The Safety of a Conservative Fluid Replacement Strategy in Adults Hospitalised with Malaria

BackgroundA conservative approach to fluid resuscitation improves survival in children with severe malaria; however, this strategy has not been formally evaluated in adults with the disease.MethodsAdults hospitalised with malaria at two tertiary referral hospitals in Myanmar received intravenous fluid replacement with isotonic saline, administered at a maintenance rate using a simple weight-based algorithm. Clinical and biochemical indices were followed sequentially.ResultsOf 61 adults enrolled, 34 (56%) had Plasmodium falciparum mono-infection, 17 (28%) Plasmodium vivax mono-infection and 10 (16%) mixed infection; 27 (44%) patients were at high risk of death (P. falciparum infection and RCAM score ≥ 2). In the first six hours of hospitalisation patients received a mean 1.7 ml/kg/hour (range: 1.3–2.2) of intravenous fluid and were able to drink a mean of 0.8 ml/kg/hour (range: 0–3). Intravenous fluid administration and oral intake were similar for the remainder of the first 48 hours of hospitalisation. All 61 patients survived to discharge. No patient developed Adult Respiratory Distress Syndrome, a requirement for renal replacement therapy or hypotension (mean arterial pressure < 60mmHg). Plasma lactate was elevated (> 2 mmol/L) on enrolment in 26 (43%) patients but had declined by 6 hours in 25 (96%) and was declining at 24 hours in the other patient. Plasma creatinine was elevated (> 120 μmol/L) on enrolment in 17 (28%) patients, but was normal or falling in 16 (94%) at 48 hours and declining in the other patient by 72 hours. There was no clinically meaningful increase in plasma lactate or creatinine in any patient with a normal value on enrolment. Patients receiving fluid replacement with the conservative fluid replacement algorithm were more likely to survive than historical controls in the same hospitals who had received fluid replacement guided by clinical judgement in the year prior to the study (p = 0.03), despite having more severe disease (p < 0.001).ConclusionsA conservative fluid resuscitation strategy appears safe in adults hospitalised with malaria

Statistical Machine Translation between Myanmar Sign Language and Myanmar Written Text

This paper contributes the first evaluation of the quality of automatic translation between Myanmar sign language (MSL) and Myanmar written text, in both directions. Our developing MSL-Myanmar parallel corpus was used for translations and the experiments were carried out using three different statistical machine translation (SMT) approaches: phrase-based, hierarchical phrase-based, and the operation sequence model. In addition, three different segmentation schemes were studies, these were syllable segmentation, word segmentation and sign unit based word segmentation. The results show that the highest quality machine translation was attained with syllable segmentations for both MSL and Myanmar written text

Development of Natural Language Processing based Communication and Educational Assisted Systems for the People with Hearing Disability in Myanmar

Information and communication technologies (ICTs) provide people with disabilities to better integrate socially and economically into their communities by supporting access to information and knowledge, learning and teaching situations, personal communication and interaction. Our research purpose is to develop systems that will provide communication and educational assistance to persons with hearing disability using Natural Language Processing (NLP). In this paper, we present corpus building for Myanmar sign language (MSL), Machine Translation (MT) between MSL, Myanmar written text (MWT) and Myanmar SignWriting (MSW) and two Fingerspelling keyboard layouts for Myanmar SignWriting. We believe that the outcome of this research is useful for educational contents and communication between hearing disability and general people

The overlap between miscarriage and extreme preterm birth in a limited-resource setting on the Thailand-Myanmar border: a population cohort study [version 3; referees: 1 approved, 3 approved with reservations]

Background : No universal  demarcation of gestational age  distinguishes miscarriage and stillbirth or extreme preterm birth (exPTB). This study provides a synopsis of outcome between 22 to <28 weeks gestation from a low resource setting. Methods : A retrospective record review of a population on the Thailand-Myanmar border was conducted. Outcomes were classified as miscarriage, late expulsion of products between 22 to < 28 weeks gestation with evidence of non-viability (mostly ultrasound absent fetal heart beat) prior to 22 weeks; or  exPTB (stillbirth/live born) between 22 to < 28 weeks gestation when the fetus was viable at ≥22 weeks. Termination of pregnancy and gestational trophoblastic disease were excluded. Results : From 1995-2015, 80.9% (50,046/ 61,829) of registered women had a known pregnancy outcome, of whom 99.8% (49,931) had a known gestational age. Delivery  between 22 to <28 weeks gestation included 0.9% (472/49,931) of pregnancies after removing 18 cases (3.8%) who met an exclusion criteria. Most  pregnancies had an ultrasound: 72.5% (n=329/454);  43.6% (n=197) were classified as  miscarriage and 56.4% (n=257) exPTB.  Individual record review of miscarriages estimated that fetal death had occurred at a median of 16 weeks, despite late expulsion between 22 to <28 weeks. With available data (n=252, 5 missing) the proportion of stillbirth was 47.6% (n=120), congenital abnormality 10.5% (24/228, 29 missing) and neonatal death was 98.5% (128/131, 1 missing). Introduction of ultrasound was associated with a 2-times higher odds of classification of outcome as exPTB rather than miscarriage. Conclusion : In this low resource setting few (<1%) pregnancy outcomes occurred in the 22 to <28 weeks gestational window; four in ten  were miscarriage (late expulsion) and neonatal mortality approached 100%.  In the scale-up to preventable newborns deaths (at least initially) greater benefits will be obtained by focusing on the viable newborns of ≥ 28 weeks gestation

