Evidence map›Paper›PMID 36727726›Full record

ArticleTransplantation2023

Development and Validation of a Hypertension Risk Calculator for Living Kidney Donors.

Erika S Helgeson, Shruti Vempati, Elise F Palzer, Geir Mjoen, Anders J Haugen, Arthur J Matas

Abstract read
In one paragraph

Article in Transplantation, 2023. 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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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Erika S HelgesonDivision of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN.
Shruti VempatiDivision of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN.
Elise F PalzerDivision of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN.
Geir MjoenDepartment of Transplant Medicine, Oslo University Hospital, Oslo, Norway.
Anders J HaugenDeptartment of Internal Medicine, Bærum Hospital, Sandvika, Norway.
Arthur J MatasDivision of Transplantation, Department of Surgery, University of Minnesota, Minneapolis, MN.

Funding

Outcomes in living kidney donors over 50 years compared to a healthy matched contemporaneous non-donor cohort from the same geographical regionR01DK125431 · NIDDK · UNIVERSITY OF MINNESOTA · PI ARTHUR J MATAS, ANDREW David RULE · 2020 to 2026
$3.4M
NIDDK NIH HHS R01 DK125431
6 · The paper itself

Abstract

backgroundIdeally, when deciding whether to donate, kidney donor candidates would understand their long-term risks. Using single-center data (N = 4055; median [quartiles] follow-up: 18 [9-28] y), we developed a calculator for postdonation hypertension and validated it using long-term data from an external single-center cohort (N = 1189, median [quartiles] follow-up: 9 [5-17] y).

methodsRisk factors considered were routinely obtained at evaluation from donor candidates. Two modeling approaches were evaluated: Cox proportional hazards and random survival forest models. Cross-validation prediction error and Harrell's concordance-index were used to compare accuracy for model development. Top-performing models were assessed in the validation cohort using the concordance-index and net reclassification improvement.

resultsIn the development cohort, 34% reported hypertension at a median (quartiles) of 16 (8-24) y postdonation; and in the validation cohort, 29% reported hypertension after 17 (10-22) y postdonation. The most accurate model was a Cox proportional hazards model with age, sex, race, estimated glomerular filtration rate, systolic and diastolic blood pressure, body mass index, glucose, smoking history, family history of hypertension, relationship with recipient, and hyperlipidemia (concordance-index, 0.72 in the development cohort and 0.82 in the validation cohort).

conclusionsA postdonation hypertension calculator was developed and validated; it provides kidney donor candidates, their family, and care team a long-term projection of hypertension risk that can be incorporated into the informed consent process.

Indexed as

HypertensionKidney TransplantationBody Mass IndexGlomerular Filtration RateHumansKidneyLiving DonorsNephrectomyProportional Hazards ModelsRisk Factors

Identifiers

PMID36727726
PMCPMC10205650

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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.