Does a prediction model for pregnancy of unknown location developed in the UK validate on a US population?

Multiplexed serum biomarkers to discriminate nonviable and ectopic pregnancy

OBJECTIVE

To evaluate combinations of candidate biomarkers to develop a multiplexed prediction model for identifying the viability and location of an early pregnancy. In this study, we assessed 24 biomarkers with multiple machine learning-based methodologies to assess if multiplexed biomarkers may improve the diagnosis of normal and abnormal early pregnancies.

DESIGN

A nested case-control design evaluated the predictive ability and discrimination of biomarkers in patients at risk of early pregnancy failure in the first trimester to classify viability and location.

SETTING

Three university hospitals.

PATIENTS

A total of 218 individuals with pain and/or bleeding in early pregnancy: 75 had an ongoing intrauterine gestation; 68 had ectopic pregnancies (EPs); and 75 had miscarriages.

INTERVENTIONS

Serum levels of 24 biomarkers were assessed in the same patients. Multiple machine learning-based methodologies to evaluate combinations of these top candidates to develop a multiplexed prediction model for the identification of a nonviable pregnancy (ongoing intrauterine pregnancy vs. miscarriage or EP) and an EP (EP vs. ongoing intrauterine pregnancy or miscarriage).

MAIN OUTCOME MEASURES

The predicted classification using each model was compared with the actual diagnosis, and sensitivity, specificity, positive predictive value, negative predictive value, conclusive classification, and accuracy were calculated.

RESULTS

Models using classification regression tree analysis using 3 (pregnancy-specific beta-1-glycoprotein 3 [PSG3], chorionic gonadotropin-alpha subunit, and pregnancy-associated plasma protein-A) biomarkers were able to predict a maximum sensitivity of 93.3% and a maximum specificity of 98.6%. The model with the highest accuracy was 97.4% (with 70.2% receiving classification). Models using an overlapping group of 3 (soluble fms-like tyrosine kinase-1, PSG3, and tissue factor pathway inhibitor 2) biomarkers achieved a maximum sensitivity of 98.5% and a maximum specificity of 95.3%. The model with the highest accuracy was 94.4% (with 65.6% receiving classification). When the models were used simultaneously, the conclusive classification increased to 72.7% with an accuracy of 95.9%. The predictive ability of the biomarkers in the random forest produced similar test characteristics when using 11 predictive biomarkers.

CONCLUSION

We have demonstrated a pool of biomarkers from divergent biological pathways that can be used to classify individuals with potential early pregnancy loss. The biomarkers choriogonadotropin alpha, pregnancy-associated plasma protein-A, and PSG3 can be used to predict viability, and soluble fms-like tyrosine kinase-1, tissue factor pathway inhibitor 2, and PSG3 can be used to predict pregnancy location.

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

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.

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.

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

OBJECTIVE

To 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.

DESIGN

Retrospective cohort study.

SETTING

University Hospital.

PARTICIPANTS

Women with PUL.

METHODS

All 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 MEASURES

The 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.

RESULTS

1061 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%.

CONCLUSIONS

M6NP 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.

Pregnancies lost and found: a quality improvement project to increase follow-up for early pregnancy complications

