Objective: To examine the external validity of the new Fetal Medicine Foundation (FMF) competing-risks model for prediction in midgestation of small-for-gestational-age (SGA) neonates.
Methods: This was a single-center prospective cohort study of 25 484 women with a singleton pregnancy undergoing routine ultrasound examination at 19 + 0 to 23 + 6 weeks' gestation. The FMF competing-risks model for the prediction of SGA combining maternal factors and midgestation estimated fetal weight by ultrasound scan (EFW) and uterine artery pulsatility index (UtA-PI) was used to calculate risks for different cut-offs of birth-weight percentile and gestational age at delivery. The predictive performance was evaluated in terms of discrimination and calibration.
Results: The validation cohort was significantly different in composition compared with the FMF cohort in which the model was developed. In the validation cohort, at a 10% false-positive rate (FPR), maternal factors, EFW and UtA-PI yielded detection rates of 69.6%, 38.7% and 31.7% for SGA < 10 percentile with delivery at < 32, < 37 and ≥ 37 weeks' gestation, respectively. The respective values for SGA < 3 percentile were 75.7%, 48.2% and 38.1%. Detection rates in the validation cohort were similar to those reported in the FMF study for SGA with delivery at < 32 weeks but lower for SGA with delivery at < 37 and ≥ 37 weeks. Predictive performance in the validation cohort was similar to that reported in a subgroup of the FMF cohort consisting of nulliparous and Caucasian women. Detection rates in the validation cohort at a 15% FPR were 77.4%, 50.0% and 41.5% for SGA < 10 percentile with delivery at < 32, < 37 and ≥ 37 weeks, respectively, which were similar to the respective values reported in the FMF study at a 10% FPR. The model had satisfactory calibration.
Conclusion: The new competing-risks model for midgestation prediction of SGA developed by the FMF performs well in a large independent Spanish population. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1002/uog.26210 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A Bayesian Joint Model of Multiple Nonlinear Longitudinal and Competing Risks Outcomes for Dynamic Prediction in Multiple Myeloma: Joint Estimation and Corrected Two-Stage Approaches.
Stat Med
February 2025
Hoffmann-La Roche Ltd, Basel, Switzerland.
Predicting cancer-associated clinical events is challenging in oncology. In Multiple Myeloma (MM), a cancer of plasma cells, disease progression is determined by changes in biomarkers, such as serum concentration of the paraprotein secreted by plasma cells (M-protein). Therefore, the time-dependent behavior of M-protein and the transition across lines of therapy (LoT), which may be a consequence of disease progression, should be accounted for in statistical models to predict relevant clinical outcomes.
View Article and Find Full Text PDFStatistical models versus machine learning approach for competing risks in proctological surgery.
Updates Surg
January 2025
Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy.
Clinical risk prediction models are ubiquitous in many surgical domains. The traditional approach to develop these models involves the use of regression analysis. Machine learning algorithms are gaining in popularity as an alternative approach for prediction and classification problems.
View Article and Find Full Text PDFSocio-economic inequalities in second primary cancer incidence: A competing risks analysis of women with breast cancer in England between 2000 and 2018.
Int J Cancer
January 2025
Inequalities in Cancer Outcomes Network (ICON) group, Department of Health Services Research and Policy, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, UK.
We aimed to investigate socio-economic inequalities in second primary cancer (SPC) incidence among breast cancer survivors. Using Data from cancer registries in England, we included all women diagnosed with a first primary breast cancer (PBC) between 2000 and 2018 and aged between 18 and 99 years and followed them up from 6 months after the PBC diagnosis until a SPC event, death, or right censoring, whichever came first. We used flexible parametric survival models adjusting for age and year of PBC diagnosis, ethnicity, PBC tumour stage, comorbidity, and PBC treatments to model the cause-specific hazards of SPC incidence and death according to income deprivation, and then estimated standardised cumulative incidences of SPC by deprivation, taking death as the competing event.
View Article and Find Full Text PDFNon-Parametric Estimation for Semi-Competing Risks Data With Event Misascertainment.
Stat Med
February 2025
Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN.
The semi-competing risks data model is a special type of disease-state model that focuses on studying the association between an intermediate event and a terminal event and proves to be a useful tool in modeling disease progression. The study of the semi-competing risk data model not only allows us to evaluate whether a disease episode is related to death but also provides a toolkit to predict death, given that the episode occurred at a certain time. However, the computation of the semi-competing risk models is a numerically challenging task.
View Article and Find Full Text PDFAbdominal visceral and subcutaneous adipose tissue associations with postmenopausal breast cancer incidence.
JNCI Cancer Spectr
January 2025
Department of Epidemiology and Biostatistics, University of California, Irvine, CA, USA.
Background: Obesity, classified by body mass index (BMI), is associated with higher postmenopausal breast cancer (BCa) risk. Yet, the associations between abdominal visceral (VAT) and subcutaneous adipose tissue (SAT) with BCa are unclear.
Methods: We assessed BCa associations with abdominal VAT and SAT in a prospective cohort of postmenopausal women without a history of cancer and with 27 years follow-up (N = 9950), during which all new cancers were adjudicated.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!