Louise C. Laurent, George R. Saade, Glenn R. Markenson, Kent D. Heyborne, Corina N. Schoen, Jason K. Baxter, Sherri A. Longo, Leonardo M. Pereira, Emily J. Su, Matthew K. Hoffman, Garrett K. Lam, Angela C. Fox, Ashoka D. Polpitiya, Md. Badahur Badsha, Tracey C. Fleischer, Thomas J. Garite, J. Jay Boniface, Paul E. Kearney
{"title":"结合临床变量和血清蛋白生物标志物的新型妊娠中期早产子痫前期风险预测因子的临床验证","authors":"Louise C. Laurent, George R. Saade, Glenn R. Markenson, Kent D. Heyborne, Corina N. Schoen, Jason K. Baxter, Sherri A. Longo, Leonardo M. Pereira, Emily J. Su, Matthew K. Hoffman, Garrett K. Lam, Angela C. Fox, Ashoka D. Polpitiya, Md. Badahur Badsha, Tracey C. Fleischer, Thomas J. Garite, J. Jay Boniface, Paul E. Kearney","doi":"10.1101/2022.12.21.22282936","DOIUrl":null,"url":null,"abstract":"Objective\nTo develop and validate mid-trimester preterm preeclampsia (PE) risk predictors combining clinical factors and serum protein analytes, and to compare their performance with those of widely used clinical and risk assessment algorithms endorsed by professional societies. Methods\nThis was a secondary analysis of data from two large, multicenter studies in pregnant individuals (PAPR, NCT01371019; TREETOP, NCT02787213), originally conducted to discover, verify, and validate a serum proteomic predictor of preterm birth risk. Serum protein abundances were determined by mass spectrometry. Classifier models combined one or two novel protein ratio(s) with a composite clinical variable, denoted as ClinRisk3, which included prior PE, pre-existing hypertension, or pregestational diabetes. Predictive performance was assessed for the full validation cohort and for a subset that had early gestational age (GA) dating via ultrasound. Classifier performance was compared directly to the U.S. Preventive Services Task Force (USPSTF) algorithm for identification of pregnancies that should receive low-dose aspirin (LDASA) for PE prevention. Results\nNine of nine prespecified classifier models were validated for risk of preterm PE with delivery <37 weeks′ gestation. Areas under the receiver operating characteristic curve ranged from 0.72-0.78 in the full validation cohort, compared to 0.68 for both ClinRisk3 alone and for the USPSTF algorithm. In the early GA dating subcohort, an exemplar predictor, ClinRisk3 + inhibin subunit beta C chain/sex hormone binding globulin (ClinRisk3+INHBC/SHBG) showed a markedly lower screen positive rate (11.1% vs 43.3%) and higher positive predictive value (13.0% vs 5.0%) and odds ratio (9.93 vs 5.24) than USPSTF. Its performance was similar in nulliparas and all parities. Conclusion\nNine preterm PE risk predictors were identified, validated in an independent cohort, and shown to be more predictive than the USPSTF-endorsed algorithm. Our results indicate that a single blood test performed in the first half of pregnancy can be used for personalized PE risk assessment, particularly for pregnancies with minimal or no identified clinical risk factors, including nulliparas. Results can be used to guide personalized pregnancy management, including but not restricted to LDASA for PE prophylaxis, and serve as a basis for developing new prevention strategies.","PeriodicalId":501409,"journal":{"name":"medRxiv - Obstetrics and Gynecology","volume":"27 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Clinical validation of novel second-trimester preterm preeclampsia risk predictors combining clinical variables and serum protein biomarkers\",\"authors\":\"Louise C. Laurent, George R. Saade, Glenn R. Markenson, Kent D. Heyborne, Corina N. Schoen, Jason K. Baxter, Sherri A. Longo, Leonardo M. Pereira, Emily J. Su, Matthew K. Hoffman, Garrett K. Lam, Angela C. Fox, Ashoka D. Polpitiya, Md. Badahur Badsha, Tracey C. Fleischer, Thomas J. Garite, J. Jay Boniface, Paul E. Kearney\",\"doi\":\"10.1101/2022.12.21.22282936\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Objective\\nTo develop and validate mid-trimester preterm preeclampsia (PE) risk predictors combining clinical factors and serum protein analytes, and to compare their performance with those of widely used clinical and risk assessment algorithms endorsed by professional societies. Methods\\nThis was a secondary analysis of data from two large, multicenter studies in pregnant individuals (PAPR, NCT01371019; TREETOP, NCT02787213), originally conducted to discover, verify, and validate a serum proteomic predictor of preterm birth risk. Serum protein abundances were determined by mass spectrometry. Classifier models combined one or two novel protein ratio(s) with a composite clinical variable, denoted as ClinRisk3, which included prior PE, pre-existing hypertension, or pregestational diabetes. Predictive performance was assessed for the full validation cohort and for a subset that had early gestational age (GA) dating via ultrasound. Classifier performance was compared directly to the U.S. Preventive Services Task Force (USPSTF) algorithm for identification of pregnancies that should receive low-dose aspirin (LDASA) for PE prevention. Results\\nNine