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Background: Preterm birth is a major worldwide public health concern, being the leading cause of infant mortality. Understanding of risk factors remains limited, and early identification of women at high risk of preterm birth is an open challenge. Objective: The aim of the study was to develop and validate a novel pre-pregnancy score for preterm delivery in nulliparous women using information from Italian healthcare utilization databases. Study Design: Twenty-six variables independently able to predict preterm delivery were selected, using a LASSO logistic regression, from a large number of features collected in the 4 years prior to conception, related to clinical history and socio-demographic characteristics of 126,839 nulliparous women from Lombardy region who gave birth between 2012 and 2017. A weight proportional to the coefficient estimated by the model was assigned to each of the selected variables, which contributed to the Preterm Birth Score. Discrimination and calibration of the Preterm Birth Score were assessed using an internal validation set (i.e., other 54,359 deliveries from Lombardy) and two external validation sets (i.e., 14,703 and 62,131 deliveries from Marche and Sicily, respectively). Results: The occurrence of preterm delivery increased with increasing the Preterm Birth Score value in all regions in the study. Almost ideal calibration plots were obtained for the internal validation set and Marche, while expected and observed probabilities differed slightly in Sicily for high Preterm Birth Score values. The area under the receiver operating characteristic curve was 60%, 61% and 56% for the internal validation set, Marche and Sicily, respectively. Conclusions: Despite the limited discriminatory power, the Preterm Birth Score is able to stratify women according to their risk of preterm birth, allowing the early identification of mothers who are more likely to have a preterm delivery.

ObjectivesTo develop a population-based risk stratification model (COVID-19 Vulnerability Score) for predicting severe/fatal clinical manifestations of SARS-CoV-2 infection, using the multiple source information provided by the healthcare utilisation databases of the Italian National Health Service.DesignRetrospective observational cohort study.SettingPopulation-based study using the healthcare utilisation database from five Italian regions.ParticipantsBeneficiaries of the National Health Service, aged 18–79 years, who had the residentship in the five participating regions. Residents in a nursing home were not included. The model was built from the 7 655 502 residents of Lombardy region.Main outcome measureThe score included gender, age and 29 conditions/diseases selected from a list of 61 conditions which independently predicted the primary outcome, that is, severe (intensive care unit admission) or fatal manifestation of COVID-19 experienced during the first epidemic wave (until June 2020). The score performance was validated by applying the model to several validation sets, that is, Lombardy population (second epidemic wave), and the other four Italian regions (entire 2020) for a total of about 15.4 million individuals and 7031 outcomes. Predictive performance was assessed by discrimination (areas under the receiver operating characteristic curve) and calibration (plot of observed vs predicted outcomes).ResultsWe observed a clear positive trend towards increasing outcome incidence as the score increased. The areas under the receiver operating characteristic curve of the COVID-19 Vulnerability Score ranged from 0.85 to 0.88, which compared favourably with the areas of generic scores such as the Charlson Comorbidity Score (0.60). A remarkable performance of the score on the calibration of observed and predicted outcome probability was also observed.ConclusionsA score based on data used for public health management accurately predicted the occurrence of severe/fatal manifestations of COVID-19. Use of this score may help health decision-makers to more accurately identify high-risk citizens who need early preventive or treatment interventions.