Pregnancy of unknown location (PUL) and medically managed ectopic pregnancy (EP) require longitudinal surveillance to avoid adverse outcomes; however, patients with PUL/EP in the United States (U.S.) are often unable to adhere to recommended treatment plans. This quality improvement (QI) project sought to improve PUL/EP follow-up using a three-pronged intervention: standardised recall procedures, direct patient-provider communication and electronic medical record (EMR) documentation templates and tracking. We compared patients with PUL/EP managed before and after the QI project. Our primary outcome was completion of PUL/EP clinical care. Demographics, initial diagnoses and adverse outcomes were similar between 87 pre-QI and 81 post-QI patients. Significantly more patients completed PUL/EP clinical care post-QI (80.2 vs. 65.5% p = .03). Post-QI, more providers contacted patients at standard intervals (100 vs. 57.1%, p < .0001), and EMR documentation was improved (100 vs. 69.0%, p < .001). Simple changes to PUL/EP management improved completion of clinical care and compliance with standardised recall and documentation.IMPACT STATEMENTWhat is already known on this subject? Pregnancy of unknown location (PUL) and medically managed ectopic pregnancy (EP) require longitudinal surveillance to avoid adverse outcomes; however, patients with PUL/EP in the United States (U.S.) are often unable to adhere to recommended treatment plans.What do the results of this study add? By standardising recall procedures, ensuring direct communication between patients and providers using a dedicated cell phone, and integrating case tracking and documentation into the electronic medical record (EMR), this quality improvement (QI) project improved completion of clinical follow-up for patients with PUL/EP (overall, 80.2 vs. 65.5% pre-QI, p=.03) and for the subgroup with medically managed EP not requiring surgery (76.5 vs. 36.4% pre-QI, p= .05). We also improved providers' compliance with standardised recall procedures and EMR documentation post-QI (p < .0001). There was no difference in the number of attempts to contact patients, or in the number of surveillance blood draws actually performed. Post-QI, survey responses indicated that patients were easily able to contact their provider and understood the importance of follow-up processes.What are the implications of these findings for clinical practice and/or research? Early pregnancy care providers can utilise simple strategies to improve follow-up of patients with PUL and medically managed EP, without increasing burdens to their health systems. Patients' favourable experiences with this management support its implementation.

Management of early gestations with low beta-human chorionic gonadotropin conceived by assisted reproductive technologies: performance of M4 predictive model

OBJECTIVES

To assess the safety and performance of the M4 model for classifying as high risk or low risk for ectopic pregnancy (EP) pregnancies conceived by assisted reproductive technologies (ART) that present with low beta-human chorionic gonadotropin (β-hCG) concentration in early gestation.

METHODS

This was a prospective cohort study of 243 pregnancies conceived by ART with low β-hCG levels (5-50 IU/L) at 4 + 0 to 4 + 2 weeks' gestation. After subsequent β-hCG testing at 48 h, pregnancies were classified according to the M4 model into the following categories: (i) high risk, probable EP/persistent pregnancy of unknown location (PPUL), when the risk for EP was ≥ 5%; (ii) low risk, probable intrauterine pregnancy (IUP), when the risk of EP was < 5% and the likelihood of IUP was greater than that of a failed pregnancy of unknown location (FPUL); and (iii) low risk, probable FPUL, when the risk of EP was < 5% and the likelihood of a FPUL was greater than that of an IUP. The predictive performance of the M4 model for EP and its ability to discriminate between high- and low-risk pregnancies was assessed using the final pregnancy outcome at 11 to 13 weeks of gestation as reference, which was classified as EP/PPUL, FPUL or IUP.

RESULTS

The sensitivity and specificity of the M4 model in detecting a high-risk pregnancy (EP/PPUL) were 60.0% (95% CI, 43.6-74.4%) and 79.8% (95% CI, 73.8-84.7%), respectively. The area under the receiver-operating-characteristics curve of the M4 model for discriminating between high-risk and low-risk (FPUL/IUI) pregnancies was 0.72 (95% CI, 0.62-0.81). The model had a positive likelihood ratio of 2.97 (95% CI, 2.03-4.36) and a negative likelihood ratio of 0.50 (95% CI, 0.33-0.76). The kappa index was 0.30 (95% CI, 0.16-0.43), indicating a low degree of agreement between the model classification and the final diagnosis. No serious adverse events related directly to the application of the M4 model were observed, although 14 pregnancies classified ultimately as high risk had been categorized initially as low risk by the M4 model. Of these, seven resolved with expectant management, five with methotrexate (MTX) and two required laparoscopic surgery (one after failure of medical treatment with MTX and one after deviation from the follow-up protocol). There were no cases of EP/PPUL with additional complications or need for blood or other blood product transfusion. Of the 243 ART pregnancies with low β-hCG concentration in early gestation, only 47 (19.3%) had an IUP, half (24/47) of which had an early miscarriage, resulting in only 9.5% (23/243) cases having an ongoing pregnancy.

