Predictive Analytic Model for Diagnosis of Ectopic Pregnancy

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

OBJECTIVES

Ectopic pregnancy (EP) is a major high-risk outcome following a pregnancy of unknown location (PUL) classification. Biochemical markers are used to triage PUL as high vs low risk to guide appropriate follow-up. The M6 model is currently the best risk-prediction model. We aimed to update the M6 model and evaluate whether performance can be improved by including clinical factors.

METHODS

This prospective cohort study recruited consecutive PUL between January 2015 and January 2017 at eight units (Phase 1), with two centers continuing recruitment between January 2017 and March 2021 (Phase 2). Serum samples were collected routinely and sent for β-human chorionic gonadotropin (β-hCG) and progesterone measurement. Clinical factors recorded were maternal age, pain score, bleeding score and history of EP. Based on transvaginal ultrasonography and/or biochemical confirmation during follow-up, PUL were classified subsequently as failed PUL (FPUL), intrauterine pregnancy (IUP) or EP (including persistent PUL (PPUL)). The M6 models with (M6P ) and without (M6NP ) progesterone were refitted and extended with clinical factors. Model validation was performed using internal-external cross-validation (IECV) (Phase 1) and temporal external validation (EV) (Phase 2). Missing values were handled using multiple imputation.

RESULTS

Overall, 5473 PUL were recruited over both phases. A total of 709 PUL were excluded because maternal age was < 16 years or initial β-hCG was ≤ 25 IU/L, leaving 4764 (87%) PUL for analysis (2894 in Phase 1 and 1870 in Phase 2). For the refitted M6P model, the area under the receiver-operating-characteristics curve (AUC) for EP/PPUL vs IUP/FPUL was 0.89 for IECV and 0.84-0.88 for EV, with respective sensitivities of 94% and 92-93%. For the refitted M6NP model, the AUCs were 0.85 for IECV and 0.82-0.86 for EV, with respective sensitivities of 92% and 93-94%. Calibration performance was good overall, but with heterogeneity between centers. Net Benefit confirmed clinical utility. The change in AUC when M6P was extended to include maternal age, bleeding score and history of EP was between -0.02 and 0.01, depending on center and phase. The corresponding change in AUC when M6NP was extended was between -0.01 and 0.03. At the 5% threshold to define high risk of EP/PPUL, 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.

CONCLUSIONS

The updated M6 model offers accurate diagnostic performance, with excellent sensitivity for EP. Adding clinical factors to the model improved performance in some centers, especially when progesterone levels were not suitable or unavailable. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Recent advances in the understanding of tubal ectopic pregnancy

Ectopic pregnancy (EP) is described as the implantation of an embryo outside the normal uterine cavity. It most commonly occurs in the fallopian tube, hence termed a tubal ectopic pregnancy (tEP). It is a gynaecological emergency and remains the leading cause of direct maternal mortality related to the first trimester of pregnancy worldwide. This article explores the emergence of additional risk factors for tEP, showing new evidence for identifying patient risk factors and highlighting potential areas of research. Additionally, we discuss the up-to-date patient-centred approach for the diagnosis, management and counselling of patients with tEP and ongoing clinical trials for the improvement of medical management.

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

Objective

Ectopic pregnancy (EP) is well known for its critical maternal outcome. Early detection could make the difference between life and death in pregnancy. Our aim was to make a prompt diagnosis before the rupture occur. Thus, the predictive analytical models using both conventional statistics and machine learning (ML) methods were studied.

Materials and methods

A retrospective cohort study was conducted on 407 pregnancies with unknown location (PULs): 306 PULs for internal validation and 101 PULs for external validation, randomized with a nested cross-validation technique. Using a set of 22 study features based on clinical factors, serum marker and ultrasound findings from electronic medical records, analyzing with neural networks (NNs), decision tree (DT), support vector machines (SVMs), and a statistical logistic regression (LR). Diagnostic performances were compared with the area under the curve (ROC-AUC), including sensitivity and specificity for decisional use.

Results

Comparing model performance (internal validation) to predict EP, LR ranked first, with a mean ROC-AUC ± SD of 0.879 ± 0.010. In testing data (external validation), NNs ranked first, followed closely by LR, SVMs, and DT with average ROC-AUC ± SD of 0.898 ± 0.027, 0.896 ± 0.034, 0.882 ± 0.029, and 0.856 ± 0.033, respectively. For clinical aid, we report sensitivity of mean ± SD in LR: 90.20% ± 3.49%; SVM: 89.79% ± 3.66%; DT: 89.22% ± 4.53%; and NNs: 86.92% ± 3.24%, consecutively. However, specificity ± SD was ranked by NNs, followed by SVMs, LR, and DT, which were 82.02 ± 8.34%, 80.37 ± 5.15%, 79.65% ± 6.01%, and 78.97% ± 4.07%, respectively.

Conclusion

Both statistics and the ML model could achieve satisfactory predictions for EP. In model learning, the highest ranked model was LR, showing that EP prediction might possess linear or causal data pattern. However, in new testing data, NNs could overcome statistics. This highlights the potency of ML in solving complicated problems with various patterns, while overcoming generalization error of data.

Pelvic inflammatory disease in the adolescent and young adult: An update

Pelvic inflammatory disease (PID) is an infection of the female upper genital tract that is typically polymicrobial with classic core involvement of Neisseria gonorrhoeae and/or Chlamydia trachomatis, though other endogenous flora from the vagino-cervical areas can be involved as well. It is often a sexually transmitted disease but other etiologic routes are also noted. A variety of risk factors have been identified including adolescence, young adulthood, adolescent cervical ectropion, multiple sexual partners, immature immune system, history of previous PID, risky contraceptive practices and others. An early diagnosis and prompt treatment are necessary to reduce risks of PID complications such as chronic pelvic pain, ectopic pregnancy and infertility. Current management principles of PID are also reviewed. It is important for clinicians to screen sexually active females for common sexually transmitted infections such as Chlamydia trachomatis and provide safer sex education to their adolescent and young adult patients. Clinicians should provide comprehensive management to persons with PID and utilize established guidelines such as those from the US Centers for Disease Control and Prevention (CDC).