Evidence map›Paper›PMID 35945608›Full record

ArticleBMC medical informatics and decision making2022

Research on early warning of renal damage in hypertensive patients based on the stacking strategy.

Qiubo Bi, Zemin Kuang, E Haihong, Meina Song, Ling Tan, Xinying Tang, Xing Liu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.8field-weighted citation impact, top 18% 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.

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 3 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

7 authors at 4 institutions in 1 country.

Qiubo Bi *School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
Zemin Kuang *Department of Hypertension, Beijing Anzhen Hospital of Capital Medical University, Beijing, 100029, China.
E HaihongSchool of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China. ehaihong@bupt.edu.cn.ORCID 0000-0003-2087-586X
Meina SongSchool of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
Ling TanSchool of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
Xinying TangDepartment of Cardiology, The First People's Hospital of Chenzhou, The University of South China, Chenzhou, 423000, China.
Xing LiuDepartment of Anesthesiology, Third Xiangya Hospital, Central South University, Changsha, 410013, China.
Beijing University of Posts and Telecommunications · CNCapital Medical University · CNCentral South University · CNChenzhou First People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAmong the problems caused by hypertension, early renal damage is often ignored. It can not be diagnosed until the condition is severe and irreversible damage occurs. So we decided to screen and explore related risk factors for hypertensive patients with early renal damage and establish the early-warning model of renal damage based on the data-mining method to achieve an early diagnosis for hypertensive patients with renal damage.

methodsWith the aid of an electronic information management system for hypertensive out-patients, we collected 513 cases of original, untreated hypertensive patients. We recorded their demographic data, ambulatory blood pressure parameters, blood routine index, and blood biochemical index to establish the clinical database. Then we screen risk factors for early renal damage through feature engineering and use Random Forest, Extra-Trees, and XGBoost to build an early-warning model, respectively. Finally, we build a new model by model fusion based on the Stacking strategy. We use cross-validation to evaluate the stability and reliability of each model to determine the best risk assessment model.

resultsAccording to the degree of importance, the descending order of features selected by feature engineering is the drop rate of systolic blood pressure at night, the red blood cell distribution width, blood pressure circadian rhythm, the average diastolic blood pressure at daytime, body surface area, smoking, age, and HDL. The average precision of the two-dimensional fusion model with full features based on the Stacking strategy is 0.89685, and selected features are 0.93824, which is greatly improved.

conclusionsThrough feature engineering and risk factor analysis, we select the drop rate of systolic blood pressure at night, the red blood cell distribution width, blood pressure circadian rhythm, and the average diastolic blood pressure at daytime as early-warning factors of early renal damage in patients with hypertension. On this basis, the two-dimensional fusion model based on the Stacking strategy has a better effect than the single model, which can be used for risk assessment of early renal damage in hypertensive patients.

Indexed as

Blood Pressure Monitoring, AmbulatoryHypertensionBlood PressureCircadian RhythmHumansReproducibility of ResultsData miningFeature engineeringHypertensionRenal damageRisk assessmentStacking model

Identifiers

PMID35945608
PMCPMC9361646
OpenAlexW4291020126

What OpenQuestion holds

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LicenceCC BY
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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.