CONCLUSIONS

Application of the M4 model in pregnancies conceived by ART with low β-hCG concentration in early gestation showed limited capacity in classifying them as being at low or high risk for EP, therefore, its use in pregnancies of this type is not recommended. No serious adverse events or complications related to the use of the model were observed. These pregnancies have a low probability of ending in an IUP as well as a high rate of early miscarriage. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

Predictive Analytic Model for Diagnosis of Ectopic Pregnancy

Objective: Ectopic pregnancy (EP) is a serious condition. Delayed diagnosis could lead to life-threatening outcomes. The study aimed to develop a diagnostic predictive model for EP to approach suspected cases with prompt intervention before the rupture occurred. Methods: A retrospective cross-sectional study enrolled 347 pregnant women presenting first-trimester complications (abdominal pain or vaginal bleeding) with diagnosis suspected of pregnancy of unknown location, who were eligible and underwent chart review. The data including clinical risk factors, signs and symptoms, serum human chorionic gonadotropin (hCG), and ultrasound findings were analyzed. The statistical predictive score was developed by performing logistic regression analysis. The testing data of 30 patients were performed to test the validation of predictive scoring. Results: From a total of 22 factors, logistic regression method-derived scoring model was based on five potent factors (history of pelvic inflammatory disease, current use of emergency pills, cervical motion tenderness, serum hCG ≥1,000 mIU/ml, and ultrasound finding of adnexal mass) using a cutoff score ≥3. This predictive index score was able to determine ectopic pregnancy with an accuracy of 77.8% [95% confidence interval (CI) = 73.1-82.1], specificity of 91.0% (95% CI = 62.1-72.0), sensitivity of 67.0% (95% CI = 88.0-94.0), and area under the curve of 0.906 (95% CI = 0.875-0.937). In the validation group, no patient with negative result of this score had an EP. Conclusion: Statistical predictive score was derived with high accuracy and applicable performance for EP diagnosis. This score could be used to support clinical decision making in routine practice for management of EP.

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

BACKGROUND

The diagnosis of a pregnancy of unknown location (PUL) is made when there is an elevated serum β human chorionic gonadotropin (βhCG) and no pregnancy on transabdominal and transvaginal ultrasound. Most of these pregnancies end as intra-uterine pregnancies or unsuccessful pregnancies and can be safely managed expectantly. However, up to 20% of these women will have an ectopic pregnancy. Several mathematical models, including the M4 and M6 protocols, have been developed using biochemical markers to triage PUL presentations. This rationalises numbers of tests and visits made without compromising safety and allowing timely intervention.

AIMS

We aimed to externally validate the M4 and M6 models in an Australian tertiary early pregnancy assessment service (EPAS).

MATERIALS AND METHODS

We performed a retrospective single-centre cohort study across five years. Our study population included all women attending our EPAS with a PUL who had at least two serum βhCG levels and one progesterone level measured. The M4 and M6 models were retrospectively applied.

RESULTS

Of the 360 women in the study population, there were 26 confirmed ectopic pregnancies (7.2%) and six persisting PULs (2%). The M4 model had a sensitivity and specificity of 72%. The M6P model had a sensitivity of 91% and specificity of 63%. The M6P misclassified two ectopic pregnancies into the low-risk group, compared with seven in the M4 model.

CONCLUSIONS

The M6P model has the highest sensitivity of the three models and a negative predictive value of 99%. These numbers are comparable to the original United Kingdom population. Further prospective validation is planned.

External validation of risk prediction model M4 in an Australian population: Rationalising the management of pregnancies of unknown location

BACKGROUND

The prediction model M4 can successfully classify pregnancy of unknown location (PUL) into a low- or high-risk group in developing ectopic pregnancy. M4 was validated in UK centres but in very few other countries outside UK.

