Fetal fibronectin test for threatened preterm delivery 48h after admission: Cost-effectiveness study

Benefits of Premaquick® Combined Detection of IL-6/Total IGFBP-1/Native IGFBP-1 to Predict Preterm Delivery

We conducted a prospective double-blind study to compare two vaginal diagnostic methods in singleton pregnancies with threatened preterm labor (TPL) at the University Hospital of Clermont-Ferrand (France) from August 2018 to December 2020. Our main objective was to compare the diagnostic capacity at admission, in terms of positive predictive value (PPV) and negative predictive value (NPV), of Premaquick® (combined detection of IL-6/total IGFBP-1/native IGFBP-1) and QuikCheck fFN™ (fetal fibronectin) for delivery within 7 days in cases of TPL. We included 193 patients. Premaquick® had a sensitivity close to 89%, equivalent to QuikCheck fFN™, but a higher statistical specificity of 49.5% against 38.6% for QuikCheck fFN™. We found no superiority of Premaquick® over QuickCheck fFN™ in terms of PPV (6.6% vs. 7.9%), with NPV being equivalent in predicting childbirth within 7 days in cases of TPL (98.6% vs. 98.9%). Nevertheless, the combination of positive native and total IGFBP-1 and the combination of all three positive markers were associated with a higher PPV. Our results, though non-significant, support this combined multiple-biomarker approach to improve testing in terms of predictive values.

Prediction of Preterm Labor from the Electrohysterogram Signals Based on Different Gestational Weeks

Timely preterm labor prediction plays an important role for increasing the chance of neonate survival, the mother's mental health, and reducing financial burdens imposed on the family. The objective of this study is to propose a method for the reliable prediction of preterm labor from the electrohysterogram (EHG) signals based on different pregnancy weeks. In this paper, EHG signals recorded from 300 subjects were split into 2 groups: (I) those with preterm and term labor EHG data that were recorded prior to the 26th week of pregnancy (referred to as the PE-TE group), and (II) those with preterm and term labor EHG data that were recorded after the 26th week of pregnancy (referred to as the PL-TL group). After decomposing each EHG signal into four intrinsic mode functions (IMFs) by empirical mode decomposition (EMD), several linear and nonlinear features were extracted. Then, a self-adaptive synthetic over-sampling method was used to balance the feature vector for each group. Finally, a feature selection method was performed and the prominent ones were fed to different classifiers for discriminating between term and preterm labor. For both groups, the AdaBoost classifier achieved the best results with a mean accuracy, sensitivity, specificity, and area under the curve (AUC) of 95%, 92%, 97%, and 0.99 for the PE-TE group and a mean accuracy, sensitivity, specificity, and AUC of 93%, 90%, 94%, and 0.98 for the PL-TL group. The similarity between the obtained results indicates the feasibility of the proposed method for the prediction of preterm labor based on different pregnancy weeks.

A prospective, double-blinded cohort study using quantitative fetal fibronectin testing in symptomatic women for the prediction of spontaneous preterm delivery

BACKGROUND

Spontaneous preterm birth (PTB) affects 6.5% of deliveries in Hong Kong. Quantitative fetal fibronectin (fFN) is under-utilised as a test for PTB prediction in Hong Kong. Our objective was to evaluate the effectiveness of quantitative fFN in predicting spontaneous PTB in women with symptoms of threatened preterm labour (TPTL) in our population.

METHODS

A prospective, double-blinded cohort study of women with a singleton gestation and TPTL symptoms presenting to a tertiary hospital in Hong Kong between 24 + 0 to 33 + 6 weeks was performed from 1st October 2020 and 31st October 2021. Women with vaginal bleeding, ruptured membranes, and cervical dilation > 3 cm were excluded. The primary outcome was to test the characteristics of quantitative fFN in predicting spontaneous PTB < 37 weeks. Secondary outcome was to investigate the relationship between fFN value and time to PTB. Test characteristics of quantitative fFN at different thresholds were evaluated.

RESULTS

48 women with TPTL were recruited. All had fFN testing at admission with the results being concealed from the obstetrician managing the patient. 10 mothers had PTB (< 37 weeks' gestation). 7/48 (15%) had a subsequent PTB within 14 days from testing and 5 (10%) delivered within 48 h. The negative predictive value (NPV) of predicting delivery within 14 days was 97.3% and 100% when using a cut-off of < 50ng/ml and < 10ng/ml respectively. Using > 200 ng/ml as cut-off can also reliably predict delivery within 48 h - 7 days with positive predictive value PPV of 100%; as well as PTB before 37 weeks.

CONCLUSIONS

Quantitative fFN has predictive value for spontaneous PTB prediction in symptomatic women in a Hong Kong population. fFN concentration could help clinicians rule out PTB and avoid unnecessary interventions and hospitalisation.

Prediction of Preterm Delivery from Unbalanced EHG Database

Objective: The early prediction of preterm labor can significantly minimize premature delivery complications for both the mother and infant. The aim of this research is to propose an automatic algorithm for the prediction of preterm labor using a single electrohysterogram (EHG) signal. Method: The proposed method firstly employs empirical mode decomposition (EMD) to split the EHG signal into two intrinsic mode functions (IMFs), then extracts sample entropy (SampEn), the root mean square (RMS), and the mean Teager–Kaiser energy (MTKE) from each IMF to form the feature vector. Finally, the extracted features are fed to a k-nearest neighbors (kNN), support vector machine (SVM), and decision tree (DT) classifiers to predict whether the recorded EHG signal refers to the preterm case. Main results: The studied database consists of 262 term and 38 preterm delivery pregnancies, each with three EHG channels, recorded for 30 min. The SVM with a polynomial kernel achieved the best result, with an average sensitivity of 99.5%, a specificity of 99.7%, and an accuracy of 99.7%. This was followed by DT, with a mean sensitivity of 100%, a specificity of 98.4%, and an accuracy of 98.7%. Significance: The main superiority of the proposed method over the state-of-the-art algorithms that studied the same database is the use of only a single EHG channel without using either synthetic data generation or feature ranking algorithms.