Objective To identify and determine the optimal method to screening for fetal Down＇s syndrome （DS）. Methods Three large cohorts with 17 118, 39 903, 16 646 subjects were enrolled for the first trimester double mark...Objective To identify and determine the optimal method to screening for fetal Down＇s syndrome （DS）. Methods Three large cohorts with 17 118, 39 903, 16 646 subjects were enrolled for the first trimester double marker （pregnancy-associated plasma protein A and free [B-human chorionic gonadotropin） screening （FTDMS）, second trimester double marker （c{-fetoprotein and free B-human chorionic gonadotropin） screening （STDMS）, and second trimester triple marker （a-fetoprotein, free 13-human chorionic gonadotropin and unconjugated estriol 3） screening （STTMS）, respectively. The sensitivity, specificity, false positive rate （FPR）, false negative rate （FNR） and the areas under ROC curves （AUCs） were estimated in order to determine the optimal screening method in women under or above 35 years old. Results For women under 35 years old, STTMS was the best method with a detection rate of 68.8% and FPR of 4.3% followed by the STDMS with a detection rate （sensitivity） of 66.7% and FPR of 4.9%. The FTDMS had a lower detection rate of 61.1% and FPR of 6.3%. For women above 35 years old, the detection rate of all the methods was similar, but STTMS method had a lowest FPR of 15.9%. For women under 35 years old AUCs were 0.77 （95% CI, 0.64 to 0.91）, 0.81 （95% CI, 0.71 to 0.91）, and 0.82 （95% CI, 0.69 to 0.96） for FTDMS, STDMS, and STTMS methods, respectively; for those above 35 years old, AUCs were 0.70 （95% CI, 0.56 to 0.83）, 0.70 （95% CI, 0.59 to 0.82）, 0.78 （95% Cl, 0.58 to 0.97） for FTDMS, STDMS and SITMS, respectively. Conclusion Findings from our study revealed that STDMS is optimal for the detection of fetal DS in pregnant women aged under 35. For individual women, if economic condition permits, STFMS is the best choice, while for women aged above 35, STTMS is the best choice in this regard.展开更多
Objective:To compare the efficiency and related financial parameters of the double- and triple-marker test for the second-trimester maternal serum screening for Down's syndrome. Methods:The serum samples were coll...Objective:To compare the efficiency and related financial parameters of the double- and triple-marker test for the second-trimester maternal serum screening for Down's syndrome. Methods:The serum samples were collected from the 2^(nd) trimester pregnant women in this hospital and were examined for three biomedical markers[alpha-fetoprotein(AFP),freeβ-human chorionic gonadotropin(freeβ-hCG) and unconjugated estriol(uE_3)]by TR-FIA.The pregnancy outcomes were followed up and screening efficiency calculated for double-marker(AFP+freeβ-hCG) and triple-marker(AFP+ freeβ-hCG+uE_3) test. Results:(1)A total of 4,707 serum samples of 2^(nd) trimester pregnancy were collected in this study,of which 4,245 pregnancy outcomes got followed up by May 30,2009,with 462 cases lost to follow-up.The follow-up rate was 90.2%.3 cases of Down's syndrome,4 cases of other chromosome abnormalities and 1 case of neural tube defect (NTD) were found.There was no medically induced miscarriage by invasive tests.(2) Detection rate and false positive rate of triple marker test for Down's syndrome screening were 66.7%and 5.26%,respectively,while those in double marker test were 33.3%and 4.01%,respectively.The detection rate of all chromosome abnormalities was 75%in triple marker test and 37.5%in double marker test.The detection rate of NTD was 100%either in double or triple marker test.(3) It costs 499,375 RMB to avoid one Down's syndrome birth by using triple marker test and 781,200 RMB by using double marker test. Conclusion:Triple-marker test is superior to double marker test in 2nd trimester maternal serum screening for Down's syndrome,and costs less to avoid a Down's syndrome birth.展开更多