AIM

To validate the M4 model's ability to correctly classify PULs in a cohort of Australian women.

MATERIALS AND METHODS

A retrospective analysis of women classified with PUL, attending a Sydney-based teaching hospital between 2006 and 2018. The reference standard was the final characterisation of PUL: failed PUL (FPUL) or intrauterine pregnancy (IUP; low risk) vs ectopic pregnancy (EP) or persistent PUL (PPUL; high risk). Each patient was entered into the M4 model calculator and an estimated risk of FPUL/IUP or EP/PPUL was recorded. Diagnostic accuracy of the M4 model was evaluated.

RESULTS

Of 9077 consecutive women who underwent transvaginal sonography, 713 (7.9%) classified with a PUL. Six hundred and seventy-seven (95.0%) had complete study data and were included. Final outcomes were: 422 (62.3%) FPULs, 150 (22.2%) IUPs, 105 (15.5%) EPs and PPULs. The M4 model classified 455 (67.2%) as low-risk PULs of which 434 (95.4%) were FPULs/IUPs and 21 (4.6%) were EPs or PPULs. EPs/PPULs were correctly classified with sensitivity of 80.0% (95% CI 71.1-86.5%), specificity of 75.9% (95% CI 72.2-79.3%), positive predictive value of 37.8% (95% CI 33.8-42.1%) and negative predictive value of 95.3% (95% CI 93.1-96.9%).

CONCLUSIONS

We have externally validated the prediction model M4. It classified 67.2% of PULs as low risk, of which 95.4% were later characterised as FPULs or IUPs while still classifying 80.0% of EPs as high risk.

The diagnostic role of the β-hCG discriminatory zone combined with the endometrial pattern for ectopic pregnancy in Chinese women

Previous studies have regarded the discriminatory serum β-hCG zone (DSZ) as a valuable tool for the diagnosis of ectopic pregnancy (EP). However, the wide range of the DSZ makes achieving a clinical diagnosis of EP difficult, and these reports do not indicate whether the DSZ is suitable for an EP diagnosis in Chinese women. Several studies have indicated that the endometrial pattern in patients with EPs is different from that in patients with intrauterine pregnancies (IUPs). The aims of this study were to define the DSZ cutoff value for Chinese women, test whether the endometrial pattern is a suitable predictor for EP, and assess the diagnostic value of these indicators. We enrolled participants with IUPs or EPs with abdominal pain and/or vaginal bleeding, and serum β-hCG level measurements and transvaginal ultrasound (TVS) were performed to assess the diagnostic value of the indicators for EP. The sensitivity and specificity for identifying an EP were improved by combining the DSZ, endometrial thickness and trilaminar pattern indexes. The results of this study might be helpful toward providing further options for the diagnosis of EP, especially for patients without hemoperitoneum or colporrhagia.

Pregnancy of unknown location

Pregnancy of unknown location is a situation in which a positive pregnancy test occurs, but a transvaginal ultrasound does not show intrauterine or ectopic gestation. One great concern of pregnancy of unknown location is that they are cases of ectopic pregnancy whose diagnosis might be postponed. Transvaginal ultrasound is able to identify an ectopic pregnancy with a sensitivity ranging from 87% to 94% and a specificity ranging from 94% to 99%. A patient with pregnancy of unknown location should be followed up until an outcome is obtained. The only valid biomarkers with clinical application and validation are serum levels of the beta fraction of hCG and progesterone. A single serum dosage of hCG is used only to determine whether the value obtained is above or below the discriminatory zone, that means the value of serum hCG above which an intrauterine gestational sac should be visible on ultrasound. Serum progesterone levels are a satisfactory marker of pregnancy viability, but they are unable to predict the location of a pregnancy of unknown location: levels below 5 ng/mL are associated with nonviable gestations, whereas levels above 20 ng/mL are correlated with viable intrauterine pregnancies. Most cases are low risk and can be monitored by expectant management with transvaginal ultrasound and serial serum hCG levels, in addition to the serum progesterone levels. To minimize diagnostic error and intervene during progressive intrauterine gestation, protocol indicates active treatment only in situations when progressive intrauterine pregnancy is excluded and a high possibility of ectopic pregnancy exists.

