First-trimester prediction of preterm prelabour rupture of membranes incorporating cervical length measurement

Objectives: To examine early pregnancy risk factors for preterm prelabour rupture of membranes (PPROM) and develop a predictive model. Study design: Retrospective analysis of a cohort of mixed-risk singleton pregnancies screened in the first and second trimesters in three Danish tertiary fetal medicine centres, including a cervical length measurement at 11–14 weeks, at 19–21 weeks and at 23–24 weeks of gestation. Univariable and multivariable logistic regression analyses were employed to identify predictive maternal characteristics, biochemical and sonographic factors. Receiver operating characteristic (ROC) curve analysis was used to determine predictors for the most accurate model. Results: Of 3477 screened women, 77 (2.2%) had PPROM. Maternal factors predictive of PPROM in univariable analysis were nulliparity (OR 2.0 (95% CI 1.2–3.3)), PAPP-A < 0.5 MoM (OR 2.6 (1.1–6.2)), previous preterm birth (OR 4.2 (1.9–8.9)), previous cervical conization (OR 3.6 (2.0–6.4)) and cervical length ≤ 25 mm on transvaginal imaging (first-trimester OR 15.9 (4.3–59.3)). These factors all remained statistically significant in a multivariable adjusted model with an AUC of 0.72 in the most discriminatory first-trimester model. The detection rate using this model would be approximately 30% at a false-positive rate of 10%. Potential predictors such as bleeding in early pregnancy and pre-existing diabetes mellitus affected very few cases and could not be formally assessed. Conclusions: Several maternal characteristics, placental biochemical and sonographic features are predictive of PPROM with moderate discrimination. Larger numbers are required to validate this algorithm and additional biomarkers, not currently used for first-trimester screening, may improve model performance

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Linking quality of care and training costs:cost-effectiveness in health professions education

OBJECTIVE: To provide a model for conducting cost‐effectiveness analyses in medical education. The model was based on a randomised trial examining the effects of training midwives to perform cervical length measurement (CLM) as compared with obstetricians on patients' waiting times. (CLM), as compared with obstetricians. METHODS: The model included four steps: (i) gathering data on training outcomes, (ii) assessing total costs and effects, (iii) calculating the incremental cost‐effectiveness ratio (ICER) and (iv) estimating cost‐effectiveness probability for different willingness to pay (WTP) values. To provide a model example, we conducted a randomised cost‐effectiveness trial. Midwives were randomised to CLM training (midwife‐performed CLMs) or no training (initial management by midwife, and CLM performed by obstetrician). Intervention‐group participants underwent simulation‐based and clinical training until they were proficient. During the following 6 months, waiting times from arrival to admission or discharge were recorded for women who presented with symptoms of pre‐term labour. Outcomes for women managed by intervention and control‐group participants were compared. These data were then used for the remaining steps of the cost‐effectiveness model. RESULTS: Intervention‐group participants needed a mean 268.2 (95% confidence interval [CI], 140.2‒392.2) minutes of simulator training and a mean 7.3 (95% CI, 4.4‒10.3) supervised scans to attain proficiency. Women who were scanned by intervention‐group participants had significantly reduced waiting time compared with those managed by the control group (n = 65; mean difference, 36.6 [95% CI 7.3‒65.8] minutes; p = 0.008), which corresponded to an ICER of 0.45 EUR minute(−1). For WTP values less than EUR 0.26 minute(−1), obstetrician‐performed CLM was the most cost‐effective strategy, whereas midwife‐performed CLM was cost‐effective for WTP values above EUR 0.73 minute(−1). CONCLUSION: Cost‐effectiveness models can be used to link quality of care to training costs. The example used in the present study demonstrated that different training strategies could be recommended as the most cost‐effective depending on administrators' willingness to pay per unit of the outcome variable