Predicting preeclampsia in early pregnancy using clinical and laboratory data via machine learning model
Abstract Background This study was performed to characterize the relationship of various laboratory test indicators with clinical information and Preeclampsia (PE) development. Then, prediction models for early-onset preeclampsia (EOPE), late-onset preeclampsia (LOPE), and preterm preeclampsia (Pret...
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2025-05-01
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| author | Songchang Chen Jia Li Xiao Zhang Wenqiu Xu Zhixu Qiu Siyao Yan Wenrui Zhao Zhiguang Zhao Peirun Tian Qiang Zhao Qun Zhang Weiping Chen Huahua Li Xiaohong Ruan Gefei Xiao Sufen Zhang Liqing Hu Jie Qin Wuyan Huang Zhongzhe Li Shunyao Wang Rui Zhang Shang Huang Xin Wang Yao Yao Jian Ran Danling Cheng Qi Luo Teng Pan Ruyun Gao Jing Zheng Yuxuan Wang Cong Liu Xianling Cao Xuanyou Zhou Naixin Xu Lanlan Zhang Xu Han Haolin Wang Suihua Feng Shuyuan Li Jianguo Zhang Lijian Zhao Fengxiang Wei |
| author_facet | Songchang Chen Jia Li Xiao Zhang Wenqiu Xu Zhixu Qiu Siyao Yan Wenrui Zhao Zhiguang Zhao Peirun Tian Qiang Zhao Qun Zhang Weiping Chen Huahua Li Xiaohong Ruan Gefei Xiao Sufen Zhang Liqing Hu Jie Qin Wuyan Huang Zhongzhe Li Shunyao Wang Rui Zhang Shang Huang Xin Wang Yao Yao Jian Ran Danling Cheng Qi Luo Teng Pan Ruyun Gao Jing Zheng Yuxuan Wang Cong Liu Xianling Cao Xuanyou Zhou Naixin Xu Lanlan Zhang Xu Han Haolin Wang Suihua Feng Shuyuan Li Jianguo Zhang Lijian Zhao Fengxiang Wei |
| author_sort | Songchang Chen |
| collection | DOAJ |
| description | Abstract Background This study was performed to characterize the relationship of various laboratory test indicators with clinical information and Preeclampsia (PE) development. Then, prediction models for early-onset preeclampsia (EOPE), late-onset preeclampsia (LOPE), and preterm preeclampsia (Preterm PE) were developed using maternal characteristics and laboratory data. Methods Between January 2019 and December 2021, we retrospectively recruited 144 EOPE, 363 LOPE, 231 Preterm PE, and 1458 healthy participants from six hospitals. We utilized all available clinical and laboratory data obtained during routine prenatal visits in early pregnancy. The models for EOPE, LOPE, and Preterm PE were created using ensemble machine learning models with patient clinical and laboratory data. Results: By comparing laboratory variables between PE patients and healthy controls, we identified 7, 18, 8, 15, 7,29 laboratory markers for EOPE, LOPE, and Preterm PE, severe PE, superimposed PE, first-time PE respectively. The ensemble EOPE and LOPE models incorporating clinical and laboratory predictors outperformed the clinical factor models respectively. The ensemble EOPE model demonstrated good sensitivity (72.22%,95% confidence interval [CI]: 57.59%-86.85%) and specificity (85.25%,95% CI: 80.54%-89.97%) in distinguishing EOPE from controls in early pregnancy. Similarly, the ensemble LOPE model showed good accuracy in differentiating LOPE from healthy participants (sensitivity: 69.57%, 95% CI: 56.27%-82.86%; specificity: 85.25%, 95% CI: 80.54%-89.97%). The prediction scores demonstrated notable positive correlations with blood pressure at admission, while they showed inverse correlations with 24-hour urine protein levels and fetal growth restriction among PE patients. In conclusion, our study identified key laboratory indicators for forecasting PE. The developed models exhibited good predictive capability for assessing preeclampsia risk and severity based on clinical and laboratory data. Clinical trial number Not applicable. |
| format | Article |
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| institution | OA Journals |
| issn | 1472-6947 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
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| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-d1d220786f154a33ab1fa8a996d4d9a42025-08-20T01:47:32ZengBMCBMC Medical Informatics and Decision Making1472-69472025-05-0125111510.1186/s12911-025-02999-5Predicting preeclampsia in early pregnancy using clinical and laboratory data via machine learning modelSongchang Chen0Jia Li1Xiao Zhang2Wenqiu Xu3Zhixu Qiu4Siyao Yan5Wenrui Zhao6Zhiguang Zhao7Peirun Tian8Qiang Zhao9Qun Zhang10Weiping Chen11Huahua Li12Xiaohong Ruan13Gefei Xiao14Sufen Zhang15Liqing Hu16Jie Qin17Wuyan Huang18Zhongzhe Li19Shunyao Wang20Rui Zhang21Shang Huang22Xin Wang23Yao Yao24Jian Ran25Danling Cheng26Qi Luo27Teng Pan28Ruyun Gao29Jing Zheng30Yuxuan Wang31Cong Liu32Xianling Cao33Xuanyou Zhou34Naixin Xu35Lanlan Zhang36Xu Han37Haolin Wang38Suihua Feng39Shuyuan Li40Jianguo Zhang41Lijian Zhao42Fengxiang Wei43Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan UniversityHebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI GenomicsHebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI GenomicsHebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI GenomicsHebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI GenomicsHebei Medical UniversityHebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI GenomicsHebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI GenomicsBGI GenomicsDepartment of Obstetrics and Gynecology, Jiangmen Central HospitalDepartment of Obstetrics and Gynecology, Jiangmen Central HospitalDepartment of Obstetrics and Gynecology, Jiangmen Central HospitalDepartment of Obstetrics and Gynecology, Jiangmen Central HospitalDepartment of Obstetrics and Gynecology, Jiangmen Central HospitalDepartment of Medical Genetics and Prenatal Diagnosis, Zhuhai Center for Maternal and Child Health CareDepartment of Medical Genetics and Prenatal Diagnosis, Zhuhai Center for Maternal and Child Health CareDepartment of Medical Genetics and Prenatal Diagnosis, Zhuhai Center for Maternal and Child Health CareDepartment of Medical Genetics and Prenatal Diagnosis, Zhuhai Center for Maternal and Child