Diagnostic protocols for the management of pregnancy of unknown location: a systematic review and meta-analysis

BACKGROUND

There is no international consensus on how to manage women with a pregnancy of unknown location (PUL).

OBJECTIVES

To present a systematic quantitative review summarising the evidence related to management protocols for PUL.

SEARCH STRATEGY

MEDLINE, COCHRANE and DARE databases were searched from 1 January 1984 to 31 January 2017. The primary outcome was accurate risk prediction of women initially diagnosed with a PUL having an ectopic pregnancy (high risk) as opposed to either a failed PUL or intrauterine pregnancy (low risk).

SELECTION CRITERIA

All studies written in the English language, which were not case reports or series that assessed women classified as having a PUL at initial ultrasound.

DATA COLLECTION AND ANALYSIS

Forty-three studies were included. QUADAS-2 criteria were used to assess the risk of bias. We used a novel, linear mixed-effects model and constructed summary receiver operating characteristic curves for the thresholds of interest.

MAIN RESULTS

There was a high risk of differential verification bias in most studies. Meta-analyses of accuracy were performed on (i) single human chorionic gonadotrophin (hCG) cut-off levels, (ii) hCG ratio (hCG at 48 hours/initial hCG), (iii) single progesterone cut-off levels and (iv) the 'M4 model' (a logistic regression model based on the initial hCG and hCG ratio). For predicting an ectopic pregnancy, the areas under the curves (95% CI) for these four management protocols were as follows: (i) 0.42 (0.00-0.99), (ii) 0.69 (0.57-0.78), (iii) 0.69 (0.54-0.81) and (iv) 0.87 (0.83-0.91), respectively.

CONCLUSIONS

The M4 model was the best available method for predicting a final outcome of ectopic pregnancy. Developing and validating risk prediction models may optimise the management of PUL.

TWEETABLE ABSTRACT

Pregnancy of unknown location meta-analysis: M4 model has best test performance to predict ectopic pregnancy.

Endocannabinoids as biomarkers of human reproduction

BACKGROUND

Infertility is a condition of the reproductive system that affects ∼10-15% of couples attempting to conceive a baby. More than half of all cases of infertility are a result of female conditions, while the remaining cases can be attributed to male factors, or to a combination of both. The search for suitable biomarkers of pregnancy outcome is a challenging issue in human reproduction, aimed at identifying molecules with predictive significance of the reproductive potential of male and female gametes. Among the various candidates, endocannabinoids (eCBs), and in particular anandamide (AEA), represent potential biomarkers of human fertility disturbances. Any perturbation of the balance between synthesis and degradation of eCBs will result in local changes of their tone in human female and male reproductive tracts, which in turn regulates various pathophysiological processes, oocyte and sperm maturation included.

METHODS

PubMed and Web of Science databases were searched for papers using relevant keywords like 'biomarker', 'endocannabinoid', 'infertility', 'pregnancy' and 'reproduction'.

RESULTS

In this review, we discuss different studies on the measurements of AEA and related eCBs in human reproductive cells, tissues and fluids, where the local contribution of these bioactive lipids could be critical in ensuring normal sperm fertilizing ability and pregnancy.

CONCLUSION

Based on the available data, we suggest that the AEA tone has the potential to be exploited as a novel diagnostic biomarker of infertility, to be used in association with assays of conventional hormones (e.g. progesterone, β-chorionic gonadotrophin) and semen analysis. However further quantitative research of its predictive capacity is required.

