Evidence map›Paper›PMID 35360013›Full record

ArticleFrontiers in cardiovascular medicine2022

Development and Validation of Prediction Models for Hypertensive Nephropathy, the PANDORA Study.

Xiaoli Yang, Bingqing Zhou, Li Zhou, Liufu Cui, Jing Zeng, Shuo Wang, Weibin Shi, Ye Zhang, Xiaoli Luo, Chunmei Xu and 10 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 33% 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.

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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
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

20 authors at 8 institutions in 2 countries.

Xiaoli YangDepartment of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.
Bingqing ZhouDepartment of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.
Li ZhouDepartment of Epidemiology, School of Public Health and Management, Chongqing Medical University, Chongqing, China.
Liufu CuiDepartment of Cardiology, Kailuan General Hospital, Tangshan, China.
Jing ZengDepartment of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.
Shuo WangDepartment of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.
Weibin ShiDepartment of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.
Ye ZhangDepartment of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.
Xiaoli LuoDepartment of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.
Chunmei XuDepartment of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.
Yuanzheng XueDepartment of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.
Hao ChenDepartment of Epidemiology, School of Public Health and Management, Chongqing Medical University, Chongqing, China.
Shuohua ChenDepartment of Cardiology, Kailuan General Hospital, Tangshan, China.
Guodong WangDepartment of Cardiology, Kailuan General Hospital, Tangshan, China.
Li GuoDepartment of Endocrinology, Southwest Hospital, Third Military Medical University, Chongqing, China.
Pedro A JoseDivision of Renal Disease & Hypertension, The George Washington University School of Medicine & Health Sciences, Washington, DC, United States.
Christopher S WilcoxDivision of Nephrology and Hypertension, Department of Medicine and Center for Hypertension, Kidney and Vascular Health, Georgetown University, Washington, DC, United States.
Shouling WuDepartment of Cardiology, Kailuan General Hospital, Tangshan, China.
Gengze WuDepartment of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.
Chunyu ZengDepartment of Cardiology, Daping Hospital, Third Military Medical University, Chongqing, China.
Army Medical University · CNDaping Hospital · CNKailuan General Hospital · CNChongqing Medical University · CNChongqing Public Health Medical Center · CNGeorgetown University · USGeorge Washington University · USUniversity of Chinese Academy of Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Hypertension is a leading cause of end-stage renal disease (ESRD), but currently, those at risk are poorly identified. Objective: To develop and validate a prediction model for the development of hypertensive nephropathy (HN). Design Setting and Participants: Individual data of cohorts of hypertensive patients from Kailuan, China served to derive and validate a multivariable prediction model of HN from 12, 656 individuals enrolled from January 2006 to August 2007, with a median follow-up of 6.5 years. The developed model was subsequently tested in both derivation and external validation cohorts. Variables: Demographics, physical examination, laboratory, and comorbidity variables. Main Outcomes and Measures: Hypertensive nephropathy was defined as hypertension with an estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m Results: About 8.5% of patients in the derivation cohort developed HN after a median follow-up of 6.5 years that was similar in the validation cohort. Eight variables in the derivation cohort were found to contribute to the risk of HN: salt intake, diabetes mellitus, stroke, serum low-density lipoprotein, pulse pressure, age, hypertension duration, and serum uric acid. The discrimination by concordance statistics (C-statistics) was 0.785 (IQR, 0.770-0.800); the calibration slope was 1.129, the intercept was -0.117; and the overall accuracy by adjusted Conclusions and Relevance: A prediction model of HN over 8 years had high discrimination and calibration, but this model requires prospective evaluation in other cohorts, to confirm its potential to improve patient care.

Indexed as

chronic kidney diseasehypertensionhypertensive nephropathypulse pressurerisk model

Identifiers

PMID35360013
PMCPMC8960139
OpenAlexW4221015183

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.