BACKGROUND Down syndrome(DS)is one of the most common chromosomal aneuploidy diseases.Prenatal screening and diagnostic tests can aid the early diagnosis,appropriate management of these fetuses,and give parents an inf...BACKGROUND Down syndrome(DS)is one of the most common chromosomal aneuploidy diseases.Prenatal screening and diagnostic tests can aid the early diagnosis,appropriate management of these fetuses,and give parents an informed choice about whether or not to terminate a pregnancy.In recent years,investigations have been conducted to achieve a high detection rate(DR)and reduce the false positive rate(FPR).Hospitals have accumulated large numbers of screened cases.However,artificial intelligence methods are rarely used in the risk assessment of prenatal screening for DS.AIM To use a support vector machine algorithm,classification and regression tree algorithm,and AdaBoost algorithm in machine learning for modeling and analysis of prenatal DS screening.METHODS The dataset was from the Center for Prenatal Diagnosis at the First Hospital of Jilin University.We designed and developed intelligent algorithms based on the synthetic minority over-sampling technique(SMOTE)-Tomek and adaptive synthetic sampling over-sampling techniques to preprocess the dataset of prenatal screening information.The machine learning model was then established.Finally,the feasibility of artificial intelligence algorithms in DS screening evaluation is discussed.RESULTS The database contained 31 DS diagnosed cases,accounting for 0.03%of all patients.The dataset showed a large difference between the numbers of DS affected and non-affected cases.A combination of over-sampling and undersampling techniques can greatly increase the performance of the algorithm at processing non-balanced datasets.As the number of iterations increases,the combination of the classification and regression tree algorithm and the SMOTETomek over-sampling technique can obtain a high DR while keeping the FPR to a minimum.CONCLUSION The support vector machine algorithm and the classification and regression tree algorithm achieved good results on the DS screening dataset.When the T21 risk cutoff value was set to 270,machine learning methods had a higher DR and a lower FPR than statistical methods.展开更多
Background: DiGeorge syndrome (also known as velo-cardio-facial syndrome) is a rare multisystem genetic disorder occurring in approximately 1 in 4000 to 1 in 6000 live births [1]. Although advances in genetic screenin...Background: DiGeorge syndrome (also known as velo-cardio-facial syndrome) is a rare multisystem genetic disorder occurring in approximately 1 in 4000 to 1 in 6000 live births [1]. Although advances in genetic screening have improved diagnosis in developed countries, the condition remains underdiagnosed in developing nations such as the Republic of Moldova, where access to genetic testing and family planning services is limited. Routine prenatal screening usually includes regular ultrasounds, monitoring of blood pressure, complete blood counts, coagulation studies, glucose, urine protein, and urine culture. Current ultrasound techniques have limitations in detecting this syndrome due to variability in interpretation, and genetic testing is often performed based on clinical discretion. The ultrasound could potentially point towards a genetic problem, as in DiGeorge, if multiple cardiac malformations are spotted in utero, but most cases such as this one are diagnosed after birth while being described as totally normal on prenatal ultrasound. Purpose: This study aims to highlight the diagnostic challenges and the need for comprehensive evaluation in identifying DiGeorge syndrome, emphasizing the importance of considering the syndrome as a whole rather than focusing on isolated organ system issues. Method: We present a case report of a 6-month-old girl who, after an uneventful pregnancy and normal prenatal ultrasound, presented with cardiac insufficiency. Following extensive investigations and multiple surgical interventions, DiGeorge syndrome was diagnosed at 9 months of age. Results: The patient’s diagnosis was delayed due to the lack of prenatal markers and the reliance on separate investigations of affected organ systems. Despite several interventions aimed at managing her symptoms, the final diagnosis was made after observing the association of multiple clinical features and conducting comprehensive genetic testing. Conclusions: This case underscores the importance of a holistic approach to diagnosis, which involves a thorough patient history, integration of diverse diagnostic tests, and recognition of the syndrome’s multi-system nature. It highlights the necessity for improved diagnostic protocols and increased awareness in regions with limited access to advanced genetic testing to prevent delays in identifying DiGeorge syndrome and to facilitate timely and appropriate management.展开更多
