a significant decrease in systolic and diastolic BP was achieved in both groups, with no significant difference between the groups (16.1/7.9 mmHg in the intervention group versus 13.1/8.6 mmHg in the control group, P=0.3/0.7). BP control (treatment target <140/90 mmHg) was achieved significantly more often in the intervention group (43% versus 25%; P=0.037; number needed to treat of 5). Adherence with home BP measurement overall was high, with a trend in favor of the intervention group (98.6% versus 96.2%; P=0.1). CONCLUSION:Color-coded BP self-monitoring significantly improved BP control (number needed to treat of 5, meaning that every fifth patient utilizing color-coded self-monitoring achieved better BP control after 6 months), but no significant between-group difference was observed in BP change. A markedly higher percentage of patients achieved BP values in the normal range. This simple, inexpensive approach of color-coded BP self-monitoring is user-friendly and applicable in primary care, and should be implemented in the care of patients with arterial hypertension.

Dataset Information

A color-coded tape for uterine height measurement: a tool to identify preterm pregnancies in low resource settings.

Introduction

Methods

Results

Conclusions

Publications

A color-coded tape for uterine height measurement: a tool to identify preterm pregnancies in low resource settings.

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