Management and Outcomes of Fetal Hydrops in a Tertiary Care Centre in Singapore

Objective This study aims to analyze the etiology and perinatal outcome of nonimmune hydrops fetalis (NIHF) in Southern China. Methods All cases with NIHF diagnosed antenatally from January 1, 2007 to December 31, 2014 were identified and analyzed. Results Total 482 cases of NIHF were identified during the study period. The most common cause of NIHF was hemoglobin (Hb) Bart's disease (61.8%), followed by chromosomal abnormalities (13.5%), idiopathic etiology (13.1%), cardiac abnormalities (6.4%), and others (5.2%). After 20 weeks' gestation, a total of 408 cases of NIHF presented, including Hb Bart's disease (279 cases), cardiac abnormalities (27 cases), and infection (7 cases). NIHF caused by chromosomal abnormalities mainly presented between 15 and 19 weeks' gestation. Of the 482 cases, 459 cases elected termination of pregnancy. The remaining 23 cases elected to continue their pregnancy. Among them, 14 (60.9%) resulted in intrauterine fetal death, 6 had neonatal death, 3 infants survived to 1 year of age. Of the three infants, one has cerebral palsy, and the remaining two are normal. Conclusions Hb Bart's disease is the most common cause of NIHF in Southern China. An effective prenatal screening and counseling program for thalassemia in this region may be the most effective way to lower the incidence NIHF.

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ThalPred: a web-based prediction tool for discriminating thalassemia trait and iron deficiency anemia

Laengsri

Shoombuatong

Adirojananon

et al. 2019

BMC Med Inform Decis Mak

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BackgroundThe hypochromic microcytic anemia (HMA) commonly found in Thailand are iron deficiency anemia (IDA) and thalassemia trait (TT). Accurate discrimination between IDA and TT is an important issue and better methods are urgently needed. Although considerable RBC formulas and indices with various optimal cut-off values have been developed, distinguishing between IDA and TT is still a challenging problem due to the diversity of various anemic populations. To address this problem, it is desirable to develop an improved and automated prediction model for discriminating IDA from TT.MethodsWe retrospectively collected laboratory data of HMA found in Thai adults. Five machine learnings, including k-nearest neighbor (k-NN), decision tree, random forest (RF), artificial neural network (ANN) and support vector machine (SVM), were applied to construct a discriminant model. Performance was assessed and compared with thirteen existing discriminant formulas and indices.ResultsThe data of 186 patients (146 patients with TT and 40 with IDA) were enrolled. The interpretable rules derived from the RF model were proposed to demonstrate the combination of RBC indices for discriminating IDA from TT. A web-based tool ‘ThalPred’ was implemented using an SVM model based on seven RBC parameters. ThalPred achieved prediction results with an external accuracy, MCC and AUC of 95.59, 0.87 and 0.98, respectively.ConclusionThalPred and an interpretable rule were provided for distinguishing IDA from TT. For the convenience of health care team experimental scientists, a web-based tool has been established at http://codes.bio/thalpred/ by which users can easily get their desired screening test result without the need to go through the underlying mathematical and computational details.

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Management and Outcomes of Fetal Hydrops in a Tertiary Care Centre in Singapore

Cited by 3 publications

References 15 publications

Fetal Death

Fetal Death

Etiology and Perinatal Outcome of Nonimmune Hydrops Fetalis in Southern China

ThalPred: a web-based prediction tool for discriminating thalassemia trait and iron deficiency anemia

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