A mixed methods evaluation of Advanced Life Support in Obstetrics (ALSO) and Basic Life Support in Obstetrics (BLSO) in a resource-limited setting on the Thailand-Myanmar border [version 1; peer review: 2 approved]

Background: Short emergency obstetric care (EmOC) courses have demonstrated improved provider confidence, knowledge and skills but impact on indicators such as maternal mortality and stillbirth is less substantial. This manuscript evaluates Advanced Life Support in Obstetrics (ALSO) and Basic Life Support (BLSO) as an adult education tool, in a protracted, post-conflict and resource-limited setting. Methods: A mixed methods evaluation was used. Basic characteristics of ALSO and BLSO participants and their course results were summarized. Kirkpatrick’s framework for assessment of education effectiveness included: qualitative data on participants’ reactions to training (level 1); and quantitative health indicator data on change in the availability and quality of EmOC and in maternal and/or neonatal health outcomes (level 4), by evaluation of the post-partum haemorrhage (PPH) related maternal mortality ratio (MMR) and stillbirth rate in the eight years prior and following implementation of ALSO and BLSO. Results: 561 Thailand-Myanmar border health workers participated in ALSO (n=355) and BLSO (n=206) courses 2008-2020. Pass rates on skills exceeded 90% for both courses while 50% passed the written ALSO test. Perceived confidence significantly improved for all items assessed. In the eight-year block preceding the implementation of ALSO and BLSO (2000-07) the PPH related MMR per 100,000 live births was 57.0 (95%CI 30.06-108.3)(9/15797) compared to 25.4 (95%CI 11.6-55.4)(6/23620) eight years following (2009-16), p=0.109. After adjustment, PPH related maternal mortality was associated with birth before ALSO/BLSO implementation aOR 3.825 (95%CI 1.1233-11.870), migrant (not refugee) status aOR 3.814 (95%CI 1.241-11.718) and attending ≤four antenatal consultations aOR 3.648 (95%CI 1.189-11.191). Stillbirth rate per 1,000 total births was 18.2 (95%CI 16.2-20.4)(291/16016) before the courses, and 11.1 (95%CI 9.8-12.5)(264/23884) after, p=0.038. Birth before ALSO/ BLSO implementation was associated with stillbirth aoR 1.235 (95%CI 1.018-1.500). Conclusions: This evaluation suggests ALSO and BLSO are sustainable, beneficial, EmOC trainings for adult education in protracted, post-conflict, resource-limited settings

Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: a population-based machine learning study.

Background: Preterm birth is a major global health challenge, the leading cause of death in children under 5 years of age, and a key measure of a population's general health and nutritional status. Current clinical methods of estimating fetal gestational age are often inaccurate. For example, between 20 and 30 weeks of gestation, the width of the 95% prediction interval around the actual gestational age is estimated to be 18-36 days, even when the best ultrasound estimates are used. The aims of this study are to improve estimates of fetal gestational age and provide personalised predictions of future growth. Methods: Using ultrasound-derived, fetal biometric data, we developed a machine learning approach to accurately estimate gestational age. The accuracy of the method is determined by reference to exactly known facts pertaining to each fetus-specifically, intervals between ultrasound visits-rather than the date of the mother's last menstrual period. The data stem from a sample of healthy, well-nourished participants in a large, multicentre, population-based study, the International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st). The generalisability of the algorithm is shown with data from a different and more heterogeneous population (INTERBIO-21st Fetal Study). Findings: In the context of two large datasets, we estimated gestational age between 20 and 30 weeks of gestation with 95% confidence to within 3 days, using measurements made in a 10-week window spanning the second and third trimesters. Fetal gestational age can thus be estimated in the 20-30 weeks gestational age window with a prediction interval 3-5 times better than with any previous algorithm. This will enable improved management of individual pregnancies. 6-week forecasts of the growth trajectory for a given fetus are accurate to within 7 days. This will help identify at-risk fetuses more accurately than currently possible. At population level, the higher accuracy is expected to improve fetal growth charts and population health assessments. Interpretation: Machine learning can circumvent long-standing limitations in determining fetal gestational age and future growth trajectory, without recourse to often inaccurately known information, such as the date of the mother's last menstrual period. Using this algorithm in clinical practice could facilitate the management of individual pregnancies and improve population-level health. Upon publication of this study, the algorithm for gestational age estimates will be provided for research purposes free of charge via a web portal. Funding: Bill & Melinda Gates Foundation, Office of Science (US Department of Energy), US National Science Foundation, and National Institute for Health Research Oxford Biomedical Research Centre