Question: Write a study description for the study titled 'Diagnostic utility of clinicodemographic, biochemical and metabolite variables to identify viable pregnancies in a symptomatic cohort during early gestation.'.
Answer: Study question: Can a novel machine learning pipeline create predictive models that identify a composite biomarker to differentiate live normally sited pregnancies (LNSP) from non-viable pregnancies in women presenting with pain and/or bleeding in early gestation? Summary answer: Serum metabolite levels and biochemical markers from a single blood sample possess modest predictive utility in differentiating LNSP from non-viable pregnancies upon first presentation, which is improved by variable selection and combination using machine learning. What is known already: Around 15% of intrauterine pregnancies are lost in the first trimester. A further 1–2% of pregnancies are located outside of the endometrial cavity and these ectopic pregnancies (EPs) are the leading cause of maternal mortality in the first trimester. Early pregnancy loss can cause significant morbidity when bleeding or infection occurs. Symptoms of pregnancy loss and EP are very similar (including pain and bleeding); however, these symptoms are also common in LNSP. To date, no biomarkers have been identified to differentiate LNSP from non-viable pregnancies. Study design, size, duration: This is a prospective cohort study that included 370 participants who attended the early pregnancy assessment unit (EPAU) at Liverpool Women’s Hospital between 5/11/2018 and 08/12/2021 with pain and/or bleeding ≤10 weeks of gestation.  Participants/materials, setting, methods: A single blood sample was prospectively collected from participants prior to final clinical diagnosis of pregnancy outcome: LNSP, miscarriage, pregnancy of unknown location (PUL) or tubal EP (tEP). Human chorionic gonadotrophin β (β-hCG) and progesterone concentrations were measured in plasma. Serum samples were subjected to untargeted metabolomics profiling using nuclear magnetic resonance (NMR) spectroscopy. Metabolite abundances underwent statistical analyses together with patient demographic data and biochemical markers. The cohort was randomly split into train and validation data sets. The train data set was subjected to variable selection using linear mixed models and 10-fold cross-validation with Least Absolute Shrinkage and Selection Operator (LASSO) selection. Random forest models were constructed using selected metabolites, biochemical markers, and metadata variables.  Main results and the role of chance: β-hCG and progesterone concentrations were significantly higher in the LNSP group compared with miscarriage, PUL and tEP. Thirty-two unique metabolites were identified in serum, along with 70 unlabelled signals of unknown origin. Of these, 21 metabolite signals exhibited significantly different abundances in the LNSP group when compared with miscarriage, PUL and tEP. When miscarriage, PUL and tEP were grouped together into combined adverse outcomes (CAO), 15 metabolites were significantly different compared with LNSP. LASSO selection identified nine metabolite signals as key discriminators of LNSP versus CAO. Addition of covariates β-hCG, progesterone, participant age, BMI and gestational age improved group separation. Random forest models were constructed using stable metabolite signals alone, or in combination with plasma hormone concentrations and demographic data. Using the independent validation data set. Model performance was poor when all four pregnancy outcomes were included, regardless of variable inclusion (accuracy = 0.48–0.58). When comparing LNSP with CAO, a model with stable metabolite signals only demonstrated a modest predictive accuracy (0.68), which was comparable to a model of β-hCG and progesterone (0.71). The best model for LNSP prediction comprised stable metabolite signals and hormone concentrations (accuracy = 0.79), while the addition of clinicodemographic variables did not improve the model further. Large scale data: NMR data is available via the EMBL-EBI MetaboLights repository (ID: MTBLS6219).Limitations, reasons for caution: Women with LNSPs presented at the EPAU with pain and/or bleeding. The presence of symptoms may influence the metabolome of this group versus LNSPs without symptoms, thus limiting the translation of these findings. Furthermore, environmental factors were not controlled (e.g., fasting status), making it likely that cohort heterogeneity was enhanced. Finally, a larger study should be conducted to establish the robustness of composite biomarkers for LNSP diagnosis in an independent cohort. Wider implications of the findings: A diagnostic test to confirm LNSP and thus exclude potentially life-threatening pregnancy outcomes would be invaluable in emergency situations. Study funding/competing interest(s): This work was funded by a Wellbeing of Women Project Grant (RG2137). C.J.H. is supported by the Wellbeing of Women (RG2137), SRI/Bayer and Wellcome Trust IFFS3. N.T. is supported by an Academic Clinical Lectureship from the National Institute of Health Research (NIHR). P.B. was supported by an NIHR Academic Clinical Fellowship. P.J.D. was supported by a Clinical Research Fellowship from the Liverpool Women’s Hospital NHS Foundation Trust. D.K.H. is supported by the Wellbeing of Women (RG2137) and A.M. and D.K.H. are supported by an MRC Clinical Research Training Fellowship (MR/V007238/1). A.W.H. has received honoraria for consultancy for Ferring, Roche, Nordic Pharma, Gesynta, Joii and Abbvie. A.W.H. has received payment for presentations from Theramex and Gideon Richter. The remaining authors have no competing interests to report.