基金supported by the National Natural Science Foundation of China (81101655)the grant from the China Postdoctoral Science Foundation (2011M501282)the grant from Hunan Provincial Science & Tecnology Departemnt(2009SK3048)
文摘Objective To identify and determine the optimal method to screening for fetal Down＇s syndrome （DS）. Methods Three large cohorts with 17 118, 39 903, 16 646 subjects were enrolled for the first trimester double marker （pregnancy-associated plasma protein A and free [B-human chorionic gonadotropin） screening （FTDMS）, second trimester double marker （c{-fetoprotein and free B-human chorionic gonadotropin） screening （STDMS）, and second trimester triple marker （a-fetoprotein, free 13-human chorionic gonadotropin and unconjugated estriol 3） screening （STTMS）, respectively. The sensitivity, specificity, false positive rate （FPR）, false negative rate （FNR） and the areas under ROC curves （AUCs） were estimated in order to determine the optimal screening method in women under or above 35 years old. Results For women under 35 years old, STTMS was the best method with a detection rate of 68.8% and FPR of 4.3% followed by the STDMS with a detection rate （sensitivity） of 66.7% and FPR of 4.9%. The FTDMS had a lower detection rate of 61.1% and FPR of 6.3%. For women above 35 years old, the detection rate of all the methods was similar, but STTMS method had a lowest FPR of 15.9%. For women under 35 years old AUCs were 0.77 （95% CI, 0.64 to 0.91）, 0.81 （95% CI, 0.71 to 0.91）, and 0.82 （95% CI, 0.69 to 0.96） for FTDMS, STDMS, and STTMS methods, respectively; for those above 35 years old, AUCs were 0.70 （95% CI, 0.56 to 0.83）, 0.70 （95% CI, 0.59 to 0.82）, 0.78 （95% Cl, 0.58 to 0.97） for FTDMS, STDMS and SITMS, respectively. Conclusion Findings from our study revealed that STDMS is optimal for the detection of fetal DS in pregnant women aged under 35. For individual women, if economic condition permits, STFMS is the best choice, while for women aged above 35, STTMS is the best choice in this regard.
文摘Objective:To compare the efficiency and related financial parameters of the double- and triple-marker test for the second-trimester maternal serum screening for Down's syndrome. Methods:The serum samples were collected from the 2^(nd) trimester pregnant women in this hospital and were examined for three biomedical markers[alpha-fetoprotein(AFP),freeβ-human chorionic gonadotropin(freeβ-hCG) and unconjugated estriol(uE_3)]by TR-FIA.The pregnancy outcomes were followed up and screening efficiency calculated for double-marker(AFP+freeβ-hCG) and triple-marker(AFP+ freeβ-hCG+uE_3) test. Results:(1)A total of 4,707 serum samples of 2^(nd) trimester pregnancy were collected in this study,of which 4,245 pregnancy outcomes got followed up by May 30,2009,with 462 cases lost to follow-up.The follow-up rate was 90.2%.3 cases of Down's syndrome,4 cases of other chromosome abnormalities and 1 case of neural tube defect (NTD) were found.There was no medically induced miscarriage by invasive tests.(2) Detection rate and false positive rate of triple marker test for Down's syndrome screening were 66.7%and 5.26%,respectively,while those in double marker test were 33.3%and 4.01%,respectively.The detection rate of all chromosome abnormalities was 75%in triple marker test and 37.5%in double marker test.The detection rate of NTD was 100%either in double or triple marker test.(3) It costs 499,375 RMB to avoid one Down's syndrome birth by using triple marker test and 781,200 RMB by using double marker test. Conclusion:Triple-marker test is superior to double marker test in 2nd trimester maternal serum screening for Down's syndrome,and costs less to avoid a Down's syndrome birth.