Ectopic pregnancy prediction in women with a pregnancy of unknown location: data beyond 48 h are necessary

STUDY QUESTION

Are there improvements in the accuracy of prediction of ectopic pregnancy (EP) in women with early symptomatic pregnancy using human chorionic gonadotrophin (hCG) curves when clinicians consider visits beyond the first 48 h after initial presentation?

SUMMARY ANSWER

Two hCG values, measured 48 h (2 days) apart, are often not sufficient to accurately predict the outcome of a woman with a pregnancy of unknown location (PUL), but adding a third visit on Day 4 or 7 significantly improved the prediction for 1 in 15 women.

WHAT IS KNOWN ALREADY

The use of serial hCG values is commonly used to aid in the prediction of the final diagnosis in women with a PUL. Initial outcome predictions based on two hCG values may often be incorrect.

STUDY DESIGN, SIZE, DURATION

This retrospective multicenter cohort study included 646 women with a PUL, recruited over 2 years. Of these women, 146 were ultimately diagnosed with EP.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Women presenting to the emergency room with first trimester pain or bleeding, with a PUL, at least 2 hCG values and a definitive final diagnosis from the University of Pennsylvania, University of Miami and University of Southern California, were recruited from 2007 to 2009.

MAIN RESULTS AND THE ROLE OF CHANCE

Using currently recommended prediction rules, adding a third hCG evaluation on Day 4 after initial presentation significantly improved the accuracy of initial prediction from the first two values (48 h apart, or Day 2) by 9.3% (P = 0.015). Adding a third value on Day 7 improved prediction significantly by 6.7% (P = 0.031), compared with prediction based on first two values. The improvement in prediction by assessing four hCG values (Days 0, 2, 4 and 7) compared with three values (Days 0, 2 and 4) was 1.3% and not statistically significant.

LIMITATIONS, REASONS FOR CAUTION

Missing data imputation likely biased results toward the null; predicted outcomes may not match those made by clinicians; and the study does not predict intrauterine pregnancy and spontaneous miscarriage separately.

WIDER IMPLICATIONS OF THE FINDINGS

This study provides useful information for the prediction of outcomes for women with a symptomatic first trimester pregnancy of unknown location, but may not be generalizable to all pregnant women.

STUDY FUNDING/COMPETING INTEREST(S)

Supported by NIH grant numbers R01-HD036455 to Dr Barnhart and Dr Sammel, K24HD060687 to Dr Barnhart, and 5T32MH065218 to Ms. Zee. The authors have no conflicts of interest to declare.

Biomarkers in reproductive medicine: the promise, and can it be fulfilled?

A biomarker can be used for early diagnosis of a disease, identification of individuals for disease prevention, as a potential drug target, or as a potential marker for a drug response. A biomarker may also limit the use of drug (and therefore costs) to the population of patients for which the drug will be safe and efficacious. A biomarker in reproduction could be used to improve assessment of exposure, identify subgroups susceptible to treatment, predict outcome, and/or differentiate subgroups with potentially different etiologies of disease. Despite many potential uses there is low participation in reproductive biology to develop molecular biomarkers, which may be directly related to the low number of new molecular entities entering clinical trials. As the number of candidate markers in reproductive medicine is increasing, it is important to understand the pathway of development from discovery to clinical utility and recognize that the vast majority of potential markers will not be clinically useful, owing to a variety of pitfalls. Extensive testing, validation, and modification needs to be performed before a biomarker is demonstrated to have clinical utility. New opportunities and partnerships exist and should hasten the development of biomarkers in reproduction. As more biomarkers are moved into practice, a better-educated biomarker consumer will enhance the possibility that biomarker(s) will realize their great potential.

Biomarkers for ectopic pregnancy and pregnancy of unknown location

Early pregnancy failure is the most common complication of pregnancy, and 1% to 2% of all pregnancies will be ectopic. As one of the leading causes of maternal morbidity and mortality, diagnosing ectopic pregnancy and determining the fate of a pregnancy of unknown location are of great clinical concern. Several serum and plasma biomarkers for ectopic pregnancy have been investigated independently and in combination. The following is a review of the state of biomarker discovery and development for ectopic pregnancy and pregnancy of unknown location.

