Towards optimising local reviews of severe incidents in maternity care: messages from a comparison of local and external reviews.

BACKGROUND: Detailed local case review is commonly used as a strategy to improve care. However, recent reports have highlighted concerns over quality of local reviews in maternity care. The aim of this project was to describe the methods used for conducting local reviews of care of women with severe maternal morbidity, and to compare lessons identified for future care through external and local reviews. METHODS: Thirty-three anonymised clinical records from women with severe maternal morbidities were obtained, together with the report of the local review of their care. The methodology used for the local reviews was described, including specific tools used, team members involved, their disciplines, report format and whether an action plan with recommendations for audit was produced. Multidisciplinary external reviewers considered the records using a standard confidential enquiry approach. A thematic analysis of lessons learned from the two approaches was undertaken. RESULTS: A formal report of the local review was produced for 11/33 cases; 4 of these used root cause analysis. A further 12 local reviews consisted of a group discussion with output noted in a spreadsheet; 5 consisted of a timeline with good practice points and 5 had no formal review. Patients were involved in five local reviews; only one was multidisciplinary. Action plans were recorded in 14 local reviews; 3 of these included a recommendation to audit the proposed changes. External reviews identified additional messages for care and highlighted aspects of good care in every case, whereas only 55% (n=18) of local reviews identified good care (p<0.0005). CONCLUSIONS: The quality of local reviews can clearly be improved. Very few of the reviews involved patients. Local reviews should be multidisciplinary, generate an action plan, and the implementation of recommendations should be audited. Improvements in local reviews may be achieved by standardised training or development of national protocols.

Maternal colonization with Streptococcus agalactiae and associated stillbirth and neonatal disease in coastal Kenya.

Streptococcus agalactiae (group B streptococcus, GBS) causes neonatal disease and stillbirth, but its burden in sub-Saharan Africa is uncertain. We assessed maternal recto-vaginal GBS colonization (7,967 women), stillbirth and neonatal disease. Whole-genome sequencing was used to determine serotypes, sequence types and phylogeny. We found low maternal GBS colonization prevalence (934/7,967, 12%), but comparatively high incidence of GBS-associated stillbirth and early onset neonatal disease (EOD) in hospital (0.91 (0.25-2.3)/1,000 births and 0.76 (0.25-1.77)/1,000 live births, respectively). However, using a population denominator, EOD incidence was considerably reduced (0.13 (0.07-0.21)/1,000 live births). Treated cases of EOD had very high case fatality (17/36, 47%), especially within 24 h of birth, making under-ascertainment of community-born cases highly likely, both here and in similar facility-based studies. Maternal GBS colonization was less common in women with low socio-economic status, HIV infection and undernutrition, but when GBS-colonized, they were more probably colonized by the most virulent clone, CC17. CC17 accounted for 267/915 (29%) of maternal colonizing (265/267 (99%) serotype III; 2/267 (0.7%) serotype IV) and 51/73 (70%) of neonatal disease cases (all serotype III). Trivalent (Ia/II/III) and pentavalent (Ia/Ib/II/III/V) vaccines would cover 71/73 (97%) and 72/73 (99%) of disease-causing serotypes, respectively. Serotype IV should be considered for inclusion, with evidence of capsular switching in CC17 strains.

"You cannot know if it's a baby or not a baby": uptake, provision and perceptions of antenatal care and routine antenatal ultrasound scanning in rural Kenya.

BACKGROUND: Antenatal care early in pregnancy enables service providers to identify and manage risks to mother and fetus. In the global north, ultrasound scans are routinely offered in pregnancy to provide an accurate estimate of gestational age and identify potential problems. In sub-Saharan Africa, such services are rarely available and women often delay initiating antenatal care. This study describes the uptake and provision of antenatal care in a rural Kenyan hospital and explores how pregnant women and healthcare providers perceived the provision of ultrasound scanning, following its introduction in an international foetal growth study. METHODS: A descriptive study, using qualitative and quantitative methods, was conducted in Kilifi District Hospital, Kenya, between June 2011 and April 2012. In-depth interviews were conducted with 10 nurses working in the antenatal clinic (ANC) and 59 pregnant women attending ANC. Structured observations of 357 ANC consultations and 30 ultrasound scans were made. RESULTS: Women sought antenatal care for information about the health of their baby and the protection provided by the ANC services. Uncertainty about pregnancy status contributed to delay in ANC attendance; more than 78 % of women were over 20 weeks' gestation at their first visit. Healthcare workers found it difficult to detect pregnancies below 16 weeks gestation and, accurate assessment of gestational age below 20 weeks' gestation could be problematic. Provision of services depended on the pregnancy being detected and gestational age assessed. The "seeing", made possible through ultrasound scanning was perceived by pregnant women and healthcare workers to be beneficial: confirming the pregnancy, and providing reassurance about the fetus' condition. Few participants raised concerns about ultrasound scanning. CONCLUSIONS: Uncertainty about pregnancy status and gestational age for women and healthcare providers is a key factor influencing timing of ANC attendance, contributing to delays and restricting early provision of ANC services. Ultrasound scanning was perceived to enhance antenatal care through confirmation of pregnancy status and enabling more accurate estimation of gestational age and the health status of the fetus. There is a need to make available more affordable means of pregnancy testing as a strategy towards encouraging early attendance, and delivery of antenatal care.

