Evidence map›Paper›PMID 38460023›Full record

ArticleWorld journal of urology2024

Machine learning models for screening clinically significant nephrolithiasis in overweight and obese populations.

Hao-Wei Chen, Jung-Ting Lee, Pei-Siou Wei, Yu-Chen Chen, Jeng-Yih Wu, Chia-I Lin, Yii-Her Chou, Yung-Shun Juan, Wen-Jeng Wu, Chung-Yao Kao

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In one paragraph

Article in World journal of urology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.3field-weighted citation impact, top 21% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Observational
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

10 authors at 3 institutions in 1 country.

Hao-Wei ChenGraduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, 100, Shih-Chuan 1st Road, Kaohsiung, Taiwan.
Jung-Ting LeeSchool of Medicine, National Sun Yat-Sen University, Kaohsiung, Taiwan.
Pei-Siou WeiDepartment of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan.
Yu-Chen ChenGraduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, 100, Shih-Chuan 1st Road, Kaohsiung, Taiwan.
Jeng-Yih WuDepartment of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
Chia-I LinHealth Management Center, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung, Taiwan.
Yii-Her ChouGraduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, 100, Shih-Chuan 1st Road, Kaohsiung, Taiwan.
Yung-Shun JuanGraduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, 100, Shih-Chuan 1st Road, Kaohsiung, Taiwan.
Wen-Jeng WuGraduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, 100, Shih-Chuan 1st Road, Kaohsiung, Taiwan.
Chung-Yao KaoDepartment of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan. cykao@mail.ee.nsysu.edu.tw.ORCID http://orcid.org/0000-0002-0838-7751
Kaohsiung Medical University · TWNational Sun Yat-sen University · TWKaohsiung Municipal Ta-Tung Hospital · TW

Funding

National Science and Technology Council NSTC 111-2221-E-037-008National Science and Technology Council NSTC 111-2320-B-110-002
6 · The paper itself

Abstract

purposesOur aim is to build and evaluate models to screen for clinically significant nephrolithiasis in overweight and obesity populations using machine learning (ML) methodologies and simple health checkup clinical and urine parameters easily obtained in clinics.

methodsWe developed ML models to screen for clinically significant nephrolithiasis (kidney stone > 2 mm) in overweight and obese populations (body mass index, BMI ≥ 25 kg/m

resultsOf the 2928 subjects we enrolled, 1148 (39.21%) had clinically significant nephrolithiasis and 1780 (60.79%) did not. The testing dataset consisted of data collected from 574 subjects, 235 (40.94%) with clinically significant nephrolithiasis and 339 (59.06%) without. One model had a testing area under curve of 0.965 (95% CI, 0.9506-0.9794), a sensitivity of 0.860 (95% CI, 0.8152-0.9040), a specificity of 0.947 (95% CI, 0.9230-0.9708), a positive predictive value of 0.918 (95% CI, 0.8820-0.9544), and negative predictive value of 0.907 (95% CI, 0.8756-0.9371).

conclusionThis ML-based model was found able to effectively distinguish the overweight and obese subjects with clinically significant nephrolithiasis from those without. We believe that such a model can serve as an easily accessible and reliable screening tool for nephrolithiasis in overweight and obesity populations and make possible early intervention such as lifestyle modifications and medication for prevention stone complications.

Indexed as

Diabetes MellitusKidney CalculiNephrolithiasisBody Mass IndexHumansObesityOverweightHealth servicesMachine learningNephrolithiasisObesityOverweight

Identifiers

PMID38460023
OpenAlexW4392619308

What OpenQuestion holds

Textmetadata
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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 OpenQuestion graph.