Validation of a clinical risk scoring system, based solely on clinical presentation, for the management of pregnancy of unknown location

OBJECTIVE

To assess a scoring system to triage women with a pregnancy of unknown location.

DESIGN

Validation of prediction rule.

SETTING

Multicenter study.

PATIENT(S)

Women with a pregnancy of unknown location.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

Scores assigned to factors identified at clinical presentation, total score calculated to assess risk of ectopic pregnancy (EP) in women with a pregnancy of unknown location, and a proposed three-tiered clinical action plan.

RESULT(S)

The cohort of 1,400 women (284 ectopic pregnancies, 759 miscarriages, and 357 intrauterine pregnancies) was more diverse than the original cohort used to develop the decision rule. The recommendations of the action plan were low risk, intermediate risk, and high risk; the recommendation based on the model score was compared with clinical diagnosis. A total of 29.4% intrauterine pregnancies were identified for less frequent follow-up observation, and 18.4% nonviable gestations were identified for more frequent follow-up observation (to rule out an ectopic pregnancy) compared with intermediate risk (i.e., monitor in current standard fashion). For a decision of possible less frequent monitoring, the specificity was 90.8% (89.0-92.6) with negative predictive value of 79.0% (76.7-81.3). For a decision of more intense follow-up observation, the specificity was 95.0% (92.7-97.2). Test characteristics using the scoring system were replicated in the diverse validation cohort.

CONCLUSION(S)

A scoring system based on symptoms at presentation has value to stratify risk and influence the intensity of outpatient surveillance for women with pregnancy of unknown location but does not serve as a diagnostic tool.

How and when human chorionic gonadotropin curves in women with an ectopic pregnancy mimic other outcomes: differences by race and ethnicity

OBJECTIVE

To investigate the hCG profiles in a diverse patient group with ectopic pregnancy (EP) and to understand when they may mimic the curves of an intrauterine pregnancy (IUP) or spontaneous abortion (SAB).

DESIGN

Retrospective cohort study.

SETTING

Three university hospitals.

PATIENT(S)

One hundred seventy-nine women with symptomatic pregnancy of unknown location.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

Slope of log hCG; days and visits to final diagnosis.

RESULT(S)

Of women with an EP, 60% initially exhibited an increase in hCG values, with a median slope of 32% increase in 2 days; 40% of subjects initially had an hCG decrease, with the median slope calculated as a 15% decline in 2 days. In total, the hCG curves in 27% of women diagnosed with EP resembled that of a growing IUP or SAB. Of the EP hCG curves, 16% demonstrated a change in the direction of the slope of the curve. This was more common in African Americans and less evident in Hispanics. Furthermore, it was associated with more clinical visits and days until final diagnosis.

CONCLUSION(S)

The rate of change in serial hCG values can be used to distinguish EP from an IUP or SAB in only 73% of cases. The number of women who had a change in direction of serial hCG values was associated with race and ethnicity.

The diagnostic value of a multivariate logistic regression analysis model with transvaginal power Doppler ultrasonography for the prediction of ectopic pregnancy

OBJECTIVE

A multivariate logistic regression analysis model for predicting ectopic pregnancy in women with pregnancy of unknown location was designed and evaluated clinically.

METHODS

Endometrial thickness, symmetry, resonance, pattern of echogenicity, helicine artery blood flow and blood flow resistance index (RI) in 129 patients with suspected early ectopic pregnancy were assessed by transvaginal power Doppler ultrasonography. Variables significant in univariate logistic regression analysis were included in a multivariate predictive logistic regression analysis model.