Health CareDepartment of Medical Genetics and Prenatal Diagnosis, Zhuhai Center for Maternal and Child Health CareDepartment of Prevention and Health Care, Zhuhai Center for Maternal and Child Health CareBGI GenomicsDivision of Maternal-Fetal Medicine, Shenzhen Bao’ an Women’s and Children’s HospitalThe Genetics Laboratory, Longgang District Maternity and Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College)The Genetics Laboratory, Longgang District Maternity and Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College)The Genetics Laboratory, Longgang District Maternity and Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College)The Genetics Laboratory, Longgang District Maternity and Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College)The Genetics Laboratory, Longgang District Maternity and Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College)School of Basic Medical Sciences, Jiamusi UniversityThe Genetics Laboratory, Longgang District Maternity and Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College)School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human HealthSchool of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human HealthSchool of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human HealthObstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan UniversityObstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan UniversityObstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan UniversityObstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan UniversityInternational Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong UniversityInternational Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong UniversitySchool of Computer Science, Guangzhou College of Technology and BusinessDepartment of Obstetrics and Gynecology, Jiangmen Central HospitalInternational Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong UniversityHebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI GenomicsHebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI GenomicsThe Genetics Laboratory, Longgang District Maternity and Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College)Abstract Background This study was performed to characterize the relationship of various laboratory test indicators with clinical information and Preeclampsia (PE) development. Then, prediction models for early-onset preeclampsia (EOPE), late-onset preeclampsia (LOPE), and preterm preeclampsia (Preterm PE) were developed using maternal characteristics and laboratory data. Methods Between January 2019 and December 2021, we retrospectively recruited 144 EOPE, 363 LOPE, 231 Preterm PE, and 1458 healthy participants from six hospitals. We utilized all available clinical and laboratory data obtained during routine prenatal visits in early pregnancy. The models for EOPE, LOPE, and Preterm PE were created using ensemble machine learning models with patient clinical and laboratory data. Results: By comparing laboratory variables between PE patients and healthy controls, we identified 7, 18, 8, 15, 7,29 laboratory markers for EOPE, LOPE, and Preterm PE, severe PE, superimposed PE, first-time PE respectively. The ensemble EOPE and LOPE models incorporating clinical and laboratory predictors outperformed the clinical factor models respectively. The ensemble EOPE model demonstrated good sensitivity (72.22%,95% confidence interval [CI]: 57.59%-86.85%) and specificity (85.25%,95% CI: 80.54%-89.97%) in distinguishing EOPE from controls in early pregnancy. Similarly, the ensemble LOPE model showed good accuracy in differentiating LOPE from healthy participants (sensitivity: 69.57%, 95% CI: 56.27%-82.86%; specificity: 85.25%, 95% CI: 80.54%-89.97%). The prediction scores demonstrated notable positive correlations with blood pressure at admission, while they showed inverse correlations with 24-hour urine protein levels and fetal growth restriction among PE patients. In conclusion, our study identified key laboratory indicators for forecasting PE. The developed models exhibited good predictive capability for assessing preeclampsia risk and severity based on clinical and laboratory data. Clinical trial number Not applicable.https://doi.org/10.1186/s12911-025-02999-5PreeclampsiaLaboratory dataMachine learning model |
| spellingShingle | Songchang Chen Jia Li Xiao Zhang Wenqiu Xu Zhixu Qiu Siyao Yan Wenrui Zhao Zhiguang Zhao Peirun Tian Qiang Zhao Qun Zhang Weiping Chen Huahua Li Xiaohong Ruan Gefei Xiao Sufen Zhang Liqing Hu Jie Qin Wuyan Huang Zhongzhe Li Shunyao Wang Rui Zhang Shang Huang Xin Wang Yao Yao Jian Ran Danling Cheng Qi Luo Teng Pan Ruyun Gao Jing Zheng Yuxuan Wang Cong Liu Xianling Cao Xuanyou Zhou Naixin Xu Lanlan Zhang Xu Han Haolin Wang Suihua Feng Shuyuan Li Jianguo Zhang Lijian Zhao Fengxiang Wei Predicting preeclampsia in early pregnancy using clinical and laboratory data via machine learning model BMC Medical Informatics and Decision Making Preeclampsia Laboratory data Machine learning model |
| title | Predicting preeclampsia in early pregnancy using clinical and laboratory data via machine learning model |
| title_full | Predicting preeclampsia in early pregnancy using clinical and laboratory data via machine learning model |
| title_fullStr | Predicting preeclampsia in early pregnancy using clinical and laboratory data via machine learning model |
| title_full_unstemmed | Predicting preeclampsia in early pregnancy using clinical and laboratory data via machine learning model |
| title_short | Predicting preeclampsia in early pregnancy using clinical and laboratory data via machine learning model |
| title_sort | predicting preeclampsia in early pregnancy using clinical and laboratory data via machine learning model |
| topic | Preeclampsia Laboratory data Machine learning model |
| url | https://doi.org/10.1186/s12911-025-02999-5 |
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