Evidence map›Paper›PMID 41593149›Full record

ArticleScientific reports2026

Life expectancy after kidney transplantation in a population-based retrospective cohort.

Tanya Babich, Vered Daitch, Leonard Leibovici, Hefziba Green, Adi Turjeman

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Tanya BabichResearch authority, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel. tanyanoa@gmail.com.
Vered DaitchDina Recanati School of Medicine, Reichman University, Herzliya, Israel.
Leonard LeiboviciResearch authority, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel.
Hefziba GreenInternal Medicine A, Kaplan Medical Center, Rehovot, Israel.
Adi TurjemanResearch authority, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Kidney transplantation is the treatment of choice for end-stage renal disease, offering significant survival benefits. However, outcomes vary based on donor type and patient characteristics. We aimed to identify factors influencing long-term survival following kidney transplantation. This was a population-based retrospective cohort study of kidney transplant recipients from 2005 to 2018 in Israel, assessing survival from one-month post-transplant. Univariate and multivariate Cox regression analyses were performed to identify predictors of mortality. Among 1,847 recipients, 679 received kidneys from living donors and 1,168 from deceased donors. The median age was 52 years (IQR 38–62), and the mean post-transplant survival time was 11.9 years (95% CI 11.7–12.3). Increased mortality risk was associated with age over 65 years (HR 3.73), current or past smoking (HR= 1.32), diabetes mellitus (HR= 1.79), heart failure (HR= 1.49), and atrial fibrillation (HR= 1.79). Receiving a kidney from a living donor (HR= 0.49) and higher pre-transplant hemoglobin levels (HR= 0.75) were linked to improved survival. Recipient comorbidities and donor type are key determinants of post-transplant outcomes. These findings support a tailored approach to transplant evaluation, balancing potential survival and quality-of-life benefits with individual risk profiles.

Indexed as

Kidney Failure, ChronicKidney TransplantationLife ExpectancyAdultAgedFemaleHumansIsraelLiving DonorsMaleMiddle AgedQuality of LifeRetrospective StudiesRisk FactorsDonor typeHigh-risk patientsKidney transplantationLife-expectancyRisk factors

Identifiers

PMID41593149
PMCPMC12905445

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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.