Evidence map›Paper›PMID 42438723›Full record

ArticleKidney international reports2026

PREDICT Tool for Pregnancy-Associated CKD Progression.

Elizabeth Ralston, Mairéad Hamill, Shalini Santhakumaran, Michelle Hladunewich, Graham Smith, Lavanya Bathini, Nivethika Jeyakumar, Amit X Garg, Kate Bramham, PREDICT Investigation Group9 and 2 more

Abstract read
In one paragraph

Article in Kidney international reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Elizabeth RalstonDepartment of Women and Children's Health, Faculty of Life Sciences and Medicine, School of Life Course and Population Sciences, King's College London, London, UK.
Mairéad HamillDepartment of Women and Children's Health, Faculty of Life Sciences and Medicine, School of Life Course and Population Sciences, King's College London, London, UK.
Shalini SanthakumaranUK Renal Registry, UK Kidney Association, Bristol, UK.
Michelle HladunewichDivision of Nephrology, Sunnybrook Health Sciences Centre, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
Graham SmithInstitute for Clinical Evaluative Sciences (ICES), Toronto, Ontario, Canada.
Lavanya BathiniLondon Health Sciences Centre Research Institute, London, Ontario, Canada.
Nivethika JeyakumarDepartment of Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
Amit X GargInstitute for Clinical Evaluative Sciences (ICES), Toronto, Ontario, Canada.
Kate BramhamDepartment of Women and Children's Health, Faculty of Life Sciences and Medicine, School of Life Course and Population Sciences, King's College London, London, UK.
PREDICT Investigation Group9
RaDaR Consortium
PREDICT Investigation Group

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Prepregnancy counselling is recommended for women with chronic kidney disease (CKD) to discuss potential adverse outcomes; however, no tools exist to estimate individual risk. We aimed to develop and externally validate 2 prediction models for outcomes prioritized by people with CKD and health care professionals: The primary outcome was the probability of ≥25% reduction in estimated glomerular filtration rate (eGFR) or kidney replacement therapy (KRT) within 12 months postpartum. The secondary outcome was the probability of small-for-gestational-age (SGA) (< 3rd percentile) infant and/or preterm delivery (< 34 weeks). Methods: The development cohort used linked data from the National Registry of Rare Kidney Disease (RaDar), UK Renal Registry (UKRR) and NHS Hospital Episode Statistics (HES). Individuals with eGFR < 90 ml/min per 1.73 m Results: The development cohort included 746 women (median prepregnancy eGFR: 58 ml/min per 1.73 m Conclusion: We developed high performing models for individuals with CKD to predict coselected adverse kidney and neonatal outcomes from contemporaneous cohorts. Individualized pregnancy risk assessment tools could support future parents and health care professionals to make informed choices.

Indexed as

chronic kidney diseaseobstetric nephrologyprediction tool

Identifiers

PMID42438723
PMCPMC13356684

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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.