RESULTS

The final predictive model included three factors: endometrial thickness≤9 mm; a multilayered endometrial echogenicity pattern with prominent outer and midline hyperechogenic lines and an inner hypoechogenic region; and visible endometrial arterial blood flow. The area under the receiver operating characteristic curve of the model was 0.980. When RI was >0.65 and the predictive probability>0.50, diagnostic accuracy was high. The model correctly diagnosed 52/55 (94.5%) clinically confirmed ectopic pregnancy cases.

CONCLUSION

This multivariate predictive logistic regression analysis model has clinical value for the differential diagnosis of early ectopic pregnancy when the pregnancy location is unknown.

Performance of human chorionic gonadotropin curves in women at risk for ectopic pregnancy: exceptions to the rules

OBJECTIVE

To investigate the accuracy of serial hCG to predict outcome of a pregnancy of unknown location in an ethnically and geographically diverse setting.

DESIGN

Multisite cohort study.

SETTING

University hospital.

PATIENT(S)

Women with a pregnancy of unknown location.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

Patients were followed until diagnosed with ectopic pregnancy (EP), intrauterine pregnancy (IUP), or miscarriage. To predict outcome, observed hCG level was compared with recommended thresholds to assess deviation from defined normal curves. Predicted outcome was compared with standard of care. Sensitivity, specificity, predictive value, and accuracy were calculated, stratified by diagnosis.

RESULT(S)

The final diagnosis of 1,005 patients included 179 EPs, 259 IUPs, and 567 miscarriages. The optimal balance in sensitivity and specificity used the minimal expected 2-day increase in hCG level of 35%, and the minimal 2-day decrease in hCG level of 36%-47% (depending on the level) achieving 83.2% sensitivity, 70.8% specificity to predict EP. However, 16.8% of EPs and 7.7% of IUPs would be misclassified solely using serial hCG levels. Consideration of a third hCG and early ultrasound decreased IUP misclassification to 2.7%.

CONCLUSION(S)

Solely using serial hCG values can result in misclassification. Clinical judgment should trump prediction rules and continued surveillance with a third hCG may be prudent, especially when initial values are low or when values are near suggested thresholds.

Improving automated case finding for ectopic pregnancy using a classification algorithm

BACKGROUND

Research and surveillance work addressing ectopic pregnancy often rely on diagnosis and procedure codes available from automated data sources. However, the use of these codes may result in misclassification of cases. Our aims were to evaluate the accuracy of standard ectopic pregnancy codes; and, through the use of additional automated data, to develop and validate a classification algorithm that could potentially improve the accuracy of ectopic pregnancy case identification.

METHODS

Using automated databases from two US managed-care plans, Group Health Cooperative (GH) and Kaiser Permanente Colorado (KPCO), we sampled women aged 15-44 with an ectopic pregnancy diagnosis or procedure code from 2001 to 2007 and verified their true case status through medical record review. We calculated positive predictive values (PPV) for code-selected cases compared with true cases at both sites. Using additional variables from the automated databases and classification and regression tree (CART) analysis, we developed a case-finding algorithm at GH (n = 280), which was validated at KPCO (n = 500).

RESULTS

Compared with true cases, the PPV of code-selected cases was 68 and 81% at GH and KPCO, respectively. The case-finding algorithm identified three predictors: ≥ 2 visits with an ectopic pregnancy code within 180 days; International Classification of Diseases, 9th Revision, Clinical Modification codes for tubal pregnancy; and methotrexate treatment. Relative to true cases, performance measures for the development and validation sets, respectively, were: 93 and 95% sensitivity; 81 and 81% specificity; 91 and 96% PPV; 84 and 79% negative predictive value. Misclassification proportions were 32% in the development set and 19% in the validation set when using standard codes; they were 11 and 8%, respectively, when using the algorithm.

CONCLUSIONS

The ectopic pregnancy algorithm improved case-finding accuracy over use of standard codes alone and generalized well to a second site. When using administrative data to select potential ectopic pregnancy cases, additional widely available automated health plan data offer the potential to improve case identification.