How do the M4 and M6 models perform in an Australian pregnancy of unknown location population?

Ooi, Sara; Vries, Bradley de; Ludlow, J.

doi:10.1111/ajo.13252

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…In this study, three of these criteria were assessed and it was found that the mathematical model using logistic regression analysis fares much better than the criterion of progesterone and hCG ratio. Our results are similar to other studies in which the mathematical model was utilized ( 11 , 12 ). A recent meta-analysis showed that model M4 is the best available method to predict outcome of ectopic pregnancy ( 13 ).…”

Section: Discussionsupporting

confidence: 91%

“…Although the value of ultrasound in evaluating pregnancy of unknown location cannot be sufficiently emphasized, many algorithms have been developed to ensure a desired and wanted pregnancy. Clinicians should keep current with the utility of different clinical criteria that help assess the fate in controversial cases ( 11 , 12 ). In this study, three of these criteria were assessed and it was found that the mathematical model using logistic regression analysis fares much better than the criterion of progesterone and hCG ratio.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Triaging Women with Pregnancy of Unknown Location: Evaluation of Protocols Based on Single Serum Progesterone, Serum hCG Ratios, and Model M4

Izhar¹,

Husain²,

Tahir³

et al. 2022

JRI

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Background: The purpose of the current study was to evaluate the ability of three protocols to triage women presenting with pregnancy of unknown location (PUL). Methods: Women with pregnancy of unknown location were recruited from Aziz Medical Centre from 1st August, 2018 to 31st July, 2020. The criterion of progesterone, human chorionic gonadotrophin (hCG) ratio, and M4 algorithm were used to predict risk of adverse pregnancy outcomes and classify women. Finally, 3 groups were established including ectopic pregnancy, failed pregnancy of unknown location, and intrauterine pregnancy (IUP). The primary outcome was to assign women to ectopic pregnancy group using these protocols. The secondary outcome was to compare the sensitivity and specificity of the three protocols relative to the final outcome. Results: Of the 288 women, 66 (22.9%) had ectopic pregnancy, 144 (50.0%) had intrauterine pregnancy, and 78 (27.1%) had failed pregnancy of unknown location. The criterion of progesterone had a sensitivity of 81.8%, specificity of 27%, negative predictive value (NPV) of 83.3%, and positive predictive value (PPV) of 25% for high risk result (ectopic pregnancy). The hCG ratio had sensitivity of 72%, specificity of 73%, NPV of 90%, and PPV of 44% for high risk result (ectopic pregnancy). However, model M4 had sensitivity of 86.4%, specificity of 91.9%, NPV of 95.8%, and PPV of 76% for high risk result. Conclusion: Based on the findings of the study, it was revealed that prediction model of M4 had the highest sensitivity, specificity, negative predictive value and positive predictive value for high risk result (ectopic pregnancy).

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Triaging Women with Pregnancy of Unknown Location: Evaluation of Protocols Based on Single Serum Progesterone, Serum hCG Ratios, and Model M4

Izhar¹,

Husain²,

Tahir³

et al. 2022

JRI

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“…We did not assess M6P (adding progesterone as a predictor) since progesterone is not used in our clinic. As previously reported in studies from the UK and Australia it is probable that M6P would perform better as compared with M6NP also in our unit 14 32…”

Section: Discussionsupporting

confidence: 68%

Pregnancy of unknown location: external validation of the hCG-based M6NP and M4 prediction models in an emergency gynaecology unit

2022

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ObjectiveTo investigate if M6NP predicting ectopic pregnancy (EP) among women with pregnancy of unknown location (PUL) is valid in an emergency gynaecology setting and comparing it with its predecessor M4.DesignRetrospective cohort study.SettingUniversity Hospital.ParticipantsWomen with PUL.MethodsAll consecutive women with a PUL during a study period of 3 years were screened for inclusion. Risk prediction of an EP was based on two serum human chorionic gonadotropin (hCG) levels taken at least 24 hours and no longer than 72 hours apart.Main outcome measuresThe area under the ROC curve (AUC) expressed the ability of a model to distinguish an EP from a non-EP (discrimination). Calibration assessed the agreement between the predicted risk of an EP and the true risk (proportion) of EP. The proportion of EPs and non-EPs classified as high risk assessed the model’s sensitivity and false positive rate (FPR). The proportion of non-EPs among women classified as low risk was the model’s negative predictive value (NPV). The clinical utility of a model was evaluated with decision curve analysis.Results1061 women were included in the study, of which 238 (22%) had a final diagnosis of EP. The AUC for EP was 0.85 for M6NP and 0.81 for M4. M6NP made accurate risk predictions of EP up to predictions of 20% but thereafter risks were underestimated. M4 was poorly calibrated up to risk predictions of 40%. With a 5% threshold for high risk classification the sensitivity for EP was 95% for M6NP, the FPR 50% and NPV 97%. M6NP had higher sensitivity and NPV than M4 but also a higher FPR. M6NP had utility at all thresholds as opposed to M4 that had no utility at thresholds≤5%.ConclusionsM6NP had better predictive performance than M4 and is valid in women with PUL attending an emergency gynaecology unit. Our results can encourage implementation of M6NP in related yet untested clinical settings to effectively support clinical decision-making.

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“…Previous research suggested that UK-developed models are not as predictive when applied to American populations 36 . However, studies in Sweden, Australia and Pakistan support both M4 and M6 as superior PUL triage techniques [43][44][45][46] . Although most of these publications are retrospective, the importance of proactive validation and revalidation is clear.…”

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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How do the M4 and M6 models perform in an Australian pregnancy of unknown location population?

Cited by 4 publications

References 18 publications

Triaging Women with Pregnancy of Unknown Location: Evaluation of Protocols Based on Single Serum Progesterone, Serum hCG Ratios, and Model M4

Triaging Women with Pregnancy of Unknown Location: Evaluation of Protocols Based on Single Serum Progesterone, Serum hCG Ratios, and Model M4

Pregnancy of unknown location: external validation of the hCG-based M6NP and M4 prediction models in an emergency gynaecology unit

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

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