Evidence map›Paper›PMID 33863296›Full record

ArticleBMC pregnancy and childbirth2021

A hierarchical procedure to select intrauterine and extrauterine factors for methodological validation of preterm birth risk estimation.

Pasquale Anthony Della Rosa, Cesare Miglioli, Martina Caglioni, Francesca Tiberio, Kelsey H H Mosser, Edoardo Vignotto, Matteo Canini, Cristina Baldoli, Andrea Falini, Massimo Candiani and 1 more

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in BMC pregnancy and childbirth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07059286 (The Combination of Maternal Risk Factors and Functional/Structural Connectivity in Fetuses and Neonates to Predict Neurodevelopment From Birth Through Early Childhood), which is not on this map. Cited by 28 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 4 pooled it
7.5field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07059286 enrolling by invitationnot on this mapstarted 2025, after this paper: background citation

The Combination of Maternal Risk Factors and Functional/Structural Connectivity in Fetuses and Neonates to Predict Neurodevelopment From Birth Through Early Childhood: A Single-Center Observational Cohort Study

TypeobservationalSponsorIRCCS San RaffaeleRan2025 to 2033Enrolled160ConditionsPregnancy, Neurodevelopment, Brain Connectivity, Neurodevelopment OutcomeArmsMaternal Frailty Inventory (MaFra) Questionnaire, Fetal Resting-State Functional MRI, Maternal Emotional Reactivity, Neonatal Resting-State Functional MRI, Longitudinal Neurodevelopmental Testing Battery
3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 4 syntheses or guidelines pooled it, 52 citations in OpenAlex.

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  5. Vaginal microbiome and its implications in preterm birth.Obstetrics & gynecology science · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 3 institutions in 2 countries.

Pasquale Anthony Della Rosa *Neuroradiology Department, IRCCS San Raffaele Hospital and University, via Olgettina 62, Milan, 20132, Italy.ORCID http://orcid.org/0000-0002-3137-1731
Cesare Miglioli *Research Center for Statistics, University of Geneva, Boulevard du Pont-d'Arve 40, Geneva, 1205, Switzerland.ORCID http://orcid.org/0000-0002-1181-5016
Martina CaglioniObstetrics and Gynaecology Department, IRCCS San Raffaele Hospital and University, via Olgettina 62, Milan, 20132, Italy.
Francesca TiberioObstetrics and Gynaecology Department, IRCCS San Raffaele Hospital and University, via Olgettina 62, Milan, 20132, Italy.
Kelsey H H MosserNeuroradiology Department, IRCCS San Raffaele Hospital and University, via Olgettina 62, Milan, 20132, Italy.
Edoardo VignottoResearch Center for Statistics, University of Geneva, Boulevard du Pont-d'Arve 40, Geneva, 1205, Switzerland.
Matteo CaniniNeuroradiology Department, IRCCS San Raffaele Hospital and University, via Olgettina 62, Milan, 20132, Italy.
Cristina BaldoliNeuroradiology Department, IRCCS San Raffaele Hospital and University, via Olgettina 62, Milan, 20132, Italy.
Andrea FaliniNeuroradiology Department, IRCCS San Raffaele Hospital and University, via Olgettina 62, Milan, 20132, Italy.
Massimo CandianiObstetrics and Gynaecology Department, IRCCS San Raffaele Hospital and University, via Olgettina 62, Milan, 20132, Italy.
Paolo CavorettoObstetrics and Gynaecology Department, IRCCS San Raffaele Hospital and University, via Olgettina 62, Milan, 20132, Italy. cavoretto.paolo@hsr.it.ORCID http://orcid.org/0000-0001-8097-366X
Istituti di Ricovero e Cura a Carattere Scientifico · ITVita-Salute San Raffaele University · ITUniversity of Geneva · CH

Funding

Italian Ministry of Health's "Ricerca Finalizzata 2016" RF-2016-02364081
6 · The paper itself

Abstract

backgroundEtiopathogenesis of preterm birth (PTB) is multifactorial, with a universe of risk factors interplaying between the mother and the environment. It is of utmost importance to identify the most informative factors in order to estimate the degree of PTB risk and trace an individualized profile. The aims of the present study were: 1) to identify all acknowledged risk factors for PTB and to select the most informative ones for defining an accurate model of risk prediction; 2) to verify predictive accuracy of the model and 3) to identify group profiles according to the degree of PTB risk based on the most informative factors.

methodsThe Maternal Frailty Inventory (MaFra) was created based on a systematic review of the literature including 174 identified intrauterine (IU) and extrauterine (EU) factors. A sample of 111 pregnant women previously categorized in low or high risk for PTB below 37 weeks, according to ACOG guidelines, underwent the MaFra Inventory. First, univariate logistic regression enabled p-value ordering and the Akaike Information Criterion (AIC) selected the model including the most informative MaFra factors. Second, random forest classifier verified the overall predictive accuracy of the model. Third, fuzzy c-means clustering assigned group membership based on the most informative MaFra factors.

resultsThe most informative and parsimonious model selected through AIC included Placenta Previa, Pregnancy Induced Hypertension, Antibiotics, Cervix Length, Physical Exercise, Fetal Growth, Maternal Anxiety, Preeclampsia, Antihypertensives. The random forest classifier including only the most informative IU and EU factors achieved an overall accuracy of 81.08% and an AUC of 0.8122. The cluster analysis identified three groups of typical pregnant women, profiled on the basis of the most informative IU and EU risk factors from a lower to a higher degree of PTB risk, which paralleled time of birth delivery.

conclusionsThis study establishes a generalized methodology for building-up an evidence-based holistic risk assessment for PTB to be used in clinical practice. Relevant and essential factors were selected and were able to provide an accurate estimation of degree of PTB risk based on the most informative constellation of IU and EU factors.

Indexed as

AdolescentAdultFemaleHumansMiddle AgedPregnancyPremature BirthRisk FactorsYoung AdultAkaike information criterionExtrauterineFuzzy clusteringIntrauterinePrecision medicinePregnancyPreterm deliveryRandom forestRisk factors

Identifiers

PMID33863296
PMCPMC8052693
OpenAlexW3153759712

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.