Predictive analytical model for ectopic pregnancy diagnosis: Statistics vs. machine learning

Rueangket, Ploywarong; Rittiluechai, Kristsanamon; Prayote, Akara

doi:10.3389/fmed.2022.976829

Cited by 2 publications

(1 citation statement)

References 39 publications

(24 reference statements)

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“…However, it can be subject to bias evolving from validation procedures, and calibration is often insufficient 47,48 . In fact, publications comparing machine learning to statistical logistic regression have concluded equivocal performance [48][49][50] . Whilst the current statistical methodology (logistic regression) is sound, a machine-learning-based M6 model could be developed and its performance compared.…”

Section: Discussionmentioning

confidence: 99%

Updating M6 pregnancy of unknown location risk‐prediction model including evaluation of clinical factors

Kyriacou,

Ledger,

Bobdiwala

et al. 2024

Ultrasound in Obstet & Gyne

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IntroductionEctopic pregnancy (EP) is the major high‐risk outcome following a pregnancy of unknown location (PUL) classification. Biochemical markers are used to triage PUL to high versus low risk to decide appropriate follow up. The M6 model is currently the best risk prediction model. We aimed to update the M6 model and evaluate whether the model can be improved by including clinical factors.MethodsThis prospective cohort study recruited consecutive PUL between January 2015 and January 2017 (phase one) at eight units, with two center recruitment between January 2017 and March 2021 (phase two). Serum samples were routinely collected and sent for BhCG and progesterone measurement. Clinical factors recorded were maternal age, pain score, bleeding score and history of previous EP. Based on transvaginal ultrasonography and/or biochemical confirmation during follow‐up, PUL were subsequently classified as failed PUL, intrauterine pregnancy, or EP (including persistent PUL (PPUL)). The M6 models with (M6P) and without progesterone (M6NP) were refitted and extended with clinical factors. Model validation was performed using internal‐external cross validation (IECV ‐ phase one), and external validation (EV ‐ phase two). Missing values were handled using multiple imputation.Results5473 PUL were recruited over both phases. 709 PUL were excluded because maternal age was <16 years or initial BhCG was ≤25 IU/L, leaving 4764 (86%) PUL for analysis (2894 in phase one, 1870 in phase 2). For the refitted M6P, the area under the receiver operating characteristic curve (AUROC) for EP/PPUL vs other was 0.89 for IECV and 0.84‐0.88 with EV. For the refitted M6NP, the AUROC was 0.85 for IECV and 0.82‐0.86 with EV. Calibration performance was good overall, but with heterogeneity between centers. Net Benefit confirmed clinical utility. The change in AUROC when extending M6P with maternal age, bleeding score, and EP history was ‐0.02 to 0.01 depending on center and phase. The change in AUROC when extending M6NP was ‐0.01 to 0.03. At the 5% threshold defining high EP/PPUL risk, extending M6P altered sensitivity by ‐0.02 to ‐0.01, specificity by 0.03 to 0.04, and Net Benefit by ‐0.005 to 0.006. Extending M6NP altered sensitivity by ‐0.03 to ‐0.01, specificity by 0.05 to 0.07, and Net Benefit by ‐0.005 to 0.006.ConclusionsThe updated M6 model offers accurate diagnostic performance, with excellent sensitivity for EP. Adding clinical factors to the model improves performance in some centers, especially when progesterone levels are not suitable or unavailable.This article is protected by copyright. All rights reserved.

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Section: Discussionmentioning

confidence: 99%

Updating M6 pregnancy of unknown location risk‐prediction model including evaluation of clinical factors

Kyriacou,

Ledger,

Bobdiwala

et al. 2024

Ultrasound in Obstet & Gyne

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Conventional and modern markers of pregnancy of unknown location: Update and narrative review

Hou,

Liang,

Zeng

et al. 2024

Intl J Gynecology & Obste

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Pregnancy of unknown location (PUL) is a temporary pathologic or physiologic phenomenon of early pregnancy that requires follow up to determine the final pregnancy outcome. Evidence indicated that PUL patients suffer a remarkably higher rate of adverse pregnancy outcomes, represented by ectopic gestation and early pregnancy loss, than the general population. In the past few decades, discussion about PUL has never stopped, and a variety of markers have been widely investigated for the early and accurate evaluation of PUL, including serum biomarkers, ultrasound imaging features, multivariate analysis, and the diagnosis of ectopic pregnancy based on risk stratification. So far, machine learning (ML) methods represented by M4 and M6 logistic regression have gained a level of recognition and are continually improving. Nevertheless, the heterogeneity of PUL markers, mainly caused by the limited sample size, the differences in population and technical maturity, etc., have hampered the management of PUL. With the advancement of multidisciplinary integration and cutting‐edge technologies (e.g. artificial intelligence, prediction model development, and telemedicine), novel markers, and strategies for the management of PUL are expected to be developed. In this review, we summarize both conventional and novel markers (represented by artificial intelligence) for PUL assessment and management, investigate their advancements, limitations and challenges, and propose insights on future research direction and clinical application.

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Predictive analytical model for ectopic pregnancy diagnosis: Statistics vs. machine learning

Cited by 2 publications

References 39 publications

Updating M6 pregnancy of unknown location risk‐prediction model including evaluation of clinical factors

Updating M6 pregnancy of unknown location risk‐prediction model including evaluation of clinical factors

Conventional and modern markers of pregnancy of unknown location: Update and narrative review

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