Evidence map›Paper›PMID 35688469›Full record

ArticleClinical journal of the American Society of Nephrology : CJASN2022

Machine Learning-Derived Integer-Based Score and Prediction of Tertiary Hyperparathyroidism among Kidney Transplant Recipients: An Integer-Based Score to Predict Tertiary Hyperparathyroidism.

Namki Hong, Juhan Lee, Hyung Woo Kim, Jong Ju Jeong, Kyu Ha Huh, Yumie Rhee

Registry-linked trialOpen access · greenAbstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical journal of the American Society of Nephrology : CJASN, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02042963 (KoreaN Cohort Study for Outcome in Patients With Kidney Transplantation), which is not on this map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 32% 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.

NCT02042963 unknown statusnot on this map

KoreaN Cohort Study for Outcome in Patients With Kidney Transplantation : A 9-year Longitudinal Cohort Study of the Kidney Transplantation (KNOW-KT)

Typeobservational_patient_registrySponsorSeoul National University HospitalRan2012Enrolled1,000ConditionsKidney Transplant Recipients, Complications After Kidney Transplant
3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 6 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Namki HongDepartment of Internal Medicine, Endocrine Research Institute, Yonsei University College of Medicine, Seoul, South Korea.ORCID 0000-0002-8246-1956
Juhan LeeDepartment of Surgery, The Research Institute for Transplantation, Yonsei University College of Medicine, Seoul, South Korea.
Hyung Woo KimDepartment of Internal Medicine, Institute of Kidney Disease Research, Yonsei University College of Medicine, Seoul, South Korea.
Jong Ju JeongDepartment of Surgery, Endocrine Research Institute, Yonsei University College of Medicine, Seoul, South Korea.ORCID 0000-0002-4155-6035
Kyu Ha HuhDepartment of Surgery, The Research Institute for Transplantation, Yonsei University College of Medicine, Seoul, South Korea.
Yumie RheeDepartment of Internal Medicine, Endocrine Research Institute, Yonsei University College of Medicine, Seoul, South Korea.ORCID 0000-0003-4227-5638
Yonsei University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesTertiary hyperparathyroidism in kidney allograft recipients is associated with bone loss, allograft dysfunction, and cardiovascular mortality. Accurate pretransplant risk prediction of tertiary hyperparathyroidism may support individualized treatment decisions. We aimed to develop an integer score system that predicts the risk of tertiary hyperparathyroidism using machine learning algorithms. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We used two separate cohorts: a derivation cohort with the data of kidney allograft recipients (

resultsTertiary hyperparathyroidism requiring parathyroidectomy occurred in 5% and 2% of the derivation and validation cohorts, respectively. Three top predictors (dialysis duration, pretransplant intact parathyroid hormone, and serum calcium level measured at the time of admission for kidney transplantation) were identified to create an integer score system (dialysis duration, pretransplant serum parathyroid hormone level, and pretransplant calcium level [DPC] score; 0-15 points) to predict tertiary hyperparathyroidism. The median DPC score was higher in participants with post-transplant parathyroidectomy than in those without (13 versus three in derivation; 13 versus four in external validation;

conclusionsA simple integer-based score predicted the risk of tertiary hyperparathyroidism in kidney allograft recipients, with improved classification by serial measurement compared with single-time measurement. CLINICAL TRIAL REGISTRY NAME AND REGISTRATION NUMBER: Korean Cohort Study for Outcome in Patients with Kidney Transplantation (KNOW-KT), NCT02042963 PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_06_10_CJN15921221.mp3.

Indexed as

HyperparathyroidismKidney TransplantationCalciumCohort StudiesHumansMachine LearningParathyroidectomyParathyroid HormoneRetrospective StudiesCalciumParathyroid Hormoneartificial intelligencecalciumhyperparathyroidismmachine learningparathyroid hormonetransplantationtransplant recipients

Identifiers

PMID35688469
PMCPMC9269627
OpenAlexW4281756742

What OpenQuestion holds

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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.