Evidence map›Paper›PMID 34605917›Full record

ArticleJAMA network open2021

Diagnostic Yield of Population-Based Screening for Chronic Kidney Disease in Low-Income, Middle-Income, and High-Income Countries.

Marcello Tonelli, Sophanny Tiv, Shuchi Anand, Deepa Mohan, Guillermo Garcia Garcia, José Alfonso Gutiérrez Padilla, Scott Klarenbach, Guillermo Navarro Blackaller, Sidy Seck, Jinwei Wang and 2 more

Open access · goldAbstract read
In one paragraph

Article in JAMA network open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.7field-weighted citation impact, top 15% 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

13 citing papers in PubMed, 22 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

12 authors at 10 institutions in 6 countries.

Marcello TonelliUniversity of Calgary, Calgary, Alberta, Canada.
Sophanny TivDivision of Nephrology, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
Shuchi AnandDivision of Nephrology, Department of Medicine, Stanford University, Stanford, California.
Deepa MohanMadras Diabetes Research Foundation, Chennai, India.
Guillermo Garcia GarciaHospital Civil de Guadalajara Fray Antonio Alcalde, University Center for Health Science, University of Guadalajara, Guadalajara, Jalisco, Mexico.
José Alfonso Gutiérrez PadillaHospital Civil de Guadalajara Fray Antonio Alcalde, University Center for Health Science, University of Guadalajara, Guadalajara, Jalisco, Mexico.
Scott KlarenbachDivision of Nephrology, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
Guillermo Navarro BlackallerHospital Civil de Guadalajara Fray Antonio Alcalde, University Center for Health Science, University of Guadalajara, Guadalajara, Jalisco, Mexico.
Sidy SeckDepartment of Internal Medicine and Nephrology, Gaston Berger University, Saint-Louis, Senegal.
Jinwei WangPeking University Institute of Nephrology, Peking University First Hospital, Beijing, China.
Luxia ZhangPeking University Institute of Nephrology, Peking University First Hospital, Beijing, China.
Paul MuntnerDepartment of Epidemiology, School of Public Health, University of Alabama, Tuscaloosa.
Hospital Civil de Guadalajara · MXUniversity of Alberta · CAMadras Diabetes Research Foundation · INPeking University · CNPeking University First Hospital · CNStanford University · USUniversidad de Guadalajara · MXUniversité Gaston Berger · SNUniversity of Alabama · USUniversity of Calgary · CA

Funding

Chronic kidney disease of unknown etiology: applying a multidisciplinary approach to investigate the world's most common tubulointerstitial kidney diseaseR01DK127138 · NIDDK · STANFORD UNIVERSITY · PI MONTEZ RATH, MARIA · 2021 to 2025
$2.7M
NIDDK NIH HHS R01 DK127138
6 · The paper itself

Abstract

Importance: Population-based screening for chronic kidney disease (CKD) is sometimes recommended based on the assumption that detecting CKD is associated with beneficial changes in treatment. However, the treatment of CKD is often similar to the treatment of hypertension or diabetes, which commonly coexist with CKD. Objective: To determine the frequency with which population-based screening for CKD is associated with a change in recommended treatment compared with a strategy of measuring blood pressure and assessing glycemia. Design, Setting, and Participants: This cohort study was conducted using data obtained from studies that evaluated CKD in population-based samples from China (2007-2010), India (2010-2014), Mexico (2007-2008), Senegal (2012), and the United States (2009-2014), including a total of 126 242 adults screened for CKD. Data were analyzed from January 2020 to March 2021. Main Outcomes and Measures: The primary definition of CKD was estimated glomerular filtration rate less than 60 mL/min/1.73 m2. For individuals with CKD, the need for a treatment change was defined as not taking an angiotensin-converting enzyme inhibitor or angiotensin receptor blocker or having blood pressure levels of 140/90 mm Hg or greater. For individuals with CKD who also had diabetes, the need for a treatment change was also defined as having hemoglobin A1c levels of 8% or greater or fasting glucose levels of 178.4 mg/dL (9.9 mmol/L) or greater. Case finding was defined as testing for CKD only in adults with hypertension or diabetes. Results: Among 126 242 adults screened for CKD, there were 47 204 patients in the China cohort, 9817 patients in the India cohort, 51 137 patients in the Mexico cohort, 2441 patients in the Senegal cohort, and 15 643 patients in the US cohort. The mean age of participants was 49.6 years (95% CI, 49.5-49.7 years) in the China cohort, 42.9 years (95% CI, 42.6-43.2 years) in the India cohort, 51.6 years (95% CI, 51.5-51.7 years) in the Mexico cohort, 48.2 years (95% CI, 47.5-48.9 years) in the Senegal cohort, and 47.3 years (95% CI, 46.6-48.0 years) in the US cohort. The proportion of women was 57.3% (95% CI, 56.9%-57.7%) in the China cohort, 53.4% (95% CI, 52.4%-54.4%) in the India cohort, 68.8% (95% CI, 68.4%-69.2%) in the Mexico cohort, 56.0% (95% CI, 54.0%-58.0%) in the Senegal cohort, and 51.9% (51.0%-52.7%) in the US cohort. The prevalence of CKD was 2.5% (95% CI, 2.4%-2.7%) in the China cohort, 2.3% (95% CI, 2.0%-2.6%) in the India cohort, 10.6% (95% CI, 10.3%-10.9%) in the Mexico cohort, 13.1% (95% CI, 11.7%-14.4%) in the Senegal cohort, and 6.8% (95% CI, 6.2%-7.5%) in the US cohort. Screening for CKD was associated with the identification of additional adults whose treatment would change (beyond those identified by measuring blood pressure and glycemia) per 1000 adults: China: 8 adults (95% CI, 8-9 adults); India: 5 adults (95% CI, 4-7 adults); Mexico: 26 adults (95% CI, 24-27 adults); Senegal: 59 adults (95% CI, 50-69 adults); and the US: 19 adults (95% CI, 16-23 adults). Case finding was associated with the identification of 46.2% (95% CI, 45.1%-47.4%) to 86.4% (95% CI, 85.4%-87.3%) of individuals with CKD depending on the country, an increase in the proportion of individuals requiring a treatment change by as much 89.6% (95% CI, 80.4%-99.3%) in the US, and a decrease in the proportion of individuals needing GFR measurements by as much as 57.8% (95% CI, 56.3%-59.3%) in the US. Conclusions and Relevance: This study found that most additional individuals with CKD identified by population-based screening programs did not need a change in treatment compared with a strategy of measuring blood pressure and assessing glycemia and that case finding was more efficient than screening for early detection of CKD.

Indexed as

ChinaCohort StudiesDeveloping CountriesDiabetes MellitusDiagnostic Screening ProgramsHumansHypertensionIndiaMexicoPrevalenceRenal Insufficiency, ChronicRisk FactorsSenegalUnited States

Identifiers

PMID34605917
PMCPMC8491102
OpenAlexW3202401628

What OpenQuestion holds

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Registered trials

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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.