基金Supported by Science and Technology Department of Jilin Province,No.20190302073GX.
文摘BACKGROUND Down syndrome(DS)is one of the most common chromosomal aneuploidy diseases.Prenatal screening and diagnostic tests can aid the early diagnosis,appropriate management of these fetuses,and give parents an informed choice about whether or not to terminate a pregnancy.In recent years,investigations have been conducted to achieve a high detection rate(DR)and reduce the false positive rate(FPR).Hospitals have accumulated large numbers of screened cases.However,artificial intelligence methods are rarely used in the risk assessment of prenatal screening for DS.AIM To use a support vector machine algorithm,classification and regression tree algorithm,and AdaBoost algorithm in machine learning for modeling and analysis of prenatal DS screening.METHODS The dataset was from the Center for Prenatal Diagnosis at the First Hospital of Jilin University.We designed and developed intelligent algorithms based on the synthetic minority over-sampling technique(SMOTE)-Tomek and adaptive synthetic sampling over-sampling techniques to preprocess the dataset of prenatal screening information.The machine learning model was then established.Finally,the feasibility of artificial intelligence algorithms in DS screening evaluation is discussed.RESULTS The database contained 31 DS diagnosed cases,accounting for 0.03%of all patients.The dataset showed a large difference between the numbers of DS affected and non-affected cases.A combination of over-sampling and undersampling techniques can greatly increase the performance of the algorithm at processing non-balanced datasets.As the number of iterations increases,the combination of the classification and regression tree algorithm and the SMOTETomek over-sampling technique can obtain a high DR while keeping the FPR to a minimum.CONCLUSION The support vector machine algorithm and the classification and regression tree algorithm achieved good results on the DS screening dataset.When the T21 risk cutoff value was set to 270,machine learning methods had a higher DR and a lower FPR than statistical methods.
文摘Background: DiGeorge syndrome (also known as velo-cardio-facial syndrome) is a rare multisystem genetic disorder occurring in approximately 1 in 4000 to 1 in 6000 live births [1]. Although advances in genetic screening have improved diagnosis in developed countries, the condition remains underdiagnosed in developing nations such as the Republic of Moldova, where access to genetic testing and family planning services is limited. Routine prenatal screening usually includes regular ultrasounds, monitoring of blood pressure, complete blood counts, coagulation studies, glucose, urine protein, and urine culture. Current ultrasound techniques have limitations in detecting this syndrome due to variability in interpretation, and genetic testing is often performed based on clinical discretion. The ultrasound could potentially point towards a genetic problem, as in DiGeorge, if multiple cardiac malformations are spotted in utero, but most cases such as this one are diagnosed after birth while being described as totally normal on prenatal ultrasound. Purpose: This study aims to highlight the diagnostic challenges and the need for comprehensive evaluation in identifying DiGeorge syndrome, emphasizing the importance of considering the syndrome as a whole rather than focusing on isolated organ system issues. Method: We present a case report of a 6-month-old girl who, after an uneventful pregnancy and normal prenatal ultrasound, presented with cardiac insufficiency. Following extensive investigations and multiple surgical interventions, DiGeorge syndrome was diagnosed at 9 months of age. Results: The patient’s diagnosis was delayed due to the lack of prenatal markers and the reliance on separate investigations of affected organ systems. Despite several interventions aimed at managing her symptoms, the final diagnosis was made after observing the association of multiple clinical features and conducting comprehensive genetic testing. Conclusions: This case underscores the importance of a holistic approach to diagnosis, which involves a thorough patient history, integration of diverse diagnostic tests, and recognition of the syndrome’s multi-system nature. It highlights the necessity for improved diagnostic protocols and increased awareness in regions with limited access to advanced genetic testing to prevent delays in identifying DiGeorge syndrome and to facilitate timely and appropriate management.