Learning-based prediction of gestational age from ultrasound images of the fetal brain.

We propose an automated framework for predicting gestational age (GA) and neurodevelopmental maturation of a fetus based on 3D ultrasound (US) brain image appearance. Our method capitalizes on age-related sonographic image patterns in conjunction with clinical measurements to develop, for the first time, a predictive age model which improves on the GA-prediction potential of US images. The framework benefits from a manifold surface representation of the fetal head which delineates the inner skull boundary and serves as a common coordinate system based on cranial position. This allows for fast and efficient sampling of anatomically-corresponding brain regions to achieve like-for-like structural comparison of different developmental stages. We develop bespoke features which capture neurosonographic patterns in 3D images, and using a regression forest classifier, we characterize structural brain development both spatially and temporally to capture the natural variation existing in a healthy population (N=447) over an age range of active brain maturation (18-34weeks). On a routine clinical dataset (N=187) our age prediction results strongly correlate with true GA (r=0.98,accurate within±6.10days), confirming the link between maturational progression and neurosonographic activity observable across gestation. Our model also outperforms current clinical methods by ±4.57 days in the third trimester-a period complicated by biological variations in the fetal population. Through feature selection, the model successfully identified the most age-discriminating anatomies over this age range as being the Sylvian fissure, cingulate, and callosal sulci.

Predicting fetal neurodevelopmental age from ultrasound images.

We propose an automated framework for predicting age and neurodevelopmental maturation of a fetus based on 3D ultrasound (US) brain image appearance. A topology-preserving manifold representation of the fetal skull enabled design of bespoke scale-invariant image features. Our regression forest model used these features to learn a mapping from age-related sonographic image patterns to fetal age and development. The Sylvian Fissure was identified as a critical region for accurate age estimation, and restricting the search space to this anatomy improved prediction accuracy on a set of 130 healthy fetuses (error ± 3.8 days; r = 0.98 performing the best current clinical method. Our framework remained robust when applied to a routine clinical population.

Single point biochemical measurement algorithm for early diagnosis of ectopic pregnancy.

OBJECTIVES: Tubal rupture as a result of an ectopic pregnancy is the leading cause of first trimester maternal mortality. Currently, the diagnosis of ectopic pregnancy depends on transvaginal ultrasound and serial serum measurements of human chorionic gonadotrophin (hCG), which requires follow up. The objective of this study was to examine whether single point measurements at presentation could distinguish between women with ectopic pregnancy, viable pregnancy, and spontaneous miscarriage. DESIGN AND METHODS: Serum total hCG (hCGt), hyperglycosylated hCG (hCGh), free beta subunit of hCG (hCGß), progesterone (P), and CA-125 were measured by chemiluminescence immunoassay over a 3 month period in 441 women presenting at the emergency room with abdominal pain and a positive pregnancy test. Patient outcomes were followed and confirmed by histology. 65 samples were excluded due to poor sample storage, or lost to follow up. RESULTS: The pregnancy outcomes were 175 viable pregnancies, 175 spontaneous miscarriages, and 26 ectopic pregnancies. A serum hCGt <3736 mIU/mL cut off was 100% sensitive, with 76% specificity, for distinguishing ectopic pregnancy from viable pregnancy; but did not differentiate spontaneous miscarriage. Serum CA125 <41.98 U/mL produced 100% sensitivity and 43% specificity in distinguishing ectopic pregnancy from spontaneous miscarriage. Sequential application of hCGt and CA-125 cut off followed by ultrasound could detect 100% of ectopic pregnancies with 87% specificity for all intrauterine pregnancies. CONCLUSION: The combination of serum hCGt <3736 mIU/mL, followed by CA125 <41.98 U/mL is a promising algorithm for detecting all ectopic pregnancy at initial presentation.