Methods for Evaluation of medical prediction Models, Tests And Biomarkers (MEMTAB) 2018 Symposium: Utrecht, The Netherlands. 2-3 July 2018. Diagnostic and Prognostic Research, 2(S1):12, s41512–018–0036–3, July, 2018.
Methods for Evaluation of medical prediction Models, Tests And Biomarkers (MEMTAB) 2018 Symposium: Utrecht, The Netherlands. 2-3 July 2018 [link]Paper  doi  abstract   bibtex   
Background: Risk prediction models for early-onset pre-eclampsia (requiring delivery \textless34 weeks’ gestation) may improve maternal and infant health outcomes by identifying women who will benefit from management such as aspirin prophylaxis. Risk models using routinely measured factors are needed in settings where specialised tests are not available. However, few such models have been externally validated. Objective: To assess the performance of the Baschat (2014) [1] risk model that incorporates history of chronic hypertension, diabetes and mean arterial pressure (MAP) to predict early-onset preeclampsia in early pregnancy using the Perinatal Antiplatelet Review of International Studies (PARIS) randomised controlled trial dataset. Methods: A retrospective individual-participant data meta-analysis to validate the Baschat model (reported sensitivity 55%/66% at 10%/ 20% false positive rates (FPRs) respectively, area-under-curve (AUC) 0.83). Trials were eligible if they did not select women based on the presence/absence of high-risk factors; enrolled women \textless28 weeks’ gestation; and reported model predictors and pre-eclampsia. Women assigned to the control arm were included. Model performance was assessed by estimating sensitivity, specificity, positive (PPV) and negative (NPV) predictive value for predicting early-onset preeclampsia at: (i) 0.7% risk threshold to classify low- versus high-risk; and (ii) 10%/20% FPRs as reported in the original publication. The AUC and 95% confidence interval (CI) was calculated. Model calibration was assessed using the Hosmer and Lemeshow goodness-of-fit test and a calibration plot. Results: Three eligible trials included 4510 women. Pre-eclampsia prevalence was 4.9%. For prediction of early-onset pre-eclampsia (n=25, 0.6%), model sensitivity was 28.0% (95% CI 14.3-47.6%), specificity 84.3% (83.2-85.3%), PPV 1.0% (0.5%-2.0%), NPV 99.5% (99.399.7%). At 10% and 20% FPRs, sensitivity was 20.0% (8.9-39.1%) and 32.0% (17.2-51.6%) respectively; AUC=0.55 (0.43-0.68), goodness-of-fit p=0.86. Conclusion: Model performance for predicting early-onset pre-eclampsia was poor in this validation population. Determining appropriate risk thresholds for assessment of clinical performance will be important for ongoing model development.
@article{noauthor_methods_2018-1,
	title = {Methods for {Evaluation} of medical prediction {Models}, {Tests} {And} {Biomarkers} ({MEMTAB}) 2018 {Symposium}: {Utrecht}, {The} {Netherlands}. 2-3 {July} 2018},
	volume = {2},
	issn = {2397-7523},
	shorttitle = {Methods for {Evaluation} of medical prediction {Models}, {Tests} {And} {Biomarkers} ({MEMTAB}) 2018 {Symposium}},
	url = {https://diagnprognres.biomedcentral.com/articles/10.1186/s41512-018-0036-3},
	doi = {10.1186/s41512-018-0036-3},
	abstract = {Background: Risk prediction models for early-onset pre-eclampsia (requiring delivery {\textbackslash}textless34 weeks’ gestation) may improve maternal and infant health outcomes by identifying women who will benefit from management such as aspirin prophylaxis. Risk models using routinely measured factors are needed in settings where specialised tests are not available. However, few such models have been externally validated. Objective: To assess the performance of the Baschat (2014) [1] risk model that incorporates history of chronic hypertension, diabetes and mean arterial pressure (MAP) to predict early-onset preeclampsia in early pregnancy using the Perinatal Antiplatelet Review of International Studies (PARIS) randomised controlled trial dataset. Methods: A retrospective individual-participant data meta-analysis to validate the Baschat model (reported sensitivity 55\%/66\% at 10\%/ 20\% false positive rates (FPRs) respectively, area-under-curve (AUC) 0.83). Trials were eligible if they did not select women based on the presence/absence of high-risk factors; enrolled women {\textbackslash}textless28 weeks’ gestation; and reported model predictors and pre-eclampsia. Women assigned to the control arm were included. Model performance was assessed by estimating sensitivity, specificity, positive (PPV) and negative (NPV) predictive value for predicting early-onset preeclampsia at: (i) 0.7\% risk threshold to classify low- versus high-risk; and (ii) 10\%/20\% FPRs as reported in the original publication. The AUC and 95\% confidence interval (CI) was calculated. Model calibration was assessed using the Hosmer and Lemeshow goodness-of-fit test and a calibration plot. Results: Three eligible trials included 4510 women. Pre-eclampsia prevalence was 4.9\%. For prediction of early-onset pre-eclampsia (n=25, 0.6\%), model sensitivity was 28.0\% (95\% CI 14.3-47.6\%), specificity 84.3\% (83.2-85.3\%), PPV 1.0\% (0.5\%-2.0\%), NPV 99.5\% (99.399.7\%). At 10\% and 20\% FPRs, sensitivity was 20.0\% (8.9-39.1\%) and 32.0\% (17.2-51.6\%) respectively; AUC=0.55 (0.43-0.68), goodness-of-fit p=0.86. Conclusion: Model performance for predicting early-onset pre-eclampsia was poor in this validation population. Determining appropriate risk thresholds for assessment of clinical performance will be important for ongoing model development.},
	language = {en},
	number = {S1},
	urldate = {2019-05-02},
	journal = {Diagnostic and Prognostic Research},
	month = jul,
	year = {2018},
	pages = {12, s41512--018--0036--3},
	file = {2018 - Methods for Evaluation of medical prediction Model.pdf:/Users/neil.hawkins/Zotero/storage/M7RRZMC7/2018 - Methods for Evaluation of medical prediction Model.pdf:application/pdf},
}
Downloads: 0
About
Documentation
Keywords
Search
Users
Terms and Conditions of Use
Privacy Policy
Sitemap
© BibBase.org 2021