Evidence map›Paper›PMID 35633387›Full record

ArticleAbdominal radiology (New York)2022

The predictive value of renal parenchymal information for renal function impairment in patients with ADPKD: a multicenter prospective study.

Yuhang Xie, Mengmiao Xu, Yajie Chen, Xiaolan Zhu, Shenghong Ju, Yuefeng Li

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Abdominal radiology (New York), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

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0cells of the map it votes in
8citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yuhang Xie *Department of Radiology, Affiliated Hospital of Jiangsu University, No. 438, Jiefang Road, Zhenjiang, 212001, Jiangsu, China.
Mengmiao Xu *Department of Radiology, Affiliated Hospital of Jiangsu University, No. 438, Jiefang Road, Zhenjiang, 212001, Jiangsu, China.
Yajie ChenDepartment of Radiology, Affiliated Hospital of Jiangsu University, No. 438, Jiefang Road, Zhenjiang, 212001, Jiangsu, China.
Xiaolan ZhuDepartment of Central Laboratory, The Fourth Affiliated Hospital of Jiangsu University, No. 20, Zhengdong Road, Zhenjiang, 212001, Jiangsu, China. 13775552517@163.com.
Shenghong JuDepartment of Radiology, Southeast University Affiliated Zhongda Hospital, No. 87, Dingjiaqiao Road, Nanjing, 210009, Jiangsu, China. jshtougao666@126.com.
Yuefeng LiDepartment of Radiology, Affiliated Hospital of Jiangsu University, No. 438, Jiefang Road, Zhenjiang, 212001, Jiangsu, China. jiangdalyf@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveAlthough the guideline indicates that total kidney volume (TKV) is an important detection indicator in patients with autosomal dominant polycystic kidney disease (ADPKD), this study attempted to demonstrate that renal parenchymal information, combined with parenchymal volume and radiomics features, may have more valuable clinical guiding significance.

methodsA totals of 340 ADPKD patients with normal renal function were prospectively collected and followed-up for five years, with renal function tests and non-contrast computed tomography (CT) performed every six months. The relationship between renal function impairment and renal parenchymal volume (RPV) as along with radiomics features was explored using a multiple linear regression model and multiple logistic regression. Then, a combined model of RPV with radiomics features was constructed to comprehensively evaluate its predictive value.

resultsCompared with TKV, decreased RPV presented a closer relationship with renal function impairment, namely, with the impairment rate (RPV: 82.3% vs. TVK: 67.1%) and eGFR (RPV: r = 0.614, p < 0.001 vs. TKV: r = -0.452, p < 0.001), and showed higher predictive power (RPV: AUC = 0.752 [95%CI 0.692-0.805], p < 0.001 vs. TKV: AUC = 0.711 [95%CI 0.649-0.768], p < 0.001). Correspondingly, the radiomics analysis that was derived from the renal parenchyma also showed a satisfactory result (AUC = 0.849 [95%Cl 0.797-0.892], p < 0.001). Importantly, the predictive power for renal function impairment was further improved in the combined model of RPV and radiomics features (AUC = 0.902 [95%Cl 0.857-0.937], p < 0.001).

conclusionRenal parenchyma information may be a sensitive biomarker of renal function impairment in ADPKD, which can provide a new approach for clinically monitoring renal function, and may greatly improve the pre-hospital prevention and treatment effects.

Indexed as

Polycystic Kidney, Autosomal DominantRenal InsufficiencyDisease ProgressionGlomerular Filtration RateHumansKidneyOrgan SizePrognosisProspective StudiesNon-contrast computed tomographyPolycystic kidney diseaseRadiomics analysisRenal function impairmentRenal parenchymal volume

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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.