ObjectivesTo identify risk factors for spontaneous preterm birth (birth <37 weeks gestation) with intact membranes (SPTB-IM) and SPTB after prelabour rupture of the membranes (SPTB-PPROM) for nulliparous pregnant women.DesignProspective international multicentre cohort.Participants3234 healthy nulliparous women with a singleton pregnancy, follow up was complete in 3184 of participants (98.5%).ResultsOf the 3184 women, 156 (4.9%) had their pregnancy complicated by SPTB; 96 (3.0%) and 60 (1.9%) in the SPTB-IM and SPTB-PPROM categories, respectively. Independent risk factors for SPTB-IM were shorter cervical length, abnormal uterine Doppler flow, use of marijuana pre-pregnancy, lack of overall feeling of well being, being of Caucasian ethnicity, having a mother with diabetes and/or a history of preeclampsia, and a family history of low birth weight babies. Independent risk factors for SPTB-PPROM were shorter cervical length, short stature, participant’s not being the first born in the family, longer time to conceive, not waking up at night, hormonal fertility treatment (excluding clomiphene), mild hypertension, family history of recurrent gestational diabetes, and maternal family history of any miscarriage (risk reduction). Low BMI (<20) nearly doubled the risk for SPTB-PPROM (odds ratio 2.64; 95% CI 1.07–6.51). The area under the receiver operating characteristics curve (AUC), after internal validation, was 0.69 for SPTB-IM and 0.79 for SPTB-PPROM.ConclusionThe ability to predict PTB in healthy nulliparous women using clinical characteristics is modest. The dissimilarity of risk factors for SPTB-IM compared with SPTB-PPROM indicates different pathophysiological pathways underlie these distinct phenotypes.Trial RegistrationACTR.org.au ACTRN12607000551493

Self-Organising Map (SOM) clustering methods applied to the monthly and seasonal averaged flowering intensity records of eight Eucalypt species are shown to successfully quantify, visualise and model synchronisation of multivariate time series. The SOM algorithm converts complex, nonlinear relationships between high-dimensional data into simple networks and a map based on the most likely patterns in the multiplicity of time series that it trains. Monthly- and seasonal-based SOMs identified three synchronous species groups (clusters): E. camaldulensis, E. melliodora, E. polyanthemos; E. goniocalyx, E. microcarpa, E. macrorhyncha; and E. leucoxylon, E. tricarpa. The main factor in synchronisation (clustering) appears to be the season in which flowering commences. SOMs also identified the asynchronous relationship among the eight species. Hence, the likelihood of the production, or not, of hybrids between sympatric species is also identified. The SOM pattern-based correlation values mirror earlier synchrony statistics gleaned from Moran correlations obtained from the raw flowering records. Synchronisation of flowering is shown to be a complex mechanism that incorporates all the flowering characteristics: flowering duration, timing of peak flowering, of start and finishing of flowering, as well as possibly specific climate drivers for flowering. SOMs can accommodate for all this complexity and we advocate their use by phenologists and ecologists as a powerful, accessible and interpretable tool for visualisation and clustering of multivariate time series and for synchrony studies.