of nine prespecified classifier models were validated for risk of preterm PE with delivery <37 weeks′ gestation. Areas under the receiver operating characteristic curve ranged from 0.72-0.78 in the full validation cohort, compared to 0.68 for both ClinRisk3 alone and for the USPSTF algorithm. In the early GA dating subcohort, an exemplar predictor, ClinRisk3 + inhibin subunit beta C chain/sex hormone binding globulin (ClinRisk3+INHBC/SHBG) showed a markedly lower screen positive rate (11.1% vs 43.3%) and higher positive predictive value (13.0% vs 5.0%) and odds ratio (9.93 vs 5.24) than USPSTF. Its performance was similar in nulliparas and all parities. Conclusion\\nNine preterm PE risk predictors were identified, validated in an independent cohort, and shown to be more predictive than the USPSTF-endorsed algorithm. Our results indicate that a single blood test performed in the first half of pregnancy can be used for personalized PE risk assessment, particularly for pregnancies with minimal or no identified clinical risk factors, including nulliparas. Results can be used to guide personalized pregnancy management, including but not restricted to LDASA for PE prophylaxis, and serve as a basis for developing new prevention strategies.\",\"PeriodicalId\":501409,\"journal\":{\"name\":\"medRxiv - Obstetrics and Gynecology\",\"volume\":\"27 2\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"medRxiv - Obstetrics and Gynecology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1101/2022.12.21.22282936\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv - Obstetrics and Gynecology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2022.12.21.22282936","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
目的建立并验证结合临床因素和血清蛋白分析的中期早产先兆子痫(PE)风险预测指标,并将其与专业学会认可的广泛使用的临床和风险评估算法进行比较。方法:本研究是对两项针对孕妇的大型多中心研究(PAPR, NCT01371019;TREETOP, NCT02787213),最初进行的目的是发现、验证和验证早产风险的血清蛋白质组学预测因子。用质谱法测定血清蛋白丰度。分类器模型将一个或两个新的蛋白质比率与一个复合临床变量(表示为ClinRisk3)结合在一起,其中包括既往PE、既往高血压或妊娠糖尿病。对整个验证队列和通过超声测定早期胎龄(GA)的子集的预测性能进行评估。分类器的性能直接与美国预防服务工作组(USPSTF)算法进行比较,用于识别应该接受低剂量阿司匹林(LDASA)预防PE的妊娠。结果9个预先设定的分类模型中,有9个模型对妊娠37周早产PE的风险进行了验证。在整个验证队列中,受试者工作特征曲线下的面积为0.72-0.78,而单独ClinRisk3和USPSTF算法下的面积为0.68。在早期GA dating亚群中,典型预测因子ClinRisk3+抑制素亚单位β C链/性激素结合球蛋白(ClinRisk3+INHBC/SHBG)的筛查阳性率明显低于USPSTF (11.1% vs 43.3%),阳性预测值(13.0% vs 5.0%)和优势比(9.93 vs 5.24)高于USPSTF。它在无parliparas和所有当事方的表现相似。结论:9个早产儿PE风险预测因子被确定,并在独立队列中验证,结果显示比uspstf认可的算法更具预测性。我们的研究结果表明,在妊娠前半期进行的单次血液检查可用于个性化PE风险评估,特别是对于具有最小或没有确定的临床风险因素的妊娠,包括无孕产。结果可用于指导个性化妊娠管理,包括但不限于LDASA用于PE预防,并可作为制定新的预防策略的基础。
Clinical validation of novel second-trimester preterm preeclampsia risk predictors combining clinical variables and serum protein biomarkers
Objective
To develop and validate mid-trimester preterm preeclampsia (PE) risk predictors combining clinical factors and serum protein analytes, and to compare their performance with those of widely used clinical and risk assessment algorithms endorsed by professional societies. Methods
This was a secondary analysis of data from two large, multicenter studies in pregnant individuals (PAPR, NCT01371019; TREETOP, NCT02787213), originally conducted to discover, verify, and validate a serum proteomic predictor of preterm birth risk. Serum protein abundances were determined by mass spectrometry. Classifier models combined one or two novel protein ratio(s) with a composite clinical variable, denoted as ClinRisk3, which included prior PE, pre-existing hypertension, or pregestational diabetes. Predictive performance was assessed for the full validation cohort and for a subset that had early gestational age (GA) dating via ultrasound. Classifier performance was compared directly to the U.S. Preventive Services Task Force (USPSTF) algorithm for identification of pregnancies that should receive low-dose aspirin (LDASA) for PE prevention. Results
Nine of nine prespecified classifier models were validated for risk of preterm PE with delivery <37 weeks′ gestation. Areas under the receiver operating characteristic curve ranged from 0.72-0.78 in the full validation cohort, compared to 0.68 for both ClinRisk3 alone and for the USPSTF algorithm. In the early GA dating subcohort, an exemplar predictor, ClinRisk3 + inhibin subunit beta C chain/sex hormone binding globulin (ClinRisk3+INHBC/SHBG) showed a markedly lower screen positive rate (11.1% vs 43.3%) and higher positive predictive value (13.0% vs 5.0%) and odds ratio (9.93 vs 5.24) than USPSTF. Its performance was similar in nulliparas and all parities. Conclusion
Nine preterm PE risk predictors were identified, validated in an independent cohort, and shown to be more predictive than the USPSTF-endorsed algorithm. Our results indicate that a single blood test performed in the first half of pregnancy can be used for personalized PE risk assessment, particularly for pregnancies with minimal or no identified clinical risk factors, including nulliparas. Results can be used to guide personalized pregnancy management, including but not restricted to LDASA for PE prophylaxis, and serve as a basis for